WO2013152989A2 - Biomarker assay and uses thereof for diagnosis, therapy selection, and prognosis of cancer - Google Patents

Biomarker assay and uses thereof for diagnosis, therapy selection, and prognosis of cancer Download PDF

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WO2013152989A2
WO2013152989A2 PCT/EP2013/057111 EP2013057111W WO2013152989A2 WO 2013152989 A2 WO2013152989 A2 WO 2013152989A2 EP 2013057111 W EP2013057111 W EP 2013057111W WO 2013152989 A2 WO2013152989 A2 WO 2013152989A2
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protein
group
fragments
biomarkers
cdh5
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WO2013152989A3 (en
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Silvia SURINOVA
Rudolf Aebersold
Marian Hajduch
Josef Srovnal
Jiri DRABEK
Lenka Radova
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Eth Zurich
Univerzita Palackeho V Olomouci
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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
    • 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
    • G01N33/57423—Specifically defined cancers of lung
    • 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
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney
    • 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
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6842—Proteomic analysis of subsets of protein mixtures with reduced complexity, e.g. membrane proteins, phosphoproteins, organelle proteins
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/81—Protease inhibitors
    • G01N2333/8107—Endopeptidase (E.C. 3.4.21-99) inhibitors
    • G01N2333/811—Serine protease (E.C. 3.4.21) inhibitors
    • G01N2333/8121—Serpins
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/902—Oxidoreductases (1.)
    • G01N2333/90287—Oxidoreductases (1.) oxidising metal ions (1.16)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

  • Biomarker assay and uses thereof for diagnosis, therapy selection, and prognosis of cancer
  • the present invention relates to the field of cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assays for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer.
  • Stable indicators of prognosis such as protein biomarkers measured non-invasively in blood would be highly valuable to enable personalized therapy selection and improve the overall disease management.
  • biomarker development pipeline for the discovery and validation of novel colorectal cancer biomarkers and used it to discover panels of biomarker candidates.
  • Plasma from the systemic circulation is routinely sampled in a non-invasive way and represents an ideal sample type for biomarker screening.
  • the first clinical hypothesis was designed to evaluate the effect of tumor removal on potential biomarkers.
  • the second clinical hypothesis was designed to identify biomarker candidates with a concentration gradient between the tumor drainage vein and the systemic circulation.
  • CRC patients we have again included paired samples, where blood was sampled before and after surgery, for a few patients to examine the effect of tumor excision on potential biomarker candidates.
  • LTQ-FT High-resolution mass spectrometry
  • LC nanoflow liquid chromatography
  • Targeted measurement of biomarker candidates by selected reaction monitoring (SRM) Targeted analysis by SRM represents the most selective and sensitive MS analysis to date, and was employed to screen our candidate proteins in plasma and validate the detectable proteins in the large clinical sample cohort.
  • SRM assays were developed for candidate proteins and used to screen them in patient plasma samples to assess their detectability.
  • a single multiplex SRM method was developed to simultaneously profile all verified candidate proteins over hundreds of clinical samples. Sophisticated statistical methods have been developed for this analysis and generated accurately quantified and validated biomarker candidates of CRC.
  • CRC detection this has been shown by combining the FDA-approved biomarker carcinoembryonic antigen (CEA) with additional proteins such as CA 19-9 or CA 72-4 and lead to an increased sensitivity and specificity as compared to CEA alone, although still insufficient for a reliable diagnosis.
  • CEA biomarker carcinoembryonic antigen
  • Univariate methods for statistical testing e.g. Kruskal-Wallis test
  • survival analysis Kaplan-Meier curves with a log-rank test
  • regression analysis e.g. Spearman correlation
  • candidate biomarkers that are significantly changing and/or associated with prognostic and/or predictive molecular factors, disease stage and/or grade, presence or absence of metastasis, and localization of cancer.
  • the present invention relates to a cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer comprising the combined measurement of at least two, preferably at least all three protein/peptide biomarkers and/or fragments of protein biomarkers selected from a first group consisting of:; CP; SERPINA3; PON1; optionally in combination with at least one or both protein/peptide biomarkers and/or fragments of protein biomarkers selected from a second group consisting of: IGFBP3; ATRN; LRG1; TIMP1, in human serum, plasma or a derivative of blood, or blood itself.
  • At most one of the first group and/or at most one or two of the second group can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following third group: CD44; FGG; MMRN1 ; CTSD; IGHG2; ECM1; IGHA2; FHR3; ITIH4; HP; ORM1; FN1; PRG4; LGALS3BP;
  • all three protein/peptide biomarkers and/or fragments of protein biomarkers of the first group are measured in combination with LRG1 and/or TIMP1 from the second group, or are measured with IGFBP3 and/or ATRN from the second group, or are measured with all protein/peptide biomarkers and/or fragments of protein biomarkers from the second group.
  • the invention relates to a cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer comprising the combined measurement of at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the large group consisting of: ATRN; A1AG2; APMAP; APOB; CD44; CLU; C04A; CP; CFH; DKFZp686C02220; ECM1; F5; FETUB; FGA; FHR3; HP; HRG; HYOU1; IGHA2; IGHG1; IGHM; IGHG2; LUM; LAMP2; PLPT; PRG4; PTPRJ; LRG1; MMRN1; MST1; ORM1; SERPINA
  • the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or all five protein/peptide biomarkers and/or fragments of protein biomarkers selected from the first group consisting of: CP; SERPINA3; PON1; optionally in combination with at least one, two, three or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the second group consisting of: IGFBP3; ATRN; LRG1; TIMP1, in human serum, plasma or a derivative of blood, or blood itself.
  • the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: GOLM1; HLA-A; HYOU1; MRC2; NCAM1; SERPINA3, in human serum, plasma or a derivative of blood, or blood itself.
  • protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: A1AG2; AFM; AHSG; ANT3; AOC3; ATRN; APOB; BTD; C20orf3; CADM1; CD109; CD163; CDH5; CD44; CFH; CFI.
  • the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: PTPRJ; PIGR; HPX; FETUB; IGHG2; VTN; APOB; ATRN, in human serum, plasma or a derivative of blood, or blood itself.
  • PTPRJ PIGR
  • HPX HPX
  • FETUB IGHG2
  • VTN APOB
  • ATRN ATRN
  • at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: SERPINA1 ; ITIH4; F5; TNC.
  • the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: IGHG2; IGHA2; F5; LYVE1; ITIH4; FHR3, in human serum, plasma or a derivative of blood, or blood itself.
  • protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: LRG1; CP; SERPINA6; KDR; HP; MRC2; GOLM1; SERPINA7; PROC; VTN; CADM1; DKFZp686N02209; CD 109; TNC; HPX.
  • the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: F5; VWF; FETUB; IGHA2; IGFBP3;
  • ORMl in human serum, plasma or a derivative of blood, or blood itself.
  • PROC PROC; SERPI A1 ; Q6N091; PLXNB2.
  • the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: HLA-A; VWF; TNC; MRC2; FHR3; FCGBP; PTPRJ; CD 109, in human serum, plasma or a derivative of blood, or blood itself.
  • protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: HP; CD44; CDH5; PGCP; THBS1; HP; PLXNB2; LUM; PROC; DSG2; DKFZp686N02209; PLTP; F5; CD44; KDR; LCN2; HPX; ATRN; MPO.
  • the invention and fully or partially independently of the above biomarkers relates to a biomarker comprising the combined measurement of at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: LGALS3BP; PROC; CD163; AOC3 in human serum, plasma or a derivative of blood, or blood itself, with the proviso that at least one of the group consisting of PROC; CD163 is measured. It correspondingly also relates to methods for the cancer diagnosis/therapy/prognosis/patient stratification using such biomarker assays.
  • At least PROC and CD 163 are measured in combination with at least two or more further protein/peptide biomarkers and/or fragments of protein biomarkers.
  • protein/peptide biomarkers and/or fragments of protein biomarkers of the group consisting of: LGALS3BP; PROC; CD163; AOC3 are measured.
  • the present invention also relates to a biomarker assay comprising/involving the combined measurement of at least one (e.g. additional to the above-mentioned list), preferably at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: ATRN; A1AG2; APMAP; APOB;
  • CD44 CD44; CLU; C04A; CP; CFH; DKFZp686C02220: ECM1; F5; FETUB; FGA; FHR3;
  • TIMP1 TIMP1
  • VWF for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer.
  • proximal plasma from the tumor drainage vein VWF is measured as present at higher concentration and at least one of CD44, DKFZp686C02220 at lower concentration in the proximal plasma as compared to the systemic circulation.
  • measured as upregulated as an effect of tumor excision are at least one of ATRN, CLU, DKFZp686C02220, ECM1, F5, FETUB, HRG, IGHA2, IGHGl , IGHG2, LUM, and PLPT.
  • the present invention also relates to a biomarker assay or a corresponding method in which for diagnostic applications a signature comprising at least THBS1, PRG4, FHR3, SERPINAl, DKFZp686C02220, CD44, C04A, CFH, is measured preferably either the signature consisting of THBS1+ HP+ CP + APMAP+ PRG4+ APOB+ IGHG1+ FHR3+ IGHG2+ SERPINAl + LGALS3BP+ DKFZp686C02220 + CD44 + VWF + TIMP1+ C04A+ CFH, or the signature consisting of CD44+CFH+ECM 1 +F5+FHR3+IGHM+PRG4+C04 A+ DKFZp686C02220 + SERPINAl +THBS1, leading to high sensitivity, specificity as well as accuracy in the validation set.
  • a signature comprising at least THBS1, PRG4, FHR3, SERPINAl, DKF
  • CD109, ORMl, A1AG2, and/or VTN should be measured.
  • These proteins can thus e.g. be used to determine the location where the doctor should start the intervention to detect the cancer.
  • a disease-free (DFS) survival status is determined based on at least one of AFM, KLKB1, KNG1, LGALS3BP, and PTPRJ and overall survival (OS) with at least one of C04A, MRC2, and BTD, wherein significantly associated with both DFS and OS are HYOUl, IGHM, ORMl, A1AG2, VTN, SERPINA7, AHSG, IGJ, CFH, F5, HP, ITIH4, LUM, PIGR, PROC, SERPINAl , and SERPINA6.
  • Preferably 4 protein combinations are significantly associated with DFS: IGHG2+ATRN non-adjusted, CDH5+ATRN non-adjusted, MST1+CD109 age-, gender-, and stage- adjusted, and MST1+MRC2 age-, gender-, and stage-adjusted; and 5 protein combinations significantly are associated with OS: HRG+AHSG non-adjusted, C04A+CADM1 non- adjusted, LCN2+APMAP age- and gender-adjusted, and CDH5+FCGBP age-, gender-, and stage-adjusted, CDH5+IGFBP3 age-, gender-, and stage-adjusted; and 1 protein combination is significantly associated with both DFS and OS: CD163&CD109 age-, gender, and stage-adjusted; and 12 age-, gender-, and stage-adjusted protein combinations are significantly associated with 5-year OS : CDH5 + AHSG + MST1; CDH5
  • At least one of ATRN, APOB, PRG4, and SERPINA3, preferably a combination thereof, is associated with the status of the KRAS gene where higher protein expression is observed in patients with the mutated gene, and at least one of, preferably a combination of HYOU1, IGHM, FGA, THBS1, and VWF is associated with the status of the KRAS gene where lower protein expression is observed in patients with the mutated gene.
  • a prognostic and predictive indicator of CRC associated with microsatellite status at least one of MST1, SERPINA7, LAMP2, and IGHG1 can also be measured.
  • For grading, staging, and cancer assessment at least one of IGHG2 and ORM1 can be measured.
  • metastasis, and/or EGFR dephosphorylation PTPRJ can be measured; and for metastasis status ATRN can be measured.
  • Last but not least the present invention also relates to a biomarker assay characterized in that it is an affinity reagent-based assay, preferably antibody-based assay such as an Enzyme-Linked Immunosorbent Assay (ELISA) for the detection of the proposed biomarker candidates.
  • a biomarker assay characterized in that it is an affinity reagent-based assay, preferably antibody-based assay such as an Enzyme-Linked Immunosorbent Assay (ELISA) for the detection of the proposed biomarker candidates.
  • ELISA Enzyme-Linked Immunosorbent Assay
  • the invention relates to a method for the diagnosis and/or for the therapy and/or for the prognosis and/or for the monitoring of colorectal cancer and/or lung cancer and/or pancreatic cancer, using a biomarker assay according to any of the preceding claims, wherein preferably the measurement is carried out using tandem mass spectrometry techniques, preferably selected reaction monitoring (SRM), more preferably in combination with liquid chromatography, and/or Enzyme-Linked Immunosorbent Assays (ELISA) for the detection of these proteins/fragments thereof.
  • SRM reaction monitoring
  • ELISA Enzyme-Linked Immunosorbent Assays
  • Fig. 1 shows the biomarker development pipeline; samples, procedures, analysis, and outcomes are outlined for the different phases of the pipeline; A, outlines the rationale for sample type selection during the three phases of development; B, outlines the procedures used throughout the pipeline;
  • a) - e) show the development and evaluation of a diagnostic biomarker signature, wherein in A, random forests (RFs) were employed to select and rank the best predictor proteins and the 20 most important proteins were selected for logistic regression; observation histograms indicate the frequency occurrence of proteins in the 100 best models; in B, the 10 best prediction models with the best bootstrap validation are listed, where each box has 100 values due to the 100-fold cross validation; the first box represents a random model; in C, the best predictive model with the highest median AUC (in solid black) and the proteins it is comprised of are indicated; in D, evaluation of the best model on samples in the validation set (1/3 of all samples); reproducibility and consistency is demonstrated by the similar AUC of the model in both training and validation analysis; in E, contingency table indicating the actual classification of cases at a given specificity and sensitivity threshold; cases of other malignancies were included in the final classification and their classification results are indicative of specificity to a given cancer type;
  • Fig. 6 shows biomarker candidates associated with the status of the KRAS gene
  • A Patients with a differential abundance of a biomarker candidate based on wildtype or mutated KRAS (Kruskal-Wallis test); Mutated and wild-type genes are depicted by "mut” and "wt", respectively;
  • B Biomarker candidates associated with the KRAS gene status and 5-year patient survival;
  • Fig. 9 shows an association of biomarker candidates with tumor characteristics, wherein in A, Tumor grade and in B, Tumor stage. Dukes (left) and TNM (right) classification systems are reported, and in C, Presence of metastasis;
  • Fig. 10 shows the biomarker signature development on the training dataset of the second analysis; protein significance testing (p-value ⁇ 0.05, abundance fold change (FC) ⁇ 1.1) between CRC and controls and stepwise selection of discriminative proteins into logistic regression models was employed within 10-fold cross-validation (CV) to generate the biomarker signature; CRC disease probability determined based on the regression model cut-off was plotted against the relative protein abundance with linear regression line (LOESS method); confidence bounds were added on each line; protein abundance fold change (FC) between CRC and controls is shown in brackets behind the protein labels; protein predictors were enumerated with brute force search and the best preforming predictors based on the area under the curve (AUC) were ranked; predictor distribution of the best 2097 models that were identical in their
  • Fig. 11 shows the biomarker signature evaluation on the validation dataset of the second analysis; a, Performance of the biomarker signature on the full validation dataset; b, Evaluation of the predictive power of the signature for distinct clinical stages; the significance of differences between the corresponding AUCs is determined by statistical testing (significance level p ⁇ 0.05); c, Evaluation of the predictive power of the signature for patients grouped by their tumor size; The significance of differences between the corresponding AUCs is determined by statistical testing (significance level p ⁇ 0.05).
  • Proteins relevant for CRC detection were characterized by comparing the average protein abundance in CRC and control groups, where 23 proteins were found to be significantly differentially abundant (A1AG2, APMAP, APOB, CFH, C04A, CP, ECM1, F5, FHR3, HP, IGHA2, IGHGl, IGHG2, LGALS3BP, LRG1, MMRN1, ORM1, SERPINAI, SERPINA3, SERPINA7, THBS1, TIMP1, and VWF). Of these, the majority was present at higher levels and only 3 proteins (IGHA2, IGHGl, IGHG2) were present at lower levels in the CRC population as compared to the healthy controls.
  • Proteins were reported if their P value was ⁇ 0.05 rounded for either OS or DFS.
  • age-, gender-, and stage-adjusted Cox multivariate regression analysis characterized multiple regressions with two proteins, i.e. MST1+CD109 significantly associated with DFS, CDH5+FCGBP and CDH5+IGFBP3 significantly associated with OS, and CD163+CD109 significantly associated with both DFS and OS (table B).
  • MST1+CD109 significantly associated with DFS
  • CDH5+FCGBP and CDH5+IGFBP3 significantly associated with OS
  • CD163+CD109 significantly associated with both DFS and OS
  • Three protein combination regression analysis further identified twelve protein combinations associated with 5-year OS and five protein combinations associated with 5- year DFS, of which three combinations overlapped between OS and DFS (table B).
  • CDH5+IGFBP3 0.021, 0.008 -
  • CDH5+AHSG+MST1 0.032, 0.027,0.026
  • CDH5+AHSG+ORM1 0.032, 0.022, 0.008
  • CDH5+CFH+ORM 1 0.012, 0.019, 0.004 -
  • CDH5+FCGBP+IGFBP3 0.008, 0.011, 0.021 0.072, 0.018, 0.022
  • CDH5+FCGBP+ORM1 0.018, 0.018, 0.027 -
  • CDH5+FCGBP+SERPINA 1 0.008, 0.021, 0.026 -
  • CDH5+ IGHM+ORM1 0.016, 0.014, 0.012 0.047, 0.018, 0.003
  • CDH5+ LGALS3BP+ORM1 0.020, 0.013, 0.011 -
  • Proteins were reported if the P value of either protein in the combination (comma separated) was ⁇ 0.05 for either OS or DFS and if the P value of the likelihood ratio test was also ⁇ 0.05. In all cases, no significant association was observed between the proteins and age, gender or stage.
  • the above described proteins and their combinations provide prognostic value due to their association with patient outcome for newly diagnosed patients based on their protein expression profiles measured non-invasively from blood.
  • KRAS is a downstream mediator of EGFR signaling and activating mutations in KRAS negatively predict the response to EGFR antibody therapy and are associated with a worse prognosis.
  • 9 proteins could individually significantly discriminate between patients with wildtype and mutated KRAS gene, where ATRN, APOB, PRG4, and SERPINA3 had higher protein expression, and HYOUl, IGHM, FGA, THBS1, and VWF had lower protein expression in patients with the mutated form than in patients with the wild-type gene ( Figure 6A).
  • HYOUl and IGHM are also significantly associated with patient outcome in 5 -year survival analysis, where patients with lower protein abundance showed a worse outcome.
  • patients with lower abundance of these two biomarker candidates were associated with the KRAS mutation and thereby also represent the patients with a worse prognosis (figure 6B).
  • proteins represent biomarker candidates with both predictive and prognostic value, and could be used for a non-invasive selection of appropriate therapy and also to determine which patients have a better prognosis.
  • Another prognostic and predictive indicator of CRC is the stability of microsatellites.
  • MST1, SERPINA7, LAMP2, and IGHG1 proteins that were individually significantly associated with microsatellite status of patients, where all proteins exhibited a higher expression in the stable form (MSS) as compared to the instable form, where only one of the microsatellite sequences is mutated (MSI-low) (figure 8).
  • MSS stable form
  • MSI-low the instable form
  • FIG. 8 Another prognostic and predictive indicator of CRC is the stability of microsatellites.
  • Grading represents a measure of cellular differentiation of tumor cells as compared to the normal cells in the tissue of origin.
  • IGHG2 and ORMl significantly associated with the grade of CRC patients.
  • Patient clinical stage represents the extent of the disease.
  • the two major staging systems of CRC are TNM and Dukes classification.
  • PTPRJ protein, tyrosine-protein phosphatase receptor-type J
  • Figure 9B tyrosine-protein phosphatase receptor-type J
  • Figure 9C tyrosine-protein phosphatase receptor-type J
  • PTPRJ is a candidate tumor suppressor in the colonic epithelium, was found to inhibit proliferation and migration of CRC cells, is upregulated by protective nutrients in the tumor, and is a negative regulator of EGFR signaling pathway, through EGFR dephosphorylation.
  • Table 1 Clinical cohorts employed in the validation phase.
  • TOTAL 555 Note by: Samples were collected at surgery prior to administration of any therapy, except for a subset of CRC cases, which was sampled at two time points, i.e. at surgery and one month after surgery, and is indicated as "paired samples”.
  • CD34 P28906 Hematopoietic progenitor cell antigen CD34
  • CD36 P16671 Platelet glycoprotein 4 CD38 P28907 ADP-ribosyl cyclase 1
  • CEACAM1 P13688 Carcinoembryonic antigen-related cell adhesion molecule 1
  • CEACAM5 P06731 Carcinoembryonic antigen-related cell adhesion molecule 5
  • CEACAM7 Q14002 Carcinoembryonic antigen-related cell adhesion molecule 7
  • CEACAM8 P31997 Carcinoembryonic antigen-related cell adhesion molecule 8
  • CLPTM1 096005 Cleft lip and palate transmembrane protein 1
  • CTSC P53634 Dipeptidyl peptidase 1
  • GGT1 PI 9440 Gamma-glutamyltranspeptidase 1
  • HAPLN1 P10915 Hyaluronan and proteoglycan link protein 1
  • HLA-A P01892 HLA class I histocompatibility antigen, A-2 alpha chain
  • ICAM1 P05362 Intercellular adhesion molecule 1
  • NCAM1 P13591 Neural cell adhesion molecule 1
  • PRNP P04156 Major prion protein
  • CD44 PI 6070 CD44 antigen (Extracellular validated yes matrix receptor III)
  • HLA-A P01892 HLA class I validated yes histocompatibility antigen, A- 2 alpha chain
  • ICAM1 P05362 Intercellular adhesion validated yes molecule 1 (CD54)
  • ICAM2 P13598 Intercellular adhesion validated yes molecule 2 (CD 102)
  • IGFBP3 P17936 Insulin-like growth factor- validated yes binding protein 3
  • NCAM1 P13591 Neural cell adhesion validated yes molecule 1 (CD56)
  • Phase 1 Biomarker candidate discovery in tumor epithelia: To maximize the identification of colorectal cancer (CRC) biomarkers in the circulation, a phased biomarker development pipeline was used. Human primary tumors together with adjacent normal mucosa were selected from 16 patients (the set of patients included early progression and advanced stages, localised and metastatic as well as stage I-IV) as the best suitable source of biomarkers, and tissue epithelia were manually dissected to enrich for cells of cancer origin and to obtain samples with homogenous protein composition.
  • CRC colorectal cancer
  • glycoproteins which generally are cell surface and extracellular proteins prone to secretion or shedding, and represent the vast majority of currently approved biomarkers.
  • Epithelial lysates derived from 32 paired tumor and normal samples were subjected to proteolysis, followed by solid-phase extraction of N-linked glycopeptides.
  • Purified N- glycosite peptides were analyzed by high-resolution liquid chromatography tandem mass spectrometry (LC-MS/MS), which lead to the identification of 2301 glycopeptides and 673 inferred glycoproteins.
  • Prediction analysis of secondary protein structures annotated 73% of proteins to be secreted and 53% of proteins to contain at least one transmembrane domain, which is indicative of a strong enrichment for proteins of the circulatory system.
  • Phase 2 Screening of biomarker candidates in patient plasma: The hypothesis that secreted and cell surface protein candidates of CRC are destined to reach the circulation was tested in the screening phase, where differentially abundant glycoproteins in CRC were supplemented with additional proteins identified in the tumor glycoproteome and a few biomarker candidates identified in other ongoing biomarker studies to test the detection of these proteins in plasma. In combination this protein biomarker candidate list represents proteins regulated by and playing major roles in CRC tumorigenesis.
  • Targeted mass spectrometry based on selected reaction monitoring (SRM) was employed to screen for tissue-derived candidates in plasma-enriched N-glycosite samples from 19 patients.
  • SRM selected reaction monitoring
  • the dynamic range of the plasma proteome spreads over more than 10 orders of magnitude and poses a limitation to its comprehensive analysis.
  • the results demonstrate that the detected and quantified candidates cover 6 orders of magnitude, which currently represents the largest abundance range quantifiable in a single LC-MS analysis of plasma.
  • GIT Non-malignant gastrointestinal tract
  • the validation cohort was conceived to test the discovered biomarker signature on independent samples and to evaluate the classification of CRC patients with respect to clinical stage.
  • Plasma samples were subjected to parallel N-glycoprotein extraction in a 96 well format followed by targeted SRM analysis. Candidates, together with two protein standards, were combined into a 90-plex SRM method and used to profile the biomarker candidates over the plasma- enriched N-glycosite samples. Of the 88 biomarker candidates, 70 proteins were consistently quantified across the two sample sets and comprise by far the largest clinical dataset measured by LC-MS to date.
  • the discovered consensus protein combination was comprised of ceruloplasmin (CP), serum paraoxonase/arylesterase 1 (PON1), serpin peptidase inhibitor, clade A (SERPINA3), leucine-rich alpha-2-glycoprotein (LRG1), and tissue inhibitor of metalloproteinases 1 (TIMP1).
  • CP ceruloplasmin
  • PON1 serum paraoxonase/arylesterase 1
  • SERPINA3 serpin peptidase inhibitor
  • LRG1 leucine-rich alpha-2-glycoprotein
  • TRIP1 tissue inhibitor of metalloproteinases 1
  • the approach is based on prioritizing proteins with significant differences in protein abundance between the CRC and control groups, and a subsequent stepwise selection of the most discriminative proteins into the biomarker signature.
  • a second Method all protein combinations of up to five proteins in the training dataset were enumerated by exhaustive search and 100-fold bootstrapped cross-validationS and evaluated the obtained logistic regression models by their area under the receiver operating characteristic (ROC) curve (AUG) ( Figure 10).
  • the best models were found to have a similar cross-validation performance and therefore the proteins present in these models were ranked by their frequency of occurrence among these models.
  • the top ranked proteins include the protems selected into our diagnostic signature by significance testing and stepwise selection. Further, a few other proteins were also ranked high on the list and could in theory be used as 'back-up' proteins in case a future assay for a protein within the diagnostic signature will not fulfill required analytical criteria.
  • Biomarker signature development within 10-fold CV. a Differentially abundant proteins characterised as significant in the individual folds of the training dataset. b, Proteins selected into logistic regression models in individual folds. The consensus model contains proteins with a high frequency of occurrence in the individual folds.
  • the diagnostic signature model was then parameterized on the full training dataset and predicted the class of the discovery cohort cases with an agreement of 70% (Figure 10).
  • the class prediction ability of the diagnostic signature was then assessed on the independent testing dataset acquired on the validation cohort.
  • the correct class of CRC and control cases was assigned for 72% of the cases ( Figure 11a), which shows a high agreement in performance on the two independent datasets.
  • biomarker signatures for colorectal cancer were identified:
  • Non-invasive detection of CRC is a critical clinical need because it can help to diagnose CRC at early stages. Furthermore, it can help the screening program to reduce the number of false positive cases determined by the current screening standard - feacal occult blood test (FOBT) - that need to be evaluated by invasive colonoscopy.
  • FOBT screening standard - feacal occult blood test
  • CRC tumors There are three regional distinctions of CRC tumors based on anatomical location: Right- sided tumors proximal to the splenic flexure, Left-sided tumors distal to the splenic flexure, and rectum tumors.
  • Non-invasive biomarkers of the regional diagnosis of CRC would assist the oncologist with the site where to begin colonoscopic intervention.
  • Non-invasive indication of advanced disease with the presence of metastases may help the oncologist to provide the patient with best possible treatment.
  • KRAS is determined from the DNA extracted from the tumor. However, for about 20% of patients the quality of DNA in the tumor is not good enough to perform this test and thus could receive this therapy without any benefit but with potential side effects. Non-invasive KRAS status determination would therefore be highly desired.
  • Tissue epithelia were homogenized in a Microdismembrator S (Sartorius), subjected to protein extraction in lysis buffer (as above) and solubilized with 1% Rapigest (Waters) in 250mM ammonium bicarbonate. Ultra sonication in a vial-tweeter ultrasonicator (Hielscher) at 4°C was used to further disintegrate the homogenized tissue. Proteins were denatured at 60°C for 2h, reduced with 5mM dithiotreitol (DTT) at 60°C for 30 min, and alkylated with 25mM iodoacetamide (IAA) at 25°C for 45 min in the dark.
  • DTT dithiotreitol
  • IAA iodoacetamide
  • Samples were diluted to 15% TFE in lOOmM ammonium bicarbonate and proteolyzed with sequencing grade porcine trypsin (Promega) at a protease to substrate ratio of 1:100, at 37°C for 15h.
  • Peptide mixtures were desalted with Sep-Pak tC18 cartridges (Waters, Milford, MA, USA), eluted with 50% acetonitrile / 0.1% formic acid, evaporated to dryness, and resolubilized in ⁇ 20mM sodium acetate, lOOmM sodium chloride, pH 5.
  • Glycopeptide enrichment Glycopeptides were isolated as described previously. N-linked glycosylated peptides were released with N-glycosidase F (PNGase F; Roche and New England Biolabs). Formerly glycosylated peptides were desalted as above and resolubilised in ⁇ HPLC grade water / 2% acetonitrile / 0.1% formic acid.
  • LC -MS/MS analyses were carried out on a hybrid LTQ-FT-ICR mass spectrometer (Thermo Electron) interfaced to a nanoelectrospray ion source (Thermo Electron) coupled to a Tempo NanoLC system (ABI/MDS Sciex).
  • N-glycosite samples were loaded from a cooled (4°C) autosampler (ABI/MDS Sciex) and separated on a 15 cm fused silica emitter, 75 ⁇ diameter, packed in-house with a Magic CI 8 AQ 3 ⁇ resin (Michrom BioResources) using a linear gradient from 5% to 35% acetonitrile / 0.1% formic acid over 60 or 90 min, at a flow rate of 300 nl/min.
  • CID collision-induced dissociation
  • 106 ions were accumulated in the ICR cell over a maximum time of 500 ms and scanned at a resolution of 100 000 full- width at half-maximum nominal resolution settings.
  • MS2 spectra were acquired using the normal scan mode, a target setting of 104 ions, and an accumulation time of maximally 250 ms.
  • Charge state screening was used to select ions with at least two charges and to reject ions with unassigned charge state. Normalized collision energy was set to 32%, and one microscan was acquired for each spectrum. Samples were acquired in duplicates or triplicates.
  • the search criteria were set to: cleavage after lysine or arginine, unless followed by proline, at least at one tryptic terminus; maximally one missed cleavage allowed; cysteine carbamidomethylation set as fixed modification; methionine oxidation and asparagine deamidation set as variable modifications; monoisotopic parent and fragment ion masses; and precursor ion mass tolerance of 50 ppm.
  • the database search results were further validated with the Trans- Proteomic Pipeline (TPP), with a false positive rate was set to 1% on both peptide and protein level, as determined by PeptideProphet9 and ProteinProphetlO, respectively. Data was uploaded to the PeptideAtlas (http://www.peptideatlas.org/) and processed with the settings described above.
  • Protein topology prediction Prediction of secondary protein structure was performed from the amino acid sequence with Phobius (http://phobius.sbc.su.se/).
  • Blood collection and plasma preparation Patients from the screening, discovery, training, and validation cohorts all have signed an informed consent document. Blood was drawn prior to surgery from the cubital vein and collected into tubes processed with EDTA. Blood was directly centrifuged at 4500rpm for 3min at 4°C. Plasma was collected into a new tube, frozen at -20°C and stored at -80°C. In the training cohort, blood was drawn before bowel preparation for colonoscopy or prior to large bowel surgery and centrifuged at 2123xg for lOmin.
  • Glycoprotein enrichment from plasma Glycoproteins were isolated as described previously6 and above, starting with 50 ⁇ of plasma. Prior to the enrichment, bovine standard N-glycoproteins (Fetuin and Alpha- 1 -acid glycoprotein) were spiked into samples at equal concentration (lOpmol/protein). Counter to above, glycoproteins were first oxidised, immobilised on resin, non-bound proteins were thoroughly washed away with urea buffer (8M urea, lOOmM ammonium bicarbonate, 0.1% SDS, 5mM EDTA), then proteolysed at 2M urea, and N-linked glycosylated peptides were enzymatically released as above.
  • bovine standard N-glycoproteins Fretuin and Alpha- 1 -acid glycoprotein
  • the protocol was adapted to a Sirroco 96-well plate (Waters) where Affi-gel hydrazine resin (Bio-Rad) was used. Formerly glycosylated peptides were desalted as above in 96-well MacroSpin column plates filled with Vydac C18 silica (The Nest Group Inc.) and resolubilized in ⁇ HPLC grade water / 2% acetonitrile / 0.1% formic acid. Targeted LC-SRM analysis of plasma N-glycosites.
  • Samples from the screening and validation cohorts were analyzed on a hybrid triple quadrupole/ion trap (4000 QTrap, ABI/MDS Sciex) equipped with a nanoelectrospray ion source and a Tempo NanoLC system (ABI/MDS Sciex) coupled to a 15 cm fused silica emitter, 75 ⁇ diameter, packed in-house with a Magic CI 8 AQ 5 ⁇ resin (Michrom BioResources). Samples were loaded from a cooled (4°C) autosampler (ABI/MDS Sciex) and separated over a linear gradient from 5% to 35% acetonitrile / 0.1% formic acid over 35 min, at a flow rate of 300 nl/min.
  • the instrument was operated in scheduled SRM mode (retention time window of 300 sec, target scan time of 3 sec), at a unit resolution (0.7 m/z half maximum peak width) of both Ql and Q3 analysers.
  • SRM assays were retrieved from the N-glycosite SRM atlas (http://www.srmatlas.org/), reanalyzed to select the best transitions for endogenous detection in plasma, split to multiple SRM methods or used to optimize a single SRM method.
  • peptides were loaded onto a 75- ⁇ X 10.5- cm fused silica microcapillary reverse phase column, in-house packed with Magic CI 8 AQ material (200 A pore, 5-m diameter; Michrom BioResources).
  • Magic CI 8 AQ material 200 A pore, 5-m diameter; Michrom BioResources.
  • solvent B solvent A: 98% water, 2% acetonitrile, 0.1% formic acid
  • solvent B 98% acetonitrile, 2% water, 0.1% formic acid
  • the mass spectrometer was operated in the positive ion mode using ESI with a capillary temperature of 270 °C, a spray voltage of +1350 V, and a collision gas pressure of 1.5 mTorr. SRM transitions were monitored with a mass window of 0.7 half-maximum peak width (unit resolution) in Ql and Q3. All of the measurements were performed in scheduled mode, applying a retention time window of 3 min, a cycle time of 2 s, and a dwell time of 25 ms (depending on the number of transitions measured per run, which was in the range of 400 - 600).
  • CE Collision energies
  • the intensities of the standard proteins were modeled to obtain a single sample value representative of their quantity in individual samples and these sample quantities were correlated with the median of the total intensities of plasma samples by Pearson correlation. A correlation of >0.6 was considered significant.
  • the sample intensities of the standard proteins were used to normalize the endogenous plasma intensities across all runs to remove the systematic bias created during sample preparation.
  • a linear model with expanded scope of technical replication and restricted scope of biological replication was specified. Comparisons of mean protein abundance between groups were carried out using quantities from the model and p-values were adjusted as above. Normalized data were used to calculate model-based estimation of sample quantification for individual proteins.
  • Prediction analysis Proteins with more than 40% missing values were excluded. 10-fold cross-validation was used to find the most discriminative proteins in the training dataset. For each fold, proteins with significantly differential abundance between groups were used in logistic regression models. Statistical significance analysis of differential abundance was performed as described above at FDRO.05 and fold change cut-off ⁇ 1.1. After fitting the model with protein quantification data, the best model for each fold was chosen by stepwise selection, choosing the model by repetitively adding or dropping proteins until minimizing Akaike information criterion (AIC). The final predictive model was comprised of proteins which were selected more than five times among the ten folds, and was then parameterized on the full training dataset. The performance of the final model was assessed on the validation dataset. The threshold was determined based on the best accuracy in the training dataset. The pROC package in R was used to draw the ROCs, to calculate the AUCs and the CIs with bootstrap methods, and to compare different AUCs with bootstrap methods.
  • AIC Akaike information criterion

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Abstract

Cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer comprising the combined measurement of fragments of protein biomarkers selected from a first group consisting of: CP; SERPINA3; PON1; optionally in combination with at least one or both protein/peptide biomarkers and/or fragments of protein biomarkers selected from a second group consisting of: IGFBP3; ATRN; LRG1; TIMP1, in human serum, plasma or a derivative of blood, or blood itself and/or of at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: LGALS3BP; PROC; CD163; AOC3 in human serum, plasma or a derivative of blood, or blood itself.

Description

TITLE
Biomarker assay and uses thereof for diagnosis, therapy selection, and prognosis of cancer
TECHNICAL FIELD
The present invention relates to the field of cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assays for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer.
PRIOR ART
The diagnosis of localized CRC, despite large efforts in mass screening programs, remains a major challenge. This is mainly due to a largely clinically silent disease that can progress over several decades until signs of invasive cancer are presented. In most developed countries, the population at average risk can undergo a non-invasive screening test, i.e. fecal occult blood test (FOBT), which captures blood in stool potentially originating from the large intestine and the rectum. Cases with a positive test are typically followed up by colonoscopy, which is a costly and invasive procedure. The major drawback of the current screening program is that the FOBT lacks specificity and thereby generates large numbers of false-positive cases that need to undergo unnecessary colonoscopy. A reliable and non- invasive diagnostic procedure is still lacking, even though novel methodologies based on blood plasma profiling are emerging as tools to increase the overall diagnostic accuracy. Prognosis of CRC largely varies between patients and several genetic factors such as KRAS gene status or microsatellite stability, as well as clinical features such as disease location, are associated with response to therapy, disease prognosis and survival. Currently such clinical data is acquired from tumor tissue, which is only possible during tumor resection and in the case of genetic tests requires DNA of an adequate quality. This is an issue for roughly 20% of patients for which such information cannot be obtained and thus hampers the selection of appropriate therapy.
SUMMARY OF THE INVENTION
Stable indicators of prognosis such as protein biomarkers measured non-invasively in blood would be highly valuable to enable personalized therapy selection and improve the overall disease management. To enhance the accuracy of non-invasive colorectal cancer detection and to improve patient stratification based on their prognosis, we have established a biomarker development pipeline for the discovery and validation of novel colorectal cancer biomarkers and used it to discover panels of biomarker candidates. Our strategy is based on three major aspects: (I) the characterization of biomarker candidates directly in human primary tumors, subsequent verification in human blood plasma from different blood circulations and time points, and validation across large clinical cohorts, (II) the employment of cutting edge mass spectrometry-based methodologies and bioinformatics tools established in our laboratory for the isolation, identification and quantification of N- linked glycoproteins across large numbers of samples, and (III) the application of sophisticated methods to discover specific proteins and their combinations able to predict CRC detection as well as other important prognostic and predictive disease characteristics. The biomarker candidates identified with our approach hold promise of high value for the various clinical applications.
(I) From tumor tissue to blood plasma: Identification of secreted biomarker candidates
Our phased approach is based on a previously described experimental workflow, where candidate biomarkers are initially discovered in the tissue disease site and then validated in relevant clinical blood samples that are routinely obtained in a non-invasive manner. Furthermore, to enhance the likelihood of biomarker candidate detection in the circulation our focus lies on glycoproteins that are extracellular proteins prone to secretion or shedding. Modifications of the approach entail the direct use of human specimens and the rational selection of sample types for candidate discovery, verification, and validation (figure 1).
Rationale for using primary human colorectal tumor tissues
Disease models such as cell lines or animal models that mimic a certain disease hypothesis are routinely employed in biomarker research, although optimal fidelity to human disease is certainly an important implication. To ensure the identification of biomarkers most relevant and specific for CRC several considerations were taken into account: first, we selected human primary tumor tissues - the site of disease initiation and progression - as the best suitable source of biomarkers; second, epithelial cells from which cancer arises were enriched from tissues to obtain more homogenous samples in terms of cellular composition; and third, tumor tissues were excised jointly with adjacent normal mucosa representing paired samples that overcome the issue of inter-patient variability during comparative analysis.
Rationale for using patient plasma specimens for biomarker verification and validation Blood from the systemic circulation is routinely sampled in a non-invasive way and represents an ideal sample type for biomarker screening. In the verification phase, we have employed human plasma samples from the same patients as where used for candidate discovery. This allows the characterization of proteins that are detectable in plasma and thus amenable to large scale pre-clinical validation. Besides detection analysis, we selected samples to evaluate two biological hypotheses. Plasma from three sources of blood was used: (1) plasma from the systemic circulation sampled at surgery, (2) plasma from the systemic circulation sampled one month after surgery, and (3) plasma from a tumor drainage vein sampled at surgery (figure 1). The first clinical hypothesis was designed to evaluate the effect of tumor removal on potential biomarkers. The second clinical hypothesis was designed to identify biomarker candidates with a concentration gradient between the tumor drainage vein and the systemic circulation. In the validation phase, we have selected clinical cohorts that include CRC patients, controls, and patients with other malignancies to evaluate the clinical value of the verified biomarker candidates (table 1). Plasma samples from in total 329 subjects, and in a second analysis supplemented to 529 subjects, have been selected to identify candidate biomarkers that can detect CRC specifically, and/or are associated with predictive and prognostic clinical factors of CRC, and serve as a non-invasive readout of the clinical condition. In the case of CRC patients, we have again included paired samples, where blood was sampled before and after surgery, for a few patients to examine the effect of tumor excision on potential biomarker candidates.
(II) Cutting edge mass spectrometry and bioinformatics
Discovery of biomarker candidates by mass spectrometry (MS)
High-resolution mass spectrometry (LTQ-FT) coupled to nanoflow liquid chromatography (LC) was set up to generate reproducible measurements. Isolated tissue N-glycosites from tumor and control tissue samples were analyzed by LC-MS and bioinformatic postprocessing of the obtained data lead to the identification and quantification of proteins across all samples simultaneously. Sophisticated statistical analysis of the quantified data, developed in collaboration with our laboratory, lead to the generation of a list of biomarker candidates for colorectal cancer.
Targeted measurement of biomarker candidates by selected reaction monitoring (SRM) Targeted analysis by SRM represents the most selective and sensitive MS analysis to date, and was employed to screen our candidate proteins in plasma and validate the detectable proteins in the large clinical sample cohort. In the verification step, SRM assays were developed for candidate proteins and used to screen them in patient plasma samples to assess their detectability. In the validation phase, a single multiplex SRM method was developed to simultaneously profile all verified candidate proteins over hundreds of clinical samples. Sophisticated statistical methods have been developed for this analysis and generated accurately quantified and validated biomarker candidates of CRC.
(Ill) Methods to characterize biomarker candidates for CRC diagnosis, prognosis, and therapeutic applications
Rationale of using multivariate methods
Disease signatures represent a combination of biomarkers and can lead to an increased diagnostic or prognostic accuracy, when compared with the use of single biomarkers. For CRC detection, this has been shown by combining the FDA-approved biomarker carcinoembryonic antigen (CEA) with additional proteins such as CA 19-9 or CA 72-4 and lead to an increased sensitivity and specificity as compared to CEA alone, although still insufficient for a reliable diagnosis.
We have measured a panel of candidate biomarkers and identified the signatures that can best discriminate between CRC and controls. Moreover, by classifying other malignancies (lung and pancreatic) with the obtained signature we gain insights about the specificity of the signature for CRC or generality for cancer, which is a highly valuable attribute that has not been commonly exploited to date.
In order to develop a predictor (combination of biomarkers) able to classify CRC and controls, we performed a sequence of analysis steps as follows: (1) we partition our data into a training and validation set, (2) we select the most promising proteins from the training set, (3) we generate a predictor by fitting logistic regression models to the training set, and (4) we test the predictor accuracy on the validation set that contains data that has never been previously seen by the predictor and is composed of the CRC group, control group, and the group with other cancers. The classification of cases with other cancers into either group or roughly equally across both groups is indicative of the specificity of the predictor for CRC.
Other statistical methods such as multivariate age-, gender- and stage-adjusted Cox regression analysis were employed to identify proteins and their combinations significantly associated with patient survival that are able to discriminate between patients with a better and worse prognosis. Such prognostic protein signatures can be used to estimate the risk of death of newly diagnosed patients based on their protein expression profiles.
Rationale of using univariate methods
Univariate methods for statistical testing (e.g. Kruskal-Wallis test), survival analysis (Kaplan-Meier curves with a log-rank test), regression analysis on a single protein level, or correlation analysis (e.g. Spearman correlation) have been employed to discover candidate biomarkers that are significantly changing and/or associated with prognostic and/or predictive molecular factors, disease stage and/or grade, presence or absence of metastasis, and localization of cancer.
Generally speaking, the present invention relates to a cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer comprising the combined measurement of at least two, preferably at least all three protein/peptide biomarkers and/or fragments of protein biomarkers selected from a first group consisting of:; CP; SERPINA3; PON1; optionally in combination with at least one or both protein/peptide biomarkers and/or fragments of protein biomarkers selected from a second group consisting of: IGFBP3; ATRN; LRG1; TIMP1, in human serum, plasma or a derivative of blood, or blood itself. Optionally, at most one of the first group and/or at most one or two of the second group can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following third group: CD44; FGG; MMRN1 ; CTSD; IGHG2; ECM1; IGHA2; FHR3; ITIH4; HP; ORM1; FN1; PRG4; LGALS3BP;
According to a first preferred embodiment all three protein/peptide biomarkers and/or fragments of protein biomarkers of the first group are measured in combination with LRG1 and/or TIMP1 from the second group, or are measured with IGFBP3 and/or ATRN from the second group, or are measured with all protein/peptide biomarkers and/or fragments of protein biomarkers from the second group.
Generally, the invention, and fully or partially independently of the above biomarkers but including them, relates to a cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer comprising the combined measurement of at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the large group consisting of: ATRN; A1AG2; APMAP; APOB; CD44; CLU; C04A; CP; CFH; DKFZp686C02220; ECM1; F5; FETUB; FGA; FHR3; HP; HRG; HYOU1; IGHA2; IGHG1; IGHM; IGHG2; LUM; LAMP2; PLPT; PRG4; PTPRJ; LRG1; MMRN1; MST1; ORM1; SERPINA1; SERPINA3; SERP1NA7; THBS1; TIMP1 ; VWF; AFM; KLKB1; KNG1; MRC2; BTD; IGJ; ITIH4; PIGR; SERPINA6; VTN; ANT3; CD109; CADM1; FCGBP; IGFBP3; LCN2; AHSG; CDH5; PON1; HPX; LYVE1; TNC; HLA-A. GOLM1; NCAM1 in human serum, plasma or a derivative of blood, or blood itself,.
According to a preferred embodiment, for disease detection the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or all five protein/peptide biomarkers and/or fragments of protein biomarkers selected from the first group consisting of: CP; SERPINA3; PON1; optionally in combination with at least one, two, three or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the second group consisting of: IGFBP3; ATRN; LRG1; TIMP1, in human serum, plasma or a derivative of blood, or blood itself.
According to another preferred embodiment, for disease localization the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: GOLM1; HLA-A; HYOU1; MRC2; NCAM1; SERPINA3, in human serum, plasma or a derivative of blood, or blood itself. Optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: A1AG2; AFM; AHSG; ANT3; AOC3; ATRN; APOB; BTD; C20orf3; CADM1; CD109; CD163; CDH5; CD44; CFH; CFI.
According to another preferred embodiment, for detection of metastatic disease the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: PTPRJ; PIGR; HPX; FETUB; IGHG2; VTN; APOB; ATRN, in human serum, plasma or a derivative of blood, or blood itself. Optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: SERPINA1 ; ITIH4; F5; TNC.
According to another preferred embodiment, for molecular KRAS characteristics the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: IGHG2; IGHA2; F5; LYVE1; ITIH4; FHR3, in human serum, plasma or a derivative of blood, or blood itself. Optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: LRG1; CP; SERPINA6; KDR; HP; MRC2; GOLM1; SERPINA7; PROC; VTN; CADM1; DKFZp686N02209; CD 109; TNC; HPX.
According to another preferred embodiment, for molecular MSI characteristics the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: F5; VWF; FETUB; IGHA2; IGFBP3;
ORMl; PTPRJ; Q5JNX2, in human serum, plasma or a derivative of blood, or blood itself.
Optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group:
Q5JNX2; CFI; HLA-A; CD44; CD 163; LAMP2; MPO; ICAM2; PIGR; PLXDC2;
ICAM1; Fl l; HP; KNG1; CFH; SERPINA3; VTN; FGG; APOB; ATRN; Q5JNX2;
PROC; SERPI A1 ; Q6N091; PLXNB2.
According to another preferred embodiment, for prognosis the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: HLA-A; VWF; TNC; MRC2; FHR3; FCGBP; PTPRJ; CD 109, in human serum, plasma or a derivative of blood, or blood itself. Optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: HP; CD44; CDH5; PGCP; THBS1; HP; PLXNB2; LUM; PROC; DSG2; DKFZp686N02209; PLTP; F5; CD44; KDR; LCN2; HPX; ATRN; MPO.
Generally, the invention, and fully or partially independently of the above biomarkers relates to a biomarker comprising the combined measurement of at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: LGALS3BP; PROC; CD163; AOC3 in human serum, plasma or a derivative of blood, or blood itself, with the proviso that at least one of the group consisting of PROC; CD163 is measured. It correspondingly also relates to methods for the cancer diagnosis/therapy/prognosis/patient stratification using such biomarker assays.
According to a first preferred embodiment, at least PROC and CD 163 are measured in combination with at least two or more further protein/peptide biomarkers and/or fragments of protein biomarkers.
According to yet another preferred embodiment, four protein/peptide biomarkers and/or fragments of protein biomarkers of the group consisting of: LGALS3BP; PROC; CD163; AOC3 are measured.
Furthermore, and at least partly independently of the above mentioned biomarker assay, the present invention also relates to a biomarker assay comprising/involving the combined measurement of at least one (e.g. additional to the above-mentioned list), preferably at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: ATRN; A1AG2; APMAP; APOB;
CD44; CLU; C04A; CP; CFH; DKFZp686C02220: ECM1; F5; FETUB; FGA; FHR3;
HP; HRG; HYOU l ; IGHA2; IGHGl ; IGHM; IGHG2; LUM; LAMP2; PLPT; PRG4; PTPRJ; LRG1; MMRNl ; MST1; ORM1; SERPINAl ; SERPINA3; SERPINA7; THBS1 ;
TIMP1; VWF for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer.
According to a preferred embodiment, in case of proximal plasma from the tumor drainage vein VWF is measured as present at higher concentration and at least one of CD44, DKFZp686C02220 at lower concentration in the proximal plasma as compared to the systemic circulation.
According to another preferred embodiment, measured as upregulated as an effect of tumor excision are at least one of ATRN, CLU, DKFZp686C02220, ECM1, F5, FETUB, HRG, IGHA2, IGHGl , IGHG2, LUM, and PLPT.
Also possible is the measurement as relevant for CRC detection of at least one of the group: A1AG2, APMAP, APOB, CFH, C04A, CP, ECM1, F5, FHR3, HP, IGHA2, IGHGl, IGHG2, LGALS3BP, LRG1, MMRNl, ORM1, SERPINAl, SERPINA3, SERPINA7, THBS1, TIMP1, and VWF, wherein these are present at higher levels and the 3 proteins IGHA2, IGHGl, IGHG2 are present at lower levels in the CRC population as compared to healthy controls.
Furthermore the present invention also relates to a biomarker assay or a corresponding method in which for diagnostic applications a signature comprising at least THBS1, PRG4, FHR3, SERPINAl, DKFZp686C02220, CD44, C04A, CFH, is measured preferably either the signature consisting of THBS1+ HP+ CP + APMAP+ PRG4+ APOB+ IGHG1+ FHR3+ IGHG2+ SERPINAl + LGALS3BP+ DKFZp686C02220 + CD44 + VWF + TIMP1+ C04A+ CFH, or the signature consisting of CD44+CFH+ECM 1 +F5+FHR3+IGHM+PRG4+C04 A+ DKFZp686C02220 + SERPINAl +THBS1, leading to high sensitivity, specificity as well as accuracy in the validation set.
For tumor localization, so in particular for diagnostic purposes, CD109, ORMl, A1AG2, and/or VTN should be measured. These proteins can thus e.g. be used to determine the location where the doctor should start the intervention to detect the cancer.
According to yet another preferred embodiment, for prognostic applications a disease-free (DFS) survival status is determined based on at least one of AFM, KLKB1, KNG1, LGALS3BP, and PTPRJ and overall survival (OS) with at least one of C04A, MRC2, and BTD, wherein significantly associated with both DFS and OS are HYOUl, IGHM, ORMl, A1AG2, VTN, SERPINA7, AHSG, IGJ, CFH, F5, HP, ITIH4, LUM, PIGR, PROC, SERPINAl , and SERPINA6.
Preferably 4 protein combinations are significantly associated with DFS: IGHG2+ATRN non-adjusted, CDH5+ATRN non-adjusted, MST1+CD109 age-, gender-, and stage- adjusted, and MST1+MRC2 age-, gender-, and stage-adjusted; and 5 protein combinations significantly are associated with OS: HRG+AHSG non-adjusted, C04A+CADM1 non- adjusted, LCN2+APMAP age- and gender-adjusted, and CDH5+FCGBP age-, gender-, and stage-adjusted, CDH5+IGFBP3 age-, gender-, and stage-adjusted; and 1 protein combination is significantly associated with both DFS and OS: CD163&CD109 age-, gender, and stage-adjusted; and 12 age-, gender-, and stage-adjusted protein combinations are significantly associated with 5-year OS : CDH5 + AHSG + MST1; CDH5 + AHSG + ORMl; CDH5 + CFH + ORMl; CDH5 + FCGBP + IGFBP3; CDH5 + FCGBP + ORMl; CDH5 + FCGBP + PROC; CDH5 + FCGBP + SERPINAl; CDH5 + HYOUl + SERPINAl; CDH5 + IGFBP3 + SERPINAl; CDH5 + IGFBP3 + SERPINA6; CDH5 + IGHM + ORMl; CDH5 + LGALS3BP + ORMl; and 5 age-, gender-, and stage-adjusted protein combinations are significantly associated with 5-year DFS: CDH5 + FCGBP + IGFBP3; CDH5 + HYOUl + SERPINAl; CDH5 + IGHM + ORMl; CDH5 + IGHM + A1AG2; CD109 + KLKB1 + PIGR; and 3 age-, gender-, and stage-adjusted protein combination are significantly associated with both DFS and OS: CDH5 + FCGBP + IGFBP3; CDH5 + HYOUl + SERPINAl; CDH5 + IGHM + ORMl. The overlap between individual proteins and protein combinations for survival association is 8 proteins, namely AHSG, CFH, HYOU1, KLKB1, ORM1, PROC, SERPINA1 , PIGR.
For prognostic and predictive power according to another preferred embodiment at least one of ATRN, APOB, PRG4, and SERPINA3, preferably a combination thereof, is associated with the status of the KRAS gene where higher protein expression is observed in patients with the mutated gene, and at least one of, preferably a combination of HYOU1, IGHM, FGA, THBS1, and VWF is associated with the status of the KRAS gene where lower protein expression is observed in patients with the mutated gene. As a prognostic and predictive indicator of CRC associated with microsatellite status at least one of MST1, SERPINA7, LAMP2, and IGHG1 can also be measured. For grading, staging, and cancer assessment at least one of IGHG2 and ORM1 can be measured.
For clinical stage representation, metastasis, and/or EGFR dephosphorylation PTPRJ can be measured; and for metastasis status ATRN can be measured.
Last but not least the present invention also relates to a biomarker assay characterized in that it is an affinity reagent-based assay, preferably antibody-based assay such as an Enzyme-Linked Immunosorbent Assay (ELISA) for the detection of the proposed biomarker candidates.
Furthermore, the invention relates to a method for the diagnosis and/or for the therapy and/or for the prognosis and/or for the monitoring of colorectal cancer and/or lung cancer and/or pancreatic cancer, using a biomarker assay according to any of the preceding claims, wherein preferably the measurement is carried out using tandem mass spectrometry techniques, preferably selected reaction monitoring (SRM), more preferably in combination with liquid chromatography, and/or Enzyme-Linked Immunosorbent Assays (ELISA) for the detection of these proteins/fragments thereof.
Further preferred embodiment outlined in the dependent claims.
BRIEF DESCRIPTION OF THE DRAWINGS
Preferred embodiments of the invention are described in the following with reference to the drawings, which are for the purpose of illustrating the present preferred embodiments of the invention and not for the purpose of limiting the same. In the drawings,
Fig. 1 shows the biomarker development pipeline; samples, procedures, analysis, and outcomes are outlined for the different phases of the pipeline; A, outlines the rationale for sample type selection during the three phases of development; B, outlines the procedures used throughout the pipeline;
shows an overview of successful CRC candidates at different phases of the biomarker development pipeline;
shows a diagnostic signature for CRC detection (1); generalised linear models (GLM) path algorithm was used to select the most important proteins from differentially abundant proteins in training set; A, Area under the ROC curve (AUC) for the training and validation set; B, A probability cut-off was selected to determine the most specific test performance; C, The selected probability cut-off was used to test the classifier on the validation set; Furthermore, cases of other cancers were used to classify and hence to indicate the classifier's generality to cancer or the specificity to CRC;
shows a diagnostic signature for CRC detection (2); differentially abundant proteins overlapping between CRC versus controls test and CRC versus other cancers test in training set were selected as potential specific proteins for the classifier; A, Area under the ROC curve (AUC) for the training and validation set; B, A probability cut-off was selected to determine the most specific test performance; C, The selected probability cut-off was used to test the classifier on the validation set. Furthermore, cases of other cancers were used to classify and hence to indicate the classifier's generality to cancer or the specificity to CRC;
a) - e) show the development and evaluation of a diagnostic biomarker signature, wherein in A, random forests (RFs) were employed to select and rank the best predictor proteins and the 20 most important proteins were selected for logistic regression; observation histograms indicate the frequency occurrence of proteins in the 100 best models; in B, the 10 best prediction models with the best bootstrap validation are listed, where each box has 100 values due to the 100-fold cross validation; the first box represents a random model; in C, the best predictive model with the highest median AUC (in solid black) and the proteins it is comprised of are indicated; in D, evaluation of the best model on samples in the validation set (1/3 of all samples); reproducibility and consistency is demonstrated by the similar AUC of the model in both training and validation analysis; in E, contingency table indicating the actual classification of cases at a given specificity and sensitivity threshold; cases of other malignancies were included in the final classification and their classification results are indicative of specificity to a given cancer type;
F) shows biomarker candidates associated with tumor localisation; Anatomical segments: C18 = colon, C19 = rectosigmoid junction, C20 = rectum; Polytomous logistic regression of the four biomarker candidates demonstrated that CD 109 and VTN contain the highest weight of importance when assessed in a single model;
Fig. 6 shows biomarker candidates associated with the status of the KRAS gene;
A, Patients with a differential abundance of a biomarker candidate based on wildtype or mutated KRAS (Kruskal-Wallis test); Mutated and wild-type genes are depicted by "mut" and "wt", respectively; B, Biomarker candidates associated with the KRAS gene status and 5-year patient survival;
Fig. 7 shows biomarker candidates associated with the status of the KRAS gene in colorectal, pancreatic, and lung cancer; Patients with a differential abundance of a biomarker candidate based on wild-type or mutated KRAS (Kruskal-Wallis test); A, Biomarker candidates associated with the KRAS gene status in colorectal and pancreatic cancer; B, Biomarker candidates associated with the KRAS gene status in colorectal, pancreatic, and lung cancer; Mutated and wild-type genes are depicted by "mut" and "wt", respectively; C=Colorectal cancer, P=Pancreatic cancer, L=Lung cancer; Fig. 8 shows biomarker candidates associated with microsatellite status, wherein proteins with a Kruskal-Wallis test at P <0.05 are listed;
Fig. 9 shows an association of biomarker candidates with tumor characteristics, wherein in A, Tumor grade and in B, Tumor stage. Dukes (left) and TNM (right) classification systems are reported, and in C, Presence of metastasis; Fig. 10 shows the biomarker signature development on the training dataset of the second analysis; protein significance testing (p-value<0.05, abundance fold change (FC) ±1.1) between CRC and controls and stepwise selection of discriminative proteins into logistic regression models was employed within 10-fold cross-validation (CV) to generate the biomarker signature; CRC disease probability determined based on the regression model cut-off was plotted against the relative protein abundance with linear regression line (LOESS method); confidence bounds were added on each line; protein abundance fold change (FC) between CRC and controls is shown in brackets behind the protein labels; protein predictors were enumerated with brute force search and the best preforming predictors based on the area under the curve (AUC) were ranked; predictor distribution of the best 2097 models that were identical in their discriminative significance (Student's t- test, significance level a>0.05) was plotted for the top 20 proteins; bold proteins are present within the biomarker signature produced by either method 1 or 2, and proteins labelled in gold are part of the signature generated by method 1 ; and
Fig. 11 shows the biomarker signature evaluation on the validation dataset of the second analysis; a, Performance of the biomarker signature on the full validation dataset; b, Evaluation of the predictive power of the signature for distinct clinical stages; the significance of differences between the corresponding AUCs is determined by statistical testing (significance level p<0.05); c, Evaluation of the predictive power of the signature for patients grouped by their tumor size; The significance of differences between the corresponding AUCs is determined by statistical testing (significance level p<0.05).
DESCRIPTION OF PREFERRED EMBODIMENTS
Discovery, verification, and validation of biomarker candidates:
LC-MS analysis of N-glycosylated peptides originating from primary tumor and normal epithelia lead to the identification of 673 glycoproteins. Comparative statistical analysis of quantified proteins generated a list of 303 biomarker candidates (table 2) that include differentially abundant glycoproteins in CRC no matter the clinical stage, proteins that change across disease progression as well as protein changes that occur upon disease metastasis. This list of candidates was supplemented with interesting glycoproteins from literature or other internal datasets.
Using targeted analysis based on SRM measurements, we screened our biomarker candidates in patient plasma and verified 88 candidate proteins (table 3) over 50 plasma samples, which represents a 25% success rate of our approach. The analysis of proximal plasma from the tumor drainage vein lead to the characterization of three proteins, where one (VWF) was present at higher concentration and two (CD44, DKFZp686C02220) at lower concentration in the proximal plasma as compared to the systemic circulation. The paired analysis addressing the effect of tumor excision is addressed in the validation phase where additional samples have been included to corroborate the results.
The detectable candidates in plasma were multiplexed in a single SRM method and profiled over 355 clinical plasma samples (table 1), which represents the largest cohort of samples measured by SRM to date. Of the 88 proteins, 80 candidates were consistently quantified in this large dataset (table 3). The overview of successful candidate proteins over the different phases of the biomarker development pipeline is outlined in figure 2.
Newly developed linear mixed effects models were employed for statistical testing of differential abundance between clinical groups. To address the hypothesis of whether there is an effect of tumor removal on the measured biomarker candidates, we have analyzed subjects with paired samples, i.e. plasma collected before and after surgery, and performed two independent analyses, i.e. one in the verification and the other in the validation phase. We have identified 12 proteins that were all upregulated as an effect of tumor excision and showed the same trend in the two independent analyses (ATRN, CLU, DKFZp686C02220, ECM1, F5, FETUB, HRG, IGHA2, IGHG1, IGHG2, LUM, and PLPT). Interestingly, DKFZp686C02220 was matching the trend identified in the proximal tumor plasma.
Proteins relevant for CRC detection were characterized by comparing the average protein abundance in CRC and control groups, where 23 proteins were found to be significantly differentially abundant (A1AG2, APMAP, APOB, CFH, C04A, CP, ECM1, F5, FHR3, HP, IGHA2, IGHGl, IGHG2, LGALS3BP, LRG1, MMRN1, ORM1, SERPINAI, SERPINA3, SERPINA7, THBS1, TIMP1, and VWF). Of these, the majority was present at higher levels and only 3 proteins (IGHA2, IGHGl, IGHG2) were present at lower levels in the CRC population as compared to the healthy controls. Interestingly, the trend of these three immunoglobulins was matching the trend identified in the hypothesis testing the effect of tumor removal. The secondary interest was to determine whether proteins relevant for CRC detection might be specific for CRC or represent generic cancer trends. Testing for differential abundance between the CRC group and the group with other cancers, we found 8 proteins that were significant for both hypotheses, thereby representing potential specific biomarker candidates for CRC (APMAP, CFH, C04A, ECM1, F5, FHR3, SERPINAI , and THBSl). Diagnostic biomarkers:
For the generation of biomarker signatures we split the data prior to analysis into a training a validation set. We repeated the statistical analysis on the training set only to identify differentially abundant proteins between groups. In a first approach, the most important proteins from the list of differentially abundant candidates were selected by generalized linear models (GLM) path algorithm into the classifier. In total, these were 17 proteins (THBS1+ HP+ CP + APMAP+ PRG4+ APOB+ IGHG1+ FHR3+ IGHG2+ SERPINA1+ LGALS3BP+ DKFZp686C02220 + CD44 + VWF + TIMP1+ C04A+ CFH). Different probability cut-offs were used to determine a high specificity of the classifier, which was then set and used for testing the performance on the validation set. A cut-off of p=0.8 was selected, and lead to a sensitivity of 68%, a specificity of 92%, and an accuracy of 73% (figure 3). In a second approach, differentially abundant proteins overlapping between the CRC versus controls and CRC versus other cancers hypotheses were selected and represent 11 proteins (CD44+CFH+ECM1+F5+FHR3+IGHM+PRG4+C04A+ DKFZp686C02220 +SERPINA 1 +THBS 1 ) that were used for classification. Again the optimal probability cutoff was selected, and lead to a sensitivity of 64%, specificity of 83%, and an accuracy of 68% (figure 4). Interestingly, both approaches classified cases of the other cancers roughly equally into the CRC and the control group, indicating a more specific classification of CRC cases than cases of other cancers (figure 3 and 4). Finally, an alternative method, based on random forest (RF) selection of proteins and classification and using the same data split, generated a 4-protein signature (LGALS3BP+PROC+CD163+AOC3), with a sensitivity of 84%, specificity of 83%, and an accuracy of 83.7% in the validation set, which means that 83% of subjects would be correctly diagnosed with this novel biomarker signature. Cases of the other malignancies were used during classifier validation to indicate a potential specificity of our signature for CRC or cancer in general. Interestingly, the model classified cases of the other cancers and CRC cases similarly into the CRC and the control group, indicating that the protein predictors in our signature may represent more generic predictors of cancer. To summarize the specificity aspect of the obtained signatures to CRC, the higher the specificity of a signature for CRC (at the cost of sensitivity), the more specific it is for CRC and only for CRC. On the other hand, when both sensitivity and specificity of a signature are high (like in the RF approach), our results suggest that other cancers are also classified quite well with it. See Figures 5A-E in support of the 4- protein signature. The aspect of a non-invasive tumor localization to the colon, rectosigmoid junction, or rectum has the potential to influence and enable a focused sequence of interventions a gastroenterologist needs to perform. We have identified 4 proteins (CD 109, ORM1, Al AG2, and VTN) that independently showed a lower expression in the colon than in the rectum and were significantly associated with the tumor localization. These proteins possess diagnostic promise to inform the clinician with the valuable information of where to start with the intervention.
More specifically, we tested the association of tumor localization with specific biomarker candidates. The three basic anatomical sites of CRC are the colon, the rectosigmoid junction, and the rectum. Non-invasive indicators of cancer localization within these three segments are highly clinically valuable because they influence the sequence of interventions a gastroenterologist needs to perform to localize the tumor. We have identified four proteins (CD109, ORM1, A1AG2, and VTN) that independently showed a lower expression in the colon than in the rectosigmoid junction or rectum and were significantly associated with the tumor localization (figure 5F). To further evaluate the importance of the individual biomarker candidates, we performed a polytomous logistic regression with the three anatomical segments as categories and the four candidate proteins as dependent variables. CD 109 and VTN, two of the individually associated candidates, were found to be most important when assessing the four proteins in one model. Hence, these proteins possess diagnostic potential to inform the clinician with the valuable information of where to commence the intervention and thereby enable a focused investigation.
Prognostic biomarkers:
Besides diagnostic indicators, the association of biomarker candidates with prognostic factors such as patient survival was investigated, with the objective to identify biomarker candidates that can discriminate between patients with a better and worse prognosis. Kaplan-Meier 5-year survival curves (log-rank test, p<0.05 rounded) identified five proteins that were significantly associated with disease-free survival (DFS), five proteins that were significantly associated with overall survival (OS), and 15 proteins that were significantly associated with both DFS and OS (table A).
Table A Univariate analysis of 5 -year survival in patients with colorectal cancer.
Log-Rank Test Overall survival Disease-free survival
Biomarker candidates P value P value
A1AG2 0.012 0.009
AFM - 0.033
AHSG 0.018 -
BTD 0.006 -
CFH 0.013 0.033
C04A 0.02 -
F5 0.030 -
HP 0.032 0.026
HYOUl 0.018 0.052
IGHM 0.054 0.021
IGJ 0.043 0.016
ITIH4 0.021 0.031
KLKB1 - 0.053
KNGl - 0.044
LGALS3BP - 0.049
LUM 0.025 0.033
MRC2 0.054 -
ORMl 0.004 0.003
PIGR 0.029 0.049
PROC 0.048 0.025
PTPRJ - 0.039
SERPINAl 0.009 0.006
SERPINA6 0.041 0.012
SERPINA7 0.010 0.028
VTN 0.044 0.052
Proteins were reported if their P value was <0.05 rounded for either OS or DFS.
So with the means of Kaplan-Meier survival curves (log-rank p<0.05 rounded), we identified AFM, KLKB1, KNGl, LGALS3BP, and PTPRJ that were significantly associated with disease-free (DFS) survival; C04A, MRC2, and BTD that were significantly associated with overall survival (OS); and HYOUl, IGHM, ORMl, A1AG2, VTN, SERPINA7, AHSG, IGJ, CFH, F5, HP, ITIH4, LUM, PIGR, PROC, SERPINAl, and SERPINA6 that were significantly associated with both DFS and OS.
Furthermore, age-, gender-, and stage-adjusted Cox multivariate regression analysis (p<0.05) characterized multiple regressions with two proteins, i.e. MST1+CD109 significantly associated with DFS, CDH5+FCGBP and CDH5+IGFBP3 significantly associated with OS, and CD163+CD109 significantly associated with both DFS and OS (table B). Three protein combination regression analysis further identified twelve protein combinations associated with 5-year OS and five protein combinations associated with 5- year DFS, of which three combinations overlapped between OS and DFS (table B). These biomarker candidates and their combinations hold prognostic value for newly diagnosed patients due to the association of their protein expression profiles measured non-invasively in blood plasma with patient outcome.
Table B Multivariate analysis of 5-year survival in patients with colorectal cancer.
Cox Proportional Hazards Model
Disease-free
Overall survival
survival
Biomarker candidates P value P value
MST1+CD109 - 0.019, 0.016
CDH5+FCGBP 0.026, 0.017 -
CDH5+IGFBP3 0.021, 0.008 -
CD163+CD109 0.017, 0.011 0.004, 0.004
CDH5+AHSG+MST1 0.032, 0.027,0.026
CDH5+AHSG+ORM1 0.032, 0.022, 0.008
CDH5+CFH+ORM 1 0.012, 0.019, 0.004 -
CDH5+FCGBP+IGFBP3 0.008, 0.011, 0.021 0.072, 0.018, 0.022
CDH5+FCGBP+ORM1 0.018, 0.018, 0.027 -
CDH5+FCGBP+PROC 0.003, 0.008, 0.019 -
CDH5+FCGBP+SERPINA 1 0.008, 0.021, 0.026 -
CDH5+H YOU 1 +SERPIN A 1 0.007, 0.029, 0.010 0.004, 0.016, 0.005
CDH5+ IGFBP3+ SERPINA1 0.006, 0.007, 0.016 - CDH5+ IGFBP3+ SERPINA6 0.008, 0.006, 0.027 -
CDH5+ IGHM+ORM1 0.016, 0.014, 0.012 0.047, 0.018, 0.003
CDH5+ IGHM+A1AG2 - 0.022, 0.005, 0.002
CDH5+ LGALS3BP+ORM1 0.020, 0.013, 0.011 -
CD 109+KLKB 1 +PIGR - 0.031, 0.011, 0.040
Proteins were reported if the P value of either protein in the combination (comma separated) was <0.05 for either OS or DFS and if the P value of the likelihood ratio test was also <0.05. In all cases, no significant association was observed between the proteins and age, gender or stage.
The above described proteins and their combinations provide prognostic value due to their association with patient outcome for newly diagnosed patients based on their protein expression profiles measured non-invasively from blood.
Biomarkers with both prognostic and predictive power
Candidate markers discovered with our approach can be easily tested for their association with prognostic and predictive molecular factors. KRAS is a downstream mediator of EGFR signaling and activating mutations in KRAS negatively predict the response to EGFR antibody therapy and are associated with a worse prognosis. In total, 9 proteins could individually significantly discriminate between patients with wildtype and mutated KRAS gene, where ATRN, APOB, PRG4, and SERPINA3 had higher protein expression, and HYOUl, IGHM, FGA, THBS1, and VWF had lower protein expression in patients with the mutated form than in patients with the wild-type gene (Figure 6A). Interestingly, HYOUl and IGHM are also significantly associated with patient outcome in 5 -year survival analysis, where patients with lower protein abundance showed a worse outcome. Likewise, patients with lower abundance of these two biomarker candidates were associated with the KRAS mutation and thereby also represent the patients with a worse prognosis (figure 6B). To provide further clinical potential of these biomarker candidates, we examined their protein expression in 30 patients with pancreatic cancer. Approximately 95% of pancreatic cancer patients contain the mutated KRAS gene and therefore we hypothesize that the expression of biomarker candidates in pancreatic patients and colorectal cancer patients with the mutated KRAS gene ought to be similar. Indeed, this has been the case for 7 of the 9 proteins (ATRN, APOB, SERPEN A3, HYOUl, IGHM, THBS1, VWF; figure 7A). Furthermore, we also assessed the expression of these candidates in 30 lung cancer patients. In lung cancer, approximately 20% of cases have the mutated KRAS gene. Therefore, the majority of the lung cancer patient cohort should exhibit the wild-type form and a similar expression of the candidates to the wild-type KRAS patients in CRC. This has been the case for two candidate proteins: HYOU1 and IGHM (figure 7B).
These proteins represent biomarker candidates with both predictive and prognostic value, and could be used for a non-invasive selection of appropriate therapy and also to determine which patients have a better prognosis.
Another prognostic and predictive indicator of CRC is the stability of microsatellites. We have identified four proteins (MST1, SERPINA7, LAMP2, and IGHG1) that were individually significantly associated with microsatellite status of patients, where all proteins exhibited a higher expression in the stable form (MSS) as compared to the instable form, where only one of the microsatellite sequences is mutated (MSI-low) (figure 8). These proteins represent candidate markers able to discriminate between MSS and MSI- low, which is currently an area of intense research since MSI-low and MSS tumor s do not appear clinically or pathologically different in terms of quality (gross abnormalities) but do differ quantitatively (level of MSI). The median protein expression in patients with the MSI-high form (where two or more of the five microsatellite sequences have been mutated) was similar to the median expression in patients with the stable form. These proteins have the potential to provide a non-invasive protein measure reflecting that patients with microsatellite instability have a better prognosis and are unlikely to benefit from 5FU chemotherapy compared to patients with microsatellite stability.
Aspects of grading, staging, and cancer assessment would greatly benefit from a noninvasive alternative measure to traditional interventions at the level of invasive tissue biopsies. Grading represents a measure of cellular differentiation of tumor cells as compared to the normal cells in the tissue of origin. We have identified 2 proteins, IGHG2 and ORMl, significantly associated with the grade of CRC patients (Figure 9A). Patient clinical stage represents the extent of the disease. The two major staging systems of CRC are TNM and Dukes classification. We have identified a protein, tyrosine-protein phosphatase receptor-type J (PTPRJ), which has been significantly associated with both of these systems (Figure 9B), and in addition was also associated with the metastasis status, where higher expression of PTPRJ was found in patients with the presence of metastatic disease (Figure 9C). PTPRJ is a candidate tumor suppressor in the colonic epithelium, was found to inhibit proliferation and migration of CRC cells, is upregulated by protective nutrients in the tumor, and is a negative regulator of EGFR signaling pathway, through EGFR dephosphorylation. We have characterized PTPRJ to be more abundant in the tissue CRC epithelium than in the normal surrounding epithelium, and the expression of PTPRJ in plasma significantly correlated with EGFR in plasma. Another protein significantly associated with the presence of metastasis was ATRN. Staging of CRC mainly provides evidence about the invasiveness of the disease because it measures the extent of tumor growth across the membranes of the colonic epithelium. Hence, a smaller more invasive tumor will have a more advanced stage as compared to a larger but less invasive tumor. The clinical stage is therefore not directly linked with the absolute volume of a tumor. It can be hypothesized that the volume of a tumor can have an effect on the amount of biomarker secreted into the circulation. We have identified a protein, IGHG1, which was significantly correlated with the tumor volume of patients, and therefore represents a noninvasive readout of the size of a tumor . The clinical relevance of biomarker candidates of staging and grading is high because they can be used to develop patients' treatment strategy and to predict their prognosis, and have thus a prognostic and predictive promise. Together our results provide a concise list of proteins that are highly relevant for colorectal cancer due to their potential as diagnostic, prognostic, or predictive biomarkers.
Table 1 Clinical cohorts employed in the validation phase.
Clinical group Sub-group Clinical stage Cases Paired samples
Training cohort:
Colorectal cancer 100
Controls 100
Validation cohort:
Colorectal cancer (1) (la) I 43 7
(lb) II 58 14
(lc) III 49 5
(Id) IV 52
Total 202 26
Controls (2) (2)
Benign conditions (2a) 17
Healthy donors (2b) 50
Other malignancies (3) (3)
Lung cancer (3a) Consecutive 30
Pancreatic cancer (3b) Consecutive 30
TOTAL 555 Note by: Samples were collected at surgery prior to administration of any therapy, except for a subset of CRC cases, which was sampled at two time points, i.e. at surgery and one month after surgery, and is indicated as "paired samples".
Table 2 Discovery of biomarker candidates in CRC epithelia. Differentially abundant glycoproteins (adj.p-value < 0.05, fold change cut-off ±1.5) in at least one of five tested hypotheses: (1) CRC all stages tumor epithelia≠ CRC all stages normal epithelia, (2) CRC first stage tumor epithelia≠ CRC first stage normal epithelia, (3) CRC advanced stages tumor epithelia≠ CRC advanced stages normal epithelia, (4) CRC localised stages tumor epithelia≠ CRC localised stages normal epithelia, (5) CRC metastatic stage tumor epithelia≠ CRC metastatic stage normal epithelia. Gene names, protein names, and accession codes were defined according to UniProt Knowledgebase (www.uniprot.org .
Gene name Accession Protein name
1 APR3 Q6UW56 Apoptosis-related protein 3
2 A1BG P04217 Alpha- 1 B-glycoprotein
3 ABI3BP Q7Z7G0 Target of Nesh-SH3
4 ABP1 P19801 Amiloride-sensitive amine oxidase
5 ACE P12821 Angiotensin-converting enzyme
6 ACE2 Q9BYF1 Angiotensin-converting enzyme 2
7 AD AMI 0 014672 Disintegrin and metalloproteinase domain-containing protein 10
8 ADAM28 Q9UKQ2 Disintegrin and metalloproteinase domain-containing protein 28
9 ADAMDEC1 015204 ADAM DEC 1
10 ADAMTSL4 Q6UY14 ADAMTS-like protein 4
11 AEBP1 Q8IUX7 Adipocyte enhancer-binding protein 1
12 AFM P43652 Afamin
13 AGT P01019 Angiotensinogen
14 ANGPTL2 Q9UKU9 Angiopoietin-related protein 2
15 AN06 Q4KMQ2 Anoctamin-6
16 AN09 A1A5B4 Anoctamin-9
17 ANPEP P15144 Aminopeptidase N
18 AOC3 Q16853 Membrane primary amine oxidase APMAP Q9HDC9 Adipocyte plasma membrane-associated protein
APOB P04114 Apolipoprotein B-100
APOD P05090 Apolipoprotein D
APOH P02749 Beta-2-glycoprotein 1
ARHGAP20 Q9P2F6 Rho GTPase-activating protein 20
ASPH Q12797 Aspartyl/asparaginyl beta-hydroxylase
ASPN Q9BXN1 Asporin
ATP1B1 P05026 Sodium/potassium-transporting ATPase subunit beta- 1
ATP6AP1 Q 15904 V-type proton ATPase subunit SI
ATRN 075882 Attractin
AZGP1 P25311 Zinc-alpha-2-glycoprotein
AZU1 P20160 Azurocidin
B3GALT2 043825 Beta-l,3-galactosyltransferase 2
BCAM P50895 Basal cell adhesion molecule
BGN P21810 Biglycan
BSG P35613 Basigin
BTD P43251 Biotinidase
C1QA P02745 Complement Clq subcomponent subunit A
C1R P00736 Complement Clr subcomponent
C1RL Q9NZP8 Complement Clr subcomponent-like protein
C2 P06681 Complement C2
C22orf42 Q6IC83 Uncharacterized protein C22orf42
C3 P01024 Complement C3
C4A P0C0L4 Complement C4-A
CADM1 Q9BY67 Cell adhesion molecule 1
CADM3 Q8N126 Cell adhesion molecule 3
CD 109 Q6YHK3 CD 109 antigen
CD163L1 Q9NR16 Scavenger receptor cysteine-rich type 1 protein Ml 60
CD276 Q5ZPR3 CD276 antigen
CD34 P28906 Hematopoietic progenitor cell antigen CD34
CD36 P16671 Platelet glycoprotein 4 CD38 P28907 ADP-ribosyl cyclase 1
CD44 PI 6070 CD44 antigen
CD48 P09326 CD48 antigen
CD55 P08174 Complement decay-accelerating factor
CD58 PI 9256 Lymphocyte function-associated antigen 3
CD59 P13987 CD59 glycoprotein
CD6 P30203 T-cell differentiation antigen CD6
CD63 P08962 CD63 antigen
CD68 P34810 Macrosialin
CD74 P04233 HLA class II histocompatibility antigen gamma chain
CD97 P48960 CD97 antigen
CDCPl Q9H5V8 CUB domain-containing protein 1
CDH13 P55290 Cadherin-13
CDH17 Q12864 Cadherin-17
CDHR5 Q9HBB8 Cadherin-related family member 5
CEACAM1 P13688 Carcinoembryonic antigen-related cell adhesion molecule 1
CEACAM5 P06731 Carcinoembryonic antigen-related cell adhesion molecule 5
CEACAM6 P40199 Carcinoembryonic antigen-related cell adhesion molecule 6
CEACAM7 Q14002 Carcinoembryonic antigen-related cell adhesion molecule 7
CEACAM8 P31997 Carcinoembryonic antigen-related cell adhesion molecule 8
CECR1 Q9NZK5 Adenosine deaminase CECR1
CFB P00751 Complement factor B
CFH P08603 Complement factor H
CFHR2 P36980 Complement factor H-related protein 2
CFHR3 Q02985 Complement factor H-related protein 3
CGN Q9P2M7 Cingulin
CHD7 Q9P2D1 Chromodomain-helicase-DNA-binding protein 7 CLCA1 A8K7I4 Calcium-activated chloride channel regulator 1
CLPTM1 096005 Cleft lip and palate transmembrane protein 1
CLU P10909 Clusterin
CMA1 P23946 Chymase
CNPY3 Q9BT09 Protein canopy homolog 3
COL12A1 Q99715 Collagen alpha- 1
COL14A1 Q05707 Collagen alpha- 1
COL6A1 P12109 Collagen alpha- 1
COL6A2 P12110 Collagen alpha-2
COL6A3 P12111 Collagen alpha-3
CP P00450 Ceruloplasmin
CPD 075976 Carboxypeptidase D
CPE P16870 Carboxypeptidase E
CREG1 075629 Protein CREG1
CTSA P10619 Lysosomal protective protein
CTSC P53634 Dipeptidyl peptidase 1
CTSD P07339 Cathepsin D
CTSL1 P07711 Cathepsin LI
CYBB P04839 Cytochrome b-245 heavy chain
DAG1 Q14118 Dystroglycan
DCN P07585 Decorin
DMTF1 Q9Y222 Cyclin-D-binding Myb-like transcription factor 1
DPEP1 PI 6444 Dipeptidase 1
DPP4 P27487 Dipeptidyl peptidase 4
DSG2 Q14126 Desmoglein-2
ECE1 P42892 Endothelin-converting enzyme 1
EMILIN1 Q9Y6C2 EMILIN-1
EMILIN2 Q9BXX0 EMILIN-2
ENTPD5 075356 Ectonucleoside triphosphate diphosphohydrolase 5
ERAP2 Q6P179 Endoplasmic reticulum aminopeptidase 2
F2 P00734 Prothrombin
FAM3D Q96BQ1 Protein FAM3D FAS P25445 Tumor necrosis factor receptor superfamily member 6
FAT1 Q14517 Protocadherin Fat 1
FBN1 P35555 Fibrillin- 1
FCGBP Q9Y6R7 IgGFc-binding protein
FETUB Q9UGM5 Fetuin-B
FGB P02675 Fibrinogen beta chain
FGG P02679 Fibrinogen gamma chain
FGL2 Q14314 Fibroleukin
FKBP10 Q96AY3 Peptidyl-prolyl cis-trans isomerase FKBP10
FKBP9 095302 Peptidyl-prolyl cis-trans isomerase FKBP9
FMOD Q06828 Fibromodulin
FN1 P02751 Fibronectin
GAA PI 0253 Lysosomal alpha-glucosidase
GABRR1 P24046 Gamma-aminobutyric acid receptor subunit rho-1
GAL3ST2 Q9H3Q3 Galactose-3-O-sulfotransferase 2
GGT1 PI 9440 Gamma-glutamyltranspeptidase 1
GLG1 Q92896 Golgi apparatus protein 1
GOLIM4 000461 Golgi integral membrane protein 4
GOLM1 Q8NBJ4 Golgi membrane protein 1
GPNMB Q14956 Transmembrane glycoprotein NMB
GRN P28799 Granulins
HAPLN1 P10915 Hyaluronan and proteoglycan link protein 1
HLA-A P01892 HLA class I histocompatibility antigen, A-2 alpha chain
HLA-B P01889 HLA class I histocompatibility antigen, B-7 alpha chain
HLA-C P04222 HLA class I histocompatibility antigen, Cw-3 alpha chain
HLA-C Q95604 HLA class I histocompatibility antigen, Cw-17 alpha chain
HLA-DMB P28068 HLA class II histocompatibility antigen, DM beta chain HLA-DQA1 P01909 HLA class II histocompatibility antigen, DQ alpha 1 chain
HLA-DRB1 P01911 HLA class II histocompatibility antigen, DRBl-15 beta chain
HLA-DRB1 P01912 HLA class II histocompatibility antigen, DRBl-3 chain
HP P00738 Haptoglobin
H G P04196 Histidine-rich glycoprotein
HSP90B1 PI 4625 Endoplasmin
HSPG2 P98160 Basement membrane-specific heparan sulfate proteoglycan core protein
HYOU1 Q9Y4L1 Hypoxia up-regulated protein 1
ICAM1 P05362 Intercellular adhesion molecule 1
ICAM2 P13598 Intercellular adhesion molecule 2
ICAM3 P32942 Intercellular adhesion molecule 3
IDUA P35475 Alpha-L-iduronidase
IGHA1 P01876 Ig alpha- 1 chain C region
IGHA2 P01877 Ig alpha-2 chain C region
IGHG1 P01857 Ig gamma- 1 chain C region
IGHG2 P01859 Ig gamma-2 chain C region
IGHG4 P01861 Ig gamma-4 chain C region
IGHM P01871 Ig mu chain C region
ITGA1 P56199 Integrin alpha- 1
ITGA2 P17301 Integrin alpha-2
ITGA5 P08648 Integrin alpha-5
ITGA6 P23229 Integrin alpha-6
ITGAL P20701 Integrin alpha-L
ITGAV P06756 Integrin alpha-V
ITGAX P20702 Integrin alpha-X
ITGB1 P05556 Integrin beta-1
ITGB2 P05107 Integrin beta-2
ITIH1 P19827 Inter-alpha-trypsin inhibitor heavy chain HI ITIH2 PI 9823 Inter-alpha-trypsin inhibitor heavy chain H2
ITIH3 Q06033 Inter-alpha-trypsin inhibitor heavy chain H3
ITIH4 Q 14624 Inter-alpha-trypsin inhibitor heavy chain H4
ITPR1 Q 14643 Inositol 1,4,5-trisphosphate receptor type 1
KDELC2 Q7Z4H8 KDEL motif-containing protein 2
KIAA0090 Q8N766 Uncharacterized protein KIAA0090
KLKB1 P03952 Plasma kallikrein
KNGl P01042 Kininogen-1
LI CAM P32004 Neural cell adhesion molecule LI
LAMA2 P24043 Laminin subunit alpha-2
LAMA3 Q16787 Laminin subunit alpha- 3
LAMA4 Q16363 Laminin subunit alpha-4
LAMA5 015230 Laminin subunit alpha-5
LAMB1 P07942 Laminin subunit beta-1
LAMB2 P55268 Laminin subunit beta-2
LAMC1 PI 1047 Laminin subunit gamma- 1
LAMC2 Q13753 Laminin subunit gamma-2
LAMP1 PI 1279 Lysosome-associated membrane glycoprotein 1
LAMP2 P13473 Lysosome-associated membrane glycoprotein 2
LCN2 P80188 Neutrophil gelatinase-associated lipocalin
LGALS3BP Q08380 Galectin-3 -binding protein
LGMN Q99538 Legumain
LIPA P38571 Lysosomal acid lipase/cholesteryl ester hydrolase
LRGl P02750 Leucine-rich alpha-2-glycoprotein
LRP1 Q07954 Prolow-density lipoprotein receptor-related protein 1
LSAMP Q 13449 Limbic system-associated membrane protein
LTBP2 Q14767 Latent-transforming growth factor beta-binding protein 2
LTBP4 Q8N2S1 Latent-transforming growth factor beta-binding protein 4
LTF P02788 Lactotransferrin
LUM P51884 Lumican LY75 060449 Lymphocyte antigen 75
LYVE1 Q9Y5Y7 Lymphatic vessel endothelial hyaluronic acid receptor
1
M6PR P20645 Cation-dependent mannose-6-phosphate receptor
MAN2B1 000754 Lysosomal alpha-mannosidase
MCAM P43121 Cell surface glycoprotein MUC18
MFAP4 P55083 Microfibril-associated glycoprotein 4
MICA Q29983 MHC class I polypeptide-related sequence A
MPO P05164 Myeloperoxidase
MPZL2 060487 Myelin protein zero-like protein 2
MRC2 Q9UBG0 C-type mannose receptor 2
MSR1 P21757 Macrophage scavenger receptor types I and II
MUC12 Q9UKN1 Mucin- 12
MUC2 Q02817 Mucin-2
MUC4 Q99102 Mucin-4
MUC5B Q9HC84 Mucin-5B
NAAA Q02083 N-acylethanolamine-hydrolyzing acid amidase
NAV2 Q8IVL1 Neuron navigator 2
NCAM1 P13591 Neural cell adhesion molecule 1
NCEH1 Q6PIU2 Neutral cholesterol ester hydrolase 1
NCSTN Q92542 Nicastrin
NID2 Q14112 Nidogen-2
NPTN Q9Y639 Neuroplastin
NT5E P21589 5'-nucleotidase
OGN P20774 Mimecan
OLFM4 Q6UX06 Olfactomedin-4
OLFML3 Q9NRN5 Olfactomedin-like protein 3
P2RX4 Q99571 P2X purinoceptor 4
P4HA1 PI 3674 Prolyl 4-hydroxylase subunit alpha- 1
PDCD1LG2 Q9BQ51 Programmed cell death 1 ligand 2
PIGR P01833 Polymeric immunoglobulin receptor
PLA2G15 Q8NCC3 Group XV phospholipase A2 PLBD1 Q6P4A8 Phospholipase B-like 1
PLOD1 Q02809 Procollagen-lysine,2-oxoglutarate 5-dioxygenase 1
PLTP P55058 Phospholipid transfer protein
PLXNB2 015031 Plexin-B2
POGLUT1 Q8NBL1 Protein O-glucosyltransferase 1
POSTN Q15063 Periostin
PRELP P51888 Prolargin
PRNP P04156 Major prion protein
PROM1 043490 Prominin-1
PTGDS P41222 Prostaglandin-H2 D-isomerase
PTGFRN Q9P2B2 Prostaglandin F2 receptor negative regulator
PTK7 Q13308 Inactive tyrosine-protein kinase 7
PTPRC P08575 Receptor-type tyrosine-protein phosphatase C
PTPRJ Q12913 Receptor-type tyrosine-protein phosphatase eta
PTPRK Q 15262 Receptor-type tyrosine-protein phosphatase kappa
PVR P15151 Poliovirus receptor
PXDN Q92626 Peroxidasin homolog
QPCT Q 16769 Glutaminyl-peptide cyclotransferase
RALY Q9UKM9 RNA-binding protein Raly
RNF13 043567 E3 ubiquitin-protein ligase RNF13
SCARB2 Q14108 Lysosome membrane protein 2
SEL1L Q9UBV2 Protein sel-1 homolog 1
SEL1L3 Q68CR1 Protein sel-1 homolog 3
SEMA4D Q92854 Semaphorin-4D
SEPP1 P49908 Selenoprotein P
SERPINA1 P01009 Alpha- 1 -antitrypsin
SERPINA3 P01011 Alpha- 1 -antichymotrypsin
SERPINA4 P29622 Kallistatin
SERPINC1 P01008 Antithrombin-III
SERPIND1 P05546 Heparin cofactor 2
SERPING1 P05155 Plasma protease CI inhibitor
SGCE 043556 Epsilon-sarcoglycan SI P14410 Sucrase-isomaltase, intestinal
SIAE Q9HAT2 Sialate O-acetylesterase
SLC2A1 P11166 Solute carrier family 2, facilitated glucose transporter member 1
SLC3A2 P08195 4F2 cell-surface antigen heavy chain
SLC44A1 Q8WWI5 Choline transporter-like protein 1
SLC44A2 Q8IWA5 Choline transporter-like protein 2
SOD3 P08294 Extracellular superoxide dismutase
SORT! Q99523 Sortilin
SPPL2A Q8TCT8 Signal peptide peptidase-like 2A
SSR2 P43308 Translocon-associated protein subunit beta
ST6GALNAC1 Q9NSC7 Alpha-N-acetylgalactosaminide alpha-2,6- sialyltransferase 1
ST6GALNAC6 Q969X2 Alpha-N-acetylgalactosaminide alpha-2,6- sialyltransferase 6
STAB1 Q9NY15 Stabilin-1
STIMl Q13586 Stromal interaction molecule 1
STT3A P46977 Dolichyl-diphosphooligosaccharide—protein
glycosyltransferase subunit STT3A
STT3B Q8TCJ2 Dolichyl-diphosphooligosaccharide—protein
glycosyltransferase subunit STT3B
SUN2 Q9UH99 SUN domain-containing protein 2
SYPL1 Q 16563 Synaptophysin-like protein 1
TCN1 P20061 Transcobalamin- 1
TF P02787 Serotransferrin
TFRC P02786 Transferrin receptor protein 1
THBD P07204 Thrombomodulin
THBSl P07996 Thrombospondin- 1
THBS3 P49746 Thrombospondin-3
THY1 P04216 Thy-1 membrane glycoprotein
TIMP1 P01033 Metalloproteinase inhibitor 1
TM9SF3 Q9HD45 Transmembrane 9 superfamily member 3 284 TMED4 Q7Z7H5 Transmembrane emp24 domain-containing protein 4
285 TMED9 Q9BVK6 Transmembrane emp24 domain-containing protein 9
286 TMEM87A Q8NBN3 Transmembrane protein 87A
287 TMX3 Q96JJ7 Protein disulfide-isomerase TMX3
288 TNC P24821 Tenascin
289 TNXB P22105 Tenascin-X
290 TPBG Q13641 Trophoblast glycoprotein
291 TPP1 014773 Tripeptidyl-peptidase 1
292 TPSB2 P20231 Tryptase beta-2
293 TSPA 1 060635 Tetraspanin-1
294 TSPAN13 095857 Tetraspanin-13
295 TSPAN8 P19075 Tetraspanin-8
296 TXNDC15 Q96J42 Thioredoxin domain-containing protein 15
297 UGT1A9 060656 UDP-glucuronosyltransferase 1-9
298 UGT8 Q16880 2-hydroxyacylsphingosine 1-beta- galactosyltransferase
299 VCAN P13611 Versican core protein
300 VTN P04004 Vitronectin
301 VWF P04275 von Willebrand factor
302 WT1 PI 9544 Wilms tumor protein
303 ZNF844 Q08AG5 Zinc finger protein 844
Table 3 Biomarker candidates verified and validated in patient plasma (n=88). The majority of candidates was discovered in CRC tumor epithelia (n=77) and a minority was supplemented from literature (n=l l). The status of a candidate in the biomarker development process is indicated as verified if quantified in 60 samples and as validated if quantified in another 355 samples. Gene names, protein names, and accession codes were defined according to UniProt Knowledgebase (www.uriiprot.org).
Gene name UniProt Protein name Biomarker Discovered candidate in CRC status epithelia
1 A1AG2 P19652 Alpha- 1 -acid glycoprotein 2 validated yes AFM P43652 Afamin validated yes
AHSG P02765 Alpha-2-HS-glycoprotein validated yes
(Fetuin-A)
ANPEP P15144 Aminopeptidase N verified yes
ANT3 P01008 Antithrombin-III (Serpin CI) validated yes
AOC3 Q16853 Membrane primary amine validated yes oxidase
APMAP Q9HDC9 Adipocyte plasma membrane- validated yes associated protein
APOB P04114 Apolipoprotein B-100 validated yes
ATRN 075882 Attractin validated yes
B3GN2 Q9NY97 UDP-GlcNAc:betaGal beta- verified no
1,3-N- acetylglucosaminyltransferase
2
BTD P43251 Biotinidase (Biotinase) validated yes
CADM1 Q9BY67 Cell adhesion molecule 1 validated yes
CD 109 Q6YHK3 CD109 antigen validated yes
CD 163 Q86VB7 Scavenger receptor cysteine- validated yes rich type 1 protein Ml 30
CD44 PI 6070 CD44 antigen (Extracellular validated yes matrix receptor III)
(Hyaluronate receptor)
CDH5 P33151 Cadherin-5 (CD144) validated no
CFH P08603 Complement factor H validated yes
CFI P05156 Complement factor I validated yes
CLU PI 0909 Clusterin validated yes
CNTN4 Q8IWV2 Contactin-4 verified no
C04A P0C0L4 Complement C4-A validated yes
CP P00450 Ceruloplasmin validated yes
CTSD P07339 Cathepsin D validated yes
DKFZp686C02220 Q6N091 Putative uncharacterized validated yes protein DKFZp686C02220
DPEP1 PI 6444 Dipeptidase 1 verified no
DSG2 Q14126 Desmoglein-2 (Cadherin validated yes family member 5)
ECM1 Q16610 Extracellular matrix protein 1 validated yes
Fl l P03951 Coagulation factor XI validated no
F5 P12259 Coagulation factor V validated no
FCGBP Q9Y6R7 IgGFc-binding protein validated yes
FETUB Q9UGM5 Fetuin-B validated yes
FGA P02671 Fibrinogen alpha chain validated yes
FGG P02679 Fibrinogen gamma chain validated yes
FHR3 Q02985 Complement factor H-related validated yes protein 3
FN1 P02751 Fibronectin validated yes
GOLM1 Q8NBJ4 Golgi membrane protein 1 validated yes
(Golgi phosphoprotein 2)
HLA-A P01892 HLA class I validated yes histocompatibility antigen, A- 2 alpha chain
HP P00738 Haptoglobin validated yes
HPX P02790 Hemopexin (Beta-IB- validated yes glycoprotein)
HRG P04196 Histidine-rich glycoprotein validated yes
HYOU1 Q9Y4L1 Hypoxia up-regulated protein validated yes
1
ICAM1 P05362 Intercellular adhesion validated yes molecule 1 (CD54)
ICAM2 P13598 Intercellular adhesion validated yes molecule 2 (CD 102)
IGFBP3 P17936 Insulin-like growth factor- validated yes binding protein 3
IGHA2 P01877 Ig alpha-2 chain C region validated yes IGHG1 P01857 Ig gamma- 1 chain C region validated yes
IGHG2 P01859 Ig gamma-2 chain C region validated yes
IGHM P01871 Ig mu chain C region validated yes
IGJ P01591 Immunoglobulin J chain validated yes
ISLR 014498 Immunoglobulin superfamily verified no containing leucine-rich repeat
protein
ITIH4 Q 14624 Inter-alpha-trypsin inhibitor validated yes heavy chain H4
KDR P35968 Vascular endothelial growth validated yes factor receptor 2 (CD309)
KLKB1 P03952 Plasma kallikrein validated yes
(Kininogenin)
K G1 P01042 Kininogen-1 (Alpha-2-thiol validated yes proteinase inhibitor)
LAMA2 P24043 Laminin subunit alpha-2 verified yes
LAMP2 P13473 Lysosome-associated validated yes membrane glycoprotein 2
(CD107b)
LCN2 P80188 Neutrophil gelatinase- validated yes associated lipocalin
(Oncogene 24p3)
LGALS3BP Q08380 Galectin-3-binding protein validated yes
(Mac-2 BP) (Tumor- associated antigen 90K)
LRG1 P02750 Leucine-rich alpha-2- validated yes glycoprotein
LUM P51884 Lumican validated yes
LYVE1 Q9Y5Y7 Lymphatic vessel endothelial validated yes hyaluronic acid receptor 1
MFAP4 P55083 Microfibril-associated validated yes glycoprotein 4 MMR 1 Q13201 Multimerin-1 (EMILIN-4) validated yes
MPO P05164 Myeloperoxidase validated yes
MRC2 Q9UBG0 C-type mannose receptor 2 validated yes
(CD280)
MST1 Q 13043 Serine/threonine-protein validated yes kinase 4
NCAM1 P13591 Neural cell adhesion validated yes molecule 1 (CD56)
NEOl Q92859 Neogenin verified yes
ORM1 P02763 Alpha- 1 -acid glycoprotein 1 validated yes
PGCP Q9Y646 Plasma glutamate validated no carboxypeptidase
PIGR P01833 Polymeric immunoglobulin validated yes receptor (Hepatocellular
carcinoma-associated protein
TB6)
PLTP P55058 Phospholipid transfer protein validated yes
PLXDC2 Q6UX71 Plexin domain-containing validated yes protein 2 (Tumor endothelial
marker 7-related protein)
PLXNB2 015031 Plexin-B2 validated yes
PON1 P27169 Serum validated no paraoxonase/arylesterase 1
PRG4 Q92954 Proteoglycan 4 validated no
PROC P04070 Vitamin K-dependent protein validated no
C
PTPRJ Q12913 Receptor-type tyrosine- validated yes protein phosphatase eta
(CD148)
SERPINA1 P01009 Alpha- 1 -antitrypsin (Serpin validated yes
Al)
SERPINA3 P01011 Alpha- 1 -antichymotrypsin validated yes (Cell growth-inhibiting gene
24/25 protein) (Serpin A3)
81 SERPINA6 P08185 Corticosteroid-binding validated yes globulin (Serpin A6)
82 SERPINA7 P05543 Thyroxine-binding globulin validated yes
(Serpin A7)
83 THBSl P07996 Thrombospondin- 1 validated yes
84 TIMP1 P01033 Tissue inhibitor of validated yes metalloproteinases 1
85 TNC P24821 Tenascin (Tenascin-C) validated yes
86 TRF P02787 Serotransferrin (Transferrin) verified yes
87 VTN P04004 Vitronectin validated yes
88 VWF P04275 von Willebrand factor validated yes
In a second analysis the following approach was followed:
Phase 1 : Biomarker candidate discovery in tumor epithelia: To maximize the identification of colorectal cancer (CRC) biomarkers in the circulation, a phased biomarker development pipeline was used. Human primary tumors together with adjacent normal mucosa were selected from 16 patients (the set of patients included early progression and advanced stages, localised and metastatic as well as stage I-IV) as the best suitable source of biomarkers, and tissue epithelia were manually dissected to enrich for cells of cancer origin and to obtain samples with homogenous protein composition. To further enhance the capture of circulating proteins and to gain access to the lower abundant fraction of the plasma proteome, it was selectively focused on glycoproteins, which generally are cell surface and extracellular proteins prone to secretion or shedding, and represent the vast majority of currently approved biomarkers.
Epithelial lysates derived from 32 paired tumor and normal samples were subjected to proteolysis, followed by solid-phase extraction of N-linked glycopeptides. Purified N- glycosite peptides were analyzed by high-resolution liquid chromatography tandem mass spectrometry (LC-MS/MS), which lead to the identification of 2301 glycopeptides and 673 inferred glycoproteins. Prediction analysis of secondary protein structures annotated 73% of proteins to be secreted and 53% of proteins to contain at least one transmembrane domain, which is indicative of a strong enrichment for proteins of the circulatory system. Peptide MSI -level features were quantified across 74 LC-MS runs and used to characterize proteins that are changing in their abundance between tumor samples and their matched normal counterparts, and to assess differential protein expression across cancer progression. In total, 303 differentially abundant glycoproteins (adjusted p<0.05, log2 FC cut-off ±1) characterized robust protein changes in CRC no matter the clinical stage, distinct changes across disease progression and specific differences between localized and metastatic CRC.
Phase 2: Screening of biomarker candidates in patient plasma: The hypothesis that secreted and cell surface protein candidates of CRC are destined to reach the circulation was tested in the screening phase, where differentially abundant glycoproteins in CRC were supplemented with additional proteins identified in the tumor glycoproteome and a few biomarker candidates identified in other ongoing biomarker studies to test the detection of these proteins in plasma. In combination this protein biomarker candidate list represents proteins regulated by and playing major roles in CRC tumorigenesis.
Targeted mass spectrometry based on selected reaction monitoring (SRM) was employed to screen for tissue-derived candidates in plasma-enriched N-glycosite samples from 19 patients. Using the targeted approach, we detected 88 candidate proteins consistently in all plasma samples. The dynamic range of the plasma proteome spreads over more than 10 orders of magnitude and poses a limitation to its comprehensive analysis. The results demonstrate that the detected and quantified candidates cover 6 orders of magnitude, which currently represents the largest abundance range quantifiable in a single LC-MS analysis of plasma.
Phase 3: Development of a diagnostic biomarker signature: The detected biomarker candidates in plasma were then subjected to clinical evaluation in two independent cohorts of samples (n = 469) to ascertain biomarker candidates with diagnostic value (Table 4). Table 4. Clinical cohorts used for the biomarker signature development.
Discovery cohort (1)
Colorectal Cancer Controls
Stage unknown 3 Non-advanced adenoma 23
Stage I 32 Hyperplastic polyps 11
Stage II 26 Negative 66 Stage III 31 Total 100
Stage IV 8
Total 100
Validation cohort (2)
Colorectal Cancer Controls
Stage I 43 Non-malignant GIT conditions* 17
Stage II 58 Clinically healthy subjects 50
Stage III 49 Total 67
Stage IV 52
Total 202
Subjects in the discovery cohort were selected from a prospective screening study and a case-control study, and their status was colonoscopy-confirmed. *Non-malignant gastrointestinal tract (GIT) conditions include adenoma, benign state, diverticular disease, dysplastic polyp, and Crohn's disease.
The first cohort - the discovery cohort - was designed to reflect an underlying population with CRC or at risk, and used for the discovery of a diagnostic biomarker signature. It includes a control (n=100) and a disease group (n=100), and the disease status of the included subjects is confirmed by colonoscopy. The second cohort - the validation cohort - is comprised of clinically healthy subjects (n = 50), cases with non-malignant gastrointestinal conditions (n = 17), and CRC patients at distinct clinical stages (stage I: n = 43, stage II: n = 58, stage III: n= 49, stage IV: n = 52). The validation cohort was conceived to test the discovered biomarker signature on independent samples and to evaluate the classification of CRC patients with respect to clinical stage. Plasma samples were subjected to parallel N-glycoprotein extraction in a 96 well format followed by targeted SRM analysis. Candidates, together with two protein standards, were combined into a 90-plex SRM method and used to profile the biomarker candidates over the plasma- enriched N-glycosite samples. Of the 88 biomarker candidates, 70 proteins were consistently quantified across the two sample sets and comprise by far the largest clinical dataset measured by LC-MS to date.
To develop the diagnostic biomarker signature in a first Method the training dataset acquired on the discovery cohort was separated into ten equal folds to characterize protein combinations with predictive value within 10-fold cross-validation (Figure 10). First, differentially abundant proteins between CRC and control cases were characterized by statistical significance analysis and lead to the selection of the most relevant proteins for classification (Table 5a). The significant proteins in each fold were then used as input for the selection of proteins into logistic regression models and for the identification of the most predictive model in each fold. Finally, a consensus model was determined by the frequency of protein selection among the folds (Table 5b). The discovered consensus protein combination was comprised of ceruloplasmin (CP), serum paraoxonase/arylesterase 1 (PON1), serpin peptidase inhibitor, clade A (SERPINA3), leucine-rich alpha-2-glycoprotein (LRG1), and tissue inhibitor of metalloproteinases 1 (TIMP1).
The approach is based on prioritizing proteins with significant differences in protein abundance between the CRC and control groups, and a subsequent stepwise selection of the most discriminative proteins into the biomarker signature. To approximate an optimum predictor on the training dataset, in a second Method all protein combinations of up to five proteins in the training dataset were enumerated by exhaustive search and 100-fold bootstrapped cross-validationS and evaluated the obtained logistic regression models by their area under the receiver operating characteristic (ROC) curve (AUG) (Figure 10). The best models were found to have a similar cross-validation performance and therefore the proteins present in these models were ranked by their frequency of occurrence among these models. The top ranked proteins include the protems selected into our diagnostic signature by significance testing and stepwise selection. Further, a few other proteins were also ranked high on the list and could in theory be used as 'back-up' proteins in case a future assay for a protein within the diagnostic signature will not fulfill required analytical criteria.
Table 5. Biomarker signature development within 10-fold CV. a, Differentially abundant proteins characterised as significant in the individual folds of the training dataset. b, Proteins selected into logistic regression models in individual folds. The consensus model contains proteins with a high frequency of occurrence in the individual folds.
a Significant proteins for each fold (FDR<0.05, fold change cut-off ±1.1)
Fold Differentially abundant proteins
A 1 AG2,CP,CTSD,ECM 1 ,FHR3 ,HP,TT1H4,LGALS3BP,LRG 1 ,MMRN1 ,ORM 1 ,ΡΟΝ 1 , S
1 ERPINA 1 ,SERPINA3 ,THBS 1 ,Τ Ρ 1 ,CD44,CFH A1AG2,CD44,CFH,CP,CTSD,ECM1,FHR3,HP,ITIH4,LGALS3BP,LRG1,MMRN1,0R
2 M 1 , SERPINA 1 , SERPINA3 ,ΤΪ Ρ 1 , SERPINA7,F5
Al AG2,CD44,CFH,CP,CTSD,ECM1 ,FGG,FHR3,HP,ITIH4,LGALS3BP,LRG1 ,MMRN
3 1 ,QRM1 , SERPINA! ,SERPINA3,TIMP1 ,VTN
Al AG2,CFH,CP,CTSD,ECM 1 ,FHR3,ITIH4,LGALS3BP,LRG1 ,MMRN 1 ,ORM 1 ,SERPI
4 N A 1 , S ERPJN A3 ,TI M P 1 ,HP,CD44,PRG4
A 1 AG2,CP,CTSD,ECM 1 ,FHR3 ,FN 1 ,ITIH4,LGALS3BP,LRG 1 ,MMRN 1 ,ORM 1 ,ΡΟΝ 1 ,
5 SERPINA1 ,SERPINA3,TIMP1 ,HP,CD44,PRG4
A 1 AG2,CFH,CP,CTSD,ECM 1 ,FHR3 ,FN1 ,HP,IGHG2,ITIH4,LGALS3BP,LRG 1 ,MMR
6 Nl ,ORMl ,ΡΟΝ 1 , SERPINA 1 ,SERPINA3,T1MP1 ,CD44,FCGBP
AlAG2,CP,CTSD,ECMl,FHR3,IGHG2,ITIH4,LGALS3BP,LRGl,MMRNl,ORMl,PO
7 N 1 , SERPINA 1 , SERPINA3 ,ΤΙΜΡ 1 ,CFH,CD44
Al AG2,CFH,CP,CTSD,EC I ,FHR3,ITIH4,LGALS3BP,LRG 1 ,MMRN 1 ,ORM 1 ,ΡΟΝ 1 ,
8 SERPINA1 ,SERPINA3,TIMP1 ,CD44,F5,HP
A 1 AG2,CP,CTSD,ECM 1 ,FHR3 ,FN 1 ,IGHA2,IGHG2,LGALS3BP,LRG 1 ,MMRN1 ,ORM
9 1 ,ΡΟΝ 1 , SERPINA 1 ,SERPE\FA3 ,ΤΜΡ 1 ,CD44,HP,PRG4
AlAG2,CFH,CP,CTSD,ECMl,FHR3,FNl,ITIH4,LGALS3BP,LRGl,MMRNl,ORMl,S
II ERPPNA1,SERPINA3,TIMP1,HP,CD44
b Significant proteins selected into logistic regression models by stepwise selection
Predictive logistic Sub- Validasion models Sub-Training Validation Training tion (set
F (9/10 set 1) (1/10 set 1) (set 1) 2)
CP+LRGl+PONl+SERPIN
1 A3 0.747 0.75 0.750 0.828
CD44+CP+ITIH4+LRG1 +
2 ORM1 0.728 0.55 0.719 0.815
CP+FGG+HP+1T1H4+OR
3 Ml+TIM l 0.729 0.48 0.709 0.821
LRG 1 +MMRN 1 +ORM 1 +P
4 RG4 0.746 0.61 0.731 0.797
CP+PON1 +SERPP A3+TI
5 MP1 0.745 0.73 0.743 0.835
CP+CTSD+IGHG2+PON 1
6 +SERPINA3 0.764 0.72 0.762 0.840
CP+IGHG2+PON 1 +SERPI 0.768 0.68 0.766 0.853 NA3+TIMPJ
CP+ECMl+PONl+SERPI
8 NA3 0.748 0.43 0.738 0.812
CP+IGHA2+IGHG2+LGA
9 LS3BP+PO 1 +SERPIN A3 0.789 0.74 0.768 0.846
CP+FHR3+FN1+ITIH4+LR
10 G1+TMP1 0.747 0.54 0.722 0.840
CP+PON 1 +SERPINA3+LR
Consensus Gl+TIMPl 0.752 0.839
The diagnostic signature model was then parameterized on the full training dataset and predicted the class of the discovery cohort cases with an agreement of 70% (Figure 10). The class prediction ability of the diagnostic signature was then assessed on the independent testing dataset acquired on the validation cohort. Similarly to the training accuracy, the correct class of CRC and control cases was assigned for 72% of the cases (Figure 11a), which shows a high agreement in performance on the two independent datasets.
Next, the class prediction accuracy was examined across the four clinical stages of CRC. The AUCs of the distinct stages as well as of all CRC cases were not significantly different from each other (Figure l ib), which shows that our biomarker signature can predict equally well early and advanced disease. The clinical staging of CRC based on the TNM system (represents the invasiveness of the disease in terms of the tumor spread across the mucosal membrane. The tumor extent as such is not represented in this system and as a result a smaller but more invasive tumor will have a more advanced stage than a larger but less invasive tumor. This may have important implications for the biomarker secretion into the circulation because a larger tumor should theoretically secrete more biomarker than a smaller tumor. To test this hypothesis we grouped CRC patients into three groups based on their maximal tumor diameter (group 1: <3.5cm, group 2: 3-5-6 cm, group 3: >6cm) and classified the subjects within these groups with our diagnostic signature. Effectively, patients with large tumors were significantly better classified than patients with small tumors (Figure 11c).
Based on this dataset and using analogous statistical analysis, the following biomarker signatures for colorectal cancer were identified:
· Disease detection (as detailed above) Non-invasive detection of CRC is a critical clinical need because it can help to diagnose CRC at early stages. Furthermore, it can help the screening program to reduce the number of false positive cases determined by the current screening standard - feacal occult blood test (FOBT) - that need to be evaluated by invasive colonoscopy.
Signature: CP+PON 1 +SERPINA3 combined with either (+LRG1+TIMP1) or with (+IGFBP3 + ATRN)
• Disease localization
There are three regional distinctions of CRC tumors based on anatomical location: Right- sided tumors proximal to the splenic flexure, Left-sided tumors distal to the splenic flexure, and rectum tumors. Non-invasive biomarkers of the regional diagnosis of CRC would assist the oncologist with the site where to begin colonoscopic intervention.
Signature: GOLM 1 +HL A- A+H YOU 1 +MRC2+NC AM 1 +SERPIN A3
• Detection of metastatic disease
Patients with metastatic disease need to be diagnosed by imaging technologies and may not benefit from surgical resection. Non-invasive indication of advanced disease with the presence of metastases may help the oncologist to provide the patient with best possible treatment.
Signature: PTPRJ+PIGR+HPX+FETUB+IGHG2+VTN+APOB+ATRN
β Molecular characteristics: KRAS
Patients with mutant KRAS gene will not benefit from anti-EGFR therapies. Currently, KRAS is determined from the DNA extracted from the tumor. However, for about 20% of patients the quality of DNA in the tumor is not good enough to perform this test and thus could receive this therapy without any benefit but with potential side effects. Non-invasive KRAS status determination would therefore be highly desired.
Signature: IGHG2+IGHA2+F5+LYVE1+ITIH4+FHR3
• Molecular characteristics: MSI
Patients with microsatellite instability have a better prognosis and are unlikely to benefit from 5FU chemotherapy compared to patients with microsatellite stability. Similarly to above, a non-invasive determination of the microsatellite status is desired.
Signature: F5+V WF+FETUB+1GH A2+1GFBP3+ORM 1 +PTPRJ+C04A
• Prognosis
Patients with a better or worse prognosis will be provided with different treatment solutions and therefore non-invasive markers of patients outcome is highly desired and relevant for patient care.
Signature: HLA.A+VWF+TNC+MRC2+FHR3+FCGBP+PTPRJ+CD109
Experimental details for the study:
Collection and preparation of tissue epithelia. Tumor and adjacent normal musosa tissues were surgically resected based on standard oncological procedures. Frozen tissue was further processed in a pre-cooled cryostat (-20°C). Sections of 7μηι were fixed with 4% formalin, stained with Hematoxilin-Eosin and adjacent 40 μηι sections were manually dissected and placed in lysis buffer (50% PBS liquid, pH 7.4 (GIBCO, Invitrogen) and 50% 2,2,2-Trifluoroethanol (Fluka, 99.9% purity)) until 50mg of dissected epithelium was obtained per sample.
Protein extraction and peptide isolation. Tissue epithelia were homogenized in a Microdismembrator S (Sartorius), subjected to protein extraction in lysis buffer (as above) and solubilized with 1% Rapigest (Waters) in 250mM ammonium bicarbonate. Ultra sonication in a vial-tweeter ultrasonicator (Hielscher) at 4°C was used to further disintegrate the homogenized tissue. Proteins were denatured at 60°C for 2h, reduced with 5mM dithiotreitol (DTT) at 60°C for 30 min, and alkylated with 25mM iodoacetamide (IAA) at 25°C for 45 min in the dark. Samples were diluted to 15% TFE in lOOmM ammonium bicarbonate and proteolyzed with sequencing grade porcine trypsin (Promega) at a protease to substrate ratio of 1:100, at 37°C for 15h. Peptide mixtures were desalted with Sep-Pak tC18 cartridges (Waters, Milford, MA, USA), eluted with 50% acetonitrile / 0.1% formic acid, evaporated to dryness, and resolubilized in ΙΟΟμΙ 20mM sodium acetate, lOOmM sodium chloride, pH 5.
Glycopeptide enrichment. Glycopeptides were isolated as described previously. N-linked glycosylated peptides were released with N-glycosidase F (PNGase F; Roche and New England Biolabs). Formerly glycosylated peptides were desalted as above and resolubilised in ΙΟΟμΙ HPLC grade water / 2% acetonitrile / 0.1% formic acid.
Discovery-driven LC-MS of tissue N-glycosites. LC -MS/MS analyses were carried out on a hybrid LTQ-FT-ICR mass spectrometer (Thermo Electron) interfaced to a nanoelectrospray ion source (Thermo Electron) coupled to a Tempo NanoLC system (ABI/MDS Sciex). 2 μΐ of N-glycosite samples were loaded from a cooled (4°C) autosampler (ABI/MDS Sciex) and separated on a 15 cm fused silica emitter, 75μηι diameter, packed in-house with a Magic CI 8 AQ 3μιη resin (Michrom BioResources) using a linear gradient from 5% to 35% acetonitrile / 0.1% formic acid over 60 or 90 min, at a flow rate of 300 nl/min. In data-dependent analysis (DDA) mode, each MSI survey scan acquired in the ICR cell with an overall cycle time of approximately 1 s, exceeding 150 counts, was followed by collision-induced dissociation (CID) acquired in the LTQ of the three most abundant precursor ions with a dynamic exclusion of 30 s. For MSI, 106 ions were accumulated in the ICR cell over a maximum time of 500 ms and scanned at a resolution of 100 000 full- width at half-maximum nominal resolution settings. MS2 spectra were acquired using the normal scan mode, a target setting of 104 ions, and an accumulation time of maximally 250 ms. Charge state screening was used to select ions with at least two charges and to reject ions with unassigned charge state. Normalized collision energy was set to 32%, and one microscan was acquired for each spectrum. Samples were acquired in duplicates or triplicates.
Protein identification and CRC N-glycosite Peptide Atlas compilation. Raw data files were centroided and converted to the mzXML format with ReAdW (http://tools.proteomecenter.org/wiki/index.php?title=Software:ReAdW). MS/MS spectra were searched using the SORCERER-SEQUEST search tool against a semitryptic human UniProt protein database (http://www.uniprot.org). The search criteria were set to: cleavage after lysine or arginine, unless followed by proline, at least at one tryptic terminus; maximally one missed cleavage allowed; cysteine carbamidomethylation set as fixed modification; methionine oxidation and asparagine deamidation set as variable modifications; monoisotopic parent and fragment ion masses; and precursor ion mass tolerance of 50 ppm. The database search results were further validated with the Trans- Proteomic Pipeline (TPP), with a false positive rate was set to 1% on both peptide and protein level, as determined by PeptideProphet9 and ProteinProphetlO, respectively. Data was uploaded to the PeptideAtlas (http://www.peptideatlas.org/) and processed with the settings described above.
Protein topology prediction. Prediction of secondary protein structure was performed from the amino acid sequence with Phobius (http://phobius.sbc.su.se/).
Relative quantification and statistical testing of CRC tissue N-glycosites. Peptides were filtered for the glycosylation motif. Raw data were converted to a profile mzXML format as above. Label-free quantification was performed with 74 runs together with the search results by OpenMS 1.711 where PeakPicker ("high res" algorithm), FeatureFinder ("centroided" algorithm), IDMapper, MapAligner ("identification" algorithm), FeatureLinker ("unlabeled" algorithm), and ProteinQuantifier were employed. The quantitative data were logarithm base 2 transformed and a scale-normalization procedure was applied. Features missing in more than five-sixths of the runs or in an entire experimental group were removed from the dataset. Protein quantification was performed with the publicity available software MSstats
(http://www.stat.purdue.edu/~ovitek/Software.html). Comparisons of mean protein abundance between conditions were carried out in MSstats and p-values were adjusted with the Benjamini-Hochberg procedure.
Blood collection and plasma preparation. Patients from the screening, discovery, training, and validation cohorts all have signed an informed consent document. Blood was drawn prior to surgery from the cubital vein and collected into tubes processed with EDTA. Blood was directly centrifuged at 4500rpm for 3min at 4°C. Plasma was collected into a new tube, frozen at -20°C and stored at -80°C. In the training cohort, blood was drawn before bowel preparation for colonoscopy or prior to large bowel surgery and centrifuged at 2123xg for lOmin.
Glycoprotein enrichment from plasma. Glycoproteins were isolated as described previously6 and above, starting with 50 μΐ of plasma. Prior to the enrichment, bovine standard N-glycoproteins (Fetuin and Alpha- 1 -acid glycoprotein) were spiked into samples at equal concentration (lOpmol/protein). Counter to above, glycoproteins were first oxidised, immobilised on resin, non-bound proteins were thoroughly washed away with urea buffer (8M urea, lOOmM ammonium bicarbonate, 0.1% SDS, 5mM EDTA), then proteolysed at 2M urea, and N-linked glycosylated peptides were enzymatically released as above. The protocol was adapted to a Sirroco 96-well plate (Waters) where Affi-gel hydrazine resin (Bio-Rad) was used. Formerly glycosylated peptides were desalted as above in 96-well MacroSpin column plates filled with Vydac C18 silica (The Nest Group Inc.) and resolubilized in ΙΟΟμΙ HPLC grade water / 2% acetonitrile / 0.1% formic acid. Targeted LC-SRM analysis of plasma N-glycosites. Samples from the screening and validation cohorts were analyzed on a hybrid triple quadrupole/ion trap (4000 QTrap, ABI/MDS Sciex) equipped with a nanoelectrospray ion source and a Tempo NanoLC system (ABI/MDS Sciex) coupled to a 15 cm fused silica emitter, 75 μπι diameter, packed in-house with a Magic CI 8 AQ 5μηι resin (Michrom BioResources). Samples were loaded from a cooled (4°C) autosampler (ABI/MDS Sciex) and separated over a linear gradient from 5% to 35% acetonitrile / 0.1% formic acid over 35 min, at a flow rate of 300 nl/min. The instrument was operated in scheduled SRM mode (retention time window of 300 sec, target scan time of 3 sec), at a unit resolution (0.7 m/z half maximum peak width) of both Ql and Q3 analysers. SRM assays were retrieved from the N-glycosite SRM atlas (http://www.srmatlas.org/), reanalyzed to select the best transitions for endogenous detection in plasma, split to multiple SRM methods or used to optimize a single SRM method. Internal standard peptides labeled with heavy isotopes at the C-terminal lysine or arginine, +8 or +10 Da, respectively, (Thermo Scientific, Sigma- Aldrich, or JPT Peptide Technology) were used to validate peptide identity by analogy of chromatographic and fragmentation properties to the reference. Samples from the training cohort were analyzed on a TSQ Vantage QQQ mass spectrometer (Thermo Fischer Scientific) equipped with a nanoelectrospray ion source. Chromatographic separation of peptides was carried out on a nano-LC system (Eksigent). In each injection, peptides were loaded onto a 75-μιη X 10.5- cm fused silica microcapillary reverse phase column, in-house packed with Magic CI 8 AQ material (200 A pore, 5-m diameter; Michrom BioResources). For peptide separation, a linear 40-min gradient from 2 to 40% solvent B (solvent A: 98% water, 2% acetonitrile, 0.1% formic acid; solvent B: 98% acetonitrile, 2% water, 0.1% formic acid) at a 300 nl/min flow rate was applied. The mass spectrometer was operated in the positive ion mode using ESI with a capillary temperature of 270 °C, a spray voltage of +1350 V, and a collision gas pressure of 1.5 mTorr. SRM transitions were monitored with a mass window of 0.7 half-maximum peak width (unit resolution) in Ql and Q3. All of the measurements were performed in scheduled mode, applying a retention time window of 3 min, a cycle time of 2 s, and a dwell time of 25 ms (depending on the number of transitions measured per run, which was in the range of 400 - 600). Collision energies (CE) were calculated using the formula 0.034*(m/z)-0.848 for doubly charged precursor ions and 0.022*(m/z)+5.953 for triply charged precursor ions (m/z = mass-to-charge ratio of the precursor ion).
Relative quantification and statistical analysis of plasma N-glycosites. Raw data files from the screening or validation datasets were uploaded to Multi Quant 1.2 (Applied Biosystems) and from the training dataset to Skyline to perform automatic SRM peak integration. The quantitative data was further analyzed within SRMstats. Normalization was applied to logarithm base 2 transformed data based on equal concentrations of (1) internal standard reference peptides and (2) internal standard bovine proteins across runs. Median intensities of all reference peptides were used to adjust the reference intensities of all proteins globally across all runs and the associated bias in the total intensities of endogenous peptides. Protein-specific analytical variability was equalized across all runs based on the median reference intensities for each protein and the associated bias was removed from the endogenous protein-level intensities. Second, the intensities of the standard proteins were modeled to obtain a single sample value representative of their quantity in individual samples and these sample quantities were correlated with the median of the total intensities of plasma samples by Pearson correlation. A correlation of >0.6 was considered significant. The sample intensities of the standard proteins were used to normalize the endogenous plasma intensities across all runs to remove the systematic bias created during sample preparation. A linear model with expanded scope of technical replication and restricted scope of biological replication was specified. Comparisons of mean protein abundance between groups were carried out using quantities from the model and p-values were adjusted as above. Normalized data were used to calculate model-based estimation of sample quantification for individual proteins.
Normalization between training and validation datasets. The median difference of log2- intensities between the validation and the training datasets was subtracted from the endogenous intensities in validation samples. Missing values for a given protein were imputed with a minimum summarization representing its limit of detection.
Prediction analysis. Proteins with more than 40% missing values were excluded. 10-fold cross-validation was used to find the most discriminative proteins in the training dataset. For each fold, proteins with significantly differential abundance between groups were used in logistic regression models. Statistical significance analysis of differential abundance was performed as described above at FDRO.05 and fold change cut-off ±1.1. After fitting the model with protein quantification data, the best model for each fold was chosen by stepwise selection, choosing the model by repetitively adding or dropping proteins until minimizing Akaike information criterion (AIC). The final predictive model was comprised of proteins which were selected more than five times among the ten folds, and was then parameterized on the full training dataset. The performance of the final model was assessed on the validation dataset. The threshold was determined based on the best accuracy in the training dataset. The pROC package in R was used to draw the ROCs, to calculate the AUCs and the CIs with bootstrap methods, and to compare different AUCs with bootstrap methods.
Exhaustive search of protein predictors. All possible combinations of one to five proteins were systematically collected by brute force search. Every logistic regression model was validated on the training dataset with 100-fold bootstrapped cross-validation. The validated models were ranked according to their median AUC. Proteins in a set of high performing models that have an identical cross-validation performance (Student's t-test, significance level oc>0.05) were ranked according to their frequency among these models.

Claims

Cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer comprising the combined measurement of at least two, preferably at least all three protein/peptide biomarkers and/or fragments of protein biomarkers selected from a first group consisting of: CP; SERPINA3; PON1 ; optionally in combination with at least one or both protein/peptide biomarkers and/or fragments of protein biomarkers selected from a second group consisting of: IGFBP3; ATRN; LRG1 ; TIMP1, in human serum, plasma or a derivative of blood, or blood itself.
Cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay according to claim 1 , wherein all three protein/peptide biomarkers and/or fragments of protein biomarkers of the first group are measured in combination with LRG1 and/or TIMP1 from the second group, or are measured with IGFBP3 and/or ATRN from the second group, or are measured with all protein/peptide biomarkers and/or fragments of protein biomarkers from the second group.
Cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay for the identification of molecular KRAS characteristics, wherein a combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: IGHG2; IGHA2; F5; LYVEl ; ITIH4; FHR3, in human serum, plasma or a derivative of blood, or blood itself.
Cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer comprising the combined measurement of at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the large group consisting of: ATRN; A1AG2; APMAP; APOB; CD44; CLU; C04A; CP; CFH; DKFZp686C02220; ECM1 ; F5; FETUB; FGA; FHR3; HP; HRG; HYOU1 ; IGHA2; IGHG1 ; IGHM; IGHG2; LUM; LAMP2; PLPT; PRG4; PTPRJ; LRGl ; MMRN1 ; MST1 ; ORM1; SERP1NA1; SERPINA3; SERPINA7; THBS1 ; TIMP1 ; VWF; AFM; KLKBl ; KNG1; MRC2; BTD; IGJ; ITIH4; PIGR; SERPINA6; VTN; ANT3; CD109; CADM1; FCGBP; IGFBP3; LCN2; AHSG; CDH5; PON1; HPX; LYVE1; TNC; HLA-A, GOLM1; NCAM1 in human serum, plasma or a derivative of blood, or blood itself,.
5. Biomarker assay according to any of the preceding claims, wherein for disease detection the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or all five protein/peptide biomarkers and/or fragments of protein biomarkers selected from the first group consisting of: CP; SERPINA3; PON1; optionally in combination with at least one, two, three or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the second group consisting of: IGFBP3; ATRN; LRG1; TIMP1, in human serum, plasma or a derivative of blood, or blood itself, wherein optionally at most one of the first group and/or at most one or two of the second group can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following third group: CD44; FGG; MMRN1 ; CTSD; IGHG2; ECM1; IGHA2; FHR3; ITIH4; HP; ORM1; FN1; PRG4; LGALS3BP;
for disease localization the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: GOLM1; HLA-A; HYOU1; MRC2; NCAM1; SERPINA3, in human serum, plasma or a derivative of blood, or blood itself, wherein optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: A1AG2; AFM; AHSG; ANT3; AOC3; ATRN; APOB; BTD; C20orf3; CADM1; CD 109; CD 163; CDH5; CD44; CFH; CFI;
for detection of metastatic disease the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: PTPRJ; PIGR; HPX; FETUB; IGHG2; VTN; APOB; ATRN, in human serum, plasma or a derivative of blood, or blood itself, wherein optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: SERPINA1; ΓΠΗ4; F5; TNC;
for molecular KRAS characteristics the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: IGHG2; IGHA2; F5; LYVE1; ITIH4; FHR3, in human serum, plasma or a derivative of blood, or blood itself, wherein optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: LRG1; CP; SERPINA6; KDR; HP; MRC2; GOLM1; SERPINA7; PROC; VTN; CADM1; DKFZp686N02209; CD109; TNC; HPX ;
for molecular MSI characteristics the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: F5; VWF; FETUB; IGHA2; IGFBP3; ORM1; PTPRJ; Q5JNX2, in human serum, plasma or a derivative of blood, or blood itself, wherein optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: Q5JNX2; CFI; HLA-A; CD44; CD163; LAMP2; MPO; ICAM2; PIGR; PLXDC2; ICAM1 ; Fl l; HP; KNG1; CFH; SERPINA3; VTN; FGG; APOB; ATRN; Q5J X2; PROC; SERPI A1; Q6N091; PLXNB2 ;
for prognosis the combined measurement is carried out of at least two, preferably at least three, most preferably at least four or five or all protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: HLA-A; VWF; TNC; MRC2; FHR3; FCGBP; PTPRJ; CD109, in human serum, plasma or a derivative of blood, or blood itself, wherein optionally at most one or two of these can be replaced by protein/peptide biomarkers and/or fragments of protein biomarkers selected from the following second group: HP; CD44; CDH5; PGCP; THBS1; HP; PLXNB2; LUM; PROC; DSG2; DKFZp686N02209; PLTP; F5; CD44; KDR; LCN2; HPX; ATRN; MPO ;.
6. Cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification biomarker assay according to any of the preceding claims comprising the combined measurement of at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: LGALS3BP; PROC; CD163; AOC3 in human serum, plasma or a derivative of blood, or blood itself,
with the proviso that at least one of the group consisting of PROC; CD163 is measured, wherein preferably at least PROC and CD 163 are measured in combination with at least two or more further protein/peptide biomarkers and/or fragments of protein biomarkers.
7. Biomarker assay according to any of the preceding claims, wherein four protein/peptide biomarkers and/or fragments of protein biomarkers of the group consisting of: LGALS3BP; PROC; CD 163; AOC3 are measured.
8. Biomarker assay according to any of the preceding claims, wherein it comprises the combined measurement of at least one additional, preferably at least two, preferably at least three protein/peptide biomarkers and/or fragments of protein biomarkers selected from the group consisting of: ATRN; A1AG2; APMAP; APOB; CD44; CLU; C04A; CP; CFH; DKFZp686C02220; ECM1; F5; FETUB; FGA; FHR3; HP; HRG; HYOUl; IGHA2; IGHGl; IGHM; IGHG2; LUM; LAMP2; PLPT; PRG4; PTPRJ; LRGl ; MMRN1; MST1; ORM1 ; SERPINAl ; SERPINA3; SERPINA7; THBS1; TIMP1; VWF; AFM; KLKB1; KNG1; MRC2; BTD; IGJ; ITIH4; PIGR; SERPINA6; VTN; ANT3; CD 109; CADMl; FCGBP; IGFBP3; LCN2; AHSG; CDH5; with the proviso that any of these, alone or in combination, may replace at least one of the list of claim 1 or of claim 3 or of claim 4;
wherein preferably in case of proximal plasma from the tumor drainage vein VWF is measured as present at higher concentration and at least one of CD44, DKFZp686C02220 at lower concentration in the proximal plasma as compared to the systemic circulation
or wherein preferably measured as upregulated as an effect of tumor excision are at least one of ATRN, CLU, DKFZp686C02220, ECM1, F5, FETUB, HRG, IGHA2, IGHGl, IGHG2, LUM, and PLPT,
or wherein preferably measured as relevant for CRC detection are at least one of the group: A1AG2, APMAP, APOB, CFH, C04A, CP, ECM1, F5, FHR3, HP, IGHA2, IGHGl, IGHG2, LGALS3BP, LRGl, MMRN1, ORMl, SERPINAl, SERPINA3, SERPINA7. THBS1, TIMP1, and VWF, wherein these are present at higher levels and the 3 proteins IGHA2, IGHGl, IGHG2 are present at lower levels in the CRC population as compared to healthy controls.
9. Biomarker assay according to claim 8, wherein measured for diagnostic applications is a signature comprising at least THBS1, PRG4, FHR3, SERPINA1, DKFZp686C02220, CD44, C04A, CFH, preferably either the signature consisting of THBS1+ HP+ CP + APMAP+ PRG4+ APOB+ IGHG1+ FHR3+ IGHG2+ SERPINA1+ LGALS3BP+ D FZp686C02220 + CD44 + VWF + TIMP1+ C04A+ CFH, or the signature consisting of CD44+CFH+ECM1+F5+FHR3+IGHM+PRG4+C04A + DKFZp686C02220 + SERPINA1+THBS1.
10. Biomarker assay according to claim 8, wherein measured for prognostic applications a disease-free (DFS) survival status is determined based on at least one of AFM, KLKB1 , KNG1, LGALS3BP, and PTPRJ and overall survival (OS) with at least one of C04A, MRC2, and BTD, wherein significantly associated with both DFS and OS are HYOU1, IGHM ORM1, A1AG2, VTN, SERPINA7, AHSG, IGJ, CFH, F5, HP, ITIH4, LUM, PIGR, PROC, SERPINA1, CDH5, IGHM, A1AG2, CD 109, KLKBL MST1, FCGBP, IGFBP3, HYOU1 and SERPINA6;
wherein further preferably 6 protein combinations are significantly associated with DFS:
IGHG2+ATRN non-adjusted,
CDH5+ATRN non-adjusted,
MST1+CD109 age-, gender-, and stage-adjusted, and
MST1+MRC2 age-, gender-, and stage-adjusted,
CDH5+ IGHM+A1AG2 age-, gender-, and stage-adjusted, and CD 109+KLKB 1 +PIGR age-, gender-, and stage-adjusted;
and 14 protein combinations significantly are associated with OS:
HRG+AHSG non-adjusted,
C04A+CADM1 non-adjusted,
LCN2+APMAP age- and gender-adjusted,
CDH5+FCGBP age-, gender-, and stage-adjusted,
CDH5+IGFBP3 age-, gender-, and stage-adjusted.
CDH5+AHSG+MST1 age- and gender-adjusted,
CDH5+AHSG+ORM 1 age- and gender-adjusted, CDH5+CFH+ORM 1 age- and gender-adjusted,
CDH5+FCGBP+ORM1 age- and gender-adjusted,
CDH5+FCGBP+PR0C age- and gender-adjusted,
CDH5+FCGBP+SERPINA1 age- and gender-adjusted,
CDH5+ IGFBP3+ SERPINA1 age- and gender-adjusted,
CDH5+ IGFBP3+ SERP1NA6 age- and gender-adjusted, and CDH5+ LGALS3BP+0RM1 age- and gender-adjusted
and 4 protein combination is significantly associated with both DFS and OS:
CD163&CD109 age-, gender, and stage-adjusted.
CDH5+FCGBP+IGFBP3 age-, gender, and stage-adjusted,
CDH5+HYOU1+SERPINA1 age-, gender, and stage-adjusted, and
CDH5+ IGHM+ORM1 age-, gender, and stage-adjusted.
11. Biomarker assay according to claim 8, wherein for prognostic and predictive power at least one of ATRN, APOB, PRG4, and SERPINA3, preferably a combination thereof, is associated with higher protein expression, and at least one of, preferably a combination of HYOU1, IGHM, FGA, THBS1, and VWF is associated with lower protein expression in patients.
12. Biomarker assay according to claim 8, wherein as a prognostic and predictive indicator of CRC at least one of MST1, SERPINA7, LAMP2, and IGHG1 is measured.
13. Biomarker assay according to claim 8, wherein for grading, staging, and cancer assessment at least one of IGHG2 and ORMl is measured, and/or wherein for clinical stage assessment, metastatic disease assessment, and EGFR dephosphorylation assessment PTPRJ is measured.
14. Biomarker assay according to any of the preceding claims, wherein it is an affinity reagent-based assay, preferably antibody-based assay such as an Enzyme-Linked Immunosorbent Assay (ELISA) for the detection of the biomarker candidates
15. Method for the diagnosis and/or for the therapy and/or for the prognosis and/or for the monitoring of colorectal cancer and/or lung cancer and/or pancreatic cancer, using a biomarker assay according to any of the preceding claims, wherein preferably the measurement is carried out using tandem mass spectrometry techniques, preferably selected reaction monitoring (SRM), more preferably in combination with liquid chromatography, and/or Enzyme-Linked Immunosorbent Assays (ELISA) for the detection of these proteins/fragments thereof.
Cancer diagnostic and/or therapeutic and/or prognostic and/or patient stratification kit for the prognosis and/or diagnosis and/or therapy of colorectal cancer and/or lung cancer and/or pancreatic cancer comprising a biomarker assay according to any of the preceding claims.
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