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    • 专利摘要:
    IN VITRO METHOD FOR DIAGNOSING OR DETECTING COLORRETAL CANCER IN AN INDIVIDUAL; ARRANGEMENT OF AT LEAST TWO COMPOUNDS TO DIAGNOSE OR DETECT COLORRETAL CANCER; AND KIT FOR DIAGNOSING OR DETECTING CANCER IN AN INDIVIDUAL The present invention provides a method for diagnosing or detecting colorectal cancer in an individual, wherein the method comprises determining the presence and / or level of biomarkers selected from IL-8, IGFBP2 , MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1 (BETA), TGF (BETA) 1, and TIMP-1. The invention also relates to diagnostic kits that comprise reagents for determining the presence and / or level of biomarkers and methods of detecting or diagnosing colorectal cancer.
    公开号:BR112013000745B1
    申请号:R112013000745-1
    申请日:2011-07-14
    公开日:2021-02-02
    发明作者:Leah Jane Cosgrove；Antony Wilks Burgess；Bruce Tabor；Edouard Collins Nice
    申请人:Vision Tech Bio Pty Ltd.；
    IPC主号:
    专利说明:

    FIELD OF THE INVENTION
    The present invention relates to the determination of the presence and / or the level of biomarkers to detect or diagnose colorectal cancer. The invention also relates to diagnostic kits that comprise reagents for determining the presence and / or level of biomarkers and methods for detecting or diagnosing colorectal cancer. BACKGROUND OF THE INVENTION
    Colorectal cancer, also called colon cancer or bowel cancer, is the second most common cause of cancer in the world. There is an annual incidence of almost one million cases of colorectal cancer with an annual mortality of around 500,000 (Cancer in Australia: an overview, 2008). Unfortunately, 30 to 50% of patients have hidden or observable metastasis in presentation and once the tumors have been metastasized, the prognosis is very poor with a five-year survival of less than 10% (Etzioni et al., 2003). Adversely, more than 90% of patients who present although the tumor is still localized will still be alive after 5 years and can be considered cured. Therefore, early detection of colorectal lesions would significantly reduce the impact of colon cancer (Etzioni et al, 2003).
    Current screening tests in expanded use for the diagnosis of colorectal cancer are fecal occult blood test (FOBT), flexible sigmoidoscopy and colonoscopy (Lieberman, 2010). FOBT has relatively low specificity resulting in a high false positive rate. All positive FOBT must therefore be followed by colonoscopy.
    Sampling is done by individuals at home and requires that at least two consecutive fecal samples be analyzed to achieve optimal sensitivity. Some versions of FOBT also require dietary restrictions before sampling. FOBT also lacks sensitivity to early-stage cancerous lesions that do not bleed in the intestine and as determined above, are lesions for which treatment is mostly successful.
    Although screening for FOBT results in reduced mortality due to colorectal cancer, it suffers from a low compliance rate (30 to 40%), most likely due to the non-palatable nature of the test, which limits its usefulness as a screening tool. . Colonoscopy is the current gold standard and has a specificity greater than 90%, but it is intrusive and expensive with a small but finite risk of complications (2.1 per 1000 procedures) (Levin, 2004). The development of a fast, inexpensive and specific blood-based assay would overcome the compliance issues commonly seen with other screening tests (Tonus, 2006; Hundt et al., 2007) and would be more acceptable as part of a large screening trial . SUMMARY OF THE INVENTION
    The present inventors investigated sixty more biomarkers associated with colorectal cancer, but concluded that none of the biomarkers alone would be suitable as a diagnostic test. Surprisingly, it was concluded that determining the presence and / or level of at least two biomarkers associated with colorectal cancer in a sample of an individual allowed the detection or diagnosis of colorectal cancer at any of the disease stages. Determining the presence and / or level of at least two biomarkers advantageously provides a diagnostic test that is at least comparable in sensitivity and specificity to FOBT.
    Consequently, in one aspect, the present invention provides a method for diagnosing or detecting colorectal cancer in an individual, the method of which comprises: i) determining the presence and / or level of at least two biomarkers selected from IL-8, IGFBP2 , MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIPlβ, TGFβl and TIMP-1 in a sample from the individual, in which the presence and / or the level of the two biomarkers is indicative of colorectal cancer.
    In one embodiment, the method comprises determining the presence and / or the level of two biomarkers selected from M2PK, EpCam, IL-13, DKK-3, IL-8 and IGFBP2.
    In another embodiment, the method comprises determining the presence and / or the level of expression of at least three of the biomarkers.
    In one embodiment, the three biomarkers are selected from M2PK, EpCam, IL-13, DKK-3, IL-8, IGFBP2, MIPlβ, TGFβl and MAC2BP.
    In a particular modality, the method comprises determining the presence and / or the level of three biomarkers, where the three biomarkers are: i) DKK-3, M2PK, and IGFBP2; ii) M2PK, IGFBP2, and EpCAM; iii) M2PK, MIPlβ, and TGFβl; or iv) IL-8, IL-13, and MAC2BP.
    In another embodiment, the method comprises determining the presence and / or the level of expression of at least four of the biomarkers.
    In a particular modality, the method comprises determining the presence and / or the level of four biomarkers, where the four biomarkers are: i) DKK-3, M2PK, MAC2BP, and IGFBP2; ii) IL-8, IL-13, MAC2BP, and EpCam; iii) DKK3, M2PK, TGFβl, and TIMP-1; iv) M2PK, MIPlβ, IL-13, and TIMP-1; or v) IL-8, MAC2BP, IGFBP2, and EpCam.
    In yet another modality, the method comprises determining the presence and / or the level of at least five of the biomarkers. In a particular embodiment, the five biomarkers are IL-8, IGFBP2, MAC2BP, M2PK, and IL-13.
    In another embodiment, the method comprises determining the presence and / or the level of at least six of the biomarkers.
    In another embodiment, the method comprises determining the presence and / or the level of at least seven of the biomarkers.
    In a particular embodiment, the seven biomarkers are: i) IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, and TGF β 1; OR ii) IL-8, IGFBP2, MAC2BP, M2PK, IL-13, EpCam, and MIPlβ.
    In yet another modality, the method comprises determining the presence and / or the level of at least eight of the biomarkers. In one embodiment, the method comprises determining the presence and / or level of at least nine of the biomarkers.
    In yet another modality, the method comprises determining the presence and / or the level of at least ten of the biomarkers.
    In another embodiment, the method comprises determining the presence and / or the level of a combination of biomarkers as provided in any of Tables 7 to 18.
    In another modality, the method comprises detecting the presence and / or the level of at least one additional biomarker selected from IGF-I, IGF-II, IGF-BP2, Anfiregulina, VEGFA, VEGFD, MMP-1, MMP- 2, MMP-3, MMP-7, MMP-9, TIMP-1, TIMP-2, ENA-78, MCP-1, MIP-1β, IFN-y, IL-10, IL-13, IL-1β, IL-4, IL-8, IL-6, MAC2BP, Tumor Pyruvate kinase M2, M65, OPN, DKK-3, EpCam, TGF-βl and VEGFpan. In one embodiment, the method diagnoses or detects colorectal cancer with a sensitivity of at least 50%. In another modality, the method diagnoses or detects colorectal cancer with a sensitivity of at least 66%.
    In yet another modality, the method diagnoses or detects colorectal cancer with a sensitivity of at least 77%. In one embodiment, the method diagnoses or detects colorectal cancer with a specificity of at least 75%. In one embodiment, the method diagnoses or detects colorectal cancer with a specificity of at least 80%. In another modality, the method diagnoses or detects colorectal cancer with a specificity of at least 90%. In yet another modality, the method diagnoses or detects colorectal cancer with a specificity of at least 95%.
    In another modality, the method diagnoses or detects Dukes Stage A colorectal cancer with a sensitivity of at least 50% and a specificity of at least 95%.
    In yet another modality, the method diagnoses or detects Dukes Stage A colorectal cancer with a sensitivity of at least 60% and a specificity of at least 80%.
    In another modality, the method diagnoses or detects Dukes Stage A colorectal cancer with a sensitivity of at least 50% and a specificity of at least 90%.
    The person skilled in the art will understand that the Dukes Stage A corresponds to the TNM Classifications T1, N0, M0 and T2, N0, M0.
    Thus, in one modality, the method diagnoses or detects TNM Classification T1, N0, M0 or T2, N0, M0 of colorectal cancer with a sensitivity of at least 50% and a specificity of at least 95%.
    In yet another modality, the method diagnoses or detects TNM Classification T1, N0, M0 or T2, N0, M0 of colorectal cancer with a sensitivity of at least 60% and a specificity of at least 80%.
    In another modality, the method diagnoses or detects TNM Classification T1, N0, M0 or T2, N0, M0 of colorectal cancer with a sensitivity of at least 50% and a specificity of at least 90%.
    Any suitable technique for the detection of polypeptides can be used in the methods of the invention. In one embodiment, the method comprises contacting the sample with at least one compound that binds to a biomarker polypeptide. Alternatively, the method comprises detecting the polypeptides by mass spectrometry.
    In a particular embodiment, the compound is detectably labeled. In another embodiment, the compound is an antibody. In one embodiment, the compound is attached to a solid support. In the methods of the invention, determining the presence and / or level of the biomarker may comprise determining the presence and / or the level of a polynucleotide encoding the biomarker, such as a biomarker gene transcript. Thus, in one embodiment, biomarkers are polynucleotides.
    In yet another embodiment of the methods of the invention, the method comprises: i) determining the presence and / or the level of biomarkers in the sample from the individual; and ii) comparing the presence and / or level of biomarkers with a control, in which the presence and / or level in the sample that is different from the control is indicative of colorectal cancer. In one embodiment, the sample comprises blood, plasma, serum, urine, platelets, magakaryocytes or feces.
    In another aspect, the present invention provides a method of treatment comprising: (i) diagnosing or detecting colorectal cancer according to the method of the invention; and (ii) administer or recommend a therapeutic for the treatment of colorectal cancer.
    In yet another aspect, the present invention provides a method for monitoring the effectiveness of colorectal cancer treatment in an individual, the method of which comprises treating the individual with colorectal cancer and then detecting the presence and / or level of at least two biomarkers selected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIPlβ, TGFβl, and TIMP-1 in a sample from the individual, in which an absence and / or reduction in level expression of polypeptides after treatment when compared to before treatment is indicative of effective treatment.
    In another aspect, the present invention provides an array of at least two compounds for the diagnosis or detection of colorectal cancer, wherein each of the compounds binds to a different biomarker polypeptide selected from IL-8, IGFBP2, MAC2BP , M2PK, IL-13, DKK-3, EpCam, MIPlβ, TGFβl and TIMP-1.
    In yet another aspect, the present invention provides a kit for diagnosing or detecting colorectal cancer in an individual, whose kit comprises two compounds that bind to a different biomarker polypeptide selected from IL-8, IGFBP2, MAC2BP, M2PK, IL -13, DKK-3, EpCam, MIPlβ, TGFβl, and TIMP-1.
    Throughout the specification, the word "understands", or variations such as "understands" or "understands", will be understood to imply the inclusion of an element, whole number or step established, or group of elements, whole numbers or steps, but not excluding any other element, integer or step, or group of elements, integer or step.
    As will be apparent, the preferred features and characteristics of one aspect of the invention are applicable to many other aspects of the invention.
    The invention is hereinafter described by means of the following non-limiting Examples and with reference to the accompanying figures. BRIEF DESCRIPTION OF THE ANNEXED DRAWINGS
    Figure 1. In Study 3 an ideal combination of the 4 6 potential protein biomarkers was found using logistic expression modeling, resulting in a panel of seven biomarkers and is illustrated as a ROC curve (black curve). The performance of this "panel" in independent data was estimated using cross exclusion validation (gray curve). Vertical lines are sketched at points of 80% and 90% specificity - operational points of interest in screening tests. Performance statistics are given in Table 5. Figure 2. Performance of a seven biomarker model that identifies patients with normal colorectal cancer in each Dukes Stage illustrated by ROC curves for each stage. A (red) - Stage A, B (green) - 5 Stage B, C (blue) - Stage C, and D (black) - Stage D of Study 3a. Performance characteristics are given in Table 6. Figure 3. When the biomarker results from Study 4 (also referred to as Remedied Study 3) were modeled 10 in pairs a total of 5 pairs (out of 45 possible combinations selected from the list of 10 biomarkers above) could be shown to produce a sensitivity above 52% at a specificity of 95. The performance of these paired biomarker combinations is illustrated as 15 ROC curves (n = 5 curves). The performance characteristics are given in Table 7. Figure 4. An example of a model of 3 biomarkers generated from data from Study 4 that had a sensitivity of at least 50% with 95% specificity. 20 There were 968 possible within 3 to 10 biomarker combinations and approximately half of these combinations showed a performance of at least 50% sensitivity with 90% specificity. Figure 5. The ROC curves are illustrated for all 25 combinations of 3 to 10 biomarkers generated from data from Study 4 that have a sensitivity of at least 50% with 95% specificity (n = 485 curves validated by crossing within of 968 possible models). Figure 6. Frequency of each biomarker in the 30 best 485 models. These BMs represent all serum models that generated a sensitivity of at least 50% with 95%. The high representation of all 10 biomarkers in the useful models demonstrates the unity of the present selection of these 10 biomarkers. Figure 7. A model of 5 biomarkers generated from data from Study 4 is illustrated as a ROC curve (black) and ROC curve validated by crossing (gray). This model shows a sensitivity of 68% with 95% specificity when all stages of the disease are included and when cross-validation generated a sensitivity of 64%. The included biomarkers are IL-8, IGFBP2, Mac2BP, DKK-3 and M2PK. Figure 8. A model of 6 biomarkers generated from data from Study 4 is illustrated as a ROC curve (black) and ROC curve validated by crossing (gray). This model shows a sensitivity of 77% with a specificity of 95% when all stages of the disease are included and when cross-validation generated a sensitivity of 67%. The included biomarkers are IL-8, IGFBP2, Mac2BP, DKK-3, TGFβl and M2PK. Figure 9. Two alternative models of seven biomarkers generated from data from Study 3a are shown. One has been optimized for high specificity (black / new) and an alternative or model optimized for area under the curve is shown (gray / old). With 90% specificity, the sensitivity was 72% for the new model and 77% for the older model. The included biomarkers were as follows: New: IL8, IGFBP2, S90MAC2BP, M2PK, DKK-3, IL-13 and TGFβ, Old: IL8, IGFBP2, S90MAC2BP, M2PK, EpCAM, IL13 and MIP-lβ. Figure 10. A seven model of biomarkers generated from data from Study 4 is illustrated as a ROC curve (black) and ROC curve validated by crossing (gray). This model shows a sensitivity of 84% with a specificity of 95%. The biomarkers included are serum from M2PK, IL8.plasma, TGF betal.soro, IGFBP2.plasma, Mac2BP.soro, TIMP1.plasma and Dkk3 plasma. Figure 11. Cross-validated ROC curves that show the performance of a model of 3 biomarkers for each Dukes stage are illustrated. These data demonstrate the validity of the choice of three biomarkers (DKK-3, M2PK and IGFBP2) to detect cancer at different stages of disease progression. The data indicate that in Stage A if the three markers are used, the test will still achieve a significant sensitivity of 64% with 95% specificity that is comparable to the sensitivity achieved in the later disease stage (79%). That is, the panel of three biomarkers will choose early disease states that allow for early detection. The included biomarkers are Dkk3, M2PK and IGFBP2. KEY TO THE SEQUENCE LISTING
    SEQ ID NO: 1 IL-8 amino acid sequence SEQ ID NO: 2 IGFBP2 amino acid sequence SEQ ID NO: 3 MAC2BP amino acid sequence SEQ ID NO: 4 variant M2PK amino acid sequence 1 SEQ ID NO: 5 sequence amino acid sequence of M2PK variant 2 SEQ ID NO: 6 amino acid sequence of M2PK variant 3 SEQ ID NO: 7 amino acid sequence of IL-13 SEQ ID NO: 8 amino acid sequence of DKK-3 variant 1 SEQ ID NO: 9 sequence amino acid sequence of DKK-3 variant 2 SEQ ID NO: 10 amino acid sequence of DKK-3 variant 3 12/51 SEQ ID NO: 11 amino acid sequence of EpCam SEQ ID NO: 12- MIPlβ amino acid sequence SEQ ID NO: 13- TGFβl amino acid sequence SEQ ID NO: 14 - TIMP-1 amino acid sequence DETAILED DESCRIPTION
    General Techniques and Definitions Unless specifically defined otherwise, all technical and specific terms used in the present invention should be considered to have the same meaning as commonly understood by an element of common knowledge in the art (for example, in cell culture, molecular genetics, immunology, immunohistochemistry, protein chemistry and biochemistry).
    Unless otherwise indicated, the recombinant protein, cell culture and immunological techniques used in the present invention are standard procedures, well known to those skilled in the art. Such techniques are described and explained throughout the literature in sources such as, J. Perbal, A Practical Guide to Molecular
    Cloning, John Wiley and Sons (1984), J. Sambrook et al, Molecular Cloning: A Laboratory Manual, 3rd edition, Cold Spring Harbor Laboratory Press (2001), R. Scopes, Protein Purification - Principals and Practice, 3rd edition, Springer (1994), TA Brown (editor), Essential Molecular Biology: A, 25 Practical Approach, Volumes 1 and 2, IRL Press (1991), DM
    Glover and B.D. Hames (editors), DNA Cloning: A Practical Approach, Volumes 1 to 4, IRL Press (1995 and 1996), and F.M. Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley - Interscience 30 (1988, including all updates to date),
    Ed Harlow and David Lane (editors) Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, (1988), and J.E. Coligan et al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates to date).
    As used in the present invention, the term "colorectal cancer", also known as "colon cancer", "bowel cancer" or "rectal cancer", refers to all forms of cancer originating from the epithelial cells that line the large intestine and / or the rectum.
    As used in the present invention, "biomarker" refers to any molecule, such as a gene, gene transcript (eg, mRNA), peptide or protein or fragment thereof produced by an individual that is useful in differentiating individuals with colorectal cancer normal or healthy individuals.
    As used in the present invention, the term "diagnosis", and variants thereof, such as, but not limited to, "diagnosis", "diagnosed" or "diagnosing" should not be limited to a primary diagnosis of a clinical condition, but should be considered as including diagnosis of recurrent disease. As used in the present invention, the term "individual" refers to any animal that can develop colorectal cancer and includes animals such as mammals, for example, humans or non-human mammals such as dogs and cats, laboratory animals such as mice, rats, rabbits or guinea pigs, and farm animals. In a preferred embodiment, the individual is a human.
    The "sample" can be of any suitable type and can refer, for example, to a material in which the presence or level of biomarkers can be detected. Preferably, the sample is obtained from the individual so that the detection of the presence and / or the level of biomarkers can be performed in vitro. Alternatively, the presence and / or the level of biomarkers can be detected in vivo. The sample can be used as obtained directly from the source or after at least one (partial) purification step. The sample can be prepared in any convenient way that does not interfere with the method of the invention. Typically, the sample is an aqueous solution, biological fluid, cells or tissue. Preferably, the sample is blood, plasma, serum, urine, platelets, megakaryocytes or feces. Pre-treatment may involve, for example, preparing plasma from the blood, diluting viscous fluids and the like. Treatment methods may involve filtration, distillation, separation, concentration, inactivation of interfering components and the addition of reagents. The selection and pretreatment of biological samples prior to testing are well known in the art and need not be described further.
    As used in the present invention the terms "treat", "treats" or "treatment" include administering a therapeutically effective amount of a compound sufficient to reduce or delay the onset or progression of colorectal cancer, or to reduce or eliminate at least one symptom of colorectal cancer. Biomarkers
    The present inventors have shown that determining the presence and / or level of at least two biomarkers in a sample of an individual allows for the detection or diagnosis of colorectal cancer, early detection in Dukes A Stage or at some later stage such as Stages Dukes B, C or D, with specificity and sensitivity comparable to or greater than those achieved with FOBT. The at least two biomarkers that are useful in the methods of the present invention are selected from IL-8 (interleukin-8), IGFBP2 (insulin-like growth factor-binding protein 2), MAC2BP (MAC2 binding protein ; whey protein 90K), M2PK (muscle pyruvate kinase 2, pyruvate kinase 3), IL-13 (interleukin-13),
    DKK-3 (dickkopf homologue, 3), EpCAM (epithelial cell adhesion molecule), MIPlβ (macrophage inflammatory protein lβ, CCL4, MIPIbeta), TGFβl (transforming growth factor βl, TGFbetal) and TIMP-1 ( metalloproteinase tissue inhibitor 1). Reference to any of these biomarkers includes reference to all polypeptide and polynucleotide variants such as isoforms and transcript variants as would be known to the person skilled in the art. NCBI access numbers for. representative sequences for each of the biomarkers are provided in Table 1. Table 1. NCBI accession numbers for
    Detection or diagnosis of colorectal cancer
    It will be apparent from the foregoing description that the diagnostic methods of the present invention can involve a degree of quantification to determine the levels of biomarkers in patient samples. Such quantification is readily provided by the inclusion of appropriate control samples. included in the methods of the present invention. A preferred internal control is one or more samples taken from one or more healthy individuals.
    In the present context, the term "healthy individual" should be adopted to mean an individual who does not suffer from colorectal cancer, such knowledge is derived from clinical data about the individual, including, but not limited to, a diagnostic test other than the one described in the present invention.
    As will be known to those skilled in the art, when internal controls are not included in each test conducted, the control can be derived from an established data set. The data relevant to the control subjects are preferably selected from the group consisting of: 1. a data set comprising measurements of the presence or level of expression of biomarkers for a typical population of individuals known to have colorectal cancer; 2. a data set comprising measurements of the presence or level of biomarkers for the individual to be tested on which said measurements were made previously, such as, for example, when the individual was healthy or, in the case of an individual with colorectal cancer, when the individual was diagnosed or at an earlier stage of disease progression; 3. a data set comprising measurements of the presence or level of biomarkers for a healthy individual or a population of healthy individuals; and 4. a data set comprising measurements of the presence or level of biomarkers for a normal individual or a population of normal individuals.
    In the present context, the term "typical population" in relation to individuals known to have colorectal cancer should be used as a reference to a population or sample of individuals diagnosed with colorectal cancer that is representative of the spectrum of patients with colorectal cancer. This is not adopted as requiring a restricted normal distribution of morphological or clinicopathological parameters in the population, since some variation in such distribution is permissible. Preferably, a "typical population" will exhibit a spectrum of colorectal cancer at different stages of disease progression. It is particularly preferred that a "typical population" exhibits the expression characteristics of a cohort of individuals as described in the present invention.
    The term "normal individual" should be used to mean an individual who does not express a biomarker, or expresses a biomarker at a low level in a sample. As will be known to those skilled in the art, the data obtained from a sufficiently large sample of the population will normalize, allowing the generation of a data set to determine the average level of a particular biomarker.
    Those skilled in the art are readily able to determine the baseline for comparison in any diagnostic assay of the present invention without undue experimentation, based on the teaching provided in the present invention.
    Compounds that bind to a biomarker when used diagnostically can be linked to a diagnostic reagent such as a detectable label to allow easy detection of binding events in vitro or in vivo. Suitable labels include radioisotopes, cutting markers or other imaging reagents for detection and / or localization of target molecules. Compounds linked to a detectable label can be used with suitable in vivo imaging technologies such as, for example, radiology, fluoroscopy, nuclear magnetic resonance imaging (MRI), CAT scanning, positron emission tomography (PET), tomography computerized, etc.
    The diagnostic methods of the present invention are capable of diagnosing or detecting colorectal cancer with a sensitivity and specificity that are at least comparable to FOBT or greater. As would be understood by the element versed in the technique, sensitivity refers to the proportion of real positives in the diagnostic test that are correctly identified as having colorectal cancer. Specificity measures the proportion of negatives that are correctly identified as not having colorectal cancer. In one embodiment, the methods of the invention are capable of diagnosing or detecting colorectal cancer with a sensitivity of at least 50%, 60% or 66%, or at least 77%, 80%, 83%, 85%, 86%, 87 %, 88%, 89%, 90%, or at least 93%. In another embodiment, the methods of the invention are capable of diagnosing or detecting colorectal cancer with a sensitivity of at least 80%, or at least 85% or at least 90%, or at least 95%.
    In one embodiment, the methods of the invention are capable of diagnosing or detecting colorectal cancer with a specificity of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94% or at least 95% .
    Advantageously, the methods of the present invention are capable of detecting colorectal cancer in all Dukes stages with greater sensitivity than FOBT. In Stage Dukes A, the tumor penetrated, but did not cross, the wall of the intestine. In Stage Dukes B, the tumor has passed through the intestine wall, there is still no lymph node involvement. On Stage
    Dukes C, cancer involves regional lymph nodes. In the Dukes D Stage, there is distant metastasis, for example, in the liver or lung. In one embodiment, the methods of the present invention are capable of diagnosing or detecting colorectal cancer at any Dukes Stage with a sensitivity of at least 80%.
    As known to those skilled in the art, there are other systems for determining the stage of cancer that are known in the art. An example is the Classification of Malignant Tumors (TNM) which is used by the American Joint Committee on Cancer (AJCC: Colon and rectum, in Edge et al, eds; AJCC Cancer Staging Manual, 7th edition, New York, NY, USA: Springer , 2010, pages: 143 to 164). Another example is the Modified Astler-Coller Classification (MAC).
    Consequently, the person skilled in the art will observe that the Dukes Stages correspond to certain TNM Classifications. For example, the Dukes A Stage corresponds to T1, T2, N0 and M0; the Dukes B Stage corresponds to T3, T4a, T4b, N0 and M0; and the Dukes C Stage corresponds to i) T1-T2, Nl / Nlc, M0; ii) T1, N2a and M0; iii) T3-T4a, Nl / Nlc and M0; iv) T2-T3, N2a and M0; v) T1-T2, N2b and M0; vi) T4a, N2a and M0; vii) T3-T4a, N2b and M0; and viii) T4b, N1-N2 and M0. Accordingly, the person skilled in the art will understand that the reference to a Dukes Stage as used in the present invention includes reference to the corresponding TMN classification as known in the art. Protein Detection Techniques
    In one embodiment, the biomarker polypeptide is detected in a patient sample, in which the presence and / or the level of the polypeptide in the sample is indicative of colorectal cancer. For example, the method may comprise contacting a biological sample derived from the individual with a compound capable of binding a biomarker polypeptide, and detecting the complex formation between the compound and the biomarker polypeptide. The term "biomarker polypeptide" as used in the present invention includes fragments of biomarker polypeptides, including, for example, immunogenic fragments and epitopes of the biomarker polypeptide.
    In one embodiment, the compound that binds to the biomarker is an antibody. The term "antibody" as used in the present invention includes intact molecules as well as molecules that comprise or consist of fragments thereof, such as, for example Fab, F (ab ') 2, Fv and scFv, as well as designed variants including diabodies, tribodies, minibodies and single domain antibodies that are capable of binding an epitopic determinant. Thus, antibodies can exist as intact immunoglobulins, or as modifications in a variety of ways.
    In another embodiment, an antibody to a biomarker polypeptide is detected in a patient sample, in which the presence and / or the level of the antibody in the sample is indicative of colorectal cancer.
    Preferred detection systems contemplated in the present invention include any known assay for detecting proteins or antibodies in a biological sample isolated from a human individual, such as, for example, SDS / PAGE, isoelectric focusing, two-dimensional gel electrophoresis comprising SDS / PAGE and isoelectric focusing, an immunoassay, flow cytometry, for example, fluorescence-activated cell classification (FACS), a detection-based system that uses an antibody or non-antibody compound, such as, for example, a small molecule (for example , a chemical compound, agonist, antagonist, allosteric modulator, competitive inhibitor or non-competitive protein inhibitor). According to these modalities, the antibody or small molecule can be used in any standard solid phase or solid phase assay format receptive to protein detection. Optical or fluorescent detection, such as, for example, with the use of mass spectrometry, MALDI-TOF, biosensor technology, evanescent optical fiber or fluorescence resonance energy transfer, is clearly covered by the present invention. Test systems suitable for use in high-throughput screening of mass samples, for example, a high-throughput spectroscopy resonance method (for example MALDI-TOF, electro-spray MS or nano-electro-spray MS) are also contemplated. Another suitable protein detection technique involves the use of Multiple Reaction Monitoring (MRM) in LC-MS (LC / MRM-MS) (Anderson and Hunter, 2006).
    Immunoassay formats are particularly suitable, for example, selected from the group consisting of, an immunoblot, a Western blot, a dot blot, an enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA) enzyme immunoassay. Modified immunoassays using fluorescence resonance energy transfer (FRET), isotope encoded affinity tags (ICAT), ion-assisted matrix / flight-time laser desorption (MALDI-TOF), electrospray ionization (ESI) , biosensor technology, evanescent fiber optic technology or protein part technology are also useful. Nucleic Acid Detection Techniques
    Any suitable techniques that allow qualitative and / or quantitative assessment of the level of a polynucleotide biomarker in a sample can be used. The terms "nucleic acid molecule" or "polynucleotide" as used in the present invention refer to an oligonucleotide, polynucleotide or any fragment thereof.
    The comparison can be made by reference to a standard control or to a level of control that is found in healthy tissue. For example, the levels of a transcribed gene can be determined by Northern blotting and / or RT-PCR. With the advent of quantitative (real-time) PCR, quantitative analysis of gene expression can be achieved through the use of appropriate primers for the gene of interest. The nucleic acid can be labeled and hybridized in a gene array, in which case the gene concentration will be directly proportional to the intensity of the radioactive or fluorescent signal generated in the array.
    Methods for direct sequencing of nucleotide sequences are well known to those skilled in the art and can be found, for example, in Ausubel et al. , eds., Short Protocols in Molecular Biology, 3rd edition, Wiley, (1995) and Sambrook et al, Molecular Cloning, 3rd edition, Cold Spring Harbor Laboratory Press, (2001). The sequencing can be performed by any suitable method, for example, Dideoxy sequencing, chemical sequencing or variations thereof. Direct sequencing has the advantage of determining the variation in any base pair of a particular sequence.
    Other PCR methods that can be used in carrying out the invention include hybridization-based PCR detection systems, TaqMan assay (US 5,962,233) and molecular beam assay (US 5,925,517). The nucleic acid can be separated from the test sample. Suitable methods will be known to those skilled in the art. For example, RNA can be isolated from a sample to be analyzed using conventional procedures, such as those provided by QIAGEN technology. This RNA is then reverse transcribed into DNA using reverse transcriptase and the DNA molecule of interest can then be amplified by PCR techniques using specific primers.
    Diagnostic procedures can also be performed directly using patient samples. Hybridization or amplification assays, such as, for example, Southern or Northern blot analysis, immunohistochemistry, single-stranded conformational polymorphism (SSCP) analysis and PCR analyzes are among the techniques that are useful in this regard. If desired, the probe or target nucleic acid can be immobilized to a solid support such as a microtiter plate, membrane, polystyrene microsphere, glass plane or other solid phase. Kits
    The present invention provides kits for the diagnosis or detection of colorectal cancer. Such kits may be suitable for the detection of nucleic acid species or alternatively they may serve for the detection of a polypeptide gene product, as discussed above.
    For polypeptide detection, antibodies will most typically be used as kit components. However, any agent capable of specifically binding to a biomarker gene product will be useful in this aspect of the invention. Other components of the kits will typically include labels, secondary antibodies, substrates (if the gene is an enzyme), inhibitors, cofactors and control gene product preparations to allow the user to quantify expression levels and / or assess whether the diagnostic experiment it worked correctly. Tests based on enzyme-linked immunosorbent assay (ELISA) and tests
    Competitive ELISA tests are particularly suitable tests that can be performed by the technically skilled element using kit components.
    Optionally, the kit further comprises means for detecting the binding of an antibody to a biomarker polypeptide. Such media include a reporter molecule such as, for example, an enzyme (such as horseradish peroxidase or alkaline phosphatase), a dye, a radionucleotide, a luminescent group, a fluorescent group, biotin or a colloidal particle, such as gold or colloidal selenium. Preferably, such a reporter molecule is directly bound to the antibody.
    In yet another embodiment, a kit may additionally comprise a reference sample. In one embodiment, a reference sample comprises a polypeptide that is detected by an antibody. Preferably, the polypeptide is of known concentration. Such a polypeptide is of particular use as a standard. Consequently, various known concentrations of such a polypeptide can be detected using a diagnostic assay described in the present invention.
    For nucleic acid detection, such kits may contain a first container such as a plastic vial or tube or a microtiter plate that contains an oligonucleotide probe. The kits can optionally contain a second container that contains the initiators. The probe can be hybridized to the DNA whose altered expression is associated with colorectal cancer and the primers are useful to amplify that DNA. Kits containing an oligonucleotide probe immobilized on a solid support could also be developed, for example, with the use of arrays (see, Supplement of Issue 21 (1) Nature Genetics, 1999).
    For PCR amplification of nucleic acid, nucleic acid primers can be included in the kit which are complementary to at least a portion of a biomarker gene as described in the present invention. The primer set typically includes at least two oligonucleotides, preferably four oligonucleotides, which are capable of specific DNA amplification. Fluorescent labeling oligonucleotides that will allow quantitative PCR determination can be included (for example, TaqMan chemistry, Molecular Bundles). Enzymes suitable for amplifying DNA will also be included. The control nucleic acid can be included for purposes of comparison or validation. Such controls could be RNA / DNA isolated from healthy tissue, or from healthy individuals or domestic genes such as β-actin or GAPDH whose mRNA levels are unaffected by colorectal cancer. Regression Algorithms and Statistics
    In order to develop a panel of biomarkers suitable for diagnosing or detecting colorectal cancer, the present inventors analyzed several biomarkers in a statistical model. Such an improvement in test performance is sometimes called "sample" performance. A fair evaluation of a test requires its evaluation with the use of individuals without a sample, that is, individuals not included in the construction of the initial predictive model. This is achieved by assessing test performance using cross-validation.
    Tests for statistical significance include linear and non-linear regression, including ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and odd number ratio, Baysian probability algorithms. As the number of biomarkers measured increases, however, it may be generally more convenient to use a more sophisticated technique such as Random Forests, simple logistics, Bayes Net, to name a few.
    For example, the Bayesian probability can be adopted. In this circumstance, a 10-fold cross-validation can be used to estimate the "unsampled" performance of the models in question. For each combination of biomarkers under consideration, data can be randomly divided into 10 subsamples, each with similar proportions of healthy individual and individuals at each stage of the disease. In turn, each subsample can be excluded, and a logical model built using the remaining 90% of individuals. This model can then be used to estimate the probability of cancer for the excluded subsample, providing an "unsampled" performance estimate. By repeating this for the remaining 9 subsamples, the "unsampled" performance can be estimated from the study data itself. These predicted unsampled probabilities can then be compared to the actual disease situation of the individuals to create a Receptor Operational Characteristic Curve (ROC), from which the cross-validation sensitivity with 95% specificity can be estimated.
    Each "unsampled" performance estimate using cross-validation (or any other method), while not targeted, has an element of variability for this. Therefore, a classification of models (based on combinations of biomarker) can be indicative only of the relative performance of such models. However, a set of biomarkers that is capable of being used in a large number of combinations to generate a diagnostic test as demonstrated through "unsampled" performance evaluations, almost certainly contains the very combinations of 27/51 biomarkers that will support the repeated evaluation. qualified as diagnostic tests prove to be useful and inexpensive and have sensitivity to a specific specificity. As an example, considering the five biomarkers: IL-8, IGFBP2, MAC2BP, M2PK and DKK-3. A model that discriminates individuals with cancer from healthy controls may be as follows:

    Here, p represents the likelihood that a person will have colorectal cancer. Each Q is the logarithm of the concentration of biomarker i in a person's plasma (or serum). Each beta (β) is a coefficient that applies to that biomarker in the units of concentration in which it is measured -% is a "deviation" or "intersection". This linear logical model is common to all the results presented in the present invention, but it is far from the only way in which a combination of biomarker concentrations can be modeled to predict the likelihood of cancer. Other linear or nonlinear logic algorithms that would be equally applicable include Random Forest, ANOVA, T-test, Fisher analysis, Support Vector Machine, Linear Models for Microarray Data (LIMMA) and / or Microarray Data Significance Analysis (SAM) ), 25 Best First, Greedy Stepwise, Naive Bayes, Previous Linear Selection, Scatter Search, Linear Discriminant Analysis (LDA), Gradual Logical Regression, Receiver Operational Characteristics and Classification Trees (CT). Thus, in the light of the teachings of the present specification, the element versed in the technique will observe that the sensitivity and specificity of a test to diagnose colorectal cancer can be modulated by selecting a different combination of biomarkers as described in the present invention. Knowledge-Based Systems
    It will be apparent from the discussion of the present invention that knowledge-based computer software and hardware for implementing an algorithm also form part of the present invention. Such computer software and / or hardware is useful for performing a method of diagnosing or detecting colorectal cancer according to the invention. Accordingly, the present invention also provides software or hardware programmed to implement an algorithm that processes data obtained by executing the method of the invention through multivariate analysis to provide a disease classification and provide or allow colorectal cancer diagnosis or detection and / or determining the progression or status of a colorectal cancer or determining whether or not a colorectal cancer has progressed or determining whether or not an individual is responding to treatment for colorectal cancer according to the results of the disease classification compared to the pre values -determined.
    In one example, a method of the invention can be used in architecture based on existing knowledge or platforms associated with pathology services. For example, the results of a method described in the present invention are transmitted over a communications network (for example, the internet) to a processing system in which an algorithm is stored and used to generate a predicted later probability value that translates the classification of probability or risk of recurrent disease or metastasis or responsiveness to treatment, which is then forwarded to an end user in the form of a diagnostic or predictive report.
    The method of the invention may, therefore, be in the form of a computer-based kit or system comprising the reagents necessary to detect the concentration of biomarkers and the computer hardware and / or software to facilitate the determination and transmission of reports to a doctor.
    The test of the present invention allows integration into existing or newly developed platform or architecture systems. For example, the present invention contemplates a method to allow a user to determine the status of an individual in relation to colorectal cancer, the method includes: (a) receiving data in the form of levels from at least two biomarkers selected from IL- 8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIPlβ, TGFβl, and TIMP-1 in a readily obtained sample, optionally in combination with another colorectal cancer marker; (b) processing individual data through multivariate analysis (for example, regression analysis) to provide a disease classification; (c) determine the individual's situation according to the results of the disease classification in comparison with the predetermined values; and (d) transfer an indication of the individual's situation to the user through the communications network in reference to the multivariate analysis that includes an algorithm that performs the multivariate analysis function.
    In one embodiment, the method for diagnosing or detecting colorectal cancer of the invention can be performed by taking a blood sample from a patient and determining the presence and / or level of any one or more of the biomarkers as described in the present invention. . If desired, measurements can be made, for example, on a biochip so that a single analysis can be used to measure the presence and / or the level of multiple biomarkers. The results of this analysis can then be inserted into a computer program that individualizes them for linear regression analysis. The computer could also contain information such as for control values or expected ranges, or the doctor, nurse, medical administrator or general practitioner could enter such data. This analysis would then provide a classification or probability of having colorectal cancer. If a second test for the patient is performed, regression analysis may indicate a change in classification, thereby indicating that the patient's illness has progressed or worsened. EXAMPLES Materials and methods Patient samples
    A collection of plasma and serum samples was taken and processed from a cohort of patients with colorectal cancer (Dukes Stages A to D) who were being treated at several hospitals.
    Blood was also collected and processed from a group of about 50 healthy volunteers over the age of 65 and a group of 15 over the age of 50.
    Four separate studies were performed with slightly different biomarkers. Study 1 observed 52 cancer samples and 50 controls, study 2 observed 55 cancer samples and 53 controls, study 3 and 4 observed 96 cancer samples and 50 controls. In Study 2, 3 and 4, patients were matched with age and gender throughout the Dukes Stages, see Table 2 for summary statistics. Table 2. Characteristics of normal volunteers and patients with colorectal cancer used in studies 2, 3 and 4.
    Biomarker Analysis
    The analysis of biomarkers was performed with commercial kits and distributed antibodies (DSL, R&D Duoset, Calbiotech, Millipore, Abnova, Genway, Peviva, Schebo, 5 Bender) and using ELISA or Luminex assays. Statistical Evaluation and Modeling of panel biomarker
    The results for each test were analyzed using the statistical software packages Prism and "R". The performance of individual markers was assessed using the nonparametric Mann-Whitney T test and individual receiver operator (ROC) characteristic curves were generated.
    Related modeling and logical regression 15 strategies were used to find combinations of biomarkers that best separated controls and patients with colorectal cancer. Four separate studies were carried out with the same samples / quotas. The results of each of these are given below. Results of Studies 1, 2 and 3 The biomarkers chosen to be measured in Studies 1 and 2 and 3 are mentioned in Table 3. The bold biomarkers were those identified as 5 promising in each study (that is, they were significantly different in samples of patients with colorectal cancer versus control and / or were identified on panels of combined biomarkers that distinguish colorectal cancer from controls). Table 3. Biomarkers analyzed in studies.

    Statistical evaluation and panel biomarker modeling To find combinations of biomarkers that best separated controls and patients with colorectal cancer, the previous variable selection with Bayesian Information Criteria to penalize logarithm probability to prevent over-adjustment. To estimate the similar performance of the biomarker panel in 10 independent data sets, cross-validation "N times" or "exclusion" was used. In this procedure, an observation in an instant was excluded while the whole model adjustment algorithm was applied to the remaining observations. The resulting model was then used to estimate the probability that the excluded observation would be a case. This was repeated for each observation in the data set.
    Thus, each observation, in turn, acted as an independent test of the model-building algorithm. The resulting dataset consisting of cases and controls with a "independently predicted" case probability can then be compared to the original model. The ability to choose from several biomarkers and weight them appropriately allows for a research strategy that optimizes performance in regions of interest on the ROC curve. The cost of deficient specificity is a high number of unnecessary colonoscopies.
    In Study 3, 48 potential biomarkers were evaluated to select a candidate panel of colorectal cancer biomarkers, with the use of random blocks within plaques to avoid bias. From that list of 48, only 42 showed measurable levels. Individually, 14 biomarkers showed a significant difference between controls and CRC as assessed by T tests; (IGFII, IGFBP2, IL-8, IL-6, MMP-1, MMP-7, s90 / Mac2BP, M2PK, EpCam, TIMP-1 (serum and plasma), M65, OPN, TGFβl, VEGFpan. As expected, none had enough sensitivity or specificity to be useful as a biomarker itself (not shown). However, with the use of a variety of modeling strategies, including the use of logarithmic values, several different panels of biomarkers were found, which exceeded the performance of FOBT especially for early to later stage disease.
    Figure 1 shows the results of a panel of 7 biomarkers that included IL8 (serum), IL-13 (serum), EpCAM (plasma), M2PK (plasma), IGFBP2 (serum) and Mac2BP (serum) and which was validated by crossing to predict its performance in independent samples.
    This model of 7 biomarkers, which is described at least conceptually as
    provided good performance at high specificity and was robust under cross-validation. The estimated coefficients 5 to generate the best model for this combination of biomarker in plasma are mentioned in Table 4. Performance statistics are provided in Table 5. This performance exceeds that quoted for FOBT (sensitivity 65.8%, specificity 95%) (Morikawa et al, 2005). Table 4. Coefficients for the combination of biomarker.
    Table 5. Performance of the 7 biomarker model and cross-validation.
    This model was also applied separately to patients at each stage of colorectal cancer (Dukes Stages A, B, C, D) and shown for equally satisfactory execution within each stage (Figure 2). The AUCs were 0.88 to 0.93 and were almost equally satisfactory in discriminating all stages of colorectal cancer. The model shows the highest sensitivity of 90% with 90% specificity for Stage C and the lowest sensitivity of 73% with 90% specificity for Stage B (Table 6). Table 6. Dukes Stage Model Performance
    Study 4 (also called "remedied Study 3") In Study 4, 10 biomarkers were remedied in the same cohort as in Study 3. Blood was collected from 96 patients with colorectal cancer and 50 normal subjects (the controls). In this study, the focus was on 10 biomarkers, namely, IGFBP2, IL8, IL13, Mac2BP, M2PK, Dkk3, EpCam, TGFbetal, TIMP-1, MlPlbeta. The tests were performed as previously described. Both serum levels and plasma levels for each of the biomarkers were measured and compared with the control values.
    When modeled in pairs (two markers), a total of 5 pairs (out of 45 possible combinations 15 selected from the list of 10 biomarkers above) could be shown to produce a sensitivity above 52% at a specificity of 95%. See Table 7 and Figure 3. Table 7. Pairs of Biomarker that Produce Useful Screening Tests in Cross Validation.

    In analyzing the combinations of three to ten of the named biomarkers, there are 968 possible combinations. The 968 combinations between 3 and 10 biomarkers consist of 120 combinations of 3 markers; 210 combinations of 4 markers; 252 combinations of 5 markers; 210 combinations of 6 markers; 120 combinations of 7 markers; 45 combinations of 8 markers; 10 combinations of 9 markers and the only combination that includes all 10 biomarkers. When they were modeled using a linear logical model, and then tested through 10-fold cross-validation, about half of the 968 combinations had a sensitivity of 50% with a specificity of 95%, see Figure 4 showing the results for a combination of three biomarkers. More than half of these combinations would have a specificity of 90% and a sensitivity of 50%.
    Figure 5 shows all 4 85 of the estimated unsampled ROC curves (10-fold cross-validation) for testing out of a total of 968 possible models with all possible combinations of 3 to 10 biomarkers. It is observed that many individual segments of the 485 ROC curves are coincident, due to the fact that each horizontal segment represents a control and each vertical segment a case. In this case, 50.1% of the combinations exceeded the sensitivity by 50%, the specificity by 95. The best "no-sample" performance estimated is a sensitivity of 76% with 95% specificity. The repetition of the cross validation will select a different set of models, the sensitivity of any combination may vary by 10% with 95% specificity due to random sampling, but result in a similar proportion of "useful screening tests". Accurate validation of individual models requires repeated experiments and larger sample sizes.
    Figure 6 shows how many of the 485 combinations with 50% sensitivity, 95% specificity, include any given biomarker. At the top end, 432 of the "useful" combinations chosen include M2PK. At the bottom end, 227 of the "useful" combinations chosen include MlPlbeta. This high representation of all 10 biomarkers in "useful" models shows the unity and self-complementarity of the selection of these 10 biomarkers.
    Figures 7 to Figure 11 demonstrate some of the results of this latest study (Study 4) for combinations of 5 and 7 biomarkers, including a model in which the samples are from a pool of plasma or serum. Figure 11 demonstrates the validity of choosing three biomarkers (DKK-3, M2PK and IGFBP2) at different stages of disease progression. The data indicate that in Stage A if the three markers are used, the test will still achieve a significant sensitivity (64%) with 95% specificity that is comparable with the sensitivity achieved in the later disease stage, 79%). That is, the panel of three biomarkers will choose early disease states that allow for early detection.
    Tables 8 to 16 list the results of various combinations of various biomarker panel sets. Depending on the linear regression that is used, as well as the cohort control and other factors such as sample derivation and assay kit technique, there may be a variation in the actual Figures or in the order of the markers. Regardless, many of these combinations will achieve good selectivity at high specificities in order to be useful for diagnosing or detecting colorectal cancer at any stage of disease progression. Table 8. Combination of three biomarkers in serum that equal or exceed 50% sensitivity with 95% specificity.
    BM1 BM2 BM3 Sensitivity Test with 95% Specificity Cross Validation Sensitivity with 95% Specificity

    Table 9. Combination of four biomarkers including DKK-3 in serum that match or exceed 50% sensitivity with 95% specificity.
    Table 10. Combination of four biomarkers including M2PK in serum that match or exceed 50% sensitivity with 95% specificity.

    Table 11. Combination of five biomarkers that match or exceed 50% sensitivity with 95% specificity.

    Table 12. Combination of seven serum biomarkers that match or exceed 50% sensitivity with 95% specificity.

    Table 13. Combinations of seven biomarkers with sensitivity between 60% and 52%.


    Table 14. Combinations of seven biomarkers with sensitivity <53%


    Table 15. Sensitivity of combinations of nine biomarkers in plasma and serum samples with 95% specificity.



    It will be noted by those skilled in the art that various variations and / or modifications can be made to the invention as shown in the specific embodiments without departing from the scope of the invention as widely described. The present modalities must, therefore, be considered in all aspects as illustrative and not restrictive.
    All publications discussed and / or referenced in the present invention are incorporated into the present invention in its entirety.
    The present application claims priority of AU 2010903140, the contents of which are incorporated by reference in the present invention.
    Any discussion of documents, acts, materials, devices, articles or the like that have been included in this specification is purely for the purpose of providing a context for the present invention. This should not be taken as an admission that any or all of these matters form part of the background of the prior art or were of general common knowledge in the field relevant to the present invention as if it existed before the priority date of each claim in this application. REFERENCES
    Anderson and Hunter (2006) Mol Cell Proteomics, 5: 573 to 588. Cancer in Australia, an overview (2008) AIHW (Australian Institute of Health and Welfare) & AACR (Australasian Association of Cancer Registries), Cancer series no. 46, Cat. No: CAN 42, Canberra: AIHW. Etzioni et al. (2003) Nat Rev Cancer, 3: 243-252. Hundt et al. (2007) Cancer Epidemiol Biomarkers Prev, 16: 1935 to 1953. Kimmel (1987) Methods Enzymol, 152: 507 to 511. Kwoh et al. (1989) Proc Natl Acad Sci USA. 86: 1173 5 to 1177. Levin (2004) Gastroenterology, 127: 1841 to 1844. Lieberman (2010) Gastroenterology, 138: 2115 to 2126. Morikawa et al. (2005) Gastroenterology, 129: 422 to 428. 10 Notomi et al. (2000) Nucleic Acids Res. 28: E63. Tonus (2006) World J Gastroenterol, 12: 7007 to 701 1. 407. Wahl and Berger (1987) Methods Enzymol, 152: 399 to 15 Walker et al. (1992a) Proc Natl Acad Sci USA. 89: 392 to 396. Walker et al. (1992b) Nucleic Acids Res. 20: 1691 to 1696.
    权利要求:
    Claims (11)
    [0001]
    1. IN VITRO METHOD FOR DIAGNOSING OR DETECTING COLORRETAL CANCER IN AN INDIVIDUAL, characterized by the method comprising: i) determining the presence and / or level of expression of at least three biomarkers in a sample from the individual, in which the three markers are: (a) tumor M2-PK, IGFBP2 and DKK-3, or (b) tumor pyruvate kinase M2 ((M2-PK), IGFBP2 and EpCAM in which the sample comprises blood, plasma or serum; and in which the presence and / or the level of the three biomarkers is indicative of colorectal cancer.
    [0002]
    2. METHOD, according to claim 1, characterized by comprising the determination of the presence and / or level of expression of one or more additional biomarkers selected from IL-13, MIPlβ, TGFβl, IL-8, TIMP1 and MAC2BP.
    [0003]
    METHOD, according to either of claims 1 or 2, characterized in that the method comprises determining the presence and / or the level of expression of four biomarkers.
    [0004]
    METHOD, according to either of claims 1 or 2, characterized in that the method comprises determining the presence and / or the level of five biomarkers.
    [0005]
    METHOD, according to either of claims 1 or 2, characterized in that the method comprises determining the presence and / or level of six biomarkers.
    [0006]
    6. METHOD, according to either of claims 1 or 2, characterized in that the method comprises determining the presence and / or level of: i) seven biomarkers; or ii) eight biomarkers; or iii) nine biomarkers; or iv) ten biomarkers.
    [0007]
    7. METHOD, according to any one of claims 1 to 6, characterized in that the method further comprises detecting the presence and / or the level of at least one additional biomarker selected from the group of IGF-I, IGF-II, Anfiregulin, VEGFA, VEGFD , MMP-1, MMP-2, MMP-3, MMP-7, MMP-9,, TIMP-2, ENA-78, MCP-1, IFN-Y, IL-10, IL-1β, IL-4, IL-6, M65, OPN, and VEGFpan.
    [0008]
    METHOD, according to any one of claims 1 to 7, characterized in that the method comprises bringing the sample into contact with at least one compound that binds to a polypeptide biomarker.
    [0009]
    9. METHOD according to claim 8, characterized in that the compound is detectably labeled.
    [0010]
    10. METHOD according to either of claims 8 or 9, characterized in that the compound is an antibody.
    [0011]
    A method according to any one of claims 8 to 10, characterized in that the compound is attached to a solid support.
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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]|
    2019-06-04| B25A| Requested transfer of rights approved|Owner name: VISION TECH BIO PTY LTD. (AU) |
    2019-07-16| B06T| Formal requirements before examination [chapter 6.20 patent gazette]|
    2020-08-11| B07A| Application suspended after technical examination (opinion) [chapter 7.1 patent gazette]|
    2020-12-22| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-02-02| 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 14/07/2011, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    AU2010903140|2010-07-14|
    AU2010903140A|AU2010903140A0|2010-07-14|Diagnostic for colorectal cancer|
    PCT/AU2011/000895|WO2012006681A1|2010-07-14|2011-07-14|Diagnostic for colorectal cancer|
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