methods to diagnose whether or not an individual has non-small cell lung cancer, or to provide infor

专利摘要:
IN VITRO METHOD FOR DIAGNOSTICING THAT AN INDIVIDUAL HAS OR HAS NO LUNG CANCER AND TO PROVIDE INFORMATION ABOUT LUNG CANCER IN AN INDIVIDUAL. The present patent application includes biological markers, methods, devices, reagents, systems and kits for the detection and diagnosis of lung cancer. In one aspect, the application provides biological markers that can be used alone or in various combinations to diagnose lung cancer or to allow differential diagnosis of pulmonary nodules as benign or malignant. In another aspect, methods are provided for diagnosing an individual's lung cancer, where the detection methods include, in a biological sample from an individual, at least one biological marker value corresponding to at least one biological marker selected from the group of biological markers provided in Table 18, Table 20, or Table 21, in which the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on the value of, at least least a biological marker.
公开号:BR112012032537B1
申请号:R112012032537-0
申请日:2011-07-11
公开日:2021-03-02
发明作者:Sheri Wilcox；Deborah Ayers；Nebojsa Janjic；Larry Gold；Michael Riel-Mehan；Thale Jarvis
申请人:Somalogic, Inc；
IPC主号:

专利说明:

Related Requests
[001] This claim claims the benefit of US Provisional Order No. 61/363, 122, filed on July 9, 2010 and US Provisional Order No. Series 61/444, 947, filed on February 21, 2011, each of which is entitled "Lung Cancer Biomarkers and Uses Thereon". For all purposes, each of these requests is hereby incorporated by reference in its entirety. Field of the Invention
[002] The present application relates generally to the detection of biological markers and the diagnosis of cancer in an individual and, more specifically, to one or more biological markers, methods, devices, reagents, systems and kits to diagnose cancer, more particularly lung cancer, in an individual. Background
[003] The description below presents a summary of the information relevant to this application and is not an admission that any information provided or publications referenced herein are prior art to this application. Lung cancer remains the most common cause of cancer-related mortality. This is true for both men and women. In 2005, lung cancer in the United States accounted for more deaths than breast cancer, prostate cancer and colon cancer combined. That year, 107,416 men and 89,271 women were diagnosed with lung cancer, and 90,139 men and 69,078 women died of lung cancer. Among men in the United States, lung cancer is the second most common cancer among white, black, Asian / Pacific Islander, American Indians / Alaska, and Hispanic men. Among women in the United States, lung cancer is the second most common cancer among white women, black women and Native American / American Indians from Alaska and the third most common cancer among Asian / Pacific Islander and Hispanic women. For those who do not quit smoking, the likelihood of death from lung cancer is 15% and remains above 5%, even for those who quit smoking at the age of 50 - 59. The annual health cost of breast cancer lung in the USA is $ 95 billion. Ninety-one percent of lung cancer caused by smoking is non-small cell lung cancer (NSCLC), which represents approximately 87% of all lung cancers. The remaining 13% of all lung cancers are small cell lung cancers, although mixed cell lung cancer does occur. Because small cell lung cancer is rare and quickly fatal, the opportunity for early detection is small. There are three main types of NSCLC: squamous cell carcinoma, large cell carcinoma and adenocarcinoma. Adenocarcinoma is the most common form of lung cancer (30% - 40% and reported to be as high as 50%) and is the most common lung cancer in smokers and non-smokers. Squamous cell carcinoma represents 25-30% of all lung cancers and is usually found in a proximal bronchus. Early stage NSCLC tends to be localized, and if detected early it can often be treated by surgery with a favorable outcome and longer survival. Other treatment options include radiation treatment, drug therapy, and a combination of these methods. The stage of NSCLC is determined by the size of the tumor and its presence in other tissues, including lymph nodes. In the hidden stage, cancer cells are found in sputum samples or wash samples and the tumor is not detectable in the lungs. In stage 0, only the innermost layer of the lungs has cancer cells and the tumor has not grown through the lining. In stage IA, the cancer is considered invasive and has grown deep in the lung tissue, but the tumor is less than 3 cm in diameter. At this stage, the tumor is not found in the bronchus or lymph nodes. In stage IB, the tumor is larger than 3 cm in diameter or has grown in the bronchus or pleura, but has not grown within the lymph nodes. In stage II, the tumor is larger than 3 cm in diameter and has grown within the lymph nodes. In stage IIB, the tumor has either been found in the lymph nodes or is larger than 3 cm in diameter or has grown in the bronchus or pleura, or the cancer is not in the lymph nodes, but is found in the chest wall, diaphragm, pleura , bronchus, or tissue that surrounds the heart. In stage IIIA, cancer cells are found in the lymph nodes near the lung and bronchi, and in those between the lungs, but on the side of the thorax, where the tumor is located. In stage III-B, the cancer cells are located on the opposite side of the chest from the tumor and in the neck. Other organs close to the lungs may also have cancer cells and multiple tumors can be found in a lobe of the lungs. In stage IV, tumors are found in more than one lobe of the same lung or in both lungs and cancer cells are found elsewhere in the body. Current methods of diagnosing lung cancer include sputum testing for cancer cells, chest radiography, fiber optic airway assessment, and low-dose spiral computed tomography (CT). Sputum cytology has a very low sensitivity. Chest radiography is also relatively insensitive, requiring lesions to be larger than 1 cm in size to be visible. Bronchoscopy requires that the tumor be visible inside the airways accessible to the bronchoscope. The most widely recognized method of diagnosis is CT, but in common with X-rays, the use of CT involves ionizing radiation, which in itself can cause cancer. CT also has significant limitations: examinations require a high level of technical skill to interpret and many of the abnormalities observed are not in fact lung cancer and substantial health costs are incurred in monitoring CT findings. The most common incidental finding is a benign pulmonary nodule. Pulmonary nodules are relatively round lesions or areas of abnormal tissue, located inside the lung and can vary in size. Lung nodules can be benign or cancerous, but most are benign. If a nodule is less than 4 mm, the prevalence is only 1.5%, if 4-8 mm the prevalence is about 6%, and if more than 20 mm, the incidence is approximately 20%. For small and medium-sized nodules, the patient is advised to undergo a repeat check within three months to a year. For very large nodules, the patient receives a biopsy (which is invasive and can lead to complications), although most of these are benign. Thus, diagnostic methods are needed that can replace or complement CT to reduce the number of surgical procedures performed and minimize the risk of surgical complications. In addition, even when pulmonary nodules are absent or unknown, methods to detect lung cancer in its early stages are needed to improve patient outcomes. Only 16% of lung cancer cases are diagnosed as early-stage localized cancer, where the 5-year survival rate is 46%, compared with 84% of those diagnosed at a late stage, where the 5-year survival rate is only 13%. This shows that relying on symptoms for diagnosis is not helpful because many of them are common to other lung diseases. These symptoms include persistent cough, bloody sputum, chest pain and recurrent bronchitis or pneumonia. Where methods of early cancer diagnosis exist, the benefits are generally accepted by the medical community. Cancers that have been widely used screening protocols have the highest 5-year survival rates, such as breast cancer (88%) and colon cancer (65%) versus 16% for lung cancer. However, 88% of lung cancer patients survive ten years or more if the cancer is diagnosed in stage 1 through screening. This demonstrates the clear need for diagnostic methods that can reliably detect NSCLC at an early stage. The progression of health status to disease is accompanied by changes in the expression of proteins in affected tissues. Comparative interrogation of the human proteome in healthy and diseased tissues can provide insight into the biology of the disease and lead to the discovery of biological markers for diagnosis, new goals for therapeutic intervention, and identification of patients most likely to benefit from specific treatments. The selection of biological marker for a specific disease state involves first identifying markers that have a statistically significant and measurable difference in a sick population compared to a control population for a specific application. Biological markers can include molecules secreted or released with the development or progression of the disease in parallel and readily diffuse into the bloodstream from lung tissue or distant tissues in response to an injury. The biological marker or a set of identified biological markers is generally clinically validated or shown to be a reliable indicator for the original intended use for which it was selected. Biological markers can include small molecules, peptides, proteins and nucleic acids. Some of the key problems that affect the identification of biological markers include over-adjusting the available data and deviations in the data. A variety of methods have been used in an attempt to identify biological markers and diagnose the disease. For protein-based markers, these include two-dimensional electrophoresis, mass spectrometry, and immune assay methods. For nucleic acid markers, these include mRNA expression profiles, microRNA profiles, FISH, serial gene expression analysis (SAGE), and large-scale gene expression matrices. The utility of two-dimensional electrophoresis is limited by the low detection sensitivity; problems with protein solubility, charge and hydrophobicity; gel reproducibility, and the possibility of a single brand representing multiple proteins. For mass spectrometry, depending on the format used, the limitations revolve around the processing and separation of the samples, the sensitivity to low abundance proteins, signal considerations in relation to noise, and the inability to immediately identify the detected protein. Limitations in immune assay approaches for the discovery of biological markers are centered on the inability of multiplexed assays based on antibodies to measure a large number of analytes. You can simply print a matrix of high quality antibodies and, without sandwiches, measure the analytes bound to those antibodies. (This would be the formal equivalent of using a complete genome of nucleic acid sequences by hybridization to measure all DNA or RNA sequences in an organism or cell. The hybridization experiment works because hybridization can be a rigorous test of identity Even very good antibodies are not rigorous enough in choosing their binding partners to work in the context of blood or cell extracts, not least because the set of proteins in these matrices has extremely different abundances). Therefore, a different approach should be used with immune assay based approaches to discover biological markers - it would be necessary to use multiplexed ELISA assays (ie sandwiches) to obtain sufficient accuracy to measure many analytes simultaneously to decide which analytes are really biological markers. . Immune sandwich assays do not scale to high content and, therefore, the discovery of biological markers using rigorous immune sandwich assays is not possible to use standard matrix formats. Finally, antibody reagents are subject to very substantial variability and instability of the reagent. The immediate platform for the discovery of biological protein markers overcomes this problem. Many of these methods require or are based on some type of sample fractionation prior to analysis. Thus, sample preparation necessary to perform a sufficiently powered study designed to statistically identify / discover biological markers in a series of well-defined sample populations is extremely difficult, expensive and time-consuming. During fractionation, a wide range of variability can be introduced in the various samples. For example, a potential marker could be unstable for the process, the concentration of the marker could be changed, improper aggregation or disaggregation could occur, and inadvertent contamination of the sample could occur and thus hide the subtle changes anticipated at the beginning of the disease. It is widely accepted that the methods of discovering and detecting biological markers using these technologies have serious limitations for the identification of biological diagnostic markers. These limitations include the inability to detect low abundance biological markers, an inability to consistently cover the entire dynamic range of the proteome, irreproducibility in the processing and fractionation of samples, and overall irreproducibility and lack of robustness of the method. In addition, these studies introduced deviations to the data and did not adequately address the complexity of the sample populations, including adequate controls, in terms of the distribution and randomization necessary to identify and validate biological markers within a target disease population. Although efforts have been made for several decades to find new and effective biological markers, efforts have largely been unsuccessful. Typically, biological markers for various diseases have been identified in academic laboratories, usually through accidental discovery when doing basic research on some disease process. Based on the discovery and with small amounts of clinical data, articles were published that suggested the identification of a new biological marker. Most of these proposed biological markers, however, have not been confirmed as real or useful biological markers mainly due to the small number of clinical samples tested provided only weak statistical evidence that an effective biological marker was indeed found. That is, the initial identification was not accurate with respect to the basic elements of statistics. In each of the years 1994 to 2003, a search of the scientific literature shows that thousands of references have been published focused on biological markers. During that same time frame, however, the FDA has approved for use in diagnosis a maximum of three new biological protein markers in one year, and in several years no biological protein marker has been approved. Based on the history of failed efforts to discover biological markers, mathematical theories have been proposed that further promote the general understanding that biological markers for disease are rare and difficult to find. Research using biological markers based on 2D gels or mass spectrometry supports these notions. Very few useful biological markers have been identified through these approaches. However, it is generally ignored that 2D gels and measurement protein mass spectrometry that are present in the blood at concentrations of about 1 nM and higher, and that this set of proteins may very well be the least likely to change with disease. Unlike the instant biological marker discovery platform, there are no proteomic biological marker discovery platforms that are able to accurately measure protein expression levels at much lower concentrations. Much is known about biochemical pathways to complex human biology. Many biochemical pathways culminate in secreted proteins that function locally within the pathology or are initiated by them, for example, growth factors are secreted to stimulate the replication of other cells in the pathology, and other factors are secreted to ward off the immune system, and so on. onwards. Although many of these secreted proteins work in a paracrine way, some operate distantly in the body. A person skilled in the art with a basic understanding of biochemical pathways would understand that many proteins of specific pathology must exist in the blood at lower concentrations (even far below), within the limits of detection of 2D gels and mass spectrometry. What must precede the identification of this relatively abundant number of biological markers of the disease is a proteomic platform that can analyze proteins in concentrations lower than those detectable by 2D gels or mass spectrometry. Thus, there is a need for biological markers, methods, devices, reagents, systems and assemblies that allow (a) the differentiation of benign pulmonary nodules from malignant pulmonary nodules, (b) the detection of biological lung cancer markers, and (c ) the diagnosis of lung cancer. To meet this need, a new aptamer-based proteomic technology has been developed for the discovery of biological markers, which is capable of simultaneously measuring thousands of proteins from small volumes of plasma or serum samples (see p. E, Pub USA No. 2010/0070191; USA Pub No. 2010/0086948, Ostroff et al Nature Precedings, http://precedings.nature.com/documents/4537/version/1(2010); Gold et al. Nature Precedings, http: //precedings.nature.com/documents/4538/version/1 (2010)). This technology, called SOMA scan, is enabled by a new generation of aptamers with low dissociation rates (SOMAmers) that contain chemically modified nucleotides, which greatly expand the physico-chemical diversity of the large random nucleic acid libraries from which the aptamers selected (see U.S. Patent No. 7,947,447). Such modifications, which are compatible with SELEX, introduce functional groups in aptamers that are often found in protein-protein interactions, antibody-antigen interactions and interactions between small drug molecules with their protein targets. In general, the use of such modifications expands the range of possible aptamer targets, improves their binding properties and facilitates the selection of aptamers with low dissociation rates.
[004] Specifically, proteins in complex matrices, such as plasma are measured with a process that transforms a signature of protein concentrations to a corresponding signature of the DNA aptamer concentration, which is then quantified using a DNA microarray platform (Gold et al. Precedings Nature, http://precedings.nature.com/documents/4538/version/1 (2010)). The test leverages balance link and kinetic challenge. Both are performed in the solution, not on a surface, to take advantage of the most favorable binding and dissociation kinetics. In essence, the assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes.
[005] The assay is capable of simultaneously measuring a large number of proteins ranging from low to high abundance in the serum. For example, samples from 1,326 individuals from four independent non-small cell lung cancer studies (NSCLC) were analyzed over the long term in populations exposed to tobacco. More than 800 proteins in 15 L μ of serum were measured and a panel of 12 proteins was developed that distinguishes NSCLC from controls with sensitivity of 91% and specificity of 84% in a training set and sensitivity of 89% and specificity 83% in connected independent verification set. It is important to note that the performance was similar for early and final stage NSCLC (Ostroff et al. Nature Precedings, http://precedings.nature.com/documents/4537/version/1 (2010)).
[006] To date, several studies of clinical biological markers of human diseases, including lung cancer (Pub. Of the USA. No. 2010/0070191), ovarian cancer (Pub. Of the USA. No. 2010 / 0086948), and chronic kidney disease were performed using this method. These studies have identified new potential biological markers for the disease of each of these diseases, as well as for cancer in general. ABSTRACT
[007] The present application demonstrates the utility of the recently discovered micromatrix platform technology for identifying disease-related biological markers from tissue. The present patent application includes biological markers, methods, reagents, devices, systems and kits for the detection and diagnosis of cancer and, more particularly, lung cancer from tissue. The biological markers of the present patent application were identified using an aptamer-based multiplexed assay which is described in detail in Example 6. Using the aptamer-based method of identifying biological markers described herein, the present application describes a surprisingly large number of biological markers of lung cancer from tissue, which are useful for the detection and diagnosis of lung cancer. In the identification of these biological markers, more than 800 proteins were measured from a number of individual samples, some of which were, in concentrations in the low phentomolar range. This is about four orders of magnitude less than the biological marker discovery experiments made with 2D gels and / or mass spectrometry.
[008] Although some of the biological lung cancer markers described are useful in themselves for the detection and diagnosis of lung cancer, methods for grouping the various subsets of the biological lung cancer markers are described here, which are useful as a panel of biological markers. Once an individual biological marker or a subset of biological markers has been identified, the detection or diagnosis of an individual's lung cancer can be performed using any test platform or format that is capable of measuring differences in the levels of the biological marker or selected biological markers (s) in a biological sample. However, it was only using the aptamer-based biological marker identification method described here, in which more than 800 different potential biological marker values were individually selected from a large number of individuals having previously been diagnosed as having or not having lung cancer it was possible to identify the biological lung cancer markers disclosed here. This discovered approach is in contrast to the discovery of biological markers of conditioned medium or lysed cells as it consults a more relevant patient system that does not require translation into human pathology.
[009] Thus, in one aspect of this patent application, one or more biological markers are provided for use alone or in various combinations to diagnose lung cancer, particularly non-small cell lung cancer (NSCLC) or allow the differential diagnosis of pulmonary nodules as benign or malignant. Exemplary modalities include the biological markers provided in Table 18, which, as stated above, were identified using an aptamer-based multiplexed assay, as generally described in Example 1, and more specifically in Example 6. The markers shown in Table 18 are useful for distinguishing benign from cancerous nodules. The markers shown in Table 18 are also useful for distinguishing asymptomatic smokers from smokers with lung cancer. In one aspect, the biological marker is MMP-7. In another aspect, the biological marker is MMP-12.
[0010] While some of the biological lung cancer markers described are useful in themselves for the detection and diagnosis of lung cancer, the methods are also described here for grouping the various subsets of the biological lung cancer markers, which are useful each as a panel of two or more markers. Thus, various embodiments of the present application provide combinations that comprise N markers, where N is at least two biological markers. In other embodiments, N is selected to be any number of 2-36 biological markers.
[0011] Yet In other modalities, N is selected to be any number from 2-7, 2-10, 2-15, 2-20, 2-25, 2-30, 2-36. In other embodiments, N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-36. In other embodiments, N is selected to be any number of 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-36. In other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5-36. In other embodiments, N is selected to be any number from 6-10, 6-15, 6-20, 6-25, 6-30, 6-36. In other embodiments, N is selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-36. In other embodiments, N is selected to be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-36. In other embodiments, N is selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-36. In other embodiments, N is selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-36. It will be appreciated that N can be selected to cover order, similar but higher ranges.
[0012] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, at least one biological marker value corresponding to at least one biological marker selected at from the group of biological markers provided in Table 18, in which the individual is classified as having lung cancer based on the value of at least one biological marker.
[0013] In another aspect, a method is provided for diagnosing an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, which correspond to at least each of the N markers biologicals selected from the group of biological markers shown in Table 18, in which the likelihood of the individual having lung cancer is determined based on the values of biological markers. In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, which correspond to at least one, of each of the N biological markers selected from the group of biological markers shown in Table 18, in which the individual is classified as having lung cancer based on the values of biological markers and, where N = 2-10.
[0014] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, which correspond to at least one, of each of the N biological markers selected from the group of biological markers shown in Table 18, in which the likelihood of the individual having lung cancer is determined based on the values of biological markers and, where N = 2-10.
[0015] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, at least one biological marker value corresponding to at least a biological marker selected from the group of biological markers shown in Table 18, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined based on the value of at least one marker biological.
[0016] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting in a biological sample from an individual who is a smoker, biological marker values each corresponding to at least one of the N biological markers selected from the group of biological markers shown in Table 18, in which the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on said biological marker values, where N = 2-10.
[0017] In another aspect, a method is provided to diagnose an individual who does not have lung cancer, the method including detecting in a biological sample from an individual, at least one biological marker value corresponding to at least one selected biological marker among the group of biological markers shown in Table 18, in which the individual is classified as not having lung cancer based on the value of at least one biological marker.
[0018] In another aspect, a method is provided to diagnose an individual who does not have lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 18, in which the individual is classified as not having lung cancer based on the values of biological markers, and where N = 2-10.
[0019] In another aspect, a method is provided to diagnose lung cancer, the method including detecting, in a biological sample from an individual, values of biological markers, each of which corresponds to a biological marker on a panel of N markers biological, in which the biological markers are selected from the group of biological markers shown in Table 18, in which the classification of the biological marker values indicates that the individual has lung cancer, and in which N = 3-10.
[0020] In another aspect, a method is provided to diagnose lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to a biological marker on a panel of N biological markers, in which the biological markers are selected from the group of biological markers shown in Table 18, in which the classification of the biological marker values indicates that the individual has lung cancer, and in which N = 3-15.
[0021] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each of which corresponds to a biological marker on a panel of N biological markers, where the biological markers are selected from the group of biological markers shown in Table 18, in which the individual is classified as having lung cancer, or the likelihood of the individual having cancer of lung is determined, based on the values of biological markers, and where N = 3 - 10.
[0022] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where the biological markers are selected from the group of biological markers shown in Table 18, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on the values of biological markers, where N = 3-15.
[0023] In another aspect, a method is provided to diagnose the absence of lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where biological markers are selected from the group of biological markers shown in Table 18, where the classification of biological marker values indicates the absence of lung cancer in the individual, and where N = 3-10 .
[0024] In another aspect, a method is provided to diagnose the absence of lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where biological markers are selected from the group of biological markers shown in Table 18, where the classification of biological marker values indicates the absence of lung cancer in the individual, and where N = 3-15 .
[0025] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers defined in Table 18, in which the individual is classified as having lung cancer based on a classification score that deviates from a predetermined threshold value, and in which N = 2-10.
[0026] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where the biological markers are selected from the group of biological markers shown in Table 18, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on a classification score that deviates from a predetermined threshold value, where N = 3-10.
[0027] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where the biological markers are selected from the group of biological markers shown in Table 18, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on a classification score that deviates from a predetermined threshold value, where N = 3-15.
[0028] In another aspect, a method is provided to diagnose the absence of lung cancer in an individual, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 18, in which said individual is classified as having no lung cancer based on the classification score that deviates from a predetermined threshold value, and in which N = 2-10.
[0029] In another aspect, a computer-implemented method is provided to indicate a likelihood of lung cancer. The method comprises: retrieving information about a computer biological marker for an individual, where the biological marker information comprises the values of biological markers, which correspond to each of at least N biological markers, where n is as defined above, selected from the group of biological markers shown in Table 18; perform with the computer a classification of each of the values of biological markers; and indicate a likelihood that the individual has lung cancer based on a plurality of classifications.
[0030] In another aspect, a computer-implemented method is provided for classifying an individual as having or not having lung cancer. The method comprises: retrieving biological marker information on a computer for an individual, in which the biological marker information comprises values of biological markers each corresponding to at least one of the N biological markers selected from the group of biological markers provided in the Table 18; perform with the computer a classification of each of the values of biological markers; and indicate whether the individual has lung cancer based on a plurality of classifications.
[0031] In another aspect, a computer program product is provided to indicate a likelihood of lung cancer. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, which comprises the program code: code that retrieves data assigned to a biological sample from an individual, wherein the data comprise values of biological markers each corresponding to at least one of the N biological markers, where N is as defined above, in the biological sample selected from the group of biological markers shown in Table 18; and the code that performs a classification method that indicates a likelihood that the individual has lung cancer as a function of the biological marker values.
[0032] In another aspect, a computer program product is provided to indicate an individual's lung cancer status. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, which comprises the program code: code that retrieves data assigned to a biological sample from an individual, wherein the data comprise values of biological markers each corresponding to at least one of the N biological markers in the biological sample selected from the group of biological markers provided in Table 18; and the code that performs a classification method that indicates an individual's lung cancer status as a function of the biological marker values.
[0033] In another aspect, a computer-implemented method is provided to indicate a likelihood of lung cancer. The method comprises retrieving biological marker information for a subject on a computer, wherein the biological marker information comprises a biological marker value corresponding to a biological marker selected from the group of biological markers shown in Table 18; perform with the computer a classification of the value of the biological marker; and indicate a likelihood that the individual has lung cancer based on the classification.
[0034] In another aspect, a computer-implemented method is provided to classify an individual as having or not having lung cancer. The method comprises retrieving biological marker information for an individual on a computer, wherein the biological marker information comprises a biological marker value corresponding to a biological marker selected from the group of biological markers provided in Table 18; perform with the computer a classification of the biological marker value; and indicate whether the individual has lung cancer based on the classification.
[0035] In yet another aspect, a computer program product is provided to indicate a likelihood of lung cancer. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, which comprises the program code: code that retrieves data assigned to a biological sample from an individual, wherein the data comprise the value of a biological marker corresponding to a biological marker in the biological sample selected from the group of biological markers shown in Table 18; and code that performs a classification method that indicates a likelihood that the individual has lung cancer as a function of the biological marker value.
[0036] In yet another aspect, a computer program product is provided to indicate an individual's lung cancer status. The computer program product includes a computer-readable medium that contains program code executable by a processor for a device or computer system, which comprises the program code: code that retrieves data assigned to a biological sample from an individual, in that the data comprise the value of a biological marker corresponding to a biological marker in the biological sample selected from the group of biological markers provided in Table 18; and code that performs a classification method that indicates an individual's lung cancer status as a function of the biological marker value.
[0037] In another embodiment of the present application, exemplary embodiments include the biological markers provided in Table 20, which as noted above, were identified using an aptamer-based multiplexed assay, as described generically in Example 1, and more specifically in Example 6. The markers shown in Table 20 are useful for distinguishing benign from cancerous nodules. The markers shown in Table 20 are also useful for distinguishing asymptomatic smokers from smokers with lung cancer. With reference to Table 20, N is selected to be any number of 2-25 biological markers. The markers shown in Table 20 were determined to be useful in both tissue and serum samples.
[0038] In still other modalities, N is selected to be any number from 2-7, 2-10, 2-15, 2-20, 2-25. In other embodiments, N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25. In other modalities, N is selected to be any number of 4-7, 4-10, 4-15, 4-20, 4-25. In other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25. In other embodiments, N is selected to be any number from 6-10, 6-15, 6-20, 6-25. In other embodiments, N is selected to be any number from 7-10, 7-15, 7-20, 7-25. In other embodiments, N is selected to be any number from 810, 8-15, 8-20, 8-25. In other embodiments, N is selected to be any number from 9-15, 9-20, 9-25. In other embodiments, N is selected to be any number from 10-15, 10-20, 10-25. It will be appreciated that N can be selected to cover orders, similar but higher ranges.
[0039] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, at least one biological marker value corresponding to at least one biological marker selected at from the group of biological markers provided in Table 20, in which the individual is classified as having lung cancer based on the value of at least one biological marker.
[0040] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 20, in which the individual's likelihood of having lung cancer is determined based on the values of biological markers.
[0041] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 20, in which the individual is classified as having lung cancer based on the values of biological markers and, where N = 2-10.
[0042] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 20, in which the individual's likelihood of having lung cancer is determined based on the values of biological markers and, where N = 2-10.
[0043] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, at least one biological marker value corresponding to at least one biological marker selected from the group of biological markers shown in Table 20, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined based on the value of at least one biological marker .
[0044] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each of which corresponds to at least least one of the N biological markers selected from the group of biological markers shown in Table 20, in which the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on said values of biological markers, where N = 2-10.
[0045] In another aspect, a method is provided to diagnose an individual who does not have lung cancer, the method including detecting, in a biological sample from an individual, at least one biological marker value corresponding to at least one biological marker selected from the group of biological markers shown in Table 20, in which the individual is classified as not having lung cancer based on the value of at least one biological marker.
[0046] In another aspect, a method is provided to diagnose an individual who does not have lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 20, in which the individual is classified as not having lung cancer based on the values of biological markers, and in which N = 2-10.
[0047] In another aspect, a method is provided to diagnose lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers , in which biological markers are selected from the group of biological markers shown in Table 20, in which a classification of the values of biological markers indicates that the individual has lung cancer, and in which N = 3-10.
[0048] In another aspect, a method is provided to diagnose lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers , in which the biological markers are selected from the group of biological markers shown in Table 20, in which a classification of the biological marker values indicates that the individual has lung cancer, and in which N = 3-15.
[0049] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where the biological markers are selected from the group of biological markers shown in Table 20, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on the values of biological markers, and where N = 3 - 10.
[0050] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where the biological markers are selected from the group of biological markers shown in Table 20, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on the values of biological markers, where N = 3-15.
[0051] In another aspect, a method is provided to diagnose the absence of lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where biological markers are selected from the group of biological markers shown in Table 20, where a classification of biological marker values indicates the absence of lung cancer in the individual, and where N = 3-10 .
[0052] In another aspect, a method is provided to diagnose the absence of lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where biological markers are selected from the group of biological markers shown in Table 20, where a classification of the biological marker values indicates the absence of lung cancer in the individual, and where N = 3-15 .
[0053] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers defined in Table 20, in which the individual is classified as having lung cancer based on a classification score that deviates from a predetermined threshold value, and in which N = 2-10.
[0054] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where the biological markers are selected from the group of biological markers shown in Table 20, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on a classification score that deviates from a predetermined threshold value, where N = 3-10.
[0055] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where the biological markers are selected from the group of biological markers shown in Table 20, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on a classification score that deviates from a predetermined threshold value, where N = 3-15.
[0056] In another aspect, a method is provided to diagnose the absence of lung cancer in an individual, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 20, in which said individual is classified as having no lung cancer based on the classification score that deviates from a predetermined threshold value, and in which N = 2-10.
[0057] In another aspect, a computer-implemented method is provided to indicate a likelihood of lung cancer. The method comprises: retrieving biological marker information for an individual on a computer, in which the biological marker information comprises the values of biological markers, each of which corresponds to at least one of the N biological markers, where N is such as defined above, selected from the group of biological markers shown in Table 20; perform with the computer a classification of each of the values of biological markers; and indicate a likelihood that the individual has lung cancer based on a plurality of classifications.
[0058] In another aspect, a computer-implemented method is provided to classify an individual as having or not having lung cancer. The method comprises: retrieving, in a computer, biological marker information for an individual, in which the biological marker information comprises values of biological markers, each corresponding to at least one of the N biological markers selected from the group of biological markers presented in Table 20, perform with the computer a classification of each of the values of biological markers; and indicate whether the individual has lung cancer based on a plurality of classifications.
[0059] In another aspect, a computer program product is provided to indicate a likelihood of lung cancer. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data comprises values of biological markers each corresponding to at least one of the N biological markers, where N is, as defined above, in the biological sample selected from the group of biological markers shown in Table 20, and code that executes a classification method that indicates the likelihood that the individual has lung cancer as a function of the values of biological markers.
[0060] In another aspect, a computer program product is provided to indicate an individual's lung cancer status. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data comprise values of biological markers each corresponding to at least one of the N biological markers in the biological sample selected from the group of biological markers provided in Table 20; and code that performs a classification method that indicates an individual's lung cancer status as a function of biological marker values.
[0061] In another aspect, a computer-implemented method is provided to indicate a likelihood of lung cancer. The method comprises retrieving biological marker information on a computer for an individual, wherein the biological marker information comprises a biological marker value corresponding to a biological marker selected from the group of biological markers shown in Table 20; perform with the computer a classification of the biological marker value; and indicate a likelihood that the individual has lung cancer based on the classification.
[0062] In another aspect, a computer-implemented method is provided to classify an individual as having or not having lung cancer. The method comprises retrieving biological marker information from a computer to an individual, wherein the biological marker information comprises a biological marker value corresponding to a biological marker selected from the group of biological markers provided in Table 20; perform with the computer a classification of the biological marker value; and indicate whether the individual has lung cancer based on the classification.
[0063] In yet another aspect, a computer program product is provided to indicate a likelihood of lung cancer. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data value comprises a biological marker value corresponding to a biological marker in the biological sample selected from the group of biological markers shown in Table 20; and code that performs a classification method that indicates a likelihood that the individual has lung cancer as a function of the biological marker value.
[0064] In yet another aspect, a computer program product is provided to indicate an individual's lung cancer status. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data value comprises a biological marker value corresponding to a biological marker in the biological sample selected from the group of biological markers provided in Table 20; and code that performs a classification method that indicates an individual's lung cancer status as a function of the biological marker value.
[0065] In another embodiment of the present application, exemplary embodiments include the biological markers provided in Table 21, which were identified using an aptamer-based multiplexed assay, as generally described in Example 1 and, more specifically, in Examples 2 and 6. The markers shown in Table 21 are useful for distinguishing benign from cancerous nodules. The markers shown in Table 21 are also useful for distinguishing asymptomatic smokers from smokers with lung cancer. With reference to Table 21, N is selected to be any number of 2-86 biological markers. All of the biological markers in Table 21 are useful for providing the requested information in both tissue and serum samples.
[0066] In yet other modalities, N is selected to be any number from 2-7, 2-10, 2-15, 2-20, 2-25, 2-30, 2-35, 2-40, 2 - 45, 2-50, 2-55, 2-60, 2-65, 2-70, 2-75, 2-80, or 2-86. In other embodiments, N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, 3-45, 3 - 50, 3-55, 3-60, 3-65, 3-70, 3-75, 3-80, or 3-86. In other modalities, N is selected to be any number of 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4 - 50, 4-55, 4-60, 4-65, 4-70, 4-75, 4-80, or 4-86. In other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5-35, 5-40, 5-45, 5 - 50, 5-55, 5-60, 5-65, 5-70, 5-75, 5-80, or 5-86. In other embodiments, N is selected to be any number from 6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45, 650, 6 - 55, 6-60, 6-65, 6-70, 6-75, 6-80, or 6-86. In other embodiments, N is selected to be any number from 7-10, 715, 7-20, 7-25, 7-30, 7-35, 7-40, 7-45, 7-50, 7 - 55, 7-60, 7-65, 7-70, 775, 7-80, or 7-86. In other embodiments, N is selected to be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 850, 8 - 55, 8-60, 8-65, 8-70, 8-75, 8-80, or 8-86. In other embodiments, N is selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9-55, 9 - 60, 9-65, 9-70, 9-75, 980, or 9-86. In other embodiments, N is selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-35, 10-40, 10-45, 10-50, 10-55, 10 - 60, 10-65, 10-70, 10-75, 10-80, or 10-86. It will be appreciated that N can be selected to cover order, similar but higher ranges.
[0067] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, at least one biological marker value corresponding to at least one biological marker selected at from the group of biological markers provided in Table 21, in which the individual is classified as having lung cancer based on the value of at least one biological marker.
[0068] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to at least one of the N biological markers selected from the group of biological markers shown in Table 21, in which the individual's probability of having lung cancer is determined based on the values of biological markers.
[0069] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 21, in which the individual is classified as having lung cancer based on the values of biological markers and, where N = 2-10.
[0070] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 21, in which the likelihood of the individual having lung cancer is determined based on the values of biological markers and, where N = 2-10.
[0071] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, at least one biological marker value corresponding to at least one biological marker selected from the group of biological markers shown in Table 21, where the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined based on the value of at least one biological marker . In another aspect, a method for screening smokers for lung cancer is provided, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 21, in which the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on said biological marker values, where N = 2-10.
[0072] In another aspect, a method is provided to diagnose that an individual does not have lung cancer, the method including detecting, in a biological sample from an individual, at least one biological marker value corresponding to at least one marker biological selected from the group of biological markers shown in Table 21, in which the individual is classified as not having lung cancer based on the value of at least one biological marker.
[0073] In another aspect, a method is provided to diagnose that an individual does not have lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 21, in which the individual is classified as not having lung cancer based on the values of biological markers, and in which N = 2-10.
[0074] In another aspect, a method is provided to diagnose lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers , in which biological markers are selected from the group of biological markers shown in Table 21, in which a classification of the values of biological markers indicates that the individual has lung cancer, and in which N = 3-10.
[0075] In another aspect, a method is provided to diagnose lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers , in which biological markers are selected from the group of biological markers shown in Table 21, in which a classification of the values of biological markers indicates that the individual has lung cancer, and in which N = 3-15.
[0076] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, in which the biological markers are selected from the group of biological markers shown in Table 21, in which the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on the values of biological markers, and where N = 3 - 10.
[0077] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, in which the biological markers are selected from the group of biological markers shown in Table 21, in which the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on the values of biological markers, where N = 3-15.
[0078] In another aspect, a method is provided to diagnose the absence of lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, where biological markers are selected from the group of biological markers shown in Table 21, where the classification of biological marker values indicates the absence of lung cancer in the individual, and where N = 3-10 .
[0079] In another aspect, a method is provided to diagnose the absence of lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, in which biological markers are selected from the group of biological markers shown in Table 21, in which a classification of the values of biological markers indicates the absence of lung cancer in the individual, and in which N = 3-15 .
[0080] In another aspect, a method is provided to diagnose an individual's lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers defined in Table 21, in which the individual is classified as having lung cancer based on a classification score that deviates from a predetermined threshold value, and in which N = 2-10.
[0081] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, in which the biological markers are selected from the group of biological markers shown in Table 21, in which the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on a classification score that deviates from a predetermined threshold value, where N = 3-10.
[0082] In another aspect, a method is provided for screening smokers for lung cancer, the method including detecting, in a biological sample from an individual who is a smoker, the values of biological markers, each corresponding to a biological marker on a panel of N biological markers, in which the biological markers are selected from the group of biological markers shown in Table 21, in which the individual is classified as having lung cancer, or the likelihood of the individual having lung cancer is determined, based on a classification score that deviates from a predetermined threshold value, where N = 3-15.
[0083] In another aspect, a method is provided to diagnose the absence of lung cancer in an individual, the method including detecting, in a biological sample from an individual, the values of biological markers, each of which corresponds to at least one of the N biological markers selected from the group of biological markers shown in Table 21, in which said individual is classified as having no lung cancer based on the classification score that deviates from a predetermined threshold value, and in which N = 2-10.
[0084] In another aspect, a computer-implemented method is provided to indicate a likelihood of lung cancer. The method comprises: retrieving biological marker information for an individual on a computer, where the biological marker information comprises the values of biological markers, which correspond to at least one of the N biological markers, where N is, as above defined, selected from the group of biological markers shown in Table 21; perform with the computer a classification of each of the values of biological markers; and, indicate a likelihood that the individual has lung cancer based on a plurality of classifications.
[0085] In another aspect, a computer-implemented method is provided to classify an individual as having or not having lung cancer. The method comprises: retrieving biological marker information for an individual on a computer, in which the biological marker information comprises values of biological markers each of which corresponds to at least one of the N biological markers selected from the group of biological markers provided in Table 21; perform with the computer a classification of each of the values of biological markers; and indicate whether the individual has lung cancer based on a plurality of classifications.
[0086] In another aspect, a computer program product is provided to indicate a likelihood of lung cancer. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data comprise values of biological markers each of which corresponds to at least one of the N biological markers, where N is, as defined above, in the biological sample selected from the group of biological markers shown in Table 21; and code that performs a classification method that indicates a probability that the individual has lung cancer as a function of the biological marker values.
[0087] In another aspect, a computer program product is provided to indicate an individual's lung cancer status. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data comprise values of biological markers each of which corresponds to at least one of the N biological markers in the biological sample, selected from the group of biological markers provided in Table 21; and code that performs a classification method that indicates an individual's lung cancer status as a function of biological marker values.
[0088] In another aspect, a computer-implemented method is provided to indicate a likelihood of lung cancer. The method comprises retrieving biological marker information for an individual on a computer, where the biological marker information comprises a biological marker value corresponding to a biological marker selected from the group of biological markers shown in Table 21; perform with the computer a classification of the biological marker value; and indicate a likelihood that the individual has lung cancer based on the classification.
[0089] In another aspect, a computer-implemented method is provided to classify an individual as having or not having lung cancer. The method comprises retrieving marker information for an individual from a computer, wherein the biological marker information comprises a biological marker value corresponding to a biological marker selected from the group of biological markers provided in Table 21; perform with the computer a classification of the value of the biological marker; and indicate whether the individual has lung cancer based on the classification.
[0090] In yet another aspect, a computer program product is provided to indicate a likelihood of lung cancer. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data value comprises a biological marker corresponding to a biological marker in the biological sample selected from the group of biological markers shown in Table 21; and code that performs a classification method that indicates the likelihood that the individual has lung cancer as a function of the biological marker value.
[0091] In yet another aspect, a computer program product is provided to indicate an individual's lung cancer status. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data value comprises a biological marker corresponding to a biological marker in the biological sample selected from the group of biological markers provided in Table 21; and the code performs a classification method that indicates an individual's lung cancer status as a function of the biological marker value.
[0092] In one aspect of the application, at least one of said biological markers N selected from Table 21, in each of the above methods is a marker selected from Table 20. In yet another embodiment said biological marker selected from Table 20 is MMP-12.
[0093] In another aspect, a method is provided to diagnose lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, which correspond to each of the biological markers on a panel of markers biologicals selected from the group of panels presented in Tables 22-25 in which a classification of the values of biological markers indicates that the individual has lung cancer.
[0094] In another aspect, a method is provided to diagnose the absence of lung cancer, the method including detecting, in a biological sample from an individual, the values of biological markers, each corresponding to a biological marker on a panel of biological markers selected from the group of panels provided in Tables 22-25, in which the classification of the values of biological markers indicates the absence of lung cancer in the individual. Brief Description of Drawings
[0095] Figure 1A is a flow chart for an exemplary method of detecting lung cancer in a biological sample.
[0096] Figure 1B is a flowchart for an exemplary method of detecting lung cancer, in a biological sample using a simple Bayes classification method.
[0097] Figure 2 shows a ROC curve for a single biological marker, SCFsR, using a simple Bayes classifier for a test that detects lung cancer in asymptomatic smokers.
[0098] Figure 3 shows the ROC curves for the biological marker panels of 1-10 biological markers using simple Bayes classifiers for a test that detects lung cancer in asymptomatic smokers.
[0099] Figure 4 illustrates the increase in the classification score (specificity + sensitivity) while the number of biological markers is increased from one to ten using the simple Bayes classification for a benign lung nodule cancer panel.
[00100] Figure 5 shows distributions of biological markers measured for SCFsR as a cumulative distribution function (CDF) in RFU transformed by logarithm for the benign nodule control group (full line) and the lung cancer disease group ( dashed line), together with their adjustment curves to a normal cdf (dashed lines) used to train simple Bayes classifiers.
[00101] Figure 6 illustrates an exemplary computer system for use with the various computer implemented methods described here.
[00102] Figure 7 is a flow chart of a method of indicating the probability that an individual has lung cancer according to a modality.
[00103] Figure 8 is a flow chart of a method of indicating the probability that an individual has lung cancer according to a modality.
[00104] Figure 9 illustrates an exemplary aptamer assay that can be used to detect one or more biological lung cancer markers in a biological sample.
[00105] Figure 10 shows a frequency histogram for which biological markers were used in the construction of classifiers to distinguish between NSCLC and benign nodules from an aggregate set of potential biological markers.
[00106] Figure 11 shows a frequency histogram for which biological markers were used in the construction of classifiers to distinguish between NSCLC and asymptomatic smokers from an aggregate set of potential biological markers.
[00107] Figure 12 shows a frequency histogram for which biological markers were used in the construction of classifiers to distinguish between NSCLC and benign nodules from a set of potential biological markers in consistent locations.
[00108] Figure 13 shows a frequency histogram for which construction markers were used to distinguish between NSCLC and asymptomatic smokers from a set of potential biological markers in consistent locations.
[00109] Figure 14 shows a frequency histogram for which biological markers were used in the construction of classifiers to distinguish between NSCLC and benign nodules from a set of potential biological markers that result from a combination of aggregated markers and in consistent locations. .
[00110] Figure 15 shows a frequency histogram for which biological markers were used in the construction of classifiers to distinguish between NSCLC and asymptomatic smokers from a set of potential biological markers that result from a combination of aggregated markers and in consistent locations .
[00111] Figure 16 shows gel images resulting from downward experiments that illustrate the specificity of aptamers as capture reagents for LBP, C9 and IgM proteins. For each gel, lane 1 is the eluate from Streptavidin-agarose globules, lane 2 is the final eluate, and lane is a MW marker lane (main lanes are 110, 50, 30, 15, and 3 , 5 kDa from top to bottom,).
[00112] Figure 17A shows a pair of histograms summarizing all possible simple protein Bayes classifier scores (sensitivity + specificity) using the biological markers indicated in Table 1, Column 5 (solid) and a set of random markers ( dotted).
[00113] Figure 17B shows a pair of histograms summarizing all possible double protein single Bayes classifier scores (sensitivity + specificity) using the biological markers indicated in Table 1, Column 5 (solid) and a set of random markers ( dotted).
[00114] Figure 17C shows a pair of histograms summarizing all possible triple protein Bayes classifier scores (sensitivity + specificity) using the biological markers indicated in Table 1, Column 5 (solid) and a set of random markers ( dotted).
[00115] Figure 18A shows a pair of histograms summarizing all possible simple protein Bayes classifier scores (sensitivity + specificity) using the biological markers indicated in Table 1, Column 6 (solid) and a set of random markers ( dotted).
[00116] Figure 18B shows a pair of histograms summarizing all possible double protein single Bayes classifier scores (sensitivity + specificity) using the biological markers indicated in Table 1, Column 6 (solid) and a set of random markers ( dotted).
[00117] Figure 18C shows a pair of histograms summarizing all possible triple protein Bayes classifier scores (sensitivity + specificity) using the biological markers indicated in Table 1, Column 6 (solid) and a set of random markers ( dotted).
[00118] Figure 19A shows the sensitivity + specificity score for simple Bayes classifiers using 2-10 markers selected from the full panel (♦) and the scores obtained reducing to the best 5 (■), 10 (▲) and 15 ( x) markers during classifier generation for the benign nodule control group.
[00119] Figure 19B shows the sensitivity + specificity score for simple Bayes classifiers using 2-10 markers selected from the full panel (♦) and the scores obtained reducing to the best 5 (■), 10 (▲) and 15 ( x) markers during the generation of the classifier for the smoker control group.
[00120] Figure 20A shows a set of ROC curves modeled from the data in Tables 38 and 39 for panels of 1-5 markers.
[00121] Figure 20B shows a set of ROC curves calculated from training data for panels of 1-5 markers as in Figure 19A.
[00122] Figure 21 shows the relative changes in protein expression for 813 proteins from eight samples of NSCLC resection between adjacent and distant tissue (Figure 21A), tumor tissue and adjacent tissue (Figure 21B) and tumor tissue and distant tissue (Figure 21C), expressed as median log2 ratios. The dashed line represents a two-fold change (log2 = 1).
[00123] Figure 22 shows a heat map of protein levels in tumor tissue samples. The samples are organized in columns and are separated into adjacent, distant, and tumor samples. Within each type of tissue, the samples are separated into adenocarcinoma (AC) and squamous cell carcinoma (CPB). The numbers above each column correspond to patient codes. The proteins are displayed in rows and have been ordered using hierarchical grouping.
[00124] Figure 23 (A-T) describes proteins with increased levels in tumor tissue compared to adjacent or distant tissue.
[00125] Figure 24 (A-P) describes proteins with reduced levels in the tumor tissue compared to the adjacent or distant tissue of the eight NSCLC samples used in this study.
[00126] Figure 25 shows SOMAmer histochemistry in frozen tissue sections for selected biological markers detected in this study. (A) Thrombospondin-2 (red) staining the fibrocollagenous matrix around a tumor nest. (B) Corresponding to a normal lung specimen colored with SOMAmer Thrombospondin-2 (red). (C) SOMAmer Macrophage Mannose Receptor (red) staining scattered macrophages in a lung adenocarcinoma. (D) SOMAmer Macrophage Mannose Receptor (red) staining numerous alveolar macrophages in a section of normal lung parenchyma. (E) Multicolored image highlighting the cytomorphological distribution of Macrophage Mannose Receptor SOMAmer coloring: Green = Cytokeratin (antibodies AE1 / AE3), Red = CD31 (Antibody EP3095) and Orange = SOMAmer. All cores in this figure are contrasted with DAPI.
[00127] Figure 26 shows changes in protein expression in NSCLC tissues compared to serum. The top two panels show the log2 (LR) ratio derived from serum samples against log relationships derived from adjacent tissue and distant tissue, respectively. The last four panels show parts of the plots above, indicated by the color of the plot (green for decreased expression and red for increased expression compared to non-tumor tissue). Analytics shown in Figures 23 and 24 have been labeled and the analytics mentioned in the publication on serum samples are shown in full red symbols.
[00128] Figure 27 illustrates the histochemical identification of Thrombospondin-2 in tissue samples. Thrombospondin-2 is identified in a serial frozen section of a single lung carcinoma specimen by (A) a polyclonal homemade rabbit polyclonal thrombospondin-2 antibody, (B) the preimmune serum of rabbits used to make the home polyclonal antibody, (C) a commercial polyclonal rabbit thrombospondin-2 (Novus) antibody, and (D) SOMAmer thrombospondin-2. Thromospondin-2 SOMAmer was then used to color frozen sections of normal and malignant lung tissue, with the development of a standard color of Avidin-Biotin-Peroxidase, to demonstrate different morphological distributions: (E) Strong staining of the fibrotic stroma surrounding tumor nests, with minimal cytosolic staining of carcinoma cells, (F) Strong staining of the fibrotic stroma surrounding tumor nests in a mucinous adenocarcinoma, without significant staining of the carcinoma cells, (G) normal lung tissue, showing strong cytoplasmic staining of the bronchial epithelium and scattered macrophages alveolar and (H) strong cytoplasmic staining of an adenocarcinoma, without significant staining of the non-fibrotic stroma, predominantly inflammatory stroma. DETAILED DESCRIPTION
[00129] The practice of the invention disclosed herein employs, unless otherwise indicated, conventional methods of chemistry, microbiology, molecular biology and recombinant DNA techniques within the level of those skilled in the art. Such techniques are fully explained in the literature. See, e.g., Sambrook, et al. Molecular Cloning: A Laboratory Manual (current edition); DNA cloning: A Practical Approach, vol. I & II (D. Glover, ed.); Oligonucleotide Synthesis (N. Gait, ed., Current Edition.); Nuclei Acid Hybridization (B. Hames & S. Higgins, eds., Current Edition.); Transcription and Translation (B. Hames & S. Higgins, eds., Current Edition; Histology for Pathologists (SE Mills, Current Edition) All publications, published patent documents, and patent applications cited in this specification are indicative of the level of experts in the technique (s) to which the invention belongs, All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as if each individual publication, published patent document, or patent application was specifically and individually indicated to be incorporated by reference.
[00130] Reference will now be made in detail to the representative modalities of the invention. Although the invention is described in conjunction with the listed modalities, it should be noted that the invention is not intended to be limited to those modalities. On the contrary, the invention is intended to cover all alternatives, modifications and equivalents that may be included within the scope of the present invention as defined by the claims.
[00131] Unless otherwise defined, the technical and scientific terms used herein have the same meaning as normally understood by someone of common knowledge in the technique to which this invention belongs. Although any methods, devices and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, the preferred methods, apparatus and materials are now described.
[00132] As used in the present application, including the appended claims, the singular forms "one", "one", and "a" include references in the plural, unless the content clearly dictates otherwise, and are used interchangeably with "at least one" and "one or more". Thus, reference to "an aptamer" includes mixtures of aptamers, reference to "a probe" includes mixtures of probes, and the like.
[00133] As used herein, the term "fence" represents an insignificant modification or variation of the numerical value such that the basic function of the point to which the numerical value refers is unchanged.
[00134] As used herein, the terms "comprises", "comprising", "includes", "including", "contains", "containing", and any variation thereof, are intended to cover a non-exclusive inclusion, of such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or a list of elements not only includes those elements but may include other elements that are not expressly listed or inherent in such a process, method, product-by-process, or composition of matter.
[00135] This application includes biological markers, methods, equipment, reagents, systems and kits for the detection and diagnosis of lung cancer.
[00136] In one aspect, one or more biological markers are provided for use both alone and in various combinations to diagnose lung cancer, to allow differential diagnosis of pulmonary nodules as benign or malignant, to monitor lung cancer recurrence, or address other clinical indications. In other respects, said biological marker (s) can be used to determine information about an individual's lung cancer, such as prognosis, cancer classification, forecasting a disease probability or selecting treatment. As described in detail below, exemplary embodiments include the biological markers provided in Tables 18, 20 and 21, which were identified using an aptamer based multiplexed assay that is generically described in Example 1 and, more specifically, in Examples 2 and 6. Each of the biological markers is useful in testing any type of sample, as defined below.
[00137] Table 1, Col. 2 presents the results obtained from the analysis of hundreds of individual blood samples from NSCLC cancer cases, and hundreds of individual equivalent blood samples from smokers and people diagnosed with benign pulmonary nodules. . The groups of smokers and benign nodules were designed to serve populations with which a lung cancer diagnostic test can have the greatest benefit. (These cases and controls were obtained from various clinical sites to mimic the range of real world conditions in which such a test can be applied). Potential biological markers were measured in individual samples, rather than combining disease and control blood, which allowed for a better understanding of individual and group differences in phenotypes associated with the presence or absence of disease (in this case lung). Since more than 800 protein measurements were made on each sample, and several hundred samples of each disease and control population were measured individually, Table 1, column 2 resulted from an analysis of an extraordinarily large set big data. The measurements were analyzed using the methods described here in the section "Classification of Biological Markers and Calculation of Disease Scores".
[00138] Table 1, column 2 lists the biological markers found to be useful in samples obtained from particular individuals with NSCLC from "control" samples obtained from smokers and individuals with benign lung nodules. Using a multiplexed aptamer assay as described herein, thirty-eight biological markers were discovered, which distinguished samples obtained from individuals who had lung cancer from samples obtained from individuals in the smoking control group (see Table 1, column 6). Likewise, using a multiplexed aptamer assay, forty biological markers were found which distinguished samples obtained from individuals with NSCLC from samples obtained from people who had benign lung nodules (see Table 1, column 5). Together, the two lists of 38 and 40 biological markers are composed of 61 unique biological markers, because there is considerable overlap between the list of biological markers to distinguish NSCLC from benign nodules and the list to distinguish NSCLC from smokers who do not have lung cancer. .
[00139] Table 18 presents the results obtained from the analysis of eight individual tissue samples from smokers diagnosed with NSCLC, as described in Example 6. All patients were smokers ranging from 47 to 75 years of age and covering stages 1A through 3B of NSCLC. Three samples were obtained from each individual: tumor tissue, adjacent healthy tissue (1 cm from the tumor) and distant lung tissue not involved. The samples were chosen to match the populations with which a lung cancer diagnostic test can have the greatest benefit. Potential biological markers were measured in individual samples, instead of combining disease and control tissue, which allowed for a better understanding of individual and group variations in phenotypes associated with the presence or absence of disease (in this case lung cancer ). The measurements were analyzed using the Mann-Whitney test.
[00140] Table 18 lists the biological markers found to be useful in distinguishing samples obtained from individuals with NSCLC from "control" samples obtained from adjacent and distant lung tissue not involved in lung cancer obtained from the same individuals. Using a multiplexed aptamer assay as described herein, thirty-six biological markers were found that distinguished tumor tissue samples from samples obtained from adjacent and distant lung tissue in individuals who had been diagnosed with NSCLC. With reference to Table 1, col. 2, it can be seen that eleven of the biological markers overlap with those identified in serum samples as described in Example 2. An additional marker which was not measured in the initial serum profile, MMP-12, was found to be useful as a biological marker both in serum and tissue. Table 21 provides a list of the total number of markers (eighty-six) identified in both the combined serum and tumor tissue samples. Table 20 lists the biological markers identified that were unique to the tumor tissue samples (twenty-five).
[00141] Although some of the biological lung cancer markers described are useful in themselves for detecting and diagnosing lung cancer, methods for grouping the various subsets of the biological lung cancer markers are also described here, in which each group or selection subgroup is useful as a panel of three or more biological markers, interchangeably referred to herein as a "biological marker panel" and a panel. Thus, various embodiments of the present application provide combinations that comprise N markers, where N is at least two biological markers. In other modalities, N is selected from 2-86 biological markers (Table 21); 2-36 biological markers (Table 18) or 225 biological markers (Table 20). In other embodiments, N is selected from 2-86 (Table 21) and at least one of said N biological markers is MMP-12. In other embodiments, N is selected from 2-25 (Table 20) and at least one of said N biological markers is MMP-12. Representative panels of 2-5 biological markers including MMP-12, as one of the markers are shown in Tables 22-25.
[00142] In still other modalities, the biological markers are selected from those listed in Table 18 and N is selected to be any number from 2-7, 2-10, 2-15, 2-20, 2-25, 2- 30, 2-36. In other embodiments, N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-36. In other embodiments, N is selected to be any number of 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-36. In other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5-36. In other embodiments, N is selected to be any number from 6-10, 615, 6-20, 6-25, 6-30, 6-36. In other embodiments, N is selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-36. In other embodiments, N is selected to be any number from 810, 8-15, 8-20, 8-25, 8-30, 8-36. In other embodiments, N is selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-36. In other embodiments, N is selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-36. It will be appreciated that N can be selected to cover similar bands, but of a higher order.
[00143] In still other modalities the biological markers are selected from those listed in Table 20 and N is selected to be any number from 2-7, 2-10, 2-15, 2-20, 2-25. In other embodiments, N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25. In other modalities, N is selected to be any number of 4-7, 4-10, 4-15, 4-20, 4-25. In other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25. In other embodiments, N is selected to be any number from 6-10, 6-15, 6-20, 6-25. In other embodiments, N is selected to be any number from 7-10, 7-15, 7-20, 7-25. In other embodiments, N is selected to be any number from 810, 8-15, 8-20, 8-25. In other embodiments, N is selected to be any number from 9-15, 9-20, 9-25. In other embodiments, N is selected to be any number from 10-15, 10-20, 10-25. In other embodiments, N is selected to be any number from 915, 9-20, 9-25. In other embodiments, N is selected to be any number from 10-15, 10-20, 10-25. It will be appreciated that N can be selected to cover similar bands, but of a higher order.
[00144] In still other modalities the biological markers are selected from those listed in Table 21 and N is selected to be any number from 2-7, 2-10, 2-15, 2-20, 2-25, 2-30 , 2-35, 240, 2-45, 2-50, 2-55, 2-60, 2-65, 2-70, 2-75, 2-80, or 2-86. In other embodiments, N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, 3-45, 3 - 50, 3-55, 3-60, 3-65, 3-70, 3-75, 3-80, or 3-86. In other modalities, N is selected to be any number of 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4 - 50, 4-55, 4-60, 4-65, 4-70, 4-75, 4-80, or 4-86. In other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5-35, 5-40, 5-45, 5 - 50, 5-55, 5-60, 5-65, 5-70, 5-75, 5-80, or 5-86. In other embodiments, N is selected to be any number from 6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45, 650, 6 - 55, 6-60, 6-65, 6-70, 6-75, 6-80, or 6-86. In other embodiments, N is selected to be any number 7-10, 7-15, 7-20, 7-25, 7-30, 7-35, 7-40, 7-45, 7-50, 7- 55, 7-60, 7-65, 7-70, 775, 7-80, or 7-86. In other embodiments, N is selected to be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 850, 8 - 55, 8-60, 8-65, 8-70, 8-75, 8-80, or 8-86. In other embodiments, N is selected to be any number from 9-15, 920, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9-55, 9 - 60, 9-65, 9-70, 9-75, 980, or 9-86. In other embodiments, N is selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-35, 10-40, 10-45, 10-50, 10-55, 10 - 60, 10-65, 10-70, 10-75, 10-80, or 10-86. It will be appreciated that N can be selected to cover similar bands, but of a higher order.
[00145] In one embodiment, the number of biological markers useful for a subset of biological markers or panel is based on the sensitivity and specificity value for the particular combination of biological marker values. The term "sensitivity" and "specificity" are used here with respect to the ability to correctly classify an individual, based on one or more values of biological markers detected in the biological sample, such as having lung cancer or not having cancer of lung. "Sensitivity" indicates the performance of the biological marker (s) in relation to correctly classifying individuals who have lung cancer. "Specificity" indicates the performance of the biological marker (s) in relation to correctly classifying individuals who do not have lung cancer. For example, the specificity of 85% and sensitivity of 90% for a panel of markers used to test a set of control samples and lung cancer samples indicates that 85% of the control samples were correctly classified as control samples by the panel , and 90% of lung cancer samples were correctly classified as lung cancer samples by the panel. The desired or preferred minimum value can be determined as described in Example 3.
[00146] In one aspect, lung cancer is detected or diagnosed in an individual by performing an assay on a biological sample from the individual and detecting values of biological markers, each of which corresponds to at least one of the MMP biological markers -7, MMP-12, or IGFBP-2 and at least N additional biological markers selected from the list of biological markers in Table 21, where N is equal to 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15. In an additional aspect, lung cancer is detected or diagnosed in an individual by performing an assay on a biological sample from the individual and detecting values of biological markers , which correspond to each of the biological markers MMP-7, MMP-12, or IGFBP-2 and one of at least N additional biological markers selected from the list of biological markers in Table 21, where N is equal to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or 13. In an additional aspect, lung cancer is detected or diagnosed in an individual by performing an assay on a biological sample from the individual and detecting values of biological markers, each corresponding to the biological marker MMP-7 and one of at least N additional biological markers selected from the list of biological markers in Table 21, where N is equal to 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15. In an additional aspect , lung cancer is detected or diagnosed in an individual by performing an assay on a biological sample from the individual and detecting values of biological markers, each of which corresponds to the biological marker MMP-12 and one of at least N additional biological markers selected from the list of biological markers in Table 21, where N is equal to 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15. In an additional aspect, lung cancer is detected or diagnosed in an individual by performing using an assay of a biological sample from the individual and detecting values of biological markers, each corresponding to the biological marker IGFBP-2 and one of at least N additional biological markers selected from the list of biological markers in Table 21, in that N is equal to 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15.
[00147] The biological lung cancer markers identified here represent a relatively large number of choices for subsets or panels of biological markers that can be used to effectively detect or diagnose lung cancer. The selection of the desired number of such biological markers depends on the specific combination of selected biological markers. It is important to remember that panels of biological markers for the detection or diagnosis of lung cancer may also include biological markers that are not found in tables 18, 20 or 21, and that the inclusion of additional biological markers not found in Tables 18, 20 or 21, can reduce the number of biological markers in a given subset or the panel that is selected from Tables 18, 20 or 21. The number of biological markers from Tables 18, 20 or 21, used in a subset or the panel can also be reduced if additional biomedical information is used in conjunction with the values of biological markers to establish acceptable sensitivity and specificity for a given assay.
[00148] Another factor that can affect the number of biological markers to be used in a subset or panel of biological markers are the processes used to obtain biological samples from individuals being diagnosed for lung cancer. In a carefully controlled sample acquisition environment, the number of biological markers needed to satisfy the desired sensitivity and specificity values will be less than in a situation where there can be no more variation in the collection, handling and storage of samples. In developing the list of biological markers established in Tables 18, 20 or 21, multiple sample collection sites were used to collect classifier training data. This provides more robust biological markers, which are less sensitive to variations in the collection, handling and storage of samples, but may also require that the number of biological markers in a subset or the panel be greater than if the training data were all obtained in similar conditions.
[00149] One aspect of the present patent application can be described generically with reference to Figures 1A and B. A biological sample is obtained from an individual or individuals of interest. The biological sample is then tested to detect the presence of one or more (N) biological markers of interest and to determine a biological marker value for each of said N markers (referred to in Figure 1B as the RFU marker). Once a biological marker has been detected and assigned a biological marker value, each marker is scored or classified as described in detail here. The marker scores are then combined to provide a total diagnostic score, which indicates the likelihood that the individual from whom the sample was obtained has lung cancer.
[00150] As used herein, "lung" can be referred to interchangeably as "pulmonary".
[00151] As used herein, "smoker" refers to an individual who has a history of inhaling tobacco smoke.
[00152] "Biological sample", "sample", and "test sample" are used here interchangeably to refer to any material, biological fluid, tissue or cell obtained otherwise derived from an individual. This includes blood (including whole blood, leukocytes, peripheral blood mononuclear cells, leather-colored lining, plasma, and serum), sputum, tears, mucus, nasal washes, nasal aspirate, breathing, urine, semen, saliva, fluids meninges, amniotic fluid, glandular fluid, lymphatic fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, cells, a cellular extract, and cerebrospinal fluid. This also includes fractions experimentally separated from all precedents. For example, a blood sample can be fractionated into serum or into fractions containing certain types of blood cells, such as red blood cells or white blood cells (leukocytes). If desired, the sample can be a combination of samples from an individual, such as a combination of a tissue and fluid sample. The term "biological sample" also includes materials that contain homogenized solid material, such as a stool sample, a tissue sample, or a tissue biopsy, for example. The term "biological sample" also includes materials derived from a tissue culture or a cell culture. Any suitable methods for obtaining a biological sample can be employed; Exemplary methods include, for example, phlebotomy, cotton swab (for example, mouth swab), and a fine needle aspirate biopsy procedure. Exemplary tissues susceptible to fine needle aspiration include lymph nodes, lung, lung lavages, BAL (bronchoalveolar lavage), thyroid, breast and liver. Samples can also be collected, for example, by micro dissection (for example, laser capture micro dissection (LCM) or laser micro dissection (LMD)), bladder washing, smear (for example, a Papanicolaou), or ductal lavage. A "biological sample" obtained or derived from an individual includes any such sample that has been processed in any suitable way after being obtained from the individual.
[00153] The "tissue sample" or "tissue" refers to a certain subset of the biological samples described above. According to this definition, tissues are sets of macromolecules in a heterogeneous environment. As used herein, tissue refers to a single cell type, a collection of cell types, an aggregate of cells, or an aggregate of macromolecules. Tissues are generally a physical matrix of macromolecules that can be both fluid and rigid, both in terms of structure and composition. The extracellular matrix is an example of a more rigid tissue, both structurally and in composition, while a double layer of membrane is more fluid in its structure and composition. Tissue includes, but is not limited to, an aggregate of cells, usually of a particular type, together with its intercellular substance, which forms one of the structural materials most used to denote the general cellular tissue of a given organ, for example, tissue kidney, brain tissue, lung tissue. The four general classes of tissue are epithelial tissue, connective tissue, nervous tissue, and muscle tissue. Methods for identifying slow out-of-rate aptamers for tissue targets are described in International Application Publication No. WO 2011/006075, published on 13 January 2011, which is incorporated herein by reference in its entirety.
[00154] Examples of tissues that fall within the scope of this definition include, but are not limited to, heterogeneous aggregates of macromolecules, such as the formation of fibrin clots, which are acellular; homogeneous or heterogeneous aggregates of cells, larger ordered structures containing cells that have a specific function, such as organs, tumors, lymph nodes, arteries, etc., and individual cells. Tissues or cells can be in their natural environment, isolated, or in tissue culture. The tissue may be intact or modified. The modification can include various changes, such as transformation, transfection, activation and isolation of substructure, for example, cell membranes, cell nuclei, cell organelles, etc.
[00155] Tissue sources, cellular or subcellular structures can be obtained from prokaryotes, as well as eukaryotes. This includes human, animal, plant, bacterial, fungal and viral structures.
[00156] In addition, it should be understood that a biological sample can be derived by taking biological samples from a large number of individuals and grouping them or grouping an aliquot of each biological sample from each individual. The combined sample can be treated as a sample from a single individual, and if the presence of cancer is established in the combined sample, then each individual biological sample can be retested to determine which individual (s) have cancer. lung.
[00157] For the purposes of this specification, the term "data attributed to an individual's biological sample" is intended to mean that the data in a derived form, or was generated using, the individual's biological sample. The data may have been reformatted, revised, or mathematically altered in some way, after having been generated, for example, by converting from units of one measurement system to units of another measurement system; but, the data is understood to have been derived from, or was generated using, the biological sample.
[00158] "Target", "target molecule" and "analyte" are used here interchangeably to refer to any molecule of interest that may be present in a biological sample. The "molecule of interest" includes any minor alteration of a specific molecule, such as, in the case of a protein, for example, small variations in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation or any other manipulation or modification, such as conjugation to a labeling component, which does not substantially alter the identity of the molecule. The "target molecule", "target", or "analyte" is a set of copies of a type or species of molecule or multi-molecular structure. "Target molecules", "targets" and "analytes" refer to more than one set of such molecules. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affinity bodies, antibody mimics, viruses, pathogens, toxic substances, substrates, metabolites, analogs transition state, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues and any fragment or part of any of the above.
[00159] As used herein, "polypeptide" peptide "," and "protein" are used interchangeably herein to refer to polymers of amino acids of any length. The polymer can be linear or branched, which can comprise the modified amino acids and can be disrupted by non-amino acids. The terms also encompass a polymer of amino acids that has been modified naturally or by intervention, for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation or any other modification, such as conjugation with a labeling component. Also included within the definition are, for example, polypeptides containing one or more analogs of an amino acid (including, for example, unnatural amino acids, etc.), as well as other modifications known in the art. Polypeptides can be single chains or associated chains. Also included within the definition are intact proteins and mature pre-proteins; peptides or polypeptides derived from a mature protein, fragments of a protein, junction variants, recombinant forms of a protein; protein variants with amino acid modifications, deletions or substitutions, digest; and post-translational modifications, such as glycosylation, acetylation, phosphorylation and the like.
[00160] As used herein, "marker" and "biological marker" are used interchangeably to refer to a target molecule that indicates or is a sign of an individual's normal or abnormal process or an individual's disease or condition . More specifically, a "marker" or "biological marker" is a parameter, anatomical, physiological, biochemical or molecular associated with the presence of a specific, normal or abnormal, physiological state or process, and, if abnormal, either chronic or acute. Biological markers are detectable and measurable by a variety of methods, including laboratory tests and medical imaging. When a biological marker is a protein, it is also possible to use the expression of the corresponding gene as a surrogate measure of the amount or the presence or absence of the corresponding biological marker protein in a biological sample, or methylation status of the gene encoding the biological marker or proteins that control the expression of the marker.
[00161] As used herein, "biological marker value", "value", "biological marker level", and "level" are used interchangeably to refer to a measurement that is made using any analytical method for detecting the biological marker in a biological sample and indicating the presence, absence, absolute quantity or concentration, relative quantity or concentration, title, a level, an expression level, a ratio of measured levels, or similar, from, to, or corresponding to biological marker in the biological sample. The exact nature of the "value" or "level" depends on the design and specific components of the analytical method employed to detect the biological marker.
[00162] When a biological marker indicates or is a sign of an abnormal process or a disease or other condition of an individual, that biological marker is generically described as being either over-expressed or under-expressed, compared to a level or expression value of the biological marker that indicates either it is a sign of a normal process or an absence of a disease or other condition of an individual.
[00163] "upward regulation", "upward regulation", "overexpression", "overexpression", and any variations thereof are used interchangeably to refer to a value or level of a biological marker in a biological sample that is greater than a value or level (or range of values or levels) of a biological marker that is normally detected in similar biological samples from healthy, or normal individuals. The terms can also refer to a value or level of a biological marker in a biological sample that is greater than a value or level (or range of values or levels) of a biological marker that can be detected at a different stage of a disease particular.
[00164] "down regulation", "down regulation", "under-expression", "under-expression", and any variation thereof, are used interchangeably to refer to a value or level of a biological marker in a biological sample that is less than a value or level (or range of values or levels) of a biological marker that is normally detected in similar biological samples from healthy, or normal individuals. The terms can also refer to a value or level of a biological marker in a biological sample that is less than a value or level (or range of values or levels) of a biological marker that can be detected at a different stage than a particular disease .
[00165] In addition, a biological marker that is either overexpressed or underexpressed can also be said to be "differentially expressed" or to have a "differential level" or "differential value" compared to a level of "normal" expression or the value of the biological marker that indicates either it is a sign of a normal process or an absence of a disease or other condition of an individual. Thus, the "differential expression" of a biological marker can also be said to be a variation from a "normal" level of expression of the biological marker.
[00166] The term "differential gene expression" and "differential expression" are used interchangeably to refer to a gene (or its expression product of the corresponding protein), whose expression is activated at a higher or lower level in an individual suffering from a specific disease, in relation to its expression in a normal or control individual. The terms also include genes (or the corresponding protein expression products), whose expression is activated at a higher or lower level at different stages of the same disease. It is also understood that a differentially expressed gene can either be activated or inhibited at the nucleic acid level or at the protein level, or it can be subjected to alternative junction to result in a product other than the polypeptide. Such differences can be seen through a variety of changes including mRNA levels, surface expression, secretion or other partitioning of a polypeptide. Differential gene expression can include a comparison of expression between two or more genes or their gene products, or a comparison of expression indices between two or more genes or their gene products, or even a comparison of two processed products unlike the same gene, which differ between normal individuals and individuals suffering from a disease, or between different stages of the same disease. Differential expression includes both quantitative and qualitative differences, in the pattern of temporal or cellular expression in a gene or its expression products, between, for example, normal and diseased cells, or between cells that have suffered disease events or different stages of the disease.
[00167] As used herein, "individual" refers to a test subject or the patient. The individual can be a mammal or a non-mammal. In various modalities, the individual is a mammal. An individual can be a human or a non-human mammal. In various modalities, the individual is a human being. A healthy or normal individual is an individual in which the disease or condition of interest (including, for example, lung disease, associated lung disease, or other lung disease) is not detectable through conventional diagnostic methods.
[00168] "Diagnosis", "diagnosis", "diagnoses" and their variations refer to the detection, determination, or recognition of an individual's state or health condition, based on one or more signs, symptoms, data or other information pertaining to that individual. An individual's health status can be diagnosed as healthy / normal (ie, a diagnosis of the absence of a disease or condition) or diagnosed as sick / abnormal (ie, a diagnosis of the presence or an assessment of the characteristics, disease or condition). The terms "diagnosis", "diagnosis", "diagnoses", etc., encompass, in relation to a particular disease or condition, the initial detection of the disease, the characterization or classification of the disease, the detection of progression, remission, or recurrence of the disease. disease, and detecting the disease response after administering treatment or therapy to the individual. The diagnosis of lung cancer includes distinguishing individuals, including smokers and non-smokers, who have cancer from individuals who do not. It also includes distinguishing benign pulmonary nodules from cancerous pulmonary nodules.
[00169] "Prognosis", "prognosis", "prognosis", and its variations refer to the prediction of a future course of a disease or condition of an individual who has the disease or condition (for example, predicting the patient survival), and such terms cover the assessment of the disease response after administering treatment or therapy to the individual.
[00170] "Assess", "assessing", "assessment", and the variation thereof cover both "diagnosis" and "prognosis" and also include determinations or predictions about the future course of a disease or condition in an individual who does not have the disease, as well as determinations or predictions about the likelihood that a disease or condition will recur in an individual who has apparently been cured of the disease. The term "assessment" also includes assessing an individual's response to treatment, such as, for example, predicting whether an individual is likely to respond favorably to a therapeutic agent, or is unlikely to respond to a therapeutic agent ( or you will experience toxic effects or other undesirable side effects, for example), the selection of a therapeutic agent for administration to an individual, or the monitoring or determination of an individual's response to a therapy that has been administered to the individual. Thus, "evaluating" lung cancer can include, for example, any of the following: predicting the future course of lung cancer in an individual; predicting the recurrence of lung cancer in an individual who has apparently been cured of lung cancer, or de-determining or predicting an individual's response to a lung cancer treatment or selecting a lung cancer treatment to administer to an individual with based on a determination of the biological marker values from the individual's biological samples.
[00171] Any of the following examples can be referred to either as "diagnosing" or "evaluating" lung cancer: initially detecting the presence or absence of lung cancer, determining a specific stage, type or sub-type of classification, or another, or characteristic of lung cancer; determine whether a pulmonary nodule is a benign lesion or a malignant lung tumor; or detect / monitor the progression of lung cancer (for example, monitoring of lung tumor growth or metastasis), remission or recurrence.
[00172] As used herein, "additional biomedical information" refers to one or more assessments of an individual, other than using any of the biological markers described here, which are associated with the risk of lung cancer. "Additional biomedical information" includes any of the following: physical descriptors of an individual, physical descriptors of a lung nodule seen by computed tomography, the height and / or weight of an individual, the gender of an individual, the ethnicity of an individual, smoking, occupational history, exposure to known carcinogens (for example, exposure to anyone from asbestos, radon gas, chemicals, smoke from fires and air pollution, which may include emissions from mobile or stationary sources, such as industrial / factory or auto / marine / aircraft emissions), exposure to second-hand smoke, family history of lung cancer (or other cancer), the presence of lung nodules, nodule size, nodule location, nodule morphology (for example, as seen through computed tomography, ground-glass opacity (GGO), solid, non-solid), nodule edge characteristics (for example, smooth, lobed, filament and smooth, spiculated, infiltrating), and the like. Smoking is usually quantified in terms of the "years package", which refers to the number of years the person has smoked multiplied by the average number of packs smoked per day. For example, a person who has smoked, on average, a pack of cigarettes a day for 35 years is said to have a 35-year pack. Additional biomedical information can be obtained from an individual using routine techniques known in the art, such as from the individual himself through the use of a routine patient questionnaire or health history questionnaire, etc., or from a medical professional, etc. Alternatively, additional biomedical information can be obtained from routine imaging techniques, including computed tomography (eg, low-dose CT) and X-rays. Assaying levels of biological markers in combination with an assessment of any additional biomedical information can, for example, improve sensitivity, specificity and / or AUC to detect lung cancer (or other uses related to lung cancer), in comparison with biological marker tests alone or evaluating any element of complementary biomedical information alone (for example, computed tomography alone).
[00173] The term "area under the curve" or "AUC" means the area under the curve of a receiver operating characteristic curve (ROC), both of which are well known in the art. AUC measurements are useful for comparing the accuracy of a classifier across the entire data range. Classifiers with a higher AUC have a greater ability to correctly classify unknowns between two interest groups (for example, lung cancer samples and normal or control samples). ROC curves are useful for plotting the performance of a particular aspect (for example, any of the biological markers described here and / or any other item of additional biomedical information) to distinguish between two populations (for example, cases with lung cancer and controls without lung cancer). Typically, characteristic data across the entire population (for example, cases and controls) is ranked in ascending order based on the value of a single characteristic. Then, for each value for that characteristic, the rates of true positives and false positives for the data are calculated. The rate of true positives is determined by scoring the number of cases above the value for that characteristic and then dividing it by the total number of cases. The rate of false positives is determined by scoring the number of controls above the value for the characteristic and then dividing it by the total number of controls. Although this definition refers to situations in which a characteristic is high in cases compared to controls, this definition also applies to situations in which a characteristic is less in cases compared to controls (in such a scenario, the samples below would be counted the value of that characteristic). ROC curves can be generated by a single characteristic, as well as for other unique outputs, for example, a combination of two or more characteristics can be mathematically combined (for example, added, subtracted, multiplied, etc.) to provide a value of simple sum, and this simple sum value can be plotted on a ROC curve. In addition, any combination of multiple characteristics, where the combination obtains a single output value, can be represented on a ROC curve. These combinations of characteristics may include a test. The ROC curve is the plot of the true positive rate (sensitivity) of a test against the false positive rate (1-specificity) of the test.
[00174] As used herein, "detecting" or "determining" in relation to a biological marker value includes the use of both the instrument necessary to observe and record a signal corresponding to a biological marker value and the material (s) ) needed to generate the signal. In several modalities, the value of the biological marker is detected by any suitable method, including fluorescence, chemiluminescence, surface plasma resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, microscopy tunneling scan, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.
[00175] "Solid support" refers here to any substrate that has a surface to which the molecules can be attached, directly or indirectly, either through covalent or non-covalent bonds. A "solid support" can have a variety of physical shapes, which can include, for example, a membrane, a chip (for example, a protein chip), a smooth surface (for example, a glass slide or lamella) , a column, a hollow, solid, semi-solid, pore or cavity containing particles, such as, for example, a granule, a gel, a fiber, which includes an optical fiber material, a matrix, and a container of sample. Exemplary sample containers include sample wells, tubes, vials, capillary vessels, and any other groove or recess container capable of holding a sample. A sample receptacle may be contained in a multi-sample platform, such as a microtiter plate, slide, microfluidic device, and the like. A support can be composed of a natural or synthetic material, an organic or inorganic material. The composition of the solid support to which the capture reagents are attached generally depends on the fixation method (e.g., covalent bond). Other exemplary containers include bulk emulsions and oil / aqueous controlled microdroplets and microfluidics within which testing and related manipulations can take place. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica, or silica-based materials, functionalized glasses, modified silicon, carbon, metals, inorganic glasses, membranes, nylon, natural fibers (such as, for example, silk, wool, and cotton), polymers, and the like. The material that makes up the solid support can include reactive groups such as, for example, carboxy, amino or hydroxyl groups, which are used for fixing the capture reagents. Solid polymeric supports can include, for example, polystyrene, polyethylene glycol tetraftalate, polyvinyl acetate, polyvinyl chloride, polyvinyl-pyrrolidone, polyacrylonitrile, polymethyl polymethacrylate, polytetrafluoroethylene, butyl rubber, polyethylene rubber, natural rubber, styrene-butadiene rubber, natural rubber polypropylene, (poly) tetrafluoroethylene, vinylidene (poly) fluoride, polycarbonate, and polymethylpentene. Suitable solid support particles that can be used include, for example, encoded particles, such as Luminex ® type encoded particles, magnetic particles, and glass particles. Exemplary Uses of Biological Markers
[00176] In several exemplary embodiments, methods are provided for diagnosing an individual's lung cancer by detecting one or more values of biological markers that correspond to one or more biological markers that are present in an individual's lung tissue, for example any number of analytical methods, including any of the analytical methods described here. These biological markers are, for example, expressed differentially in individuals with lung cancer, compared to individuals without lung cancer, particularly NSCLC. Detection of the differential expression of an individual's biological marker can be used, for example, to allow early diagnosis of lung cancer, to distinguish between a benign and malignant pulmonary nodule (such as, for example, a nodule seen on tomography (CT), to monitor the recurrence of lung cancer, or for other clinical indications, including prognosis determination and treatment methods.
[00177] Any of the biological markers described herein can be used in a variety of clinical indications for lung cancer, including any of the following: detection of lung cancer (for example, in a high-risk individual or population), featuring lung cancer (eg, determine type, subtype, or stage of lung cancer), as well as by the distinction between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) and / or between adenocarcinoma and squamous cell carcinoma (or otherwise facilitate histopathology); determine whether a lung nodule is a benign nodule or a malignant lung tumor; determine the prognosis of lung cancer, monitor the progression or remission of lung cancer, monitor the recurrence of lung cancer; monitor metastasis, treatment selection; monitor response to a therapeutic agent or other treatments; stratification of individuals for computed tomography (CT) screening, (for example, identifying those individuals at greatest risk for lung cancer and therefore most likely to benefit from spiral screening of CT, thereby increasing the positive predictive value of CT) , combine tests of biological markers with additional biomedical information, such as smoking history, etc., or with nodule size, morphology, etc. (such as to provide a diagnostic test with increased performance compared to CT or biological marker assay alone); facilitate the diagnosis of a pulmonary nodule as malignant or benign; facilitate the clinical decision made once the pulmonary nodule is seen in the CT (for example, asking for repeat CT scans if the nodule is considered to be of low risk, such as if a test based on a biological marker is negative, with or without categorization nodule size, or considering the biopsy if the nodule is considered medium to high risk, such as if a test based on a biological marker is positive, with or without the nodule size categorization), and facilitate decisions regarding clinical follow-up (for example, if repetition of CT scans, fine needle biopsy, or thoracotomy after observing a non-calcified nodule in the CT). Biological marker testing can improve the positive predictive value (PPV) on screening for CT alone. In addition to their usefulness in conjunction with CT screening, the biological markers described here can also be used in conjunction with any other imaging modalities used for lung cancer, such as chest radiography. In addition, the biological markers described may also be useful in allowing some of these uses before lung cancer indications are detected by imaging modalities or other related clinics, or before symptoms appear.
[00178] As an example of the way in which any of the biological markers described here can be used to diagnose lung cancer, the differential expression of one or more of the biological markers described in an individual who is not known to have lung cancer may indicate that the individual has lung cancer, which allows the detection of lung cancer in the early stage of the disease, when treatment is most effective, perhaps before lung cancer is detected by other means, or before the onset of symptoms . Overexpression of one or more of the biological markers during the course of lung cancer can be indicative of the progression of lung cancer, for example, a lung tumor is growing and / or is metastasizing (and thus indicate a poor prognosis), at which stage a decrease in the degree p to which one or more of the biological markers is differentially expressed (ie, in subsequent biological marker tests, the level of expression in the individual is moving in the direction or if approaching a "normal" expression level) may be indicative of lung cancer remission, for example, a lung tumor is shrinking (and thus indicate a good or better prognosis). Likewise, an increase in the degree to which one or more of the biological markers is expressed differentially (for example, in subsequent biological marker tests, the level of expression at which the individual is moving further away from an expression level " normal "), during the course of lung cancer treatment may indicate that lung cancer is progressing and, therefore, indicates that the treatment is ineffective, at the stage that a decrease in the differential expression of one or more of the biological markers during the course of lung cancer treatment can be indicative of lung cancer remission and therefore indicates that the treatment is working successfully. In addition, an increase or decrease in the differential expression of one or more of the biological markers after the individual has apparently been cured of lung cancer may be indicative of the recurrence of lung cancer. In a situation like this, for example, the individual may be restarted on therapy (or the therapeutic regimen modified in order to increase the amount of dosage and / or frequency, if the individual has maintained therapy) at an earlier stage than if the recurrence of lung cancer was not detected until later. On the other hand, a level of differential expression of one or more of the biological markers in an individual can be predictive of the individual's response to a particular therapeutic agent. In monitoring for lung cancer recurrence or progression, changes in the expression levels of biological markers may indicate the need for repeat imaging (for example, repeat CT scanning), such as to determine lung cancer activity or to determine the need for changes in treatment.
[00179] Detection of any of the markers described herein can be particularly useful after, or in conjunction with, treatment of lung cancer, such as assessing treatment success, or for monitoring lung cancer remission, recurrence, and / or progression (including metastases) after treatment. Treatment of lung cancer may include, for example, administration of a therapeutic agent to the individual, performance of surgery (for example, surgical resection of at least part of a lung tumor), administration of radiation therapy , or any other type of lung cancer treatment used in the technique, and any combination of these treatments. For example, any of the biological markers can be detected at least once after treatment, or can be detected several times after treatment (for example, at periodic intervals), or can be detected before and after treatment. Differential expression levels of any of the biological markers in an individual over time can be indicative of lung cancer progression, remission, or recurrence, examples of which include any of the following: an increase or decrease in the level of expression of biological markers after treatment compared to the level of expression of the marker before treatment, an increase or decrease in the level of expression of the marker at a later point of time compared to the level of expression of the marker at one point of time earlier after treatment, and a level of differential expression of the biological marker at a single time, after treatment, compared to normal levels of the biological marker.
[00180] As a specific example, the biological marker levels for any of the biological markers described here can be determined in the pre-surgery and post-surgery (for example, 2-4 weeks after surgery) serum samples. An increase in the level of expression of biological marker (s) in the post-surgery sample compared to the pre-surgery sample may indicate the progression of lung cancer (eg, unsuccessful surgery), to a step that a decrease in expression level of biological marker (s) in the post-surgery sample compared to the pre-surgery sample may indicate regression of lung cancer (for example, surgery has successfully removed the lung tumor). Similar analyzes of the levels of biological markers can be performed before and after other forms of treatment, such as before and after radiation therapy or the administration of a therapeutic agent or cancer vaccine.
[00181] In addition to testing the levels of biological markers as an independent diagnostic test, the levels of biological markers can also be done in conjunction with the determination of SNPs or other genetic lesions or variability that are indicative of an increased probability of susceptibility to disease. (See, for example, Amos et al., Nature Genetics 40, 616-622 (2009)).
[00182] In addition to testing the levels of biological markers as an independent diagnostic test, the levels of biological markers can also be done in conjunction with CT screening. For example, biological markers can facilitate the economic and medical justification for applying CT screening, such as for screening large asymptomatic populations at risk for lung cancer (for example, smokers). For example, a "pre-CT" test of biological marker levels could be used to stratify high-risk individuals for screening for CT, as well as to identify people who are most at risk of lung cancer based on their levels of biological markers and who should be prioritized for CT screening. If a CT test is implemented, the levels of biological markers (for example, as determined by a serum or plasma sample aptamer assay) of one or more biological markers can be measured and the diagnostic score could be assessed together with additional biomedical information (for example, tumor parameters determined by the CT test) to increase the positive predictive value (PPV) on CT or biological marker testing alone. A "post-CT" aptamer panel to determine levels of biological markers can be used to determine the likelihood that a pulmonary nodule seen by CT (or another imaging modality) is malignant or benign.
[00183] The detection of any of the biological markers described here can be useful for the post-test of CT. For example, the biological marker test can eliminate or reduce a significant number of false positives about CT alone. In addition, the biological marker test can facilitate the treatment of patients. As an example, if a lung nodule is less than 5 mm in size, the results of the biological marker test can advance patients from "watching and waiting" for biopsy at an earlier time; if a lung nodule is 5-9 mm, the biomarker test can eliminate the use of a biopsy or thoracotomy in false positive scans, and if a lung nodule is greater than 10 mm, the biomarker test can eliminate surgery for a sub-population of patients with these benign nodules. Eliminating the need for biopsy in some patients based on biological marker tests would be beneficial because there is significant morbidity associated with nodule biopsy and difficulty in obtaining nodule tissue, depending on the location of the nodule. Likewise, eliminating the need for surgery in some patients, such as those whose nodules are really benign, which would avoid unnecessary risks and the costs associated with surgery.
[00184] In addition to testing the levels of biological markers in conjunction with CT screening (for example, assessment of biological marker levels in conjunction with the size or other characteristics of a pulmonary nodule seen on a CT scan), information on biological markers can also be assessed in conjunction with other types of data, especially data indicating an individual's risk for lung cancer (eg, patient's medical history, symptoms, family history of cancer, risk factors, such as whether the individual is a smoker or not, and / or status of other biological markers, etc.). This various data can be evaluated by automated methods, such as a computer / software program, which can be incorporated into a computer or other device / device.
[00185] Any of the biological markers described can also be used in imaging studies. For example, an imaging agent can be attached to any of the described biological markers, which can be used to assist in the diagnosis of lung cancer, to monitor disease progression / remission or metastasis, to monitor recurrence of disease, or to monitor response to treatment, among other uses. Detection and Determination of Biological Markers and Biological Marker Values
[00186] A biological marker value for the biological markers described herein can be detected using any of a variety of known analytical methods. In one embodiment, a biological marker value is detected using a capture reagent. As used herein, a "capture agent" or "capture reagent" refers to a molecule that is capable of specifically binding to a biological marker. In various embodiments, the capture reagent can be exposed to the biological marker in solution or can be exposed to the biological marker while the capture reagent is immobilized on a solid support. In other embodiments, the capture reagent contains a characteristic that is reactive with a secondary characteristic on a solid support. In these embodiments, the capture reagent can be exposed to the biological marker in solution, and then the capture reagent feature can be used in conjunction with the secondary feature on the solid support to immobilize the biological marker on the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture reagents include, but are not limited to, antibodies, aptamers, adnectins, ankyrins, other mimetic antibodies and other protein frames, autoantibodies, chimeras, small molecules, an F (ab ') 2 fragment, a chain antibody fragment simple, an Fv fragment, a single-stranded Fv fragment, a nucleic acid, a lectin, a linker-binding receptor, affinity bodies, nanobodies, printed polymers, avimers, peptidomimetics, a hormone receptor, a cytokine receptor, and synthetic receptors, and modifications and fragments thereof.
[00187] In some embodiments, a biological marker value is detected using a biological marker / capture reagent complex.
[00188] In other embodiments, the biological marker value is derived from the biological marker / capture reagent complex and is detected indirectly, such as, for example, as a result of a reaction that is subsequent to the interaction of the biological marker reagent / capture, but is dependent on the formation of the biological marker / capture reagent complex.
[00189] In some modalities, the value of the biological marker is detected directly with the biological marker in a biological sample.
[00190] In one embodiment, biological markers are detected using a multiplexed format that allows the simultaneous detection of two or more biological markers in a biological sample. In a multiplexed format, capture reagents are immobilized, directly or indirectly, covalently or non-covalently, in discrete locations on a solid support. In another embodiment, a multiplexed format uses discrete solid supports, where each solid support has a unique capture reagent associated with the solid support, such as, for example, quantum dots. In another embodiment, an individual device is used to detect each of the multiple biological markers to be detected in a biological sample. The individual devices can be configured to allow each biological marker in the biological sample to be processed simultaneously. For example, a microtiter plate can be used in such a way that each well of the plate is used exclusively to analyze one of the multiple biological markers to be detected in a biological sample.
[00191] In one or more of the previous modalities, a fluorescent marker can be used to mark a component of the biological marker / capture complex to allow the detection of the biological marker value. In various embodiments, the fluorescent marker can be conjugated to a capture reagent specific for any of the markers described herein, using known techniques, and the fluorescent marker can then be used to detect the corresponding biological marker value. Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophicocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red, and other such compounds.
[00192] In one embodiment, the fluorescent marker is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule includes at least one substituted indolium ring system in which the substituent on carbon 3 of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule includes an AlexFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In other embodiments, the dye molecule comprises a first type and a second type of dye molecule, such as, for example, two different AlexaFluor molecules. In other embodiments, the dye molecule comprises a first type and a second type of dye molecule, and the two dye molecules have different emission spectra.
[00193] Fluorescence can be measured with a wide variety of instrumentation compatible with a wide variety of assay formats. For example, spectrofluorometers were designed to analyze microtiter plates, microscope slides, printed matrices, crucibles, etc. See Principles of Fluorescence Spectroscopy, by J.R. Lakowicz, Springer Science + Business Media, Inc., 2004; Bioluminescence & Chemiluminescence: Progress & Current Aplications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.
[00194] In one or more of the previous embodiments, a chemiluminescent marker can optionally be used to mark a component of the biological marker / capture complex to allow the detection of a biological marker value. Suitable chemiluminescent materials include any of oxalyl chloride, Rodamin 6G, Ru (bipy) 32+, TMAE (ethylene tetrakis (dimethylamino)), pyrogallol (1,2,3-trihydroxybenzene), Lucigenin, peroxyoxalates, aryl oxalates, esters acridinium, dioxetanes, and others.
[00195] In still other modalities, the detection method includes an enzyme / substrate combination, which generates a detectable signal that corresponds to the biological marker value. In general, the enzyme catalyzes a chemical change to the chromogenic substrate that can be measured using various techniques, including spectrophotometry, fluorescence and chemiluminescence. Suitable enzymes include, for example, luciferases, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase and glucose-6-phosphate-dehydrogenase, uricas , xanthine oxidase, lactoperoxidase, microperoxidase, and the like.
[00196] In still other modalities, the detection method can be a combination of fluorescence, chemiluminescence, combinations of radionuclides or enzyme / substrate, which generate a measurable signal. Multimodal signaling could have unique advantageous features in biological marker assay formats.
[00197] More specifically, the biological marker values for the biological markers described herein can be detected using known analytical methods including, simple aptamer assays, multiplexed aptamer assays, simple or multiplexed immune assays, mRNA expression profiles, expression profiles miRNA, mass spectrometric analysis, histological / cytological methods, etc. as detailed below. Determination of Values of Biological Markers, Using Aptamer-Based Assays
[00198] Tests aimed at the detection and quantification of physiologically significant molecules in biological samples and other samples are important tools in scientific research and in the health area. One class of such assays involves the use of a microarray that includes one or more aptamers immobilized on a solid support. Each of the aptamers is able to bind to a target molecule in a highly specific way and with very high affinity. See, p. e., U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands," see also, for example, U.S. Patent No. 6,242,246, U.S. Patent No. 6,458,543 and U.S. Patent No. 6,503,715, each of which is entitled "Nucleic Acid Ligands Diagnostic Biochip". Once the micromatrix is contacted with a sample, the aptamers bind to their respective target molecules present in the sample and, thus, allow a determination of a biological marker value corresponding to a biological marker.
[00199] As used herein, an "aptamer" refers to a nucleic acid that has a specific binding affinity to a target molecule. It is recognized that affinity interactions are a matter of degree, however, in the present context, the "specific binding affinity" of an aptamer to its target means that the aptamer binds to its target, usually to a very high degree. higher than the affinity with which it binds to other components of a test sample. An "aptamer" is a set of specimens of a type or species of a nucleic acid molecule that has a specific nucleotide sequence. An aptamer can include any suitable number of nucleotides, including any number of chemically modified nucleotides. "Aptamers" refers to more than one such set of molecules. Different aptamers can have both the same number of nucleotides and different ones. Aptamers can be DNA or RNA or chemically modified nucleic acids and can be single-stranded, double-stranded, or contain double-stranded regions, and can include higher order structures. An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently attached to its corresponding target. Any of the aptamer methods disclosed herein may include the use of two or more aptamers that specifically bind to the same target molecule. As will be described below, an aptamer may include a mark. If an aptamer includes a mark, all copies of the aptamer need not have the same mark. In addition, if each of different aptamers includes a brand, these different aptamers can have the same brand or a different brand.
[00200] An aptamer can be identified using any known method, including the SELEX process. Once identified, an aptamer can be prepared or synthesized according to any known method, including chemical synthesis methods and synthetic enzymatic methods.
[00201] The terms "SELEX" and "SELEX process" are used interchangeably here to generally refer to a combination of (1) the selection of aptamers that interact with a target molecule in a desirable manner, for example, with a high affinity for binding to a protein, with (2) amplification of the selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity for a specific target or biological marker.
[00202] SELEX generally includes preparing a candidate mixture of nucleic acids, binding the candidate mixture to the desired target molecule, to form an affinity complex, separating the affinity complexes from unbound candidate nucleic acids, separating and isolating the nucleic acid from the complex of affinity, purify the nucleic acid, and identify a specific aptamer sequence. The process can include multiple rounds to further refine the affinity of the selected aptamer. The process can include amplification steps at one or more points in the process. See, p. e., U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands". The SELEX process can be used to generate an aptamer that binds covalently to its target, as well as an aptamer that binds covalently to its target. See, p. e., U.S. Patent No. 5,705,337, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX"
[00203] The SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that confer improved characteristics on the aptamer, such as, for example, an improvement in live stability or improved delivery characteristics. Examples of such modifications include chemical substitutions in ribose and / or phosphate and / or base positions. Aptamers identified by the SELEX process containing modified nucleotides are described in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides", which describes oligonucleotides containing chemically modified nucleotide derivatives at the 5'- and 2'- pyrimidine positions. US Patent No. 5,580,737, see supra, describes highly specific aptamers that contain one or more nucleotides modified with 2'-amino (2 '-NH2), 2'-fluoro (2'-F), and / or 2'-O-methyl (2 '- OMe). See also, US Patent Application Publication 20090098549, entitled "SELEX and PHOTOSELEX", which describes the nucleic acid libraries having expanded physical and chemical properties and their use in SELEX and PHOTOSELEX.
[00204] SELEX can also be used to identify aptamers that have desirable dissociation rate characteristics. See US Patent Application Publication 20090004667, entitled "Method for Generating Aptamers with Improved Off-Rates", which describes improved SELEX methods for generating aptamers that can bind to target molecules. Methods for producing aptamers and photoaptamers with slower rates of dissociation from their target molecules are described. The methods involve contacting the candidate mixture with the target molecule, allowing the formation of target nucleic acid complexes to occur, and performing a process of enriching the slow desiccation rate, in which the target nucleic acid complex with faster rates of dissociation will dissociate and not reform, while complexes with low dissociation rates will remain intact. In addition, the methods include the use of modified nucleotides in the production of mixtures of candidate nucleic acids to generate aptamers with better dissociation rate performance.
[00205] A variation of this assay uses photoreactive aptamers that include the functional groups that allow aptamers to covalently bond or "cross-link" target molecules. See, p. e., U.S. Patent No. 6,544,776, entitled "Nucleic Acid Ligand Biochip Diagnostic." These photoreactive aptamers are also referred to as photoaptamers. See, p. e., U.S. Patent No. 5,763,177, U.S. Patent No. 6,001,577 and US Patent No. 6,291,184, each of which is entitled "Sistematic Evolution of Nucleic Acid Ligand by Exponential Enrichment: Photoselection of Nucleic Acid Ligand and Solution SELEX", see also, for example, U.S. Patent No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligand". After the micromatrix has contacted the sample and the photoaptamers have had the opportunity to bind to target molecules, the photoaptamer is photoactivated, and the solid support is washed to remove any molecules not specifically bound. Hard washing conditions can be used, since the target molecules that are attached to the photoaptamers are generally not removed, due to the covalent bonds created by the photoactivated functional group (s) on the photoaptamer. In this way, the assay allows the detection of a value corresponding to a biological marker value corresponding to a biological marker in the test sample.
[00206] In both test formats, aptamers are immobilized on the solid support before being put in contact with the sample. In certain circumstances, however, immobilization of aptamers prior to contact with the sample may not provide an optimal assay. For example, prior immobilization of aptamers may result in inefficient mixing of aptamers with target molecules on the surface of the solid support, perhaps leading to long reaction times and therefore prolonged incubation periods to allow efficient attachment of aptamers to their target molecules. In addition, when photoaptamers are used in the assay and, depending on the material used as a solid support, the solid support may tend to disperse or absorb the light used to effect the formation of covalent bonds between the photoaptamers and their target molecules. In addition, depending on the method used, the detection of target molecules linked to their aptamers may be subject to inaccuracy, since the surface of the solid support can also be exposed and influenced by any labeling agents that are used. Finally, the immobilization of the aptamers on the solid support generally involves a step of preparing the aptamer (i.e., immobilization) before the exposure of the aptamers to the sample, and this preparation step can affect the activity or functionality of the aptamers.
[00207] Aptamer assays have also been described that allow an aptamer to capture the target in solution and then use separation steps that are designed to remove specific components from the target aptamer mixture prior to detection (see Application Publication US patent 20090042206, entitled "Multiplexed Analysis of Test Samples"). The aptamer assay methods described allow the detection and quantification of a non-target nucleic acid (e.g., a target protein) in a test sample by detecting and quantifying a nucleic acid (i.e., an aptamer). The described methods create a nucleic acid substitute (ie, the aptamer) for the detection and quantification of a non-target nucleic acid, thus allowing a wide variety of nucleic acid technologies, including amplification, to be applied to a wide range of desired targets, including target proteins.
[00208] Aptamers can be constructed to facilitate the separation of test components from an aptamer biological marker complex (or covalent photoaptamer biological marker complex) and allow the isolation of the aptamer for detection and / or quantification . In one embodiment, these constructs can include a cleavable or releasable element within the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, for example, a marked or detectable component, a spacer component, or a specific binding marker or immobilization element. For example, the aptamer may include a marker connected to the aptamer by means of a cleavable portion, a marker, a spacer component separating the marker, and the cleavable portion. In one embodiment, a cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin portion and a separator section, can include an NHS group for derivatization of amines, and can be used to introduce a biotin group to an aptamer, thus allowing the release of the aptamer later on a test method.
[00209] Homogeneous tests, performed with all the test components in the solution, do not require the separation of the sample and the reagents before the detection of the signal. These methods are quick and easy to use. These methods generated signals based on a molecular capture or binding reagent that reacts with its specific target. For lung cancer, the molecular capture reagents would be an aptamer or an antibody or the like, and the specific target would be a biological lung cancer marker from Table 20.
[00210] In one embodiment, a method for signal generation takes advantage of changing the anisotropy signal due to the interaction of a fluorophore-labeled capture reagent with its specific biological marker target. When the marked capture reacts with its target, the increase in molecular weight causes the rotational movement of the fluorophore fixed to the complex to change the anisotropy value much slower. When monitoring the anisotropy change, binding events can be used to quantitatively measure the biological markers in the solutions. Other methods include fluorescence polarization assays, molecular beacons (molecular beacons), time resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, and the like.
An exemplary solution-based aptamer assay that can be used to detect a biological marker value corresponding to a biological marker in a biological sample comprises the following: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first marker and has a specific affinity for the biological marker, in which an aptamer affinity complex is formed when the biological marker is present in the sample, (b) exposing the mixture to a first solid support, including a first element of capture, and allow the first marker to associate with the first capture element, (c) remove any components from the mixture not associated with the first solid support, (d) attach a second marker to the biomarker component of the aptamer affinity complex , (e) releasing the aptamer affinity complex from the first solid support, (f) exposing the released aptamer affinity complex to a second s solid support that includes a second capture element and allowing the second marker to associate with the second capture element, (g) remove any non-complex aptamer from the mixture by partitioning the non-complex aptamer from the aptamer affinity complex, (h ) eluting the aptamer from the solid support, and (i) detecting the biological marker by detecting the aptamer component of the aptamer affinity complex. Determination of Values of Biological Markers Using Immune Assays
[00212] Immune assay methods are based on the reaction of an antibody to its corresponding target or analyte and capable of detecting the analyte in a sample according to the specific assay format. To improve the specificity and sensitivity of an assay method based on immuno-reactivity, monoclonal antibodies are often used because of their recognition of specific epitopes. Polyclonal antibodies have also been used successfully in several assays, due to their greater affinity to the target, compared to monoclonal antibodies. Immune assays are designed for use with a wide range of biological sample arrays. Immune assay formats have been designed to provide qualitative, semi-quantitative and quantitative results.
[00213] Quantitative results are generated through the use of a standard curve created with known concentrations of specific analyte to be detected. The response or signal from an unknown sample is plotted on the standard curve, and an amount or value corresponding to the target is established in the unknown sample.
[00214] Numerous immune assay formats have been designed. ELISA or EIA can be quantitative for the detection of an analyte. This method is based on placing a marker on both the analyte and the antibody, and the label component includes, either directly or indirectly, an enzyme. ELISA tests can be formatted for direct, indirect, competitive or sandwich detection of the analyte. Other methods depend on tags, such as, for example, radioisotopes (I125) or fluorescence. Other techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and others (see Immunoassay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).
[00215] Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radio immune assay, chemiluminescence, fluorescent, and time resolved fluorescence resonance (FRET) or FRET immune assays (TR-FRET FRET) . Examples of procedures for the detection of biological markers include immunoprecipitation of biological markers followed by quantitative methods that allow discrimination of size and peptide levels, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.
[00216] The methods for detecting and / or quantifying a detectable marker or signal-generating material depend on the nature of the marker. The products of the reactions catalyzed by appropriate enzymes (where the detectable marker is an enzyme; see above) can be, without limitation, fluorescent, luminescent or radioactive or can absorb visible or ultraviolet light. Examples of suitable detectors for the detection of such detectable markers include, without limitation, an X-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorimeters, luminometers and densitometers.
[00217] Any of the detection methods can be performed in any format that allows any preparation, processing and analysis of the appropriate reactions. This can be, for example, on multiple well assay plates (for example, 96 wells or 384 wells) or using any suitable matrix or microarray. Backup solutions for the various agents can be done manually or robotically, and all subsequent pipetting, dilution, distribution, mixing, washing, incubation, sample reading, data collection and analysis can be done robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting a detectable marker. Determination of Biological Marker Values using Gene Expression Profile
[00218] The measurement of mRNA in a biological sample can be used as a substitute for the detection of the corresponding protein level in the biological sample. Thus, any of the biological markers or panels of biological markers described herein can also be detected through the detection of appropriate RNA.
[00219] MRNA expression levels are measured by polymerase quantitative reverse transcription chain reaction (RT-PCR followed with qPCR). RT-PCR is used to create a cDNA from the mRNA. The cDNA can be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison with a standard curve, qPCR can produce an absolute measurement such as the number of mRNA copies per cell. Northern blots, microarrays, invader assays, and RT-PCR combined with capillary electrophoresis have been used to measure levels of mRNA expression in a sample (see, Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004).
[00220] MiRNA molecules are small RNAs that are non-coding, but can regulate gene expression. Any of the suitable methods for measuring mRNA expression levels can also be used for the corresponding miRNA. Recently, many laboratories have investigated the use of miRNAs as biological markers for disease. Many diseases involve regulation of generalized transcription, and it is not surprising that miRNAs can find a role as biological markers. The link between miRNA concentration and disease is often even less clear than the connections between protein levels and disease, but the value of biological miRNA markers can be substantial. Of course, as with any RNA differentially expressed during disease, the problems that arise with the development of an in vitro diagnostic product include the requirement that miRNAs survive in the diseased cell and be easily extracted for analysis, or that miRNAs are released into blood or other matrices where they must survive long enough to be measured. Biological protein markers have similar requirements, although many potential biological markers of protein are deliberately secreted at the site of pathology and function, during the disease, in a paracrine manner. Many biological markers of potential proteins are designed to function outside cells, within which those proteins are synthesized. Detection of Biological Markers Using Live Molecular Image Formation Technologies.
[00221] Any of the biological markers described (see Table 20), can also be used in molecular imaging tests. For example, an imaging agent can be attached to any of the described biological markers, which can be used to assist in the diagnosis of lung cancer, to monitor the progression / remission or metastasis of the disease, to monitor the recurrence of the disease. disease, or to monitor response to treatment, among other uses.
[00222] Live imaging technologies provide non-invasive methods for determining the state of a particular disease in an individual's body. For example, entire parts of the body, or even the entire body, can be seen as a three-dimensional image, thus giving valuable information regarding the morphology and structures of the body. Such technologies can be combined with the detection of the markers described in the present invention to provide information about the state of the cancer, in particular the state of the lung cancer, of an individual.
[00223] The use of live molecular imaging technologies is expanding due to several advances in technology. These advances include the development of new contrast agents or tags, such as radiolabels and / or fluorescent tags, which can provide strong signals within the body, and the development of new and powerful imaging technology, which can detect and analyze signals from outside the body, with sufficient sensitivity and precision to provide useful information. The contrast agent can be viewed in a suitable imaging system, thereby providing an image of the part or parts of the body on which the contrast agent is located. The contrast agent can be linked to or associated with a capture reagent, such as an aptamer or an antibody, for example, and / or with a peptide or protein, or an oligonucleotide (for example, for the detection of gene expression) , or a complex containing any of these with one or more macromolecules and / or other forms of particles.
[00224] The contrast agent can also characterize a radioactive atom that is useful in imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphy studies. Other readily detectable portions include, for example, magnetic resonance imaging (MRI) spin tags, such as, for example, iodine-123 again, iodine-131, indium-111, fluorine-19, carbon- 13, nitrogen-15, oxygen -17, gadolinium, manganese or iron. Such markers are well known in the art and can be easily selected by one of ordinary skill in the art.
[00225] Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography, positron emission tomography (PET), single photon emission computed tomography (SPECT), and the like. For diagnosis by live imaging, the type of detection instrument available is an important factor in the selection of a specific contrast agent, such as a given radionuclide and the particular biological marker, which is used for targeting (protein, mRNA, and the like). The chosen radionuclide usually has a type of decay that is detectable by a particular type of instrument. In addition, when selecting a radionuclide for live diagnosis, its half-life must be long enough to allow detection, at the moment of maximum absorption by the target tissue, but short enough that the harmful radiation from the host is minimized.
Exemplary imaging techniques include, but are not limited to PET and SPECT, which are imaging techniques in which a radionuclide is synthetically or locally administered to an individual. Subsequent absorption of the tracer is measured over time and used to obtain information about the target tissue and the biological marker. In view of the high energy (gamma rays), the emissions of the specific isotopes used and the sensitivity and sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity can be inferred from outside the body.
[00227] Positron-emitting nuclei commonly used in PET include, for example, carbon-11, nitrogen-13, oxygen-15 and fluorine-18. Isotopes that decay by electron capture and / or gamma emission are used in SPECT and include , for example, iodine-123 and technetium-99m. An exemplary method for labeling amino acids with technetium-99m is the reduction of the pertechnetate ion in the presence of a chelating precursor, to form the complex technetium-99m labile precursor, which, in turn, reacts with the metal of the linking group of a bifunctional chemotactic peptide modified to form a technetium-99m-chemotactic peptide conjugate.
[00228] Antibodies are often used for such live diagnostic methods by imaging. The preparation and use of antibodies for live diagnosis is well known in the art. Labeled antibodies that specifically bind to any of the biological markers in Table 20 can be injected into an individual suspected of having a certain type of cancer (for example, lung cancer), detectable according to the particular biological marker used, with the purpose of diagnosing or assessing the individual's disease status. The marker used will be selected according to the type of image formation to be used, as previously described. The location of the marker allows the determination of the spread of cancer. The amount of marker within an organ or tissue also allows the determination of the presence or absence of cancer in that organ or tissue.
[00229] Likewise, aptamers can be used for such diagnostic methods of live imaging. For example, an aptamer that was used to identify a particular biological marker, described in Table 20 (and therefore specifically binds to that particular biological marker) can be appropriately marked and injected into an individual suspected of having lung cancer, detectable according to the particular biological marker, for the purpose of diagnosing or assessing the individual's lung cancer status. The marker used will be selected according to the type of image formation to be used, as previously described. The location of the marker allows the determination of the spread of cancer. The amount of marker within an organ or tissue also allows the determination of the presence or absence of cancer in that organ or tissue. Aptamer-directed imaging agents may have unique and advantageous characteristics related to tissue penetration, tissue distribution, kinetics, elimination, potency and selectivity compared to other imaging agents.
[00230] Such techniques can also optionally be performed with labeled oligonucleotides, for example, for the detection of gene expression through image formation with antisense oligonucleotides. These methods are used for on-site hybridization, for example, with fluorescent molecules or radionuclides as the marker. Other methods for detecting gene expression include, for example, detecting the activity of a reporting gene.
[00231] Another general type of image formation technology is optical image formation, in which the fluorescent signals inside the subject are detected by an optical device that is external to the subject. These signals may be due to actual fluorescence and / or biological luminescence. Improvements in the sensitivity of optical detection devices have increased the usefulness of live optical imaging in diagnostic tests.
[00232] The use of live molecular biological marker imaging is increasing, including for clinical trials, for example, to more quickly measure clinical efficacy in trials for new cancer therapies and / or to avoid prolonged treatment with a placebo for those diseases, such as multiple sclerosis, where prolonged treatment can be considered ethically questionable.
[00233] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009. Determination of Biological Marker Values using Histology / Cytology Methods
[00234] For the evaluation of lung cancer, a variety of tissue samples can be used in histological or cytological methods. Sample selection depends on the location of the primary tumor and metastasis sites. For example, endo- and trans-bronchial biopsies, fine needle aspirations, sharp needles and core biopsies can be used for histology. Bronchial washing and brushing, pleural aspiration, and expectoration can be used for cytology. Although cytological analysis is still used to diagnose lung cancer, histological methods are known to provide better sensitivity for cancer detection. Any of the markers identified here, which have been shown to be up-regulated (see Table 19) in individuals with lung cancer can be used to color a histological specimen as an indication of disease.
[00235] In one embodiment, one or more capture reagent (s) specific for the corresponding biological marker (s) are used in a cytological evaluation of a sample of lung cells and may include one or more of the following: collecting a cell sample, fixing the cell sample, dehydrating, cleaning, immobilizing the cell sample on a microscope slide, permeabilizing the cell sample, treatment for the recovery of the analyte, coloring, discoloring, wash, block, and react with one or more capture reagents in a buffered solution. In another embodiment, the cell sample is produced from a block of cells.
[00236] In another embodiment, one or more capture reagents specific for the corresponding biological markers are used in a histological assessment of a lung tissue sample and may include one or more of the following: collect a tissue sample, fix the sample tissue, dehydrate, clean, immobilize the tissue sample on a microscope slide, permeabilize the tissue sample, treatment for analyte recovery, color, discolor, wash, block, rehydrate and react with the capture reagent in a solution buffered. In another embodiment, fixation and dehydration are replaced by freezing.
[00237] In another embodiment, the one or more aptamer / s specific for the corresponding biological marker are reacted with the histological or cytological sample and can serve as the nucleic acid target in a nucleic acid amplification method. Suitable methods of nucleic acid amplification include, for example, PCR, q-beta replicase, rolling circle amplification, chain displacement, helicase dependent amplification, mediated isothermal amplification loop, ligase chain reaction, and rolling circle amplification aided by circularization.
[00238] In one embodiment, the one or more specific capture reagent for the corresponding biological markers for use in histological or cytological assessment are mixed in a buffered solution that can include any of the following: blocking materials, competitors, detergents, stabilizers, carrier of nucleic acid, polyanionic material, etc.
[00239] A "cytology protocol" generally includes sample collection, sample fixation, sample immobilization, staining. "Cell preparation" can include several processing steps after sample collection, including the use of one or more slow dissociation rate aptamers for staining the prepared cells.
[00240] Sample collection may include placing the sample directly in an untreated transport container, placing the sample in a transport container containing some type of media, or placing the sample directly on the slide (immobilization), without any treatment or fixation.
[00241] Sample immobilization can be improved by applying a part of the collected sample to a glass slide that is treated with polylysine, gelatin, or a silane. The slides can be prepared by staining a thin, uniform layer of cells across the slide. Care is generally taken to minimize mechanical distortion and drying artifacts. Liquid samples can be processed by a cell block method. Or, alternatively, liquid samples can be mixed 1: 1 with the fixation solution for 10 minutes at room temperature.
[00242] Cell blocks can be prepared from residual spillage, sputum, urine sediment, gastrointestinal fluids, cell scraping, or fine needle aspiration. The cells are concentrated or packed by means of centrifugation or membrane filtration. A number of methods have been developed for the preparation of cell blocks. Representative procedures include fixed pellet, bacterial agar, or membrane filtration methods. In the fixed pellet method, the cell pellet is mixed with a fixing agent such as Bouins, picric acid, or buffered formalin, and then the mixture is centrifuged to form pellet of the fixed cells. The supernatant is removed, drying the cell pellet as completely as possible. The pellet is collected and wrapped in lens paper and then placed in a tissue cassette. The tissue cassette is placed in a bottle with additional fixative and processed as a tissue sample. The agar method is very similar, but the pellet is removed and dried on a paper towel and then cut in half. The cut side is placed in a drop of agar melted on a glass slide, and then the pellet is covered with agar making sure that no bubbles form on the agar. The agar is allowed to harden and then any excess agar is trimmed off. This is placed on a tissue cassette and the tissue process completed. Alternatively, the pellet can be directly suspended in 2% liquid agar at 65 ° C and the sample centrifuged. The agar cell pellet is allowed to solidify for one hour at 4 ° C. The solid agar can be removed from the centrifuge tube and cut in half. The agar is wrapped in filter paper and then the tissue cassette. Processing from this point forward is as described above. Centrifugation can be replaced in any of these processes with membrane filtration. Any of these processes can be used to generate a "cell block sample".
[00243] Cell blocks can be prepared using specialized resin, including Lowicryl, LR White, LR Gold, Unicryl and MonoStep resins. These resins have a low viscosity and can be polymerized at low temperatures and with ultraviolet (UV). The incorporation process depends on progressively cooling the sample, during dehydration, transferring the sample to the resin, and polymerizing a block at a low final temperature in an appropriate UV wavelength.
[00244] Cell block sections can be stained with hematoxylin-eosin for cytomorphological examination while additional sections are used for examination for specific markers.
[00245] If the process is cytological or histological, the sample can be fixed before further processing to avoid degradation of the sample. This process is called "fixing" and describes a wide variety of materials and procedures that can be used interchangeably. The protocol for fixing samples and reagents is best selected empirically based on the targets to be detected and the specific cell / tissue type to be analyzed. Sample fixation is based on reagents such as ethanol, polyethylene glycol, methanol, formalin, or isopropanol. Samples should be fixed as soon as possible after collection and fixation to the slide. However, the selected fixator can introduce structural changes in various molecular targets making its subsequent detection more difficult. Fixation and immobilization processes and their sequence can modify the appearance of cells and these changes must be anticipated and recognized by the cytotechnologist. Fixatives can cause shrinkage of certain types of cells and cause the cytoplasm to appear granular or reticular. Many fasteners work by using cross-linked cellular components. This can damage or alter specific epitopes, generate new epitopes, cause molecular associations, and reduce the permeability of the membrane. Formaldehyde fixation is one of the most common cytological / histological approaches. Formalin forms methyl bridges between neighboring proteins or within proteins. Precipitation or coagulation is also used for fixation and ethanol is often used in this type of fixation. A combination of crosslinking and precipitation can also be used for fixation. A strong fixation process is better for preserving morphological information while a weaker fixation process is better for preserving molecular targets.
[00246] A representative fixative is 50% absolute ethanol, 2 mM polyethylene glycol (PEG), 1.85% formaldehyde. Variations on this formulation include ethanol (50% to 95%), methanol (20% - 50%) and formalin (formaldehyde) only. Another common fixative is 2% PEG 1500, 50% ethanol, and 3% methanol. The slides are placed in the fixative for about 10 to 15 minutes at room temperature, then removed and allowed to dry. Once the slides are attached, they can be washed with a buffered solution such as PBS.
[00247] A wide variety of dyes can be used to differentially enhance and contrast or "color" cellular, sub-cellular, and tissue characteristics or morphological structures. Hematoilina is used to color nuclei of a blue or black color. Orange G-6 and Eosin Azure both color cell cytoplasm. Orange G collects keratin and cells that contain yellow glycogen. Eosin Y is used to color nucleoli, cilia, red blood cells and superficial cells of the squamous epithelium. Romanowsky dyes are used for air-dried slides and are useful in increasing pleomorphism and distinguishing extracellular material from intracytoplasmic material.
[00248] The staining process may include a treatment to increase the permeability of the cells to staining. Treating the cells with a detergent can be used to increase permeability. To increase cell and tissue permeability, fixed samples can also be treated with solvents, saponins, or non-ionic detergents. Enzymatic digestion can also improve the accessibility of specific targets in a tissue sample.
[00249] After staining, the sample is dehydrated with a succession of washes with alcohol of increasing alcohol concentration. The final wash is done with xylene or a xylene substitute, such as a citrus terpene, which has a refractive index close to that of the lamella to be applied to the slide. This final step is called cleaning. Once the sample is dehydrated and cleaned, an assembly medium is applied. The mounting medium is chosen to have a refractive index close to the glass and is able to attach the coverslip to the blade. It will also inhibit further drying, shrinking or attenuation of the cell sample.
[00250] Regardless of the color or processing used, the final evaluation of the lung cytological specimen is done by some type of microscopy to allow a visual inspection of the morphology and the determination of the presence or absence of the marker. Exemplary microscopy methods include bright field microscopy, phase contrast, fluorescence and differential interference contrast.
[00251] If secondary tests are required on the sample after examination, the coverslip can be removed and the slide discolored. Decolorization involves the use of original solvent systems used to color the slide initially without the added dye and in reverse order to the original staining procedure. Discoloration can also be completed by immersing the slide in an acid alcohol until the cells are colorless. Once colorless the slides are washed well in a water bath and the second staining process applied.
[00252] In addition, specific molecular differentiation may be possible, in conjunction with cell morphological analysis through the use of specific molecular reagents, such as antibodies or nucleic acid probes or aptamers. This improves the accuracy of the cytological diagnosis. Micro-dissection can be used to isolate a subset of cells for further evaluation, in particular, for the genetic evaluation of abnormal chromosomes, gene expression, or mutations.
[00253] The preparation of a tissue sample for histological examination involves fixation, dehydration, infiltration, incorporation and sectioning. The fixation reagents used in histology are very similar or identical to those used in cytology and have the same problems of preserving morphological characteristics at the expense of molecular ones, such as individual proteins. Time can be saved if the tissue sample is not fixed and dehydrated, but is instead frozen and sectioned while frozen. This is a smoother processing procedure and can preserve more individual markers. However, freezing is not acceptable for long-term storage of a tissue sample as subcellular information is lost due to the introduction of ice crystals. The ice in the frozen tissue sample also prevents the cutting process from producing a very thin slice and, consequently, some microscopic image resolution and subcellular structures may be lost. In addition to formalin fixation, osmium tetroxide is used to fix and color phospholipids (membranes).
[00254] The dehydration of the tissues is carried out with successive washes of alcohol of increasing concentration. Cleaning uses a material that is miscible with alcohol and embedding material, and involves a step process from 50:50 alcohol and cleaning reagent and then 100% cleaning agent (xylene or substitute) xylene). Infiltration involves incubating the tissue with a liquid form of the embedding agent (hot wax, nitrocellulose solution), first the 50:50 embedding agent: cleaning agent and the 100% embedding agent. The soaking is completed by placing the tissue in a mold or cassette and filling with melted soaking agent, such as wax, agar or gelatin. The soaking agent is allowed to harden. The sample of hardened tissue can then be cut into thin sheets for staining and subsequent examination.
[00255] Before staining, the tissue section is removed and rehydrated. Xylene is used to desenter the section, one or more changes of xylene can be used, and the fabric is rehydrated by successive washes in alcohol of decreasing concentration. Before disengaging, the tissue section can be heat immobilized onto a glass slide at about 80 ° C for about 20 minutes.
[00256] Laser capture micro-dissection allows the isolation of a subset of cells for further analysis from a section of tissue.
[00257] As in cytology, to improve the visualization of microscopic characteristics, the section of tissue or slide can be marked with a variety of stains. A large menu of commercially available stains can be used to improve or identify specific characteristics.
[00258] To further increase the interaction between molecular reagents with cytological / histological samples, a series of "analyte recovery" techniques have been developed. The first of these techniques uses high temperature heating of a fixed sample. This method is also known as heat-induced epitope recovery or HIER. A variety of heating techniques have been used, including steam heating, microwaves, autoclaving, water bathing, and pressure cooking or a combination of these heating methods. Analyte recovery solutions include, for example, water, citrate, and normal saline buffers. The key to the recovery of the analyte is time at high temperature, but lower temperatures from longer times have also been used successfully. Another key to analyte recovery is the pH of the heating solution. Low pH has been found to provide the best immunostaining but also gives rise to formations that often require the use of a second section of tissue, as a negative control. The most consistent benefit (an increase in immunostaining without increasing the background) is generally obtained with a high pH solution, regardless of the composition of the buffer. The analyte recovery process for a specific target is empirically optimized for the target using heat, time, pH, and the buffer composition as variables for process optimization. Using the microwave analyte recovery method allows sequential staining of different targets, with antibody reagents. But the time required to reach the antibody and enzyme complexes between the staining steps has also been shown to degrade cell membrane analytes. As well, microwave heating methods have improved on-site hybridization methods.
[00259] In order to start the analyte recovery process, the section is first removed and hydrated. The slide is then placed in 10 mM pH 6.0 sodium citrate buffer, in a dish or flask. A representative procedure uses an 1100W microwave and applies microwaves to the blade at 100% power for 2 minutes, followed by microwave application to the slides using 20% energy for 18 minutes after checking to make sure the blade remains covered. in the liquid. The slide is then allowed to cool in the uncovered container and then washed with distilled water. HIER can be used in combination with enzymatic digestion to improve the target's reactivity to immunochemical reagents.
[00260] Such an enzymatic digestion protocol uses proteinase K. A concentration of 20 μg / ml of proteinase K is prepared in 50 mM Tris Base buffer, 1 mM EDTA, 0.5% Triton X-100, pH 8.0. The first process involves disengagement sections in two xylene changes, 5 minutes each. Then, the sample is hydrated in two changes of 100% ethanol, for 3 minutes each, 95% and 80% ethanol for 1 minute each, and then washed in distilled water. The sections are covered with Proteinase K working solution and incubated 10-20 minutes at 37 ° C in a humid chamber (the ideal incubation time may vary depending on the type of tissue and the degree of fixation). The sections are cooled to room temperature for 10 minutes and then washed in PBS Tween 20 for 2x2 min. If desired, sections can be blocked to eliminate possible interference from endogenous compounds and enzymes. The section is then incubated with primary antibody at an appropriate dilution in primary antibody dilution buffer for 1 hour at room temperature or overnight at 4 ° C. The section is then washed with PBS Tween 20 for 2x2 min. Additional blocking can be performed, if necessary for the specific application, followed by additional washing with PBS Tween 20 for 3x2 min and then, finally, the immunostaining protocol is complete.
[00261] A simple treatment with 1% SDS at room temperature has also been shown to improve immunohistochemical staining. Analyte recovery methods were applied to blade mounted sections, as well as free floating sections. Another treatment option is to place the slide in a flask containing citric acid and 0.1 Nonident P40 at pH 6.0 and heating to 95 ° C. The slide is then washed with a buffer solution such as PBS.
[00262] For the immunological staining of tissues that can be useful to block the specific non-association of the antibody with tissue proteins by immersing the section in a protein solution such as whey or dry skimmed milk.
[00263] Blocking reactions may include the need to reduce the level of endogenous biotin; eliminate the effects of endogenous load; inactivate endogenous nucleases, and / or inactivate endogenous enzymes such as peroxidase and alkaline phosphatase. Endogenous nucleases can be inactivated by degradation with proteinase K, by thermal treatment, use of a chelating agent such as EDTA or EGTA, the introduction of a DNA or RNA carrier, treatment with a chaotropic agent such as urea, thio-urea, hydrochloride guanidine, guanidine thiocyanate, lithium perchlorate, etc., or diethyl pyrocarbonate. Alkaline phosphatase can be inactivated by treatment with 0.1 N HCl for 5 minutes at room temperature, or treatment with 1 mM levamisole. Peroxidase activity can be eliminated by treatment with 0.03% hydrogen peroxide. Endogenous biotin can be blocked by immersing the slide or section in an avidin solution (streptavidin, neutravidine can be replaced), for at least 15 minutes at room temperature. The slide or section is then washed for at least 10 minutes in buffer. This can be repeated at least three times. Then, the slide or section is soaked in a biotin solution for 10 minutes. This can be repeated at least three times with a fresh biotin solution each time. The buffer washing procedure is repeated. Blocking protocols should be minimized to avoid damage to cells or tissue or structure or the target or targets of interest, but one or more of these protocols can be combined to "block" a slide or section before reacting with one or more slow dissociation rate aptamers. See Basic Medical Histology: the Biology of Cells, Tissues and Organs, by Richard G. Kessel, Oxford University Press, 1998. Determination of Biological Markers Values using Mass Spectrometry Methods
[00264] A variety of mass spectrometer configurations can be used to detect the values of biological markers. Various types of mass spectrometers are available or can be produced with various configurations. In general, a mass spectrometer has the following main components: a sample input, an ion source, a mass analyzer, a detector, a vacuum system, a system control instrument, and a data system. The differences in sample input, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, an inlet can be a capillary column liquid chromatography source or it can be a probe or direct stage as used in. Common sources of ions are, for example, electrospray, including nano-spray and micro-spray or laser-assisted matrix desorption. Common mass analyzers include a four-pole mass filter, ion trap mass analyzer and time-of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70: 647 R-716R (1998); Kinter and Sherman Protein sequencing and identification using tandem mass spectrometry New York: Wiley-Interscience (2000).
[00265] Biological protein markers and biological marker values can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS) n , laser assisted matrix desorption desorption time-of-flight ionization mass spectrometry (MALDI-TOF-MS), improved surface laser flight time ionization / desorption mass spectrometry (SELDI-TOF-MS ), desorption / ionization in silicon (DIOS) secondary ion mass spectrometry (SIMS), time-of-flight quadrupole (Q-TOF), time-of-flight tandem technology (TOF / TOF), called UltraFlex IIITOF / TOF, spectrometry of chemical ionization mass at atmospheric pressure (APCI-MS), APCI-MS / MS, APCI- (MS) -N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS and APPI ( MS) N, quadrupolar mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry vat and ion trap mass spectrometry.
[00266] Sample preparation strategies are used to mark and enrich samples before mass spectroscopic characterization of biological protein markers and determination of biological marker values. Labeling methods include, but are not limited to, isobaric labeling for relative and absolute quantification (iTRAQ) and stable isotope labeling with cell culture amino acids (SILAC). Capture reagents used to selectively enrich samples for candidate biological marker proteins prior to mass spectroscopic analysis include, but are not limited to, aaptamers, antibodies, nucleic acid probes, chimeras, small molecules, an F (ab ') fragment 2 , a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affinity bodies, nanobodies, ankyrins, domain antibodies, alternative structures and antibodies (e.g., diabody, etc.) printed polymers, avimers, peptidomimetics, peptides, nucleic acid peptides, threose nucleic acids, a hormone receptor, a cytokine receptor, and synthetic receptors, and modifications and fragments thereof.
[00267] The previous tests allow the detection of the values of biological markers, which are useful in methods to diagnose lung cancer, where the methods comprise, detect a biological sample of an individual, at least N values of biological markers each corresponding to a biological marker selected from the group consisting of the biological markers provided in Tables 18, 20 or 21, where a classification, as described in detail below, using the values of biological markers indicates whether the individual has lung cancer. While some of the biological lung cancer markers described are useful in themselves for the detection and diagnosis of lung cancer, methods are also described here for grouping various subsets of the biological lung cancer markers, each of which are useful as a panel of three or more biological markers. Thus, various embodiments of the present application provide combinations that comprise N markers, where N is at least three markers. In other embodiments, N is selected to be any number of 2-86 biological markers. It will be appreciated that N can be selected to be any number from any of the ranges described above, as well as from similar but higher order ranges. According to any of the methods described in this document, the values of biological markers can be detected and classified individually or they can be detected and classified together, for example, in a multiplexed assay format.
[00268] In another aspect, methods are provided for detecting an absence of lung cancer, methods comprising detecting, in a biological sample from an individual, at least N values of biological markers, each corresponding to a selected biological marker from the group consisting of the biological markers provided in Tables 18, 20 or 21, in which a classification, as described in detail below, of the biological marker values indicates the absence of lung cancer in the individual. Although some of the biological lung cancer markers described are useful in themselves for detecting and diagnosing the absence of lung cancer, the methods are also described here for grouping various subsets of the biological lung cancer markers, each of which they are useful as a panel of three or more biological markers. Thus, various embodiments of the present application provide combinations that comprise N markers, where N is at least three markers. In other embodiments, N is selected to be any number of 2-86 biological markers. It will be appreciated that N can be selected to be any number from any of the ranges described above, as well as from similar but higher order ranges. In accordance with any of the methods described in this document, the values of biological markers can be detected and classified individually or they can be detected and classified together, for example, in a multiplexed assay format. Classification of Biological Markers and Calculation of Disease Scores
[00269] A biological marker "signature" for a given diagnostic test contains a set of markers, each marker having different levels in the population of interest. Different levels, in this context, may refer to different means of marker levels for individuals in two or more groups, or different variations in two or more groups, or a combination of both. For the simplest form of a diagnostic test, these markers can be used to assign an unknown sample to an individual in one of two groups, either sick or not sick. Assigning a sample to one of two or more groups is known as a classification, and the procedure used to perform this task is known as a classifier or a classification method. Classification methods can also be said as scoring methods. There are many classification methods that can be used to build a diagnostic classifier from a set of biological marker values. In general, classification methods are more easily performed using supervised learning techniques, where a set of data is collected through samples obtained from individuals within two (or more, for multiple classification states) distinct groups that you want to distinguish . Once the class (or population group) to which each sample belongs is known in advance, for each sample, the classification method can be trained to respond to the desired classification. It is also possible to use unsupervised learning techniques to produce a diagnostic classifier.
[00270] Common approaches to the development of diagnostic classifiers include decision trees; bagging + boosting + forests; rule-based learning of inference; Parzen windows; linear models; logistics, neural network methods; unsupervised grouping; K-means; ascending / descending hierarchy, semi-supervised learning, prototype methods; nearest neighbor; estimate of the kernel density; support vector machines; hidden Markov models; Boltzmann learning and classifiers can be combined both simply and in ways that minimize particular objective functions. For a review, see, for example, Pattern Classifications, R.O. Duda, et al., Editors, John Wiley & Sons, 2nd edition, 2001, see also, The Elements of Statiscal Learning - Data Mining, Inference, and Prediction, T. Hastie, et al, editors, Springer Science + Business Media, LLC, 2nd edition, 2009, each of which is incorporated by reference in its entirety.
[00271] To produce a classifier using supervised learning techniques, a set of samples called training data are obtained. In the context of diagnostic tests, training data includes samples from the different groups (classes) to which unknown samples will later be assigned. For example, samples collected from individuals in a control population and individuals from a particular disease population may constitute training data to develop a classifier that can classify unknown samples (or, more particularly, the individuals from whom the samples were obtained) or how to have the disease or how to be free from the disease. The development of the classifier from the training data is known as classifier training. The specific details about classifier training depend on the nature of the supervised learning technique. For purposes of illustration, an example of forming a simple Bayesian classifier will be described below (see for example, Pattern Classifications, RO Duda, et al., Editors, John Wiley & Sons, 2nd edition, 2001; see also, The Elements of Statiscal Learning - Data Mining, Inference, and Prediction, T. Hastie, et al, editors, Springer Science + Business Media, LLC, 2nd edition, 2009).
[00272] Since there are usually many more values of potential biological markers than samples in a training set, care should be used to avoid over-adjustment. Over-adjustment occurs when a statistical model describes random error or noise, rather than the underlying relationship. Over-adjustment can be avoided in several ways, including, for example, by limiting the number of markers used for the development of the classifier, assuming that the responses of the markers are independent of each other, by limiting the complexity of the statistical model underlying employee, and ensuring that the underlying statistical model conforms to the data.
[00273] An illustrative example of the development of a diagnostic test using a set of biological markers includes the application of a simple Bayes classifier, a simple probabilistic classifier based on Bayes' theorem with strict independent treatment of biological markers. Each biological marker is described by a class-dependent probability density function (pdf) for the measured RFU values or RFU log values (relative fluorescence units) in each class. The join pdfs for the set of bookmarks of a class is assumed to be the product of the class-dependent pdfs for each biological bookmark. Training a simple Bayes classifier in this context is equivalent to assigning parameters ("parameterization") to characterize class-dependent PDFs. Any underlying model for class-dependent PDFs can be used, but in general, the model must conform to the data observed in the training set.
[00274] Specifically, the class-dependent probability of measuring a xi value for biological marker i in the disease class is written as p (xi | d) and Bayes' overall probability is simple to observe n markers with x = values (x1 , x2, ... xn) is written as
[00275] P (x | d) = π p (xi | d) for i ranging from 1 to n, where Xi s are the levels of biological markers measured in RFU or log RFU. The classification assignment for an unknown sample is facilitated by calculating the probability of being disease-free (control) p (c | x) for the same measured values. The ratio of these probabilities is calculated from the class-dependent pdfs by applying Bayes' theorem, that is,
[00276] p (c | x) / p (d | x) = p (x | c) (1-P (d)) / p (x | d) P (d) where P (d) is the prevalence disease in the appropriate test population. Taking the logarithm of both sides of this relationship and replacing the simple Bayes class-dependent probabilities above gives: In p (c | x) / p (d | x) = ∑ln {p (Xi | c) / p (Xi | d)} + ln {(1-P (d)) / P (d)}
[00277] This form is known as the probability log ratio and simply establishes that the probability log of being free from the particular disease versus having the disease is essentially composed of the sum of the individual probability log ratios of the n individual biological markers . In its simplest form, an unknown sample (or, more particularly, the individual from whom the sample was obtained) is classified as being disease-free, if the above reason is greater than zero and having the disease, if the relationship is less than zero.
[00278] In an exemplary embodiment, the pdfs of class p (xi | c) and p (xi | d) dependent biological markers are assumed to be normal or log-normal distributions in the measured RFU xi values, or

[00279] with a similar expression for p (Xi | d) with μd, ie (αd, i) 2. The parameterization of the model requires the estimation of the two parameters for each class-dependent pdf, an μ average and an α2 variance, from the training data. This can be accomplished in a number of ways, including, for example, maximum probability estimates, least squares, and any other methods known to a person skilled in the art. Substituting the normal distributions for p (Xi | c) and p (Xi | d) within the probability log ratio defined above gives the following eXpression:

[00280] Once a set of μs and α2s have been defined for each pdf in each class from the training data and the prevalence of disease in the population is specified, the Bayes classifier is completely determined and can be used to classify unknown samples with the measured X values.
[00281] The performance of the simple Bayes classifier is dependent on the number and quality of biological markers used to build and train the classifier. A unique biological marker that will work according to your KS distance (Kolmogorov-Smirnov), as defined in Example 3, below. If a classifier performance metric is defined as the sum of sensitivity (fraction of true positives, fTP) and specificity (one minus the fraction of false positives, 1- fFP), a perfect classifier will have a score of two and a classifier of randomly, on average, will have a score of one. Using the KS distance definition, that x * value that maximizes the difference in cdf functions can be found by solving

[00282] for x leading to, p (x * | c) = p (x * | d)
[00283] that is, the KS distance occurs where the class-dependent PDFs intersect. Substituting this value of x * within the expression for the distance KS produces the following definition for,

[00284] the KS distance is one minus the total fraction of errors using a test with a cut-off point at x *, essentially a single analyte Bayesian classifier. Since we defined a sensitivity + specificity score = 2 - - fFP - fFN, combining the definition above the KS distance, we see that sensitivity + specificity = 1 + KS. We selected biological markers with a statistic that is inherently suitable for building simple Bayes classifiers.
[00285] The addition of subsequent markers with good KS distances (> 0.3, for example), in general, improves the classification performance, if the markers added subsequently are independent of the first marker. Using sensitivity plus specificity as a classification score, it is easy to generate many high-score classifiers with a variation of a "greedy" algorithm. (A "greedy" algorithm is any algorithm that follows the problem-solving metaheuristic of making the optimal choice locally at each stage, with the hope of finding the global optimum.)
[00286] The algorithm approach used here is described in detail in Example 4. Briefly, all individual analyte classifiers are generated from a table of potential biological markers and added to a list. Then, all possible additions of a second analyte to each of the stored individual analyte classifiers are then performed, saving a predetermined number of top-scoring pairs, say, for example, a thousand, in a new list. All possible three-marker classifiers are explored using this new list of the best two-marker classifiers, again saving the best (thousand) of them. This process continues until the score becomes a constant plateau or begins to deteriorate as additional markers are added. Those high score classifiers that remain after convergence can be evaluated for the desired performance for an intended use. For example, in a diagnostic application, classifiers with high sensitivity and modest specificity may be more desirable than modest sensitivity and high specificity. In another diagnostic application, classifiers with high specificity and modest sensitivity may be more desirable. The desired level of performance is usually selected based on a trade-off that must be made between the number of false positives and false negatives, which can each be tolerated for the particular diagnostic application. Such commitments generally depend on the medical consequences of an error, both false negative and false positive.
[00287] Several other techniques are known in the art and can be used to generate many potential classifiers from a list of biological markers using a simple Bayes classifier. In one embodiment, which is referred to as a genetic algorithm, it can be used to combine different markers using the aptitude score, as defined above. Genetic algorithms are particularly well suited for exploring a large diverse population of potential classifiers. In another modality, the so-called colony optimization can be used to generate sets of classifiers. Other strategies that are known in the art can also be employed, including, for example, other evolutionary strategies as well as simulated annealing ("Simulated Annealing") and other methods of stochastic research. Metaheuristic methods, such as, for example, harmony research, can also be employed.
[00288] Exemplary modalities use any number of biological lung cancer markers listed in Tables 18, 20 or 21, in various combinations for the production of diagnostic tests for the detection of lung cancer (see Examples 2 and 6 for a description detailed description of how these markers were identified). In one embodiment, a method for diagnosing lung cancer uses a simple Bayes classification method in conjunction with any number of biological lung cancer markers shown in tables 18, 20 or 21. In an illustrative example (Example 3), the simplest test for the detection of lung cancer from a population of asymptomatic smokers can be constructed using a unique biological marker, for example, SCFsR which is down regulated in lung cancer with a KS distance of 0.37 ( 1 + KS = 1.37). Using the parameters, μc, i, CTO.I, μd.i, and aa.i, for SCFsR from Table 15 and the equation for the probability log described above, a diagnostic test with a sensitivity of 63% and 73% specificity (sensitivity + specificity = 1.36) can be produced, see Table 14. The ROC curve for this test is shown in Figure 2 and has an AUC of 0.75.
[00289] The addition of the biological marker HSP90a, for example, with a KS distance of 0.5, significantly improves the performance of the classifier for a sensitivity of 76% and a specificity of 0.75% (sensitivity + specificity = 1.51 ) and an AUC = 0.84. Note that the score for a classifier made up of two biological markers is not a simple sum of the KS distances; KS distances are not additive when combining biological markers and it takes several weak markers to achieve the same level of performance as a strong marker. Adding a third marker, ERBB1, for example, increases the classifier's performance to 78% sensitivity and 83% specificity and AUC = 0.87. Adding additional biological markers, such as, for example, PTN, BTK, CD30, Kallikrein 7, LRIG3, LDH-H1, and PARC, produces a series of lung cancer tests summarized in Table 14 and presented as a series of ROC curves in Figure 3. The score of the classifiers as a function of the number of analytes used in the construction of the classifier is shown in Figure 4. The sensitivity and specificity of this exemplary ten-marker classifier is> 87% and the AUC is 0.91.
[00290] The markers listed in Tables 18, 20 or 21 can be combined in several ways to produce classifiers for diagnosing lung cancer. In some embodiments, the biological marker panels are composed of different numbers of analytes, depending on a specific diagnostic performance criterion that is selected. For example, certain combinations of biological markers will produce tests that are more sensitive (or more specific) than other combinations.
[00291] Once the panel is defined to include a particular set of biological markers from Tables 18, 20 or 21 and a classifier is built from a set of training data, the definition of the diagnostic test is complete . In one embodiment, the procedure used to classify an unknown sample is described in Figure 1A. In another embodiment, the process used to classify an unknown sample is described in Figure 1B. The biological sample is appropriately diluted and then performed in one or more relevant assays to produce the levels of quantitative biological markers used for classification. The levels of measured biological markers are used as input to the classification method that generates a classification and an optional score for the sample that reflects the confidence of the class assignment.
[00292] Table 21 identifies 86 biological markers that are useful for diagnosing lung cancer in both tissues and blood samples. Table 20 identifies 25 biological markers that have been identified in tissue samples, but which are also useful in serum and plasma samples. This is a surprisingly larger number than expected when compared to what is normally found during efforts to discover biological markers and can be attributed to the scale of the study described, which covered about 800 proteins measured in hundreds of individual samples , in some cases, at concentrations in the low femtomolar range. Presumably, the large number of biological markers discovered reflects the different biochemical pathways involved both in tumor biology and in the organism's response to the presence of the tumor, each path and process involves many proteins. The results show that no single protein from a small group of proteins is exclusively informative about such complex processes, rather that multiple proteins are involved in several relevant processes, such as apoptosis or extracellular matrix repair, for example.
[00293] Given the numerous biological markers identified during the study described, one would expect to be able to derive a large number of high-performance classifiers that can be used in various diagnostic methods. To test this notion, tens of thousands of classifiers were evaluated using the biological markers in Table 1. As described in Example 4, many subsets of the biological markers shown in Table 1 can be combined to generate useful classifiers. As an example, descriptions are provided for classifiers containing 1, 2, and 3 biological markers for each of the two uses: screening for lung cancer in high-risk smokers and diagnosing individuals who have lung nodules that are detectable by CT . As described in Example 4, all classifiers that were constructed using the biological markers in Table 1 clearly perform better than those classifiers that were constructed using "non-markers".
[00294] The performance of the classifiers obtained at random excluding some of the markers in Table 1, which resulted in smaller subsets than to construct the classifiers, was also tested. As described in Example 4, Part 3, the classifiers that were constructed from subsets of random markers in Table 1 performed similarly to the optimal classifiers, which were constructed using the complete list of markers in Table 1.
[00295] The performance of ten marker classifiers obtained excluding the "best" individual markers from the aggregation of ten markers was also tested. As described in Example 4, Part 3, classifiers built without the "best" markers in Table 1, also performed well. Many subsets of the biological markers listed in Table 1 performed close to optimal shape, even after removing 15 of the markers listed at the top of the Table. This implies that the performance characteristics of any particular classifier are probably not due to some small core group of biological markers and that the disease process is likely to impact numerous biochemical pathways, which alters the expression level of many proteins.
[00296] The results of Example 4 suggest certain possible conclusions: First, the identification of a large number of biological markers allows their aggregation in a large number of classifiers that offer an equally high performance. Second, classifiers can be constructed in such a way that specific biological markers can be replaced by other biological markers in a way that reflects the redundancies that undoubtedly permeate the complexities of the underlying disease processes. That is, the information about the disease contributed by any individual biological marker identified in Table 1 overlaps with information from other biological markers, in such a way that it is possible that no particular biological marker or small group of biological markers in Table 1 should be included in any classifier.
[00297] Exemplary modalities use simple Bayes classifiers built from the data in Tables 38 and 39 to classify an unknown sample. The procedure is described in Figures 1A and B. In one embodiment, the biological sample is optionally diluted and performed in a multiplexed aptamer assay. Assay data is normalized and calibrated as outlined in Example 3, and the resulting levels of biological markers are used as input to a Bayes classification scheme. The probability ratio log is calculated for each biological marker measured individually and then added together to produce a final classification score, which is also said to be a diagnostic result. The resulting assignment, as well as the overall rating score can be reported. Optionally, the individual probability log risk factors calculated for each level of biological markers can be reported. The details of calculating the rating score are shown in Example 3.
[00298] To demonstrate the usefulness of the aptamer-based proteomic technology described herein for use in detecting diseases related to biological markers from tissues, tissue samples from surgical resections obtained from eight non-small lung cancer cells ( NSCLC), patients were analyzed, as described in Example 6. All patients were NSCLC smokers, ranging in age from 47 to 75 years old and covering stages 1A through 3B of NSCLC (Table 17). Three samples were obtained from each resection: tumor tissue sample, adjacent non-tumor tissue. The total protein concentration was adjusted and normalized in each homogenate for proteomic profiling followed by analysis of the DNA micromatrix platform to measure the concentrations of about 800 human proteins (see Gold et al., Nature Precedings, http: // precedings. nature.eom / documents / 4538 / version / 1 (2010)).
[00299] Measurements of protein concentration, expressed as relative fluorescence units (RFU), allow for large-scale comparisons of protein signatures between samples (see Figure 21). With reference to Figure 21, firstly the levels of protein expression between adjacent and distant control tissues were compared for each patient sample (Figure 21A). In this comparison, only one analyte (fibrinogen) showed more than a two-fold difference between the samples. In general, the signals generated by most analytes were similar in the adjacent and distant tissue.
[00300] In contrast, the comparison of tumor tissues with non-tumor tissues (adjacent or distant) identified 11 (1.3%) proteins with differences greater than four times and 53 (6.5%), proteins with differences greater than twice (see Figures 21B and 21C). In the remaining 767 (93.5%), proteins showed relatively small differences between tumor and non-tumor tissue. Some proteins have been substantially suppressed while others have been elevated in tumor tissues compared to adjacent or distant tissues. The differential expression of proteins between adjacent and tumor tissue, or between tumor and distant tissue, was similar in general. Changes in distant tissue were generally greater (Figure 21), which demonstrates that most protein changes are specific to the tumor's local environment.
[00301] To identify biological markers of NSCLC tissue, the levels of protein expression between tumor tissue samples, adjacent and distant, were compared by the Mann-Whitney test, as described in Ostroff et al. Nature Precedings, http: //preeedings.nature.eom/doeuments/4537/version/1 (2010)). Thirty-six proteins with the greatest change in fold and with statistically significant differences between tumor and non-tumor tissue were identified with a cut-off false discovery rate of q <0.05 for significance (Figures 23 and 24, and Table 18) . Twenty of these proteins were up-regulated and 16 were down-regulated in tumor tissue. Although the number of samples used for this study was relatively small, a powerful individual-based study design was used, in which each tumor sample had its own controls for normal tissues. This eliminates the population variation associated with projects based on the study population. The availability of properly chosen reference samples is increasingly recognized as an important component in the research of discovering biological markers (Bossuyt (2011) J. Am Med Assoc 305: 2229-30; Ioannidis and Panagiotou (2011) J. Am Med .. Assoc 305: 2200-10; Diamandis (2010) J. Natl Cancer Inst. 102: 1462-7).
[00302] It is believed that about one third (13/36) biological markers of NSCLC tissue identified here are new. The remaining two thirds (23/36) were previously reported in the form of proteins or genes differentially expressed in NSCLC tumor tissue (Table 18).
[00303] The biological markers identified according to the method of Example 6, can be classified in general into four biological processes associated with important characteristics of tumor biology (Hanahan & Weinberg (2011) Cell 144: 646-74), as shown in Table 19: 1) angiogenesis, 2) growth and metabolism, 3) inflammation and apoptosis, and 4) invasion and metastasis. Admittedly, these are convenient yet inaccurate classifications that approximate a highly complex and dynamic system in which these molecules often play multiple roles and nuances. Therefore, the specific state of a given system ultimately affects the expression and function of any particular molecule. Biological understanding is far from complete in these systems. With the SOMA scanning platform, quantitative expression of a large number of proteins in various tissues and disease processes is possible. These data provide new coordinates to help map the dynamics of these systems, which in turn will provide a more complete understanding of the biology of lung cancer, as well as other diseases. The study results provide a new perspective on the biology of the NSCLC tumor, with both familiar and new elements. Angiogenesis
[00304] Angiogenesis directs the growth of new blood vessels to support tumor growth and metabolism. The regulation of angiogenesis is a complex biological phenomenon controlled by both positive and negative signals (Hanahan & Weinberg, (2011) Cell 144: 646-74). Among the biological markers of NSCLC tissue identified in this study, the positive and negative regulators of angiogenesis (Figures 23 and 24 and Table 19) were well known, which were previously observed in NSCLC tumor tissue (Fontanini et al. (1999) British Journal of Cancer 79 (2): 363-369, Imoto et al (1998) J. Thorac. Carciovasc. Surg. 115: 10071011, Ohta et al (2006) Ann. Thorac. Surg. 82: 1180-1184; Iizasa et al (2004) Clinical Cancer Research 10: 5361-5366). These include the VEGF angiogenesis-inducing prototype and endostatin and thrombospondin-1 (TSP-1) inhibitors. VEGF is a potent growth factor that promotes the growth of new blood vessels and has been tightly regulated upward in NSCLC tumor tissue, according to previous observations (Imoto et al. (1998) J. Thorac. Carciovasc. Surg. 115: 1007-1011), and including our study of serum samples from patients with NSCLC (Ostroff et al. Nature Precedings, http: //precedings.nature.eom/documents/4537/version/1 (2010)). Endostatin is a proteolytic fragment of collagen XVIII and a strong inhibitor of endothelial cell proliferation and angiogenesis (Iizasa et al. (Aug. 2004) Clinical Cancer Research 10: 5361-5366). TSP-1 and the related thrombospondin-2 (TSP-2) were substantially up-regulated in NSCLC tumor tissue. TSP-1 and TSP-2 are extracellular matrix proteins with complex, context-dependent effects modulated through a variety of interactions with cell surface receptors, growth factors, cytokines, matrix metalloproteinases, and other molecules. Archetypically in model systems, TSP-1 and TSP-2 inhibit angiogenesis by inhibiting the proliferation of endothelial cells through the CD47 receptor and inducing apoptosis of endothelial cells through the CD36 receptor. There is also evidence of proangiogenic influences of TSP-1 and TSP-2 (Bornstein (2009) J. Cell Commun. Signal. 3 (3-4): 189-200). Finally, TSP-1 and TSP-2 reported varying levels of relative and absolute expression in NSCLC tissue (Chijiwa et al (2009) Oncology Reports 22: 279-283, .. Chen et al (2009) J Int Med Res 37: 551 - 556; Oshika 1998, Fontanini et al (1999) British Journal of Cancer 79 (2): 363-369), probably due to their complex functions. In this study, it was found that CD36 was down-regulated in NSCLC tumor tissue, which may indicate an adaptation of the tumor cells to reduce apoptosis-mediated TSP-1 and TSP-2 sensitivity. Growth and Metabolism
[00305] Ten of the NSCLC biological markers identified are associated with growth and metabolism functions. Half of these biological markers are involved in the complex hormonal regulation of cell growth and energy metabolism. Three insulin-like growth factor-binding proteins (IGFBPs), which modulate the activity of insulin-like growth factors (IGFs), have been up-regulated in NSCLC tumors (IGFBP-2, -5, and -7) . Several reports have qualitatively evaluated IGFBP-2, -5, and -7 in NSCLC (Table 18) and suggest greater expression in NSCLC tissue than in normal tissue. Insulin and IGFs are hormones that strongly influence cell growth and metabolism, and cancer cells are often dependent on these molecules for growth and proliferation (Robert et al. (August 1999) Clinical Cancer Research 5: 2094-2102; Liu et al (June 2007) Lung Cancer 56 (3): 307-317; Singhal et al (2008) Lung Cancer 60: 313-324). These hormones are, in turn, degraded by insulin, which we find regulated upwards in NSCLC tumor tissue. The hormone adiponectin controls lipid metabolism and insulin sensitivity, and we find adiponectin down-regulated in NSCLC tumors. The remaining five biological markers, carbonic anhydrase III, NAGK, TrATPase, tryptase β-2, and MAPK13, are enzymes with functions in cell metabolism (Table 17). Inflammation and apoptosis
[00306] Inflammation and apoptosis are hallmarks of cancer biology, and a large number of potential biological markers associated with these processes, which were previously associated with NSCLC (Table 19). Caspase-3, which has been associated with metastasis (Chen et al (2010) Lung Cancer (doi: 1016 / j.lungcan.2010.10.015), has been found to be up-regulated in NSCLC tumor tissue. Another example notable is sRAGE, which has been reported to be dramatically down-regulated in NSCLC tissue (Jing et al. (2010) Neoplasm. 57: 55-61, Bartling et al. (2005) Carcinogenesis 26: 293-301). result is consistent with the measurement disclosed here, in which sRAGE had the greatest change observed for proteins that are lower in the tumor than in non-malignant tissue. Although not limited by theory, one hypothesis is that RAGE plays a role in the organization of the epithelium, and the decrease in RAGE levels in lung tumors can contribute to the loss of epithelial tissue structure, which can lead to malignant transformation (Bartling et al. (2005) Carcinogenesis 26 (2): 293-301). , such as BCA-1, CXCL16, IL-8, and NAP-2, are altered (Table 18), consistent with the hypothesis ese that the invasion of tumors with cells of the innate and adaptive arms of the immune system provide bioactive molecules that affect proliferative and angiogenic signals (Hanahan & Weinberg (2011) Cell 144: 646-74). Invasion and Metastasis
[00307] The largest group of potential biological markers contains proteins that function in cell-cell and cell-matrix and the interactions involved in invasion and metastasis. Many have previously been reported to be associated with NSCLC. Most notable are two from the metalloprotease matrix, MMP-7 and MMP-12, which contribute to the proteolytic degradation of extracellular matrix components and substrate processing, such as growth factors (see, for example Su et al. (2004 ) Chinese Journal of Clinical Oncology 1 (2): 126-130, Wegmann et al. (1993) Eur. J. Cancer 29A (11): 1578-1584). Such processes are well known to play a role in the creation of tumor microenvironments. Both MMP-7 and MMP-12 were found to be up-regulated in NSCLC tissue (Table 18), which is consistent with a similar study that used antibody-based measures (Shah et al. (2010) The Journal of Thoracic and Cardiovascular Surgery 139 (4): 984-990). Overexpression of MMP-7 and MMP-12 has been associated with a poor prognosis of NSCLC (Shah et al. (2010) The Journal of Thoracic and Cardiovascular Surgery 139 (4): 984-990). MMP-12 levels were correlated with local recurrence and metastasis disease (Hofmann et al. (2005) Clin. Cancer. Res.11: 1086-92, Hoffman et al. (2006) Oncol. Rep. 16: 58795) . ). Two of the eight subjects studied had normal levels of MMP-12, while the other six had a 15-50x elevation of MMP-12 in tumor tissues compared to non-tumor tissue. Performance of NSCLC Biological Markers as Histochemical Probes
[00308] The understanding of differences in protein expression between tumor and non-tumor tissues can be used to identify new histochemical probes. Such probes can allow for more accurate molecular characterization of tumors and their effects on the surrounding stroma. Figure 25 demonstrates the ability of two of the SOMAmeros identified to color fresh frozen tissues obtained from the same tumor resections used for the discovery of these biological markers. Thrombospondin-2 (TSP2) has been found to be increased in tumor tissue homogenates, while the macrophage mannose receptor (MRC1) has been decreased. The staining of fabrics with these SOMAmeros was consistent with the profile results. Additional examples, as well as the confirmation of antibodies to staining patterns, are shown in Figure 27. Comparison of NSCLC Tissue and Biological Serum Markers
[00309] Differential protein expression in sera from patients with NSCLC compared to cancer-free controls compared to those tissue samples with NSCLC gives useful information (Figure 26). The most surprising observation is that the relative change in protein expression is greater in tissues than in serum. This result can be expected since the tumor tissue is the source of changes in protein expression which is then, even if fully released into the circulation, diluted many times in the total volume of blood. This trend is evident in the elongated distribution of data points along the x-axis in figure 26 where the axes are drawn on the same scale to illustrate this point. Twelve of the analytes shown in Figures 23 and 24, as altered in the tumor tissue, are also differentially expressed in sera from patients with NSCLC versus controls (red circles filled in Figure 26). Most directional changes are the same between tissue and serum, but some are not. Local protein concentrations in a tissue homogenate clearly need not correlate with circulating protein levels and inverse correlations can provide clues about the redistribution of certain biological markers in diseased versus normal tissues.
[00310] The discovery of new biological markers with demonstrable diagnosis or clinical utility has been a considerable challenge in recent years (Diamandis (2010) J. Natl. Cancer Inst. 102: 1462-7). The reasons for this include the ubiquity of pre-analytical and analytical artifacts, the lack of adequate health status controls and unsophisticated study designs, and the difficulty of detecting small changes in protein levels at very low concentrations. This challenge is especially pronounced with biological cancer markers, where the goal is often to identify a tiny malignant tumor in a relatively large human body at an early stage. Regarding the last point, one way to improve the possibilities of discovering true biological cancer markers is to obtain protein expression data from both the source of the disease, such as tumor tissue, and from the circulation. The combined results may partially corroborate the validity of potential biological markers. The present patent application demonstrates that this is possible with the disclosed highly sensitive multiplexed proteomic assay. It has been shown that tissues, such as plasma or serum, are also amenable to SOMA scanning and the comparative analysis resulting from protein expression in tissues of NSCLC tumors with surrounding healthy lung tissues offers a complement to the existing biological marker data set NSCLC potentials identified from serum samples (see US Pub. No. 2010/0070191). In the present case, one third, or 12 of the 36 tissue markers reported here (BCA-1 (BCL), cadherin-1 (E-cadherin), catalase, endostatin, IGFBP-2, MRC1 (macrophage mannose receptor), MAPK-13 (MK13), MMP-7, MMP-12, NAGK, VEGF and SIM were previously identified in the serum, taken together, these data contribute to a greater understanding of the complexity of the changes that accompany the NSCLC and provide additional biological markers potential for early detection of this deadly disease.
[00311] Any combination of the biological markers in Table 20 (as well as the additional biomedical information) can be detected using a suitable kit, such as for use in carrying out the methods described here. In addition, the kit can contain any one or more detectable markers, as described here, such as a fluorescent portion, etc.
[00312] In one embodiment, a kit includes (a) one or more capture reagents (such as, for example, at least one aptamer or antibody) for the detection of one or more biological markers in a biological sample, where biological markers include any of the biological markers shown in Tables 18, 20 or 21 and, optionally, (b) one or more software products or computer programs to classify the individual from whom the biological sample was obtained as having or not having lung cancer or to determine the likelihood that the individual has lung cancer, as described in this document. Alternatively, instead of one or more computer program products, one or more instructions can be provided to manually perform the above steps by a human.
[00313] The combination of a solid support with a corresponding capture reagent and a signal generating material is referred to herein as a "detection device" or "assembly". The set can also include instructions for using the devices and reagents, sample manipulation and data analysis. In addition, the set can be used with a computer or software system to analyze and report the result of the analysis of the biological sample.
[00314] Sets can also contain one or more reagents (for example, solubilization buffers, detergents, washes, or buffers) for processing a biological sample. Any of the sets described herein may also include, for example, buffers, blocking agents, mass spectrometry matrix materials, antibody capture agents, positive control samples and negative control samples, software and information, such as protocols , guidance, and reference data.
[00315] In one aspect, the present invention provides kits for the analysis of the state of lung cancer. The sets include PCR primers for one or more biological markers selected from Tables 18, 20, or 21. The set may also include instructions for use and correlation of the biological markers with lung cancer. The set can also include a DNA matrix that contains the complement of one or more of the biological markers selected from Table 20, reagents, and / or enzymes to amplify or isolate the DNA sample. Sets can include reagents for real-time PCR, for example, TaqMan probes and / or primers, and enzymes.
[00316] For example, a kit may comprise: (a) reagents comprising at least capture reagent for the quantification of one or more biological markers in a test sample, wherein said biological markers include the biological markers shown in Tables 18 , 20, or 21, or any other biological markers or panels of biological markers described herein, and, optionally, (b) one or more algorithms or computer programs to perform the steps of comparing the value of each biological marker quantified in the sample. test at one or more predetermined cutoff points and assigning a score for each biological marker based quantified in said comparison, combining the scores assigned for each quantified biological marker to obtain a total score, comparing the total score with a predetermined score , and using said comparison to determine whether an individual has lung cancer. Alternatively, instead of one or more algorithms or computer programs, one or more instructions can be provided to manually perform the above steps by a human being. Computer and Software Methods
[00317] Once a biological marker or biological marker panel is selected, a method for diagnosing an individual may include the following: 1) collecting or obtaining a biological sample; 2) perform an analytical method to detect and measure the biological marker or biological markers on the panel in the biological sample; 3) perform any data normalization or standardization necessary for the method used to collect biological marker values, 4) calculate the marker score; 5) combining marker scores to obtain a total diagnostic score and 6) reporting the individual's diagnostic score. In this approach, the diagnostic score can be a single number determined from the sum of all marker calculations, which is compared with a pre-selected threshold value that is an indication of the presence or absence of disease. Or the diagnostic score can be a series of bars representing a value for each biological marker and the pattern of responses can be compared with a preselected pattern for determining the presence or absence of disease.
[00318] At least some modalities of the methods described here can be implemented with the use of a computer. An example of a computer system 100 is shown in Figure 6. Referring to Figure 6, system 100 is shown comprised of hardware elements that are electrically coupled via bus 108, including a processor 101, input device 102, device output 103, storage device 104, computer-readable storage media reader 105a, communication system 106 processing acceleration (for example, DSP or special purpose processors) 107 and memory 109. The readable storage media reader computer 105a is further coupled to computer-readable storage medium 105b, the combination comprehensively representing remote storage devices, fixed and / or removable locations, plus storage media, memory, etc. for temporarily and / or more permanent computer-readable information, which may include storage device 104, memory 109 and / or any another feature of such an accessible system 100. System 100 also includes software elements (shown to be currently located within working memory 191), including an operating system 192 and other code 193, such as programs, data and the like.
[00319] In relation to Figure 6, system 100 has great flexibility and configuration capacity. Thus, for example, a single architecture can be used to implement one or more servers that can be configured according to the currently desirable protocols, protocol variations, extensions, etc. However, it will be apparent to those skilled in the art that modalities may very well be used according to more specific application requirements. For example, one or more elements of the system can be implemented as sub-elements within a component of system 100 (for example, within communications system 106). Customized hardware can also be used and / or particular elements can be implemented in hardware, software or both. In addition, although connection to other computing devices, such as input / output network (not shown) may be employed, it is to be understood that the connection of wires, wireless modem, and / or others, or connections to others computational devices can also be used.
[00320] In one aspect, the system may comprise a database containing the characteristics of biological markers characteristic of lung cancer. The biological marker data (or biological marker information) can be used as an input to the computer for use as part of a computer-implemented method. The biological marker data may include the data as described herein.
[00321] In one aspect, the system further comprises one or more input data delivery devices for one or more processors.
[00322] The system also comprises a memory to store a data set of classified data elements.
[00323] In another aspect, the device for providing input data comprises a detector for detecting the characteristic of the data element, for example, such as a mass spectrometer or a gene chip reader.
[00324] The system can additionally comprise a database management system. User requests or queries can be formatted in an appropriate language understood by the database management system that processes the query to extract the relevant information from the training sets database.
[00325] The system can be connected to a network to which a network server and one or more clients are connected. The network can be a local area network (LAN) or a wide area network (WAN), as it is known in the art. Preferably, the server includes the hardware necessary to run computer program products (for example, software) to access database data to process user requests.
[00326] The system may include an operating system (for example, UNIX or Linux) for executing instructions from a database management system. In one aspect, the operating system can operate on a global communications network, such as the Internet, and use a global communications network server to connect to a network of this type.
[00327] The system may include one or more devices that comprise a graphical display interface comprising interface elements, such as buttons, menus, scroll bars, text insertion fields, and others as are routinely found in well-known graphical user interfaces in the technique. Orders entered in a user interface can be transmitted to an application program in the system to format to search for relevant information in one or more of the system's databases. Requests or queries entered by a user can be constructed in any suitable database language.
[00328] The graphical user interface can be generated by a graphical user interface code, as part of the operating system and can be used for input data and / or to display the data entered. The result of the processed data can be displayed on the interface, printed on a printer communicating with the system, saved on a memory device, and / or transmitted over the network or can be provided in a computer-readable medium.
[00329] The system may be in communication with an input device for the provision of data relating to data elements to the system (for example, expression values). In one aspect, the input device may include a system of gene expression profiles, including, for example, a mass spectrometer, gene chips or matrix reader, and the like.
[00330] The methods and apparatus for analyzing biological marker information of lung cancer according to various modalities can be implemented in any suitable way, for example, using a computer program operating a computer system. A conventional computer system comprising a processor and a random access memory can be used, such as a remotely accessible application server, network server, personal or workstation computer. Additional components of the computer system may include memory devices or information storage systems, such as a mass storage system and a user interface, for example, a conventional monitor, keyboard and tracking device. The computer system can be an independent system or part of a computer network including a server and one or more databases.
[00331] The lung cancer biological marker analysis system can provide functions and operations to complete data analysis, such as for collecting data, processing, analysis, reporting and / or diagnosis. For example, in one embodiment, the computer system can run a computer program that can receive, store, research, analyze and report information about the biological markers of lung cancer. The computer program can comprise several modules that perform various functions or operations, such as a processing module for processing raw data and generating supplementary data and an analysis module for analyzing raw data and supplementary data for generating a lung cancer status and / or diagnosis. Diagnosing lung cancer status can comprise generating or collecting any other information, including additional biomedical information, regarding the individual's condition in relation to the disease, identifying whether additional tests may be desirable, or otherwise assessing health status of the individual.
[00332] With reference to Figure 7, an example of a method of using a computer can be seen, according to the principles of a disclosed modality. In Figure 7, a flow chart 3000 is shown. In block 3004, biological marker information can be retrieved for an individual. The biological marker information can be retrieved from a computer database, for example, after the individual's biological sample test is performed. The biological marker information can comprise values of biological markers each corresponding to at least one of the N biological markers selected from a group consisting of the biological markers provided in Table 18, where N = 2-36, Table 20, where N = 2-25 or in Table 21, where N = 2-86. In block 3008, a computer can be used to classify each of the biological marker values. And, in block 3012, a determination can be made as to the likelihood that an individual will have lung cancer based on a plurality of classifications. The indication can be sent to a display or other indicating device, so that it can be seen by a person. So, for example, it can be displayed on a computer's display screen or other output device.
[00333] With reference to Figure 8, an alternative method of using a computer, according to another modality, can be illustrated by means of flowchart 3200. In block 3204, a computer can be used to retrieve information from biological markers for a individual. The biological marker information comprises a biological marker value corresponding to a biological marker selected from the group of biological markers provided in Tables 18, 20 or 21. In block 3208, the classification of the biological marker value can be performed with the computer . And, in block 3212, an indication can be made as the likelihood that the individual has lung cancer based on the classification. The indication can be sent to a display or other indicating device, so that it can be seen by a person. So, for example, it can be displayed on a display screen on a computer or other output device.
[00334] Some modalities described here can be implemented to include a computer program product. A computer program product may include a computer-readable medium having the computer-readable program code incorporated into the medium to cause an application program to run on a computer with a database.
[00335] As used herein, a "computer program product" refers to an organized set of instructions in the form of programming language or natural instructions that are contained in a physical medium of any kind (for example, written , electronic, magnetic, optical or otherwise) and that can be used with a computer or other automated data processing system. Such programming language instructions, when executed by a computer or data processing system, cause the computer or data processing system to act according to the specific content of the instructions. Computer program products include, but are not limited to: source and object programs and / or test or data libraries embedded in a computer-readable medium. In addition, the computer program product that allows a computer system or data processing equipment device to act in pre-selected forms can be provided in a variety of ways, including, but not limited to, original source code, assembly code , object code, machine language, encrypted or compressed versions of the precedents and any and all equivalents.
[00336] In one aspect, a computer program product is provided to indicate a likelihood of lung cancer. The computer program product includes a computer-readable medium that contains the program code executable by a processor for a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data comprise values of biological markers each corresponding to at least one of the N biological markers in the biological sample selected from the group of biological markers provided in Table 18, where N = 2-36, Table 20, where N = 2-25 or in Table 21, where N = 2-86; and code that performs a classification method that indicates an individual's lung disease status as a function of biological marker values.
[00337] In yet another aspect, a computer program product is provided to indicate a likelihood of lung cancer. The computer program product includes a computer-readable medium that contains the program code executable by a processor of a computer device or system, the program code comprising: code that retrieves data assigned to a biological sample from an individual, in that the data comprise a biological marker value corresponding to a biological marker in the biological sample selected from the group of biological markers provided in Table 18, where N = 2-36, Table 20, where N = 2-25 or in Table 21, where N-2-86; and the code that performs a classification method that indicates an individual's lung disease status as a function of the biological marker value.
[00338] Although several modalities have been described as methods or devices, it must be understood that modalities can be implemented through code associated with a computer, for example, code resident on the computer or accessible by the computer. For example, software and databases can be used to implement many of the methods discussed above. Thus, in addition to the modalities performed by hardware, it is also noted that these modalities can be achieved through the use of an article of manufacture comprised of a usable computer medium with a computer-readable program code incorporated in it, which the training of the functions disclosed in this description. Therefore, it is desired that modalities are also considered to be protected by this patent in their program code means. In addition, the modalities can be incorporated as codes stored in a computer-readable memory of virtually any type, including, without limitation, RAM, ROM, magnetic, optical, or magneto-optical media. Even more generally, the modalities can be implemented in software, or in hardware, or any combination of these, including, but not limited to, software running on a general purpose processor, microcode, PLAs or ASICs.
[00339] It is also foreseen that modalities could be performed as computer signals incorporated in a carrier wave, as well as signals (for example, electrical and optical) propagated through a transmission medium. Thus, the various types of information discussed above can be formatted in a structure, such as a data structure, and transmitted as an electrical signal through a transmission medium or stored in a computer-readable medium.
[00340] It is also noted that many of the structures, materials, and the acts recited here can be recited as a means to carry out a function or a step towards carrying out a function. Therefore, it must be understood that such language has the right to cover all these structures, materials or acts disclosed in the present specification and their equivalents, including the material incorporated by reference. EXAMPLES
[00341] The following examples are provided for illustrative purposes only and are not intended to limit the scope of the application, as defined by the appended claims. All of the examples described herein were performed using conventional techniques, which are well known and routine to those skilled in the art. Routine molecular biology techniques described in the following examples can be performed as described in conventional laboratory manuals, such as Sambrook et al, Molecular Cloning :. A Laboratory Manual, 3rd. ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (2001). Example 1. Analysis of Multiplexed Aptamer of Samples for Selection of Lung Cancer Biological Marker
[00342] This example describes the multiplexed aptamer assay used to analyze the samples and controls for the identification of biological markers shown in Table 1, column 2 (see Figure 9). In this case, the multiplex analysis used 820 aptamers, each unique for a specific target.
[00343] In this method, the pipette tips have been changed for each addition of solution.
[00344] Also, unless otherwise stated, most solutions transfers and wash additions used the 96 well heads of a Beckman Biomek FxP. Steps in the manually pipetted method used a twelve-channel P200 Pipetteman (Rainin Instruments, LLC, Oakland, CA), unless otherwise noted. A custom buffer referred to as SB17 was prepared at home, comprising 40 mM HEPES, 100 mM NaCl, 5 mM KCl, 5 mM MgCl2, 1 mM EDTA at pH 7.5. All steps were performed at room temperature unless otherwise indicated. 1. Preparation of Aptamer Stock Solution
[00345] For aptamers without a photo-cleavable biotin binder, customized stock aptamer solutions for 10%, 1% and 0.03% serum were prepared in 8x concentration, in 1x SB17, 0.05% Tween-20 with suitable photo-cleavable biotinylated primers, where the resulting concentration of primer was 3 times the concentration of relevant aptamer. Primers hybridized with all or part of the corresponding aptamer.
[00346] Each of the 3 8x aptamer solutions were separately diluted 1: 4 in 1xSB17, 0.05% Tween-20 (1500 µl of 8x stock in 4500 µL of 1xSB17, 0.05% Tween-20) for obtain a concentration of 2x. Each diluted aptamer master mix was then divided, 1500 μl each, into 4.2 ml of screw cap tubes and brought to 95 ° C for 5 minutes, followed by a 37 ° C incubation for 15 minutes. . After incubation, the 4, 2 ml tubes, corresponding to a particular master mixture of aptamer were combined in a reagent crucible, and 55 µl of a 2x aptamer mixture (for all three mixtures) was manually pipetted onto a plate 96-well Hybaid and the plate was sealed. The end result was 3 96-well Hybaid plates closed with foil. The concentration of individual aptamer varied in the range 0.5-4 nM, as indicated in Table 2. 2. Preparation of the Test Sample
[00347] Frozen aliquots of 100% serum, stored at - 80 ° C, were placed in a water bath at 25 ° C for 10 minutes. Thawed samples were placed on ice, carefully vortexed (defined in 4), for 8 seconds and then replaced on ice.
[00348] A 20% sample solution was prepared by transferring 16 μL of sample, using a 50 μL pipettor with 8 channels of extension in 96 wells of Hybaid plates, each well containing 64 μL of appropriate sample diluent, at 4 ° C (0.8 x SB17, 0.05% Tween-20, 2μM Z block_2, 0.6 mM MgCl2 for serum). This plate was stored on ice until the following sample dilution steps were started.
[00349] To start the sample and aptamer balance, the 20% sample plate was quickly centrifuged and placed on the Beckman FX where it was mixed by pipetting up and down with the 96 well pipettor. A 2% sample was then prepared by diluting 10μL of the 20% sample in 90 μL of 1xSB17, 0.05% Tween-20. Then the 6 μL dilution of the resulting 2% sample in 194 μL of 1xSB17, 0.05% Tween-20 made a 0.06% sample plate. Dilutions were made on the Beckman Biomek FxP. After each transfer, the solutions were mixed by pipetting up and down. The three sample dilution plates were then transferred to their respective aptamer solutions by adding 55 μL of the sample and 55 μL of the appropriate 2x aptamer mixture. The sample and aptamer solutions were mixed in the robot by pipetting up and down. 3. Sample Equilibrium Connection
[00350] The sample / aptamer plates were sealed with foil and placed in an incubator at 37 ° C for 3.5 hours, before proceeding to the Capture 1 step. 4. Preparation of the Capture 2 bead plate
[00351] An 11 ml aliquot of MyOne (Invitrogen Corp, Carlsbad, CA) streptavidin C1 beads was washed twice with equal volumes of 20 mM NaOH (5 minutes of incubation for each wash), 3 times with equal volumes of 1x SB17 , 0.05% Tween-20 and resuspended in 11 mL 1x SB17, 0.05% Tween-20. Using a span-12 multichannel pipettor, 50 µL of this solution was manually pipetted into each well of a 96-well Hybaid plate. The plate was then covered with aluminum foil and stored at 4 ° C for use in the test. 5. Preparation of Capture bead plates 1
[00352] Three 0.45 um Millipore plates (HV Durapore Cat membrane, # MAHVN4550) were equilibrated with 100 µl of 1x SB17, 0.05% Tween-20 for at least 10 minutes. The equilibration buffer was then filtered through a plate and 133.3 ml of a 7.5% streptavidin-agarose granule mud (in 1x SB17, 0.05% Tween-20) was added to each well. To keep the streptavidin-agarose beads suspended while transferring them to the filter plate, the granule solution was manually mixed with a uL 200, 12-channel pipettor, 15 times. After the granules were distributed between the 3 filter plates, a vacuum was applied to remove the supernatant from the granule. Finally, the beads were washed on filter plates with 200 ml 1x SB17, 0.05% Tween-20 and then resuspended in 200 µL 1x SB17, 0.05% Tween-20. The undersides of the filter plates were transferred and the plates stored for use in the assay. 6. Loading Cytomat
[00353] The "cytomat" was loaded with all tips, slides, all reagents in troughs (except the NHS biotin reagent which was prepared fresh just after addition to the plates), 3 capture filter plates 1 and one MyOne plate prepared. 7. Capture 1
[00354] After an equilibrium time of 3.5 hours, the sample / aptamer plates were removed from the incubator, centrifuged for about 1 minute, strips removed and placed on the Beckman Biomek FxP deck. The Beckman Biomek FxP program has been started. All subsequent steps of a capture were performed by the Beckman Biomek FxP robot, unless otherwise noted. Within the program, a vacuum was applied to capture a filter plate to remove the supernatant from the granule. One hundred microliters of each of the 10%, 1% and 0.03% binding equilibrium reactions were added to the respective capture 1 filter plates, and each plate was mixed using an orbital on-shaker platform at 800 rpm for 10 minutes.
[00355] The unbound solution was removed by means of vacuum filtration. Catch beads 1 were washed with 190 µL of 100 µM biotin in 1x SB17, 0.05% Tween-20, followed by 190 mL of 1x SB17, 0.05% Tween-20 by suppressing the solution and immediately extracting a vacuum to filter the solution through the plate.
[00356] Then, 190 ul 1x SB17, 0.05% Tween-20 was added to capture plates 1. Plates were transferred to remove droplets through a blot incubation tray and then incubated with orbital shakers at 800 rpm for 10 minutes at 25 ° C.
[00357] The robot removed this wash by means of filtration under vacuum and erased at the bottom of the filter plate to remove droplets using the blot station on the deck. 8. Tagging
[00358] An aliquot of NHS-biotin-PEO4 was thawed at 37 ° C for 6 minutes and then diluted 1: 100 with labeling buffer (SB17, pH = 7.25 to 0.05% Tween- 20). The NHS-biotin-PEO4 reagent was dissolved in 100 mM concentration in anhydrous DMSO and was stored frozen at -20 ° C. Upon a robot request, the diluted NHS-biotin-PEO4 reagent was added manually to a deck chute and the robot program was manually restarted to distribute 100 L of NHS-biotin-PEO4 in each well of each fastener with a plate filter . This solution was left to incubate with a capture granules shaking at 800 rpm for 5 minutes on the obital sieves. 9. Kinetic challenge and photo-cleavage
[00359] The labeling reaction was stopped by adding 150 µL of 20 mM glycine in 1x SB17, 0.05% Tween-20 to capture plates 1 and still containing the NHS tag. The plates were then incubated for 1 minute in orbital sieves at 800 rpm. The NHS-tag / glycine solution was removed by vacuum filtration. Then, 190 µl of 20 mM glycine (1x SB17, 0.05% Tween-20) was added to each plate and incubated for 1 minute in orbital sieves at 800 rpm before removal, by vacuum filtration.
[00360] 190 µl of 1x SB17, 0.05% Tween-20 was added to each plate and removed by vacuum filtration.
[00361] The wells of capture plates 1 were subsequently washed three times by adding 190 µL 1x SB17, 0.05% Tween-20, placing the plates on orbital sieves for 1 minute at 800 rpm followed by vacuum filtration. After the last wash, the plates were placed on top of a 1 ml plate of deep wells and removed from the deck. Catches 1 of the plates were centrifuged at 1000 rpm for 1 minute to remove as much foreign volume from the agarose beads as possible before elution.
[00362] The plates were placed back in the Beckman Biomek FxP and 85 µl of 10 mM DxSO4 in 1x SB17, 0.05% Tween-20 was added to each well of the filter plates.
[00363] The filter plates were removed from the deck, placed on a Thermoshaker Variomag (Thermo Fisher Scientific, Inc., Waltham, MA) under the BlackRay (Ted Pella, Inc., Redding, CA) light sources, and radiated for 10 minutes while shaking at 800 rpm.
[00364] The photocleaved solutions were eluted sequentially from each capture plate 1 in a common bottom plate, placing the 10% first. Lock a filter plate on top of a 1 ml deep well plate and centrifugation at 1000 rpm. for 1 minute. At 1% and 0.03% of picking up a plate, they were then centrifuged sequentially into the same well bottom plate. 10. Catch 2 catch bead
[00365] A 1 ml deep well block containing all of the capture eluates 1 was placed on the deck of the Biomek Beckman FxP for capture 2.
[00366] The robot transferred all the eluate photo cleaved from the plate 1 ml of deep wells on the plate containing the previously prepared Hybaid captures 2 magnetic MyOne beads (after removing the MyOne buffer through magnetic separation).
[00367] The solution was incubated under agitation at 1350 rpm for 5 minutes at 25 ° C in a Thermoshaker Variomag (Thermo Fisher Scientific, Inc., Waltham, MA).
[00368] The robot transferred the plate to the platform in the magnetic separator station. The plate was incubated on the magnet for 90 seconds before removing and discarding the supernatant. 1. .37 ° C glycerol 30% lava
[00369] Capture 2 plate was transferred to the shaker and 75 mL on the 1x SB17 thermal deck, 0.05% Tween-20 was transferred to each well. The plate was mixed for 1 minute at 1350 rpm and 37 ° C to resuspend and heat the beads. In each well of the capture plate 2, 75 µl of 60% glycerol, at 37 ° C and the plate was transferred, continued mixing for another minute at 1350 rpm and 37 ° C. The robot transferred from the plate to a 37 ° C magnetic separator, where it was incubated in the magnet for 2 minutes and then the robot removed and the supernatant discarded. These washes were repeated two more times.
[00370] After removing the third glycerol wash 30% of the capture two beads, 150 μl of 1x SB17, 0.05% Tween-20 was added to each well and incubated at 37 ° C, with shaking at 1350 rpm for 1 minute, before removal by magnetic separation on the magnet at 37 ° C.
[00371] Catches two beads were washed one last time with 150 ml 1x SB19, 0.05% Tween-20 with incubation for 1 minute, shaking at 1350 rpm, before magnetic separation. 12. Catch 2 Bead Elution and Neutralization
[00372] The aptamers were eluted from two capture beads, adding 105 µl of 100 mM CAPSO with 1 M NaCl, 0.05% Tween-20 to each well. The beads were incubated with this solution, with shaking at 1300 rpm for 5 minutes.
[00373] Capture 2 plate was then placed on the magnetic separator for 90 seconds, before transferring 90 mL of eluate from a new 96-well plate containing 10 µl of 500 mM HCl, 500 mM HEPES, 0.05% Tween-20 in each well. After transfer, the solution was mixed by pipetting robotically 90 uL up and down five times. 13. Hybridization
[00374] The Beckman Biomek FxP transferred 20 ul of the neutralized capture 2 eluate to a fresh Hybaid plate, and 5 ul of 10x Agilent Block, 10x containing a peak of hybridization controls, was added to each well. Then, 25 µl of 2x Agilent hybridization buffer was manually pipetted into each well of the plate containing the neutralized samples and blocking buffer and the solution was mixed by hand pipetting 25 µL up and down 15 times slowly to prevent bubbles from forming. extensive. The plate was centrifuged at 1000 rpm for 1 minute.
[00375] The sliding joint was placed inside an Agilent hybridization chamber and 40 µl of each of the samples that contained the hybridization and blocking solution was manually pipetted to each joint. A variable 8-channel pipettor that measures was used in a manner designed to minimize foaming. Agilent micromatrix custom slides (Agilent Technologies, Inc., Santa Clara, CA), with their Number barcode facing up, were then slowly lowered to the joint slides (see Agilent manual for detailed description).
[00376] The upper part of the hybridization chambers were placed on the slide / support sandwich and the fixing supports slide over the whole set. These sets were tightened by turning the screws.
[00377] to ensure the solution bubble could move freely within the sample. If the bubble does not move freely, the hybridization chamber assembly was gently tapped to release bubbles presented near the gasket.
[00378] The assembled hybridization chambers were incubated in an Agilent hybridization oven for 19 hours at 60 ° C running at 20 rpm. 14. Publish Hybridization wash
[00379] Approximately 400 mL Agilent Wash Buffer 1, was placed on each of two separate staining glass plates. One of the staining dishes was placed on a magnetic stirring plate and a slide holder and stirring bar were placed in the buffer.
[00380] A staining plate for Agilent Wash 2 was prepared by placing a stir bar on an empty glass staining plate.
[00381] A fourth glass staining dish was reserved for the final wash of acetonitrile.
[00382] Each of the six hybridization chambers has been disassembled. One-by-one, the wool / liner sandwich was removed from its hybridization chamber and submerged in the staining dish containing Wash 1. The slide / liner sandwich was parted off using a pair of tweezers, while still submerging the slide micromatrix. The slide was quickly transferred to the slide holder in the Wash 1 staining plate on the magnetic stir plate.
[00383] The blade holder was gently raised and lowered 5 times. The magnetic stirrer was turned on at a low level and the slides incubated for 5 minutes.
[00384] When there was one minute left for wash 1, Wash buffer 2 preheated to 37 ° C in an incubator was added to the second prepared staining dish. The slide holder was quickly transferred to Wash Buffer 2 and any excess buffer at the bottom of the shelf was removed by scraping on the top of the staining plate. The slide rack was gently raised and lowered 5 times. The magnetic stirrer was turned on at a low level and the slides incubated for 5 minutes.
[00385] The slide holder was slowly removed from wash 2, taking about 15 seconds to remove the slides from the solution.
[00386] With one minute remaining in Wash 2, acetonitrile (ACN) was added to the IV staining disc. The slide holder was transferred to the acetonitrile staining plate. The slide holder was gently raised and lowered 5 times. The magnetic stirrer was turned on at a low level and the slides incubated for 5 minutes.
[00387] The slide holder was slowly removed from the ACN staining plate and placed on an absorbent towel. The lower edges of the blades were quickly dried and the blade was placed in a clean blade box. 15. Micromatrix Image Formation
[00388] The microarray slides were placed in Agilent scanner slide holders and loaded into the Agilent Microassay scanner according to the manufacturer's instructions.
[00389] Images of the slides were formed in the Cy3 channel in 5 μM resolution in 100% PMT configuration and the XTD option enabled in 0.05. The resulting images in .tiff format were processed using the Agilent extraction software feature, version 10.5. Example 2. Identification of biological markers
[00390] The identification of potential biological markers of lung cancer was carried out in three different diagnostic applications, diagnosis of suspected nodules from a computed tomography, the screening of asymptomatic smokers for lung cancer, and diagnosis of an individual with lung cancer. lung. Serum samples were collected from four different sites in support of these three applications and include 48 cases of NSCLC, 218 high-risk controls composed of heavy smokers and patients with benign nodules. The aptamer affinity multiplexed assay as described in Example 1 was used to measure and report the RFU value for 820 analytes in each of these 264 samples. The KS test was then applied to each analyte. The KS distance (Kolmogorov-Smirnov statistic) between the values of two sets of samples is a non-parametric measure of the extent to which the empirical distribution of values from one set (Set A) differs from the distribution of values in the other set ( Set B). For any value of a threshold T some proportion of the values of Set A will be less than T, and some proportion of the values of Set B will be less than T. The distance KS measures the maximum (not marked) difference between the proportion values of the two sets for any choice of T.
[00391] Sets of biological markers can be used to construct classifiers that assign samples to both the control and the disease group. In fact, many classifiers were produced from these sets of biological markers and the frequency with which any biological marker was used in classifiers with good determined scores. Those biological markers that occurred most frequently among the top score classifiers were the most useful for creating a diagnostic test. In this example, Bayesian classifiers were used to explore the classification space, but many other supervised learning techniques can be employed for this purpose. The scoring aptitude of any individual classifier was assessed using the area under the receiver operating characteristic curve (AUC or ROC) of the classifier on the Bayesian surface, assuming a disease prevalence of 0.5. This scoring metric ranges from zero to one, with one being an error-free classifier. The details of the construction of a Bayesian classifier based on population measurements of biological markers are described in Example 3. Example 3. Simple Bayesian Classification for Lung Cancer
[00392] From the list of biological markers identified as useful for discriminating between NSCLC and the high risk control group, a panel of five biological markers was selected and a simple Bayes classifier was built, see Table 14. The class of probability density dependent functions (pdfs), p (xi | c) and p (xi | d), where xi is the log of the measured RFU value for marker i, and c and refer to disease control and population, were modeled as normal distribution functions characterized by a mean μ and variance α2. The parameters for pdfs of the five biological markers are listed in Table 15 and an example of the raw data, together with which the model fits a normal pdf is shown in Figure 5. The underlying assumption appears to fit the data very well as evidenced by the Figure 5.
[00393] The classification of simple Bayes for such a model is given by the following equation, where P (d) is the prevalence of the disease in the population

[00394] appropriate for the test and n = 5 here. Each of the terms in the sum is a probability ratio of the log for an individual marker and the total log probability ratio of a disease-free sample of interest (ie, in this case, NSCLC) versus having the disease is simply the sum of these individual terms plus a term that represents the prevalence of the disease. For simplicity, we assume P (d) = 0.5 so that, ln (1-P (d) / P (d) = 0
[00395] Given a measure of the unknown sample in log (RFU) for each of the 10 biological markers of the individual components comprising the log of the probability ratio for control versus disease class are tabulated and can be computed from the parameters in Table 15 and the x values. The sum of the log of the individual probability ratios is 3.47, where the probability of being disease-free versus having the disease is 32: 1, where the probability = e (3.47) = 32. All five biological markers are all consistently found to favor the control group. Multiplying the probabilities together gives the same results as explained above, the 32: 1 probability that the unknown sample is free of the disease. In fact, this sample came from the control population in the training set. Although this example demonstrates the classification of serum samples using the biological markers in Table 15, the same approach can be used on any type of tissue with any set of biological markers starting from Table 21. Example 4. Algorithm (Greedy) for Selecting Biological Marker Panels for Classifiers Part 1
[00396] This example describes the selection of biological markers from Table 21, to form panels that can be used as classifiers in any of the methods described here. Panels of biological markers containing MMP-12 and subsets of biological markers in Table 21 were selected for the construction of classifiers with good performance. This method was also used to determine which potential markers were included as biological markers in Example 2.
[00397] The measure of the classifier performance used here is the area under the ROC curve (AUC), a performance of 0.5 is the baseline expectation for a random classifier (draw), a classifier worse than random scoring between 0.0 and 0.5, a classifier with better performance than random performance would score between 0.5 and 1.0. A perfect error-free classifier would have a sensitivity of 1.0, a specificity of 1.0 and an AUC of 1.0. The methods described in Example 4 can be applied to other common measures of performance such as measure F, the sum of sensitivity and specificity, or the product of sensitivity and specificity. Specifically, one may wish to treat sensitivity and specificity with a different weight, in order to select the classifiers that perform with greater specificity, at the expense of some sensitivity, or to select the classifiers that perform with greater sensitivity at the expense of some specificity. Since the method described here, consists only of a "performance" measure, any weighting scheme that results in a single performance measure can be used. Different applications will have different benefits for true positive and true negative results, as well as different costs associated with false positive results from false negative results. For example, screening for asymptomatic smokers and the differential diagnosis of benign nodules found on CT will not, in general, have the same optimal compromise between sensitivity and specificity. The different demands of the two tests, in general, will require different weighting settings for positive and negative classification errors, reflected in the performance measure. Changing the measure of performance in general will change the exact subset of the markers selected from Table 21, for a given data set.
[00398] For the Bayesian approach to discriminating lung cancer samples from the control samples described in Example 3, the classifier was completely parameterized by the biological marker distributions in the disease training and benign samples, and the list of Biological markers were chosen from Table 21, that is, the subset of markers chosen for inclusion determined a classifier in a one-to-one way given a set of training data.
[00399] The "greedy" method used here was used to search the optimal subset of markers in Table 21. For a small number of markers or classifiers with relatively few markers, each possible subset of markers was enumerated and evaluated in terms of the performance of the classifier built with that particular set of markers (see Example 4, part 2). (This approach is well known in the field of statistics as "best subset selection", see, for example, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al, editors, Springer Science + Business Media, LLC, 2nd edition, 2009). However, for the classifiers described in this document, the number of combinations of multiple markers may be very large, and it was not possible to evaluate each possible set of five markers, for example, from the list of 86 markers (Table 21) ( that is, 34,826,302 combinations). Due to the impracticality of searching through each subset of markers, the ideal single subset cannot be found, however, using this approach, several excellent subsets have been found, and in many cases, any of these subsets can represent an optimal result.
[00400] Instead of evaluating each possible set of markers, a "greedy" approach can be taken one step further (see, for example, Dabney AR, JD Storey (2007) Optimalility Driven Nearest Centroid Classification from Genomic Data. PLoS ONE 2 (10): e1002 doi: 10.1371 / journal.pone.0001002). Using this method, a classifier is started with the best single marker (based on the KS distance for the individual markers), and is grown at each stage by attempting, in turn, each member of a marker list that does not is currently a member of the marker set in the classifier. The marker that scores best, in combination with the existing classifier, is added to the classifier. This process is repeated until no further improvement in performance is achieved. Unfortunately, this approach can miss valuable marker combinations for which some of the individual markers are not all chosen before the process stops.
[00401] The "greedy" procedure used here was an elaboration of the previous approach one step further, in which, in order to broaden the search, instead of maintaining only a single candidate classifier (marker subset) at each stage, a list of candidate classifiers. The list was seeded with each unique marker subset (using each marker in the table by itself). The list was expanded in stages by deriving new classifiers (marker subsets) from those currently on the list and adding them to the list. Each marker subset currently on the list has been extended by adding any marker from Table 1 that is not already part of the classifier, and which would not duplicate an existing subset, in addition to the subset, (these are called "allowable markers"). Each subset of existing bookmarks has been extended by each bookmark allowed from the list. Clearly, such a process would eventually generate every possible subset, and the list would run out of space. Therefore, all generated classifiers were maintained only as long as the list was less than a predetermined size (usually enough to maintain all three marker subsets). Once the list reached the predetermined size limit, it became elitist, that is, only the classifiers that showed a certain level of performance were kept on the list, and the others fell to the bottom of the list and were lost. This was achieved by maintaining the list sorted in order of the classifier's performance; new classifiers that were at least as good as the worst classifier currently on the list were added, forcing the current sub-entrepreneur to expel. An additional detail of implementation is that the list has been completely replaced in each generation step and, therefore, each classifier in the list had the same number of markers, and in each step, the number of markers per classifier grew by one.
[00402] Since this method has produced a list of candidate classifiers using different combinations of markers, it can be asked whether the classifiers can be combined in order to avoid mistakes that may be made by the best single classifier, or by minority groups of the best classifiers. Such "Ensemble" and "expert committee" methods are well known in the fields of statistics and machine learning and include, for example, "getting average", "voting", "stacking", "bagging" and "increasing" ( see, for example, The Elements of Statistical Learning- Data Mining, Interference, and Prediction, T. Hastie, et al., editors, Springer Science + Business Media, LLC, 2nd edition, 2009). These combinations of simple classifiers provide a method to reduce the variance in classifications due to noise in any particular set of markers including several different classifiers and, therefore, information from a larger set of markers from a biological marker table, effectively averaging across classifiers. An example of the usefulness of this approach is that it can prevent extreme values of a single marker from adversely affecting the classification of a single sample. The requirement to measure a greater number of signals may be impractical in conventional "one marker at a time" antibody assays, but it has no disadvantage for a fully multiplexed aptamer assay. Techniques such as these benefit from a more extensive table of biological markers and from using multiple sources of information concerning disease processes to provide a more robust classification. Part 2
[00403] The biological markers selected in Table 1 gave rise to classifiers that perform better than classifiers constructed with "non-markers" (that is, proteins that have signals that do not meet the inclusion criteria in Table 1 (as described in Example 2)).
[00404] For classifiers containing only one, two and three markers, all possible classifiers obtained using the biological markers in Table 1 have been enumerated and examined for the distribution of performance in comparison with the classifiers constructed from a similar table selected at random. signs of non-markers.
[00405] In Figure 17 and Figure 18, the sum of sensitivity and specificity was used as the measure of performance, a performance of 1.0 is the expectation of the baseline for a random classifier (draw). The performance histogram of the classifier was compared with the performance histogram of an exhaustive list of similar classifiers constructed from a table of 40 "non-marker" signals; the 40 signals were chosen at random from 400 aptamers that showed no difference in signaling between control and disease populations (KS-distance <1.4).
[00406] Figure 17 shows the performance histograms of all possible classifiers of one, two and three markers constructed from the parameters of biological markers in Table 13 for biological markers that can discriminate between benign nodules and NSCLC and compares these classifiers with all possible one, two and three marker classifiers, built with the 40 "non-marker" aptamer RFU signals. Figure 17A shows the performance histograms of the single marker classifier, Figure 17B shows the performance histogram of the two marker classifier, and Figure 17C shows the performance histogram of the three marker classifier.
[00407] In Figure 17, the solid lines represent the classifier performance histograms for all one, two and three marker classifiers using the biological marker data for benign nodules and NSCLC in Table 13. The dashed lines are the histograms of the classifier performance of all one, two and three marker classifiers using data for benign nodules and NSCLC, but using the set of random non-marker signals.
[00408] Figure 18 shows the performance histograms of all possible classifiers of one, two and three markers constructed from the parameters of biological markers in Table 12 for biological markers that can discriminate between asymptomatic smokers and NSCLC and compares this with all the possible classifiers of one, two, and three markers constructed using 40 RFU signals of aptamer "non-marker". Figure 18A shows the performance histogram of the single marker classifier, Figure 18B shows the performance histogram of the two marker classifier, and Figure 18C shows the performance histogram of the three marker classifier.
[00409] In Figure 18, the solid lines represent the classifier performance histograms for all one, two and three marker classifiers using the parameters for asymptomatic smokers in Table 12. The dashed lines are the classifier performance histograms for all the one, two and three marker classifiers using data for asymptomatic smokers and NSCLC, but using the set of random non-marker signals.
[00410] The classifiers constructed from the markers listed in Table 1 form a distinct histogram, well separated from the classifiers constructed with "non-marker" signs for comparisons of all one-marker markers to two-marker to three-marker markers. The performance and AUC score of the classifiers constructed from the biological markers in Table 1 also increases more rapidly with the number of markers than the classifiers constructed from the non-markers, the separation increases between the marker and non-marker classifiers while the number of markers per classifier increases. All classifiers built with the biological markers listed in Tables 38 and 39 perform better than classifiers built using "non-markers". Part 3
[00411] To test whether a subset of the core of markers considered for the good performance of the classifiers, half of the markers were randomly removed from the lists of biological markers in Tables 38 and 39. The performance, as measured by the more specific sensitivity, of the classifiers to distinguish benign nodules from malignant nodules fell slightly by 0.07 (1.74-1.67), and the performance of the classifiers to distinguish smokers who had cancer from those who did not have cancer also fell slightly by 0.06 (1.76-1 , 70). The implication of the performance characteristics of subsets of the biological marker table is that multiple subsets of the listed biological markers are effective in constructing a diagnostic test, and no particular core subset of markers dictates the classifier performance.
[00412] In view of these results, classifiers were tested that excluded the best markers from Tables 12 and 13. Figure 19 compares the performance of constructed classifiers with the complete list of biological markers in Tables 12 and 13, with the performance of constructed classifiers with a set of biological markers from Tables 38 and 39, excluding the best classified markers.
[00413] Figure 19 demonstrates that the classifiers built without the best markers performed well, which implies that the classifiers' performance was not due to some small core group of markers, and that changes in the underlying processes associated with the disease are reflected in the activities of many proteins. Many subsets of the biological markers in Table 1 performed close to the optimal shape, even after removing 15 from the top of the 40 markers in Table 1.
[00414] Figure 19A shows the effect on classifiers to discriminate benign NSCLC nodules constructed with 2 to 10 markers. Even after removing the top 15 markers (ranked by KS distance) from Table 13, benign nodule versus NSCLC performance increased with the number of markers selected from the table to reach more than 1.65 (Sensitivity + Specificity) .
[00415] Figure 19B shows the effect on classifiers to discriminate asymptomatic NSCLC smokers constructed with 2 to 10 markers. Even after removing the top 15 markers (ranked by KS distance) from Table 12, the performance of asymptomatic smokers versus NSCLC increased with the number of markers selected from the table to reach more than 1.7 (sensitivity + specificity ), and came close to the performance of the classifier best selected from the complete list of biological markers in Table 12.
[00416] Finally, Figure 20 shows how is the ROC performance of typical classifiers built from a list of parameters in Tables 12 and 13, according to Example 3. Figure 20A shows the performance of the model of assuming independence of the markers as in Example 3, and Figure 20B shows the actual ROC curves using the data from the set assay used to generate the parameters in Tables 12 and 13. It can be seen that the performance of a certain number of selected markers was qualitatively high. agreement, and that the quantitative agreement has degraded while increasing the number of markers. (This is consistent with the notion that information from any particular biological marker with respect to disease processes is redundant with information from other biological markers provided in Tables 12 and 13). Figure 20 therefore demonstrates that Tables 12 and 13, in combination with the methods described in Example 3, enable the construction and evaluation of a large number of classifiers useful for the discrimination of benign nodule NSCLC and the discrimination of asymptomatic smokers who have NSCLC from those who don't have NSCLC. Example 5. Demonstration of Aptamer Specificity in a Down Test
[00417] The final reading of the multiplexed assay is based on the amount of aptamer recovered after the successive capture steps in the assay. The multiplexed assay is based on the premise that the amount of aptamer recovered at the end of the assay is proportional to the amount of protein in the original complex mixture (eg, plasma). In order to demonstrate that this signal is in fact derived from the intended analyte rather than from proteins not specifically bound in the plasma, a gel-based assay suspended in the plasma was developed. This assay can be used to visually demonstrate that the desired protein is, in fact, pulled out of the plasma after equilibration with an aptamer, as well as to demonstrate that aptamers attached to their intended protein targets can survive as a complex through kinetic challenge steps in the assay. In the experiments described in the present example, protein recovery at the end of this assay downward requires that the protein remain covalently unbound to the aptamer for about two hours after equilibration. It is important to note that, in this example, we also provide evidence that proteins that are not specifically bound dissociate during these steps and do not contribute significantly to the final signal. It should be noted that the procedure described below in this example includes all the key steps in the multiplexed assay described above. Plasma Down Test
[00418] Plasma samples were prepared by diluting 50 μL of EDTA plasma to 100 μL in SB18 with 0.05% Tween-20 (SB18T) and 2 μM Z-Block. The plasma solution was equilibrated with 10 pmoles of a PBDC aptamer in a final volume of 150 μL for 2 hours at 37 ° C. After equilibration, complexes and decoupled aptamer were captured with 133 μL of a 7.5% streptavidin-agarose bead slurry by incubating with shaking for 5 minutes at room temperature on a Durapore filter plate. Bead-bound samples were washed with biotin and with vacuum buffer, as described in Example 1. After washing, bound proteins were labeled with 0.5 mM NHS-SS-biotin, 0.25 mM NHS-Alexa647 on biotin diluent for 5 minutes with stirring at room temperature. This staining step allows biotinylation to capture proteins in streptavidin beads as well as highly sensitive staining for detection on a gel. The samples were washed with buffered glycine as described in Example 1. Aptamers were released from the beads by photocleaving using a Black Light Ray source for 10 minutes with stirring at room temperature. At this point, biotinylated proteins were captured in 0.5 mg MyOne streptavidin beads by shaking for 5 minutes at room temperature. This step will capture proteins linked to aptamers, as well as proteins that may have dissociated from aptamers since initial equilibrium. The beads were washed as described in Example 1. Proteins were eluted from streptavidin MyOne beads by incubation with 50 mM DTT in SB17T for 25 minutes at 37 ° C with shaking. The eluate was then transferred to MyOne beads coated with a sequence complementary to the 3 'fixed region of the aptamer and incubated for 25 minutes at 37 ° C with shaking. This step captures all the remaining aptamer. The beads were washed twice with 100 μL SB17T for 1 minute and once with 100 μL SB19T for 1 minute. The aptamer was eluted from these final beads by incubation with 45 μL 20 mM NaOH, for 2 minutes, with shaking to break the hybridized strands. 40 μL of this eluate was neutralized with 10 μL 80 mM HCl containing 0.05% Tween-20. Aliquots representing 5% of the eluate from the first set of beads (representing all plasma proteins bound to the aptamer) and 20% of the eluate from the last set of beads (representing all remaining plasma proteins bound at the end of our test clinical) were performed on a NuPAGE 4-12% Bis-Tris gel (Invitrogen) under reducing and denaturing conditions. Images of the gels were formed on a Q Alpha Innotech FluorChem digitizer in the Cy5 channel to form images of the proteins.
[00419] Aptamer down gels were selected against LBP (~ 1x10-7 M in plasma, MW ~ 60 kDa polypeptide), C9 (~ 1x10-6 M in plasma, MW polypeptide ~ 60 kDa), and IgM (~ 9x10 -6 M in plasma, MW ~ 70 kDa and 23 kDa), respectively. (See Figure 16).
[00420] In each gel, lane 1 is the elution product from the streptavidin-agarose beads, lane 2 is the final eluate, and lane 3 is a MW lane marker (main bands are 110, 50 , 30, 15, and 3.5 kDa from top to bottom). It is evident from these gels that there is a small amount of non-specific binding of plasma proteins in the initial equilibrium, but only the target remains after performing the assay capture steps. It is clear that the single aptamer reagent is sufficient to capture your desired analyte without initial depletion or plasma fractionation. The amount of aptamer remaining after these steps is then proportional to the amount of analyte in the initial sample. Example 6. Analysis of NSCLC Surgical Resections
[00421] To demonstrate the usefulness of the platform-based technology described here to identify disease related to biological markers from tissues, samples of homogenized tissues from surgical resections obtained from eight patients with NSCLC were analyzed, all patients were smokers NSCLC, aged 47-75 years and covering the stages of NSCLC 1A through 3B (Table 17). All tissue samples were obtained by freezing the tissue within 5-10 minutes of surgical excision and then placing the tissues in medium OCT (10.24% polyvinyl alcohol, 4.26% polyethylene glycol, and 85, 5% non-reactive ingredients). Three samples were obtained from each of the resections: tumor tissue sample, the adjacent healthy tissue (1 cm from the tumor) and distant lung tissue not involved with cancer. While keeping the samples constantly frozen, five 10 μm thick sections were cut, trimmed from excess OCT around the tissue, and placed in frozen 1.5 ml microcentrifuge tubes. After adding 200 μL of homogenization buffer (SB18 buffer plus PI cocktail (Pierce HALT protease inhibitor cocktail, without magnesium), the samples were homogenized in microcentrifuge tubes with a rotating ice pestle for 30 seconds, until no there are visible tissue fragments, the samples were then centrifuged in a 21,000 g centrifuge for 10 minutes and filtered through a 0.2 μm multi-well plate filter into a sterile multiple well plate. taken for BCA protein assay and the rest of the sample was stored frozen and sealed in 96 well plates at -70 ° C.
[00422] The total protein sample was adjusted to 16 μg / mL in SB17T buffer (SB17 buffer containing 0.05% Tween 20) to create proteomic profiles. The samples prepared in this way were run in the aptamer multiplexed assay which, as noted above, measures over 800 proteins, as described previously (Ostroff et al., Nature Precedings, http://precedings.nature.com/documents/4537 / version / 1 (2010)). Among the analytes measured, most were unchanged between the tumor tissue and the adjacent and distant tissue. However, some proteins were clearly suppressed (Figure 24), while others were substantially elevated in tumor tissues (Figure 23) compared to adjacent and distant tissues.
[00423] The previous modalities and examples are intended as examples only. No particular modality, example, or element of a particular modality or example is to be interpreted as a necessary or essential critical element or functionality of any of the claims. In addition, no elements described herein are necessary for the practice of the appended claims, unless expressly described as "essential" or "critical". Various changes, modifications, substitutions, and other variations can be made to the described modalities without departing from the scope of this application, which is defined by the appended claims. The specification, including figures and examples, should be considered in an illustrative and not restrictive manner, and all such modifications and replacements are intended to be included in the scope of the order. Therefore, the scope of the application should be determined by the attached claims and their legal equivalents, rather than by the examples given above. For example, the steps recited in any of the methods or processes of the claims can be performed in any practicable order and are not limited to an application submitted in any of the modalities, the examples, or the claims. In addition, in any of the methods mentioned above, one or more biological markers from Table 18, Table 20, Table 21, can be specifically excluded either as a particular biological marker or as a biological marker on any panel. Table 1. Lung Cancer Biomarkers












Table 2. Aptamer Concentrations
Table 3
Table 4. Biomarkers Identified in Benign Nodule-NSCLC in Aggregated Data
Table 5. Bioniarkers Identified in Snioker-NSCLC in Aggregated Data
Table 6. Biomarkers Identified in Benign Nodule-NSCLC by Site
Table 7. Biomarkers Identified in Snioker-NSCLC by Site
Table 8. Bioniarkers Identified in Benign Nodule-NSCLC in Blended Data Set
Tabic 9. Biomarkers Identified in Smoker-NSCLC in Blended Data Set
Table 10.


Tabic 11


Tabic 12. Parameters for Smoker Control Group
Tabic 13. Parameters for benign nodules control group
Tabic 14.
Tabic 15.
Table 16. Naive Bayes parameters for all markers in Table 21 for both tissue and serum


Table 17. Patient demographics, resection location and tumor types for the eight NSCLC samples
Table 18. Differentially Expressed Biomarkers Between Tumor and Normal Tissue



Table 19. Categorization of NSCLC tissue bioniarkers into biological processes
Table 20. Biomarkers Identified in NSCLC Tissue *
Table 21. Biomarkers Identified in Serum and Tissue

Table 22. XI Panels of two biomarkers including MMP-12

Table 23. KM) Panels of three biomarkers including MM P-12

Table 24. 100 Panels of four hiomarkers including MMP-12

Table 25. 100 Panels of five bioπiarkers including MMP-12

权利要求:
Claims (14)
[0001]
1. In vitro method for diagnosing whether an individual has non-small cell lung cancer or not, or for providing information about non-small cell lung cancer in an individual, characterized by the fact that it comprises: detecting at least one selected biomarker between the MMP-12 and C9, MMP-7, ERBB1 and SCF sR proteins in a tissue sample from an individual, and provide a biomarker value corresponding to each biomarker protein, where said biomarker values provide an indication of the likelihood of the individual having or not having non-small cell lung cancer or providing information about non-small cell lung cancer in that individual, in which the biomarker proteins are selected by a Greedy algorithm and in which the indication as to the likelihood that the individual has or does not have non-small cell lung cancer, or information about non-small cell lung cancer in that individual o is determined by a Bayesian Naive classifier.
[0002]
2. In vitro method, according to claim 1, characterized by the fact that the detection of the biomarker values comprises the performance of an in vitro assay.
[0003]
3. In vitro method, according to claim 2, characterized by the fact that said in vitro assay comprises at least one capture reagent corresponding to each of said biomarkers, and further comprises selecting said at least one capture reagent among the group consisting of aptamers, antibodies and a nucleic acid probe.
[0004]
4. In vitro method, according to claim 2 or 3, characterized by the fact that the in vitro assay is selected from the group consisting of an immunoassay, an aptamer-based assay, a histological or cytological assay and an assay of mRNA expression level.
[0005]
5. In vitro method according to any one of claims 1 to 4, characterized by the fact that the tissue sample is lung tissue, in which the values of the biomarkers are derived from a histological or cytological analysis of the lung tissue.
[0006]
6. In vitro method according to any one of claims 1 to 5, characterized by the fact that the individual is a smoker.
[0007]
7. In vitro method according to any one of claims 1 to 6, characterized by the fact that the individual has a pulmonary nodule.
[0008]
8. In vitro method for diagnosing whether an individual has non-small cell lung cancer or not, or for providing information about non-small cell lung cancer in an individual, characterized by the fact that it comprises: detecting, in a sample of an individual's tissue, the biomarker protein MMP-12 and at least one biomarker selected from C9, MMP-7, ERBB1 and SCF sR, provide biomarker values corresponding to the biomarker protein, in which the said biomarker values, the individual displays the possibility of having or not non-small cell lung cancer, in which the biomarker proteins are selected by a Greedy algorithm and in which the indication as to the likelihood that the individual has or not non-small cell lung cancer, or information on non-small cell lung cancer in that individual is determined by a Bayesian Naive classifier.
[0009]
9. In vitro method, according to claim 8, characterized by the fact that the detection of the values of the biomarkers comprises the performance of an in vitro assay.
[0010]
10. In vitro method according to claim 9, characterized by the fact that said in vitro assay comprises at least one capture reagent corresponding to each of said biomarkers, and further comprises selecting said at least one capture reagent among the group consisting of aptamers, antibodies and a nucleic acid probe.
[0011]
11. In vitro method according to claim 9 or 10, characterized by the fact that the in vitro assay is selected from the group consisting of an immunoassay, an aptamer-based assay, a histological or cytological assay and an assay of mRNA expression level.
[0012]
12. In vitro method, according to claim 8, characterized by the fact that the tissue sample is lung tissue, in which the value of the biomarker is derived from a histological or cytological analysis of the lung tissue.
[0013]
13. In vitro method according to any of claims 8 to 12, characterized by the fact that the individual is a smoker.
[0014]
14. In vitro method according to any of claims 8 to 13, characterized by the fact that the individual has a pulmonary nodule.

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CN104777313A|2015-07-15|

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法律状态:
2018-04-10| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|

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2019-07-16| B06T| Formal requirements before examination [chapter 6.20 patent gazette]|

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2020-02-04| B07A| Application suspended after technical examination (opinion) [chapter 7.1 patent gazette]|

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2020-09-01| B06A| Patent application procedure suspended [chapter 6.1 patent gazette]|

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2021-01-05| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|

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2021-03-02| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 11/07/2011, OBSERVADAS AS CONDICOES LEGAIS. |

优先权:

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申请号 | 申请日 | 专利标题

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US36312210P| true| 2010-07-09|2010-07-09|

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US61363122|2010-07-09|

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US201161444947P| true| 2011-02-21|2011-02-21|

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US61444947|2011-02-21|

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PCT/US2011/043595|WO2012006632A2|2010-07-09|2011-07-11|Lung cancer biomarkers and uses thereof|

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