US20130345322A1 - Diagnostic for colorectal cancer - Google Patents

Diagnostic for colorectal cancer Download PDF

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US20130345322A1
US20130345322A1 US13/809,785 US201113809785A US2013345322A1 US 20130345322 A1 US20130345322 A1 US 20130345322A1 US 201113809785 A US201113809785 A US 201113809785A US 2013345322 A1 US2013345322 A1 US 2013345322A1
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m2pk
mac2bp
igfbp2
epcam
biomarkers
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Leah Jane Cosgrave
Bruce Tabor
Antony Wilks Burgess
Edouard Collins Nice
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Vision Tech Bio Pty Ltd
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Commonwealth Scientific and Industrial Research Organization CSIRO
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57419—Specifically defined cancers of colon
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present invention relates to determining the presence and/or level of biomarkers for detecting or diagnosing colorectal cancer.
  • the invention also relates to diagnostic kits comprising reagents for determining the presence and/or level of the biomarkers and methods of detecting or diagnosing colorectal cancer.
  • Colorectal cancer also referred to as colon cancer or bowel cancer
  • colon cancer is the second most common cause of cancer worldwide.
  • greater than 90% of patients who present while the tumour is still localised will still be alive after 5 years and can be considered cured.
  • the early detection of colorectal lesions would therefore significantly reduce the impact of colon cancer (Etzioni et al., 2003).
  • FOBT faecal occult blood test
  • Flexible sigmoidoscopy a faecal occult blood test
  • colonoscopy a faecal occult blood test
  • FOBT has relatively low specificity resulting in a high rate of false positives. All positive FOBT must therefore be followed up with colonoscopy. Sampling is done by individuals at home and requires at least two consecutive faecal samples to be analysed to achieve optimal sensitivity. Some versions of the FOBT also require dietary restrictions prior to sampling.
  • FOBT also lacks sensitivity for early stage cancerous lesions that do not bleed into the bowel and as stated above, these are the lesions for which treatment is most successful.
  • the present inventors investigated over sixty biomarkers associated with colorectal cancer, but found that none of the biomarkers alone would be suitable as a diagnostic test. Surprisingly, it was found that determining the presence and/or level of at least two biomarkers associated with colorectal cancer in a sample from a subject allowed for the detection or diagnosis of colorectal cancer at any of the stages of disease. 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 the FOBT.
  • the present invention provides a method for diagnosing or detecting colorectal cancer in a subject, the method comprising:
  • the method comprises determining the presence and/or level of two biomarkers selected from M2PK, EpCam, IL-13, DKK-3, IL-8 and IGFBP2.
  • the method comprises determining the presence and/or level of expression of at least three of the biomarkers.
  • the three biomarkers are selected from M2PK, EpCam, IL-13, DKK-3, IL-8, IGFBP2, MIP1 ⁇ , TGF ⁇ 1 and MAC2BP.
  • the method comprises determining the presence and/or level of three biomarkers, wherein the three biomarkers are:
  • IL-8 IL-8, IL-13, and MAC2BP.
  • the method comprises determining the presence and/or level of expression of at least four of the biomarkers.
  • the method comprises determining the presence and/or level of four biomarkers, wherein the four biomarkers are:
  • IL-8 IL-8, MAC2BP, IGFBP2, and EpCam.
  • the method comprises determining the presence and/or level of at least five of the biomarkers.
  • the five biomarkers are IL-8, IGFBP2, MAC2BP, M2PK, and IL-13.
  • the method comprises determining the presence and/or level of at least six of the biomarkers.
  • the method comprises determining the presence and/or level of at least seven of the biomarkers.
  • the seven biomarkers are:
  • IL-8 IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, and TGF ⁇ 1;
  • IL-8 IL-8, IGFBP2, MAC2BP, M2PK, IL-13, EpCam, and MIP1 ⁇ .
  • the method comprises determining the presence and/or level of at least eight of the biomarkers.
  • the method comprises determining the presence and/or level of at least nine of the biomarkers.
  • the method comprises determining the presence and/or level of at least ten of the biomarkers.
  • the method comprises determining the presence and/or level of a combination of biomarkers as provided in any of Tables 7 to 18.
  • the method comprises detecting the presence and/or level of least one additional biomarker selected from IGF-I, IGF-II, IGF-BP2, Amphiregulin, VEGFA, VEGFD, MMP-1, MMP-2, MMP-3, MMP-7, MMP-9, TIMP-1, TIMP-2, ENA-78, MCP-1, MIP-1 ⁇ , IFN- ⁇ , IL-10, IL-13, IL-1 ⁇ , IL-4, IL-8, IL-6, MAC2BP, Tumor M2 pyruvate kinase, M65, OPN, DKK-3, EpCam, TGF ⁇ -1, and VEGFpan.
  • additional biomarker selected from IGF-I, IGF-II, IGF-BP2, Amphiregulin, VEGFA, VEGFD, MMP-1, MMP-2, MMP-3, MMP-7, MMP-9, TIMP-1, TIMP-2, ENA-78, MCP-1, MIP-1 ⁇ , IFN- ⁇ , IL
  • the method diagnoses or detects colorectal cancer with a sensitivity of at least 50%.
  • the method diagnoses or detects colorectal cancer with a sensitivity of at least 66%.
  • the method diagnoses or detects colorectal cancer with a sensitivity of at least 77%.
  • the method diagnoses or detects colorectal cancer with a specificity of at least 75%.
  • the method diagnoses or detects colorectal cancer with a specificity of at least 80%.
  • the method diagnoses or detects colorectal cancer with a specificity of at least 90%.
  • the method diagnoses or detects colorectal cancer with a specificity of at least 95%.
  • the method diagnoses or detects Dukes Stage A colorectal cancer with a sensitivity of at least 50% and a specificity of at least 95%.
  • the method diagnoses or detects Dukes Stage A colorectal cancer with a sensitivity of at least 60% and a specificity of at least 80%.
  • 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 method diagnoses or detects TNM Classification T1, N0, M0 or T2, N0, M0 colorectal cancer with a sensitivity of at least 50% and a specificity of at least 95%.
  • the method diagnoses or detects TNM Classification T1, N0, M0 or T2, N0, M0 colorectal cancer with a sensitivity of at least 60% and a specificity of at least 80%.
  • the method diagnoses or detects TNM Classification T1, N0, M0 or T2, N0, M0 colorectal cancer with a sensitivity of at least 50% and a specificity of at least 90%.
  • the method comprises contacting the sample with at least one compound that binds a biomarker polypeptide.
  • the method comprises detecting the polypeptides by mass spectrometry.
  • the compound is detectably labelled.
  • the compound is an antibody.
  • the compound is bound to a solid support.
  • determining the presence and/or level of the biomarker may comprise determining the presence and/or level of a polynucleotide encoding the biomarker, such as a biomarker gene transcript.
  • the biomarkers are polynucleotides.
  • the method comprises:
  • the sample comprises blood, plasma, serum, urine, platelets, magakaryocytes or faeces.
  • the present invention provides a method of treatment comprising:
  • the present invention provides a method for monitoring the efficacy of treatment of colorectal cancer in a subject, the method comprising treating the subject for 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, MIP1 ⁇ , TGF ⁇ 1, and TIMP-1 in a sample from the subject, wherein an absence of and/or reduction in the level of expression of the polypeptides after treatment when compared to before treatment is indicative of effective treatment.
  • biomarkers selected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1 ⁇ , TGF ⁇ 1, and TIMP-1
  • 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 a different biomarker polypeptide selected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1 ⁇ , TGF ⁇ 1, and TIMP-1.
  • the present invention provides a kit for diagnosing or detecting colorectal cancer in a subject, the kit comprising two compounds that each binds a different biomarker polypeptide selected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1 ⁇ , TGF ⁇ 1, and TIMP-1.
  • FIG. 1 In Study 3 an optimum combination of the 46 potential protein biomarkers was found using logistic regression modelling, resulting in a panel of seven biomarkers and is illustrated as a ROC curve (black curve). The performance of this “panel” on independent data was estimated using “leave one out” cross-validation (grey curve). The vertical lines are drawn at points of 80% and 90% specificity—operating points of interest in screening tests. Performance statistics are given in Table 5.
  • FIG. 2 Performance of a seven biomarker model identifying colorectal cancer patients from normals at each Dukes Stage illustrated by ROC curves for each stage.
  • FIG. 3 When biomarker results from Study 4 (also referred to as Study 3 remeasured) were modelled in pairs a total of 5 pairs (out of a possible 45 combinations selected from the list of 10 biomarkers above) could be shown to produce a sensitivity above 52% at a specificity of 95.
  • FIG. 4 An example of a 3 biomarker model generated from Study 4 data which had a sensitivity of at least 50% at 95% specificity. There were 968 possible 3-biomarker combinations and approximately half of those combinations showed a performance of at least 50% sensitivity at 90% specificity.
  • FIG. 6 Frequency of each biomarker in the best 485 models. These BMs represent all serum models that gave a sensitivity of at least 50% at 95% The high representation of all 10 biomarkers in the useful models demonstrates the unity of our selection of these 10 biomarkers.
  • FIG. 7 A 5 biomarker model generated from Study 4 data is illustrated as a ROC curve (black) and cross validated ROC curve (grey). This model shows a sensitivity of 68% at 95% specificity when all stages of disease are included and when cross validated gave a sensitivity of 64%. Biomarkers included are [IL-8, IGFBP2, Mac2BP, DKK-3 and M2PK].
  • FIG. 8 A 6 biomarker model generated from Study 4 data is illustrated as a ROC curve (black) and cross validated ROC curve (Grey). This model shows a sensitivity of 77% at a specificity of 95% when all stages of disease are included and when cross validated gave a sensitivity of 67%.
  • Biomarkers included are [IL-8, IGFBP2, Mac2BP, DKK-3, TGFbeta1&M2PK].
  • FIG. 9 Two alternative seven biomarker models generated from Study 3a data are shown. One was optimised for high specificity (black/new) and an alternative or model optimised for area under the curve is shown (grey/old). At 90% specificity the sensitivity was 72% for the new model and 77% for the older model. Biomarkers included were as follows:
  • FIG. 10 A seven biomarker model generated from Study 4 data is illustrated as a ROC curve (black) and cross validated ROC curve (grey). This model shows, a sensitivity of 84% at a specificity of 95%.
  • Biomarkers included are [M2PK serum, IL8.plasma, TGF beta1.serum, IGFBP2.plasma, Mac2BP.serum, TIMP1.plasma and Dkk3 plasma.
  • FIG. 11 Cross validated ROC curves showing the performance of a 3 biomarker model for each Dukes stage is illustrated.
  • This data demonstrates the validity of the choice of three biomarkers (DKK-3, M2PK and IGFBP2) for detecting cancer at different stages of the disease progression.
  • the data indicates that at Stage A if the three markers are used, the test still will achieve a significant sensitivity of 64% at 95% specificity which is comparable to the sensitivity achieved at late stage disease (79%). That is the biomarker panel of three will pick up early disease states allowing early detection.
  • Biomarkers included are Dkk3, M2PK and IGFBP2.
  • the recombinant protein, cell culture, and immunological techniques utilized 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, 3 rd edn, Cold Spring Harbour Laboratory Press (2001), R. Scopes, Protein Purification—Principals and Practice, 3 rd edn, Springer (1994), T. A. Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991), D. M. Glover and B. D.
  • colonal cancer also known as “colon cancer”, “bowel cancer” or “rectal cancer” refers to all forms of cancer originating from the epithelial cells lining the large intestine and/or rectum.
  • biomarker refers to any molecule, such as a gene, gene transcript (for example mRNA), peptide or protein or fragment thereof produced by a subject which is useful in differentiating subjects having colorectal cancer from normal or healthy subjects.
  • diagnosis and variants thereof such as, but not limited to, “diagnose”, “diagnosed” or “diagnosing” shall not be limited to a primary diagnosis of a clinical state, but should be taken to include diagnosis of recurrent disease.
  • the term “subject” refers to any animal that may develop colorectal cancer and includes animals such as mammals, e.g. humans, or non-human mammals such as cats and dogs, laboratory animals such as mice, rats, rabbits or guinea pigs, and livestock animals. In a preferred embodiment, the subject is a human.
  • sample may be of any suitable type and may refer, e.g., to a material in which the presence or level of biomarkers can be detected.
  • the sample is obtained from the subject so that the detection of the presence and/or level of biomarkers may be performed in vitro. Alternatively, the presence and/or level of biomarkers can be detected in vivo.
  • the sample can be used as obtained directly from the source or following at least one step of (partial) purification.
  • the sample can be prepared in any convenient medium which does not interfere with the method of the invention.
  • the sample is an aqueous solution, biological fluid, cells or tissue.
  • the sample is blood, plasma, serum, urine, platelets, megakaryocytes or faeces.
  • Pre-treatment may involve, for example, preparing plasma from blood, diluting viscous fluids, and the like.
  • Methods of treatment can involve filtration, distillation, separation, concentration, inactivation of interfering components, and the addition of reagents.
  • the selection and pre-treatment of biological samples prior to testing is well known in the art and need not be described further.
  • treating 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.
  • the present inventors have shown that determining the presence and/or level of least two biomarkers in a sample from a subject allows for the detection or diagnosis of colorectal cancer, either early detection at Dukes Stage A or at some later stage such as Dukes Stage B or C or D, with specificity and sensitivity comparable to or greater than that achieved with the 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; serum protein 90K), M2PK (pyruvate kinase muscle 2, pyruvate kinase 3), IL-13 (interleukin-13), DKK-3 (dickkopf homolog, 3), EpCAM (epithelial cell adhesion molecule), MIP1 ⁇ (macrophage inflammatory protein 1 ⁇ , CCL4, MIP1beta), TGF ⁇ 1 (transforming growth factor ⁇ 1 , TGFbeta1) and TIMP-1 (tissue inhibitor of metalloproteinase 1).
  • IL-8 interleukin-8
  • IGFBP2 insulin-like growth factor binding protein-2
  • MAC2BP MAC2-binding protein
  • serum protein 90K serum protein 90K
  • M2PK pyruvate kinase muscle 2, pyruv
  • references to any of these biomarkers includes reference to all polypeptide and polynucleotide variants such as isoforms and transcript variants as would be known by the person skilled in the art. NCBI accession numbers of representative sequences for each of the biomarkers are provided in Table 1.
  • the diagnostic methods of the present invention may involve a degree of quantification to determine levels biomarkers in patient samples. Such quantification is readily provided by the inclusion of appropriate control samples.
  • internal controls are included in the methods of the present invention.
  • a preferred internal control is one or more samples taken from one or more healthy individuals.
  • the term “healthy individual” shall be taken to mean an individual who is known not to suffer from colorectal cancer, such knowledge being derived from clinical data on the individual, including, but not limited to, a different diagnostic assay to that described herein.
  • control when internal controls are not included in each assay conducted, the control may be derived from an established data set.
  • Data pertaining to the control subjects are preferably selected from the group consisting of:
  • a data set comprising measurements of the presence or level of expression of biomarkers for a typical population of subjects known to have colorectal cancer
  • a data set comprising measurements of the presence or level of biomarkers for the subject being tested wherein said measurements have been made previously, such as, for example, when the subject was known to be healthy or, in the case of a subject having colorectal cancer, when the subject was diagnosed or at an earlier stage in disease progression;
  • a data set comprising measurements of the presence or level of biomarkers for a healthy individual or a population of healthy individuals
  • a data set comprising measurements of the presence or level of biomarkers for a normal individual or a population of normal individuals.
  • the term “typical population” with respect to subjects known to have colorectal cancer shall be taken to refer to a population or sample of subjects diagnosed with colorectal cancer that is representative of the spectrum of colorectal cancer patients. This is not to be taken as requiring a strict normal distribution of morphological or clinicopathological parameters in the population, since some variation in such a distribution is permissible.
  • 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 subjects as described herein.
  • normal individual shall be taken to mean an individual that does not express a biomarker, or expresses a biomarker at a low level in a sample.
  • data obtained from a sufficiently large sample of the population will normalize, allowing the generation of a data set for determining the average level of a particular biomarker.
  • Compounds that bind a biomarker when used diagnostically may be linked to a diagnostic reagent such as a detectable label to allow easy detection of binding events in vitro or in vivo.
  • a diagnostic reagent such as a detectable label to allow easy detection of binding events in vitro or in vivo.
  • Suitable labels include radioisotopes, dye markers or other imaging reagents for detection and/or localisation 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), computerized tomography etc.
  • the diagnostic methods of the present invention are able to diagnose or detect colorectal cancer with a sensitivity and specificity that is at least comparable to FOBT, or greater.
  • sensitivity refers to the proportion of actual positives in the diagnostic test which are correctly identified as having colorectal cancer.
  • Specificity measures the proportion of negatives which are correctly identified as not having colorectal cancer.
  • the methods of the invention are able to diagnose or detect 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%.
  • the methods of the invention are able to diagnose or detect colorectal cancer with a sensitivity of at least 80%, or at least 85% or at least 90%, or at least 95%.
  • the methods of the invention are able to diagnose or detect colorectal cancer with a specificity of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94% or at least 95%.
  • the methods of the present invention are able to detect colorectal cancer at all of the Dukes Stages with greater sensitivity than the FOBT.
  • the tumor has penetrated into, but not through, the bowel wall.
  • the tumor has penetrated through the bowel wall but there is not yet any lymph node involvement.
  • the cancer involves regional lymph nodes.
  • there is distant metastasis for example, to the liver or lung.
  • the methods of the present invention are able to diagnose or detect colorectal cancer at any Dukes Stage with a sensitivity of at least 80%.
  • TMM Malignant Tumors
  • AJCC American Joint Committee on Cancer
  • MAC Modified Astler-Coller classification
  • the Dukes Stages correspond to certain TNM Classifications.
  • Dukes Stage A corresponds to T1, T2, N0 and M0
  • Dukes Stage B corresponds to T3, T4a, T4b, N0 and M0
  • Dukes Stage C corresponds to i) T1-T2, N1/N1c, M0; ii) T1, N2a and M0; iii) T3-T4a, N1/N1c 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.
  • reference to a Dukes Stage as used herein includes reference to the corresponding TMN classification as known in the art.
  • biomarker polypeptide is detected in a patient sample, wherein the presence and/or level of the polypeptide in the sample is indicative of colorectal cancer.
  • the method may comprise contacting a biological sample derived from the subject with a compound capable of binding to a biomarker polypeptide, and detecting the formation of complex between the compound and the biomarker polypeptide.
  • biomarker polypeptide as used herein includes fragments of biomarker polypeptides, including for example, immunogenic fragments and epitopes of the biomarker polypeptide.
  • the compound that binds the biomarker is an antibody.
  • antibody as used herein includes intact molecules as well as molecules comprising or consisting of fragments thereof, such as, for example Fab, F(ab′)2, Fv and scFv, as well as engineered variants including diabodies, triabodies, mini-bodies and single-domain antibodies which are capable of binding an epitopic determinant.
  • antibodies may exist as intact immunoglobulins, or as modifications in a variety of forms.
  • an antibody to a biomarker polypeptide is detected in a patient sample, wherein the presence and/or level of the antibody in the sample is indicative of colorectal cancer.
  • Preferred detection systems contemplated herein include any known assay for detecting proteins or antibodies in a biological sample isolated from a human subject, such as, for example, SDS/PAGE, isoelectric focussing, 2-dimensional gel electrophoresis comprising SDS/PAGE and isoelectric focussing, an immunoassay, flow cytometry e.g. fluorescence-activated cell sorting (FACS), a detection based system using an antibody or non-antibody compound, such as, for example, a small molecule (e.g. a chemical compound, agonist, antagonist, allosteric modulator, competitive inhibitor, or non-competitive inhibitor, of the protein).
  • FACS fluorescence-activated cell sorting
  • the antibody or small molecule may be used in any standard solid phase or solution phase assay format amenable to the detection of proteins.
  • Optical or fluorescent detection such as, for example, using mass spectrometry, MALDI-TOF, biosensor technology, evanescent fiber optics, or fluorescence resonance energy transfer, is clearly encompassed by the present invention.
  • Assay systems suitable for use in high throughput screening of mass samples e.g. a high throughput spectroscopy resonance method (e.g. MALDI-TOF, electrospray MS or nano-electrospray 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).
  • MRM Multiple Reaction Monitoring
  • Immunoassay formats are particularly suitable, e.g., 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 utilizing fluorescence resonance energy transfer (FRET), isotope-coded affinity tags (ICAT), matrix-assisted laser desorption/ionization time of flight (MALDI-TOF), electrospray ionization (ESI), biosensor technology, evanescent fiber-optics technology or protein chip technology are also useful.
  • nucleic acid molecule or “polynucleotide” as used herein refer to an oligonucleotide, polynucleotide or any fragment thereof.
  • Comparison may be made by reference to a standard control, or to a control level that is found in healthy tissue.
  • levels of a transcribed gene can be determined by Northern blotting, and/or RT-PCR.
  • quantitative (real-time) PCR quantitative analysis of gene expression can be achieved by using appropriate primers for the gene of interest.
  • the nucleic acid may be labelled and hybridised on 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.
  • PCR methods that may be used in carrying out the invention include hybridization based PCR detection systems, TaqMan assay (U.S. Pat. No. 5,962,233) and the molecular beacon assay (U.S. Pat. No. 5,925,517).
  • RNA may be isolated from a sample to be analysed using conventional procedures, such as are supplied by QIAGEN technology. This RNA is then reverse-transcribed into DNA using reverse transcriptase and the DNA molecule of interest may then be amplified by PCR techniques using specific primers.
  • Hybridisation or amplification assays such as, for example, Southern or Northern blot analysis, immunohistochemistry, single-stranded conformational polymorphism analysis (SSCP) and PCR analyses are among techniques that are useful in this respect.
  • target or probe nucleic acid may be immobilised to a solid support such as a microtitre plate, membrane, polystyrene bead, glass slide or other solid phase.
  • kits for the diagnosis or detection of colorectal cancer may be suitable for detection of nucleic acid species, or alternatively may be for detection of a polypeptide gene product, as discussed above.
  • kits For detection of polypeptides, antibodies will most typically be used as components of kits. However, any agent capable of binding specifically 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, co-factors and control gene product preparations to allow the user to quantitate expression levels and/or to assess whether the diagnosis experiment has worked correctly. Enzyme-linked immunosorbent assay-based (ELISA) tests and competitive ELISA tests are particularly suitable assays that can be carried out easily by the skilled person using kit components.
  • the kit further comprises means for the detection of the binding of an antibody to a biomarker polypeptide.
  • 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 colloidal gold or selenium.
  • an enzyme such as horseradish peroxidase or alkaline phosphatase
  • a dye such as horseradish peroxidase or alkaline phosphatase
  • a radionucleotide such as a radionucleotide
  • a luminescent group such as a fluorescent group
  • biotin or a colloidal particle such as colloidal gold or selenium.
  • a colloidal particle such as colloidal gold or selenium.
  • a kit may additionally comprise a reference sample.
  • a reference sample comprises a polypeptide that is detected by an antibody.
  • the polypeptide is of known concentration.
  • Such a polypeptide is of particular use as a standard. Accordingly, various known concentrations of such a polypeptide may be detected using a diagnostic assay described herein.
  • kits may contain a first container such as a vial or plastic tube or a microtiter plate that contains an oligonucleotide probe.
  • the kits may optionally contain a second container that holds primers.
  • the probe may be hybridisable to DNA whose altered expression is associated with colorectal cancer and the primers are useful for amplifying this DNA.
  • Kits that contain an oligonucleotide probe immobilised on a solid support could also be developed, for example, using arrays (see supplement of issue 21(1) Nature Genetics, 1999).
  • nucleic acid primers may be included in the kit that are complementary to at least a portion of a biomarker gene as described herein.
  • the set of primers typically includes at least two oligonucleotides, preferably four oligonucleotides, that are capable of specific amplification of DNA.
  • Fluorescent-labelled oligonucleotides that will allow quantitative PCR determination may be included (e.g. TaqMan chemistry, Molecular Beacons). Suitable enzymes for amplification of the DNA, will also be included.
  • Control nucleic acid may be included for purposes of comparison or validation. Such controls could either be RNA/DNA isolated from healthy tissue, or from healthy individuals, or housekeeping genes such as ⁇ -actin or GAPDH whose mRNA levels are not affected by colorectal cancer.
  • test performance In order to develop a panel of biomarkers suitable for diagnosing or detecting colorectal cancer, the present inventors have analysed numerous biomarkers in a statistical model. Such an improvement in the performance of a test is sometimes referred to as the “in-sample” performance.
  • a fair evaluation of a test requires its assessment using out-of-sample subjects, that is, subjects not included in the construction of the initial predictive model. This is achieved by assessing the test performance using cross validation.
  • Tests for statistical significance include linear and non linear regression, including ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and odds ratio, Baysian probability algorithms. As the number of biomarkers measured increases however, it can be generally more convenient to use a more sophisticated technique such as Random Forests, simple logistic, Bayes Net to name a few.
  • Bayesian probability may be adopted.
  • a 10-fold cross-validation can be used to estimate the “out-of-sample” performance of the models in question.
  • the data can be divided randomly into 10 sub-samples, each with similar proportions of healthy subject and subjects at each stage of disease.
  • each subsample can be excluded, and a logistic model built using the remaining 90% of the subjects.
  • This model can then be used to estimate the probability of cancer for the excluded sub-sample, providing an estimate of “out-of-sample” performance.
  • “out-of-sample” performance can be estimated from the study data itself.
  • These out-of sample predicted probabilities can then be compared with the actual disease status of the subjects to create a Receiver Operating Characteristic (ROC) Curve, from which the cross-validated sensitivity at 95% specificity may be estimated.
  • ROC Receiver Operating Characteristic
  • a model discriminating subjects with cancer from healthy controls can be as follows:
  • log ⁇ ( p 1 - p ) ⁇ 0 + ⁇ IL ⁇ ⁇ 8 ⁇ C IL ⁇ ⁇ 8 + ⁇ IGFBP ⁇ ⁇ 2 ⁇ C IGFBP ⁇ ⁇ 2 + ⁇ MAC ⁇ ⁇ 2 ⁇ ⁇ BP ⁇ C MAC ⁇ ⁇ 2 ⁇ ⁇ BP + ⁇ M ⁇ ⁇ 2 ⁇ ⁇ PK ⁇ C M ⁇ ⁇ 2 ⁇ PK + ⁇ DKK ⁇ ⁇ 3 ⁇ C DKK ⁇ ⁇ 3
  • Each C i is the logarithm of concentration biomarker i in the plasma (or serum) of a person.
  • Each beta ( ⁇ ) is a coefficient applying to that biomarker in the concentration units in which it is measured— ⁇ 0 is an “offset” or “intercept”. This linear logistic model is common to all results presented herein, but is far from the only way in which a combination of biomarker concentrations may be modelled to predict the probability of cancer.
  • the present invention also provides software or hardware programmed to implement an algorithm that processes data obtained by performing the method of the invention via a multivariate analysis to provide a disease score and provide or permit a diagnosis or detection of colorectal cancer and/or determine progression or status of a colorectal cancer or determine whether or not a colorectal cancer has progressed or determine whether or not a subject is responding to treatment for colorectal cancer in accordance with the results of the disease score in comparison with predetermined values.
  • a method of the invention may be used in existing knowledge-based architecture or platforms associated with pathology services. For example, results from a method described herein are transmitted via a communications network (e.g. the internet) to a processing system in which an algorithm is stored and used to generate a predicted posterior probability value which translates to the score of disease probability or risk of recurrence or metastasis or responsiveness to treatment which is then forwarded to an end user in the form of a diagnostic or predictive report.
  • a communications network e.g. the internet
  • the method of the invention may, therefore, be in the form of a kit or computer-based system which comprises the reagents necessary to detect the concentration of the biomarkers and the computer hardware and/or software to facilitate determination and transmission of reports to a clinician.
  • the assay of the present invention permits integration into existing or newly developed pathology architecture or platform systems.
  • the present invention contemplates a method of allowing a user to determine the status of a subject with respect to colorectal cancer, the method including:
  • the method for diagnosing or detecting colorectal cancer of the invention may 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 herein. If desired, the measurements may be made, for example, on a biochip so that a single analysis can be used to measure the presence and/or level of multiple biomarkers. The results of this analysis may then be inputted into into a computer program that subjects them to linear regression analysis. The computer could also contain information as to control values or expected ranges, or the clinician, nurse, medical administrator or general practitioner could input such data. This analysis wold then provide a score or likelihood of having colorectal cancer. If a second test for the patient is performed, the regression analysis may indicate a change in the score, thus indicating that the patient's disease has progressed or worsened.
  • a collection of plasma and serum samples was taken and processed from a cohort of colorectal cancer patients (Dukes Stages A-D) that 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 from a group of 15 over the age of 50.
  • Study 1 looked at 52 cancer samples and 50 controls
  • study 2 looked at 55 cancer samples and 53 controls
  • study 3 and 4 looked at 96 cancer samples and 50 controls.
  • study 2 3 and 4 the patients were age and gender matched across Dukes Stages, see Table 2 for summary statistics.
  • Biomarkers chosen to be measured in Study 1 and 2 and 3 are listed in Table 3. Biomarkers in bold were those identified as promising from each study (i.e. they were significantly different in samples from colorectal cancer patients versus control and/or they were identified in panels of combined biomarkers that distinguish colorectal cancer from controls).
  • the resulting model was then used to estimate the probability that the excluded observation is a case. This was repeated for each observation in the dataset. In this way each observation in turn acted as an independent test of the model-building algorithm.
  • the resulting dataset consisting of cases and controls each with an “independently predicted” case probability can then be compared with the original model.
  • the ability to choose from numerous biomarkers and weight them appropriately allows a search strategy which optimises performance in regions of interest on the ROC curve.
  • the cost of poor specificity is large numbers of unnecessary colonoscopies.
  • biomarkers were evaluated to select a candidate panel of colorectal cancer biomarkers, using block randomization within plates to avoid bias. From this list of 48 only 42 showed measurable levels. Individually 14 biomarkers showed 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, TGF ⁇ 1, VEGFpan. As expected, none had sufficient sensitivity or specificity to be useful as a biomarker by itself (not shown). However, using a variety of modelling strategies, including use of logarithmic values, several different panels of biomarkers were found that exceeded the performance of FOBT especially for early to late stage disease.
  • FIG. 1 shows the results from a 7 biomarker panel which included IL8 (serum), IL-13 (serum), EpCAM (plasma), M2PK (plasma), IGFBP2 (serum) and Mac2BP (serum) and which was cross validated to predict its performance on independent samples.
  • log ⁇ ( p 1 - p ) ⁇ 0 + ⁇ IL ⁇ ⁇ 8 ⁇ C IL ⁇ ⁇ 8 + ⁇ IGFBP ⁇ ⁇ 2 ⁇ C IGFBP ⁇ ⁇ 2 + ⁇ MAC ⁇ ⁇ 2 ⁇ ⁇ BP ⁇ C MAC ⁇ ⁇ 2 ⁇ ⁇ BP + ⁇ M ⁇ ⁇ 2 ⁇ ⁇ PK ⁇ C M ⁇ ⁇ 2 ⁇ PK + ⁇ DKK ⁇ ⁇ 3 ⁇ C DKK ⁇ ⁇ 3
  • biomarkers were remeasured in the same cohort as Study 3. Blood was collected from 96 colorectal cancer patients and 50 normal subjects (the controls). In this study the focus was on 10 biomarkers, namely IGFBP2, IL8, IL13, Mac2BP, M2PK, Dkk3, EpCam, TGFbeta1, TIMP-1, MIP1beta. Assays were performed as described previously. Both serum and plasma levels of each of the biomarkers were measured and compared with control values.
  • the 968 combinations of between 3 and 10 biomarkers consist of the 120 combinations of 3 marker; 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 single combination that includes all 10 biomarkers.
  • the 968 combinations had a sensitivity of 50% at a specificity of 95%, see FIG. 4 which shows the results for a three biomarker combination. More than half of these combinations would have a specificity of 90% and a sensitivity of 50%.
  • FIG. 5 shows all 485 of the estimated out-of sample (10-fold cross-validated) ROC curves for tests out of a total possible 968 models based on all possible combinations of 3 to 10 of the biomarkers. Note that many individual segments of the 485 ROC curves are coincident, due to as each horizontal segment represents one control and each vertical segment one case. In this instance 50.1% of the combinations have exceeded the 50% sensitivity, 95% specificity, The best estimated “out-of-sample” performance is a sensitivity of 76% at 95% specificity. Repeating the cross-validation will select a different set of models—the sensitivity of any one combination may vary by 10% at 95% specificity due to random sampling—but result in a similar proportion of useful “useful screening tests”. Precise validation of individual models requires repeated experiments and larger sample sizes.
  • FIG. 6 shows how many of the 485 combinations with 50% sensitivity, 95% specificity, include any given biomarker.
  • 432 of the chosen “useful” combinations include M2PK.
  • At the low end 227 of the chosen “useful” combinations include MIP1beta. This high representation of all 10 biomarkers in “useful” models shows the unity and self-complementarity of the selection of these 10 biomarkers.
  • FIG. 7 to FIG. 11 demonstrate some of the results from this last study (Study 4) for combinations of 5 and 7 biomarkers, including a model where the samples are either from plasma or serum cluster.
  • FIG. 11 demonstrates the validity of the choice of three biomarkers (DKK-3, M2PK and IGFBP2) at different stages of the disease progression. The data indicates that at Stage A if the three markers are used, the test still will achieve a significant sensitivity (64%) at 95% specificity which is comparable to the sensitivity achieved at late stage disease, 79%). That is the biomarker panel of three will pick up early disease states allowing early detection.
  • Tables 8 to 16 list results from 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 on the actual figures or order of the markers. Regardless, many of these combinations will achieve good selectivity at high specificities so as to be useful for diagnosing or detecting colorectal cancer at any stage of the disease progression.

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US10877039B2 (en) 2010-07-14 2020-12-29 Vision Tech Bio Pty. Ltd. Diagnostic for colorectal cancer
US20150285804A1 (en) * 2012-10-12 2015-10-08 Origin Sciences Limited Diagnostic method for colorectal cancer
US11208461B2 (en) * 2014-07-18 2021-12-28 Sanofi Method for predicting the outcome of a treatment with aflibercept of a patient suspected to suffer from a cancer
WO2023015354A1 (en) * 2021-08-11 2023-02-16 Vision Tech Bio Pty Ltd Method of detecting adenoma
CN114594259A (zh) * 2022-04-22 2022-06-07 北京易科拜德科技有限公司 一种新型的用于结直肠癌预后预测和诊断的模型及其应用

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