COLORECTAL CANCER SCREENING EXAMINATION AND EARLY DETECTION METHOD

Abstract

The present invention pertains to a new method for the diagnosis, prognosis, stratification and/or monitoring of a therapy, of cancer, preferably colorectal cancer (CRC), in a subject. The method is based on the determination of the level of a panel of least one, preferably 3, 4 and most preferably at least 5, protein biomarker selected from the group consisting of the protein biomarkers Amphiregulin (AREG), Carcinoembryonic antigen (CEA), Insulin like growth factor binding protein 2 (IGFBP2), Keratin, type I cytoskeletal 19 (KRT19), Mannan binding lectin serine protease 1 (MASP1), Osteopontin (OPN), Serum paraoxonase lactonase 3 (PON3) and Transferrin receptor protein 1 (TR), in the biological sample obtained from the subject. The new biomarker panel of the invention allows diagnosing and even stratifying various cancer diseases. Furthermore, provided are diagnostic kits for performing the non-invasive methods of the invention. Since the biomarker panel of the invention provides a statistically robust method independent of the protein detection technology used, and considering that the biomarker panel of the invention is detected in plasma samples of the subjects, the invention provides an early detection screening examination that may be applied to a larger population.

Claims

1. A method for the diagnosis, prognosis, stratification and/or monitoring of a therapy, of a cancer disease in a subject, comprising the steps of: (a) Providing a biological sample from the subject, (b) Determining the level (concentration) of at least one (2, 3, 4, 5, 6, or 7 or more) protein biomarker selected from the group consisting of the protein biomarkers Amphiregulin (AREG), Carcinoembryonic antigen (CEA), Insulin like growth factor binding protein 2 (IGFBP2), Keratin, type I cytoskeletal 19 (KRT19), Mannan binding lectin serine protease 1 (MASP1), Osteopontin (OPN), Serum paraoxonase lactonase 3 (PON3) and Transferrin receptor protein 1 (TR), in the biological sample, wherein a differential level of the at least one, preferably, two, three, four and most preferable 5, biomarkers in the biological sample from the subject as determined in step (b) compared to a healthy control or reference value is indicative for the presence of a cancer disease in the subject.

2. The method according to claim 1, wherein step (b) comprises determining a combination of at least 4 of said biomarkers, preferably (i) MASP1, OPN, PON3 and TR, or (ii) AREG, MASP1, OPN, PON3, and TR, or (iii) AREG, MASP1, OPN, PON3, TR, CEA and KRT19.

3. The method according to claim 1 or 2, wherein step (b) comprises determining the level of at least the protein biomarker TR, OPN, IGFBP2, MASP1, and PON3, in the biological sample.

4. The method according to claim 3, wherein step (b) comprises determining the level of one or more additional biomarkers selected from the group consisting of AREG, CEA and/or KRT19, in the biological sample.

5. The method according to claim 4, wherein step (b) comprises determining the level of at least the protein biomarker TR, OPN, IGFBP2, MASP1, PON3, AREG, CEA and KRT19, in the biological sample.

6. The method according to any of claims 1 to 5, wherein the biological sample is a tissue sample or body liquid sample, preferably a blood sample, most preferably a plasma sample.

7. The method according to any of claims 1 to 6, wherein the method is a non-invasive method, preferably an ex vivo method or in vitro method.

8. The method according to any of claims 1 to 5, wherein the method is a screening method for establishing a first diagnosis of cancer in the subject.

9. The method according to any of the preceding claims wherein the cancer is colorectal cancer, gastric cancer or pancreatic cancer, and preferably is an early stage or late stage CRC.

10. The method according to any one of the preceding claims, wherein the level of said biomarker is determined using one or more antibodies specific for one or more of the respective biomarker proteins, preferably wherein the protein biomarker is detected by western blot, ELISA, Proximity Extension Assay, or mass-spectrometrically, and most preferably is detected by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) and/or Proximity Extension Assay (PEA).

11. A diagnostic kit for performing a method according to any of the preceding claims.

12. The diagnostic kit of claim 11, comprising one or more antibodies, or antigen binding fragments thereof, for the detection of the at least one biomarker.

13. Use of an antibody, or antigen binding fragment thereof, directed to any one of the protein biomarkers selected from TR, OPN, IGFBP2, MASP1, PONS, AREG, CEA and/or KRT19, in the performance of a method according to any of claims 1 to 10.

14. A screening examination method for the early detection of a cancer disease, preferably CRC, in a subject not being diagnosed to have the cancer disease before, the method comprising (a) Providing a biological sample of the subject to be screened, (b) Performing a method according to any one of claims 1 to 12 with the so provided biological sample of the subject.

15. The method according to claim 14, wherein if the level of the determined biomarkers indicate the presence of the cancer disease, (i) the method is repeated with an independent biological sample provided of the subject, and/or (ii) the subject is scheduled for a secondary diagnosis of the cancer disease.

Description

BRIEF DESCRIPTION OF THE FIGURES AND SEQUENCES

[0074] The figures show:

[0075] FIG. 1 shows a STARD (Standards for Reporting of Diagnostic Accuracy) flow diagram BLITZ-Study; Abbreviations: AA-Advanced Adenoma; CRC-Colorectal Cancer; HPP-Hyperplastic Polyps; NAA-Non advanced Adenoma; NCP-Non classified polyp; SP-Serrated poly.

[0076] FIG. 2 shows the comparison of the ROC curves for detecting (all/early/late) stage CRC & AA vs free of neoplasm controls with five and eight marker signatures. Abbreviations: AA-Advanced Adenomas; AUC-Area under the curve; CRC-Colorectal Cancer; ROC-Receiver operating characteristics.

[0077] FIG. 3 shows table 1: characteristics of the study population. Abbreviations: AA-Advanced Adenoma; CRC-Colorectal Cancer; N-number; SD-Standard deviation.

[0078] FIG. 4 shows table 2: Diagnostic performance of individual protein biomarkers for detecting CRC. Abbreviations: AUC-Area under the Receiver Operating Curve; AUCBS-0.632+ bootstrap estimates of AUC; AUC*-apparent AUC; CRC-Colorectal Cancer; 95% CI-95% Confidence Interval; Se-Sensitivity; Sp-Specificity. All proteins abbreviations: AREG-Amphiregulin; CDH5-Cadherin 5; CEA-Carcinoembryonic antigen; Gal 3-Galectin 3; IGFBP2-Insulin like growth factor binding protein 2; KRT19-Keratin, type I cytoskeletal 19; MASP1-Mannan binding lectin serine protease 1; MMP9-Matrix metalloproteinase 9; MPO-Myeloperoxidase; OPN-Osteopontin; PON3-Serum paraoxonase lactonase 3; PRTN3-Myeloblastin; SPARC-SPARC protein; TR-Transferrin receptor protein 1.

[0079] FIG. 5 shows table 3: Diagnostic performance of multi-marker signatures for detecting CRC (all stages/early/late) and AA. Abbreviations: AA-Advanced Adenomas; AUC-Area under the Receiver Operating Curve; AUCBS-0.632+ bootstrap estimates of AUC; AUC*-apparent AUC; CRC-Colorectal Cancer; 95% CI-95% Confidence Interval; Se-Sensitivity; Sp-Specificity. All proteins abbreviations: AREG-Amphiregulin; CEA-Carcinoembryonic antigen; IGFBP2-Insulin like growth factor binding protein 2; KRT19-Keratin, type I cytoskeletal 19; MASP1-Mannan binding lectin serine protease 1; OPN-Osteopontin; PON3-Serum paraoxonase lactonase 3; TR-Transferrin receptor protein 1.

[0080] FIG. 6 shows table 4: Functions of proteins from multi-marker signatures. All proteins abbreviations: AREG-Amphiregulin; CEA-Carcinoembryonic antigen; IGFBP2-Insulin like growth factor binding protein 2; KRT19-Keratin, type I cytoskeletal 19; MASP1-Mannan-binding lectin serine protease 1; OPN-Osteopontin; PON3-Paraoxonase 3; TR-Transferrin receptor protein 1.

[0081] FIG. 7 shows table 5: Diagnostic performance of multi-marker signatures for detecting CRC (all stages/early/late) and AA. Abbreviations: AA-Advanced Adenomas; AUC-Area under the Receiver Operating Curve; AUCBS-0.632+ bootstrap estimates of AUC; AUC*-apparent AUC; CRC-Colorectal Cancer; 95% CI-95% Confidence Interval; Se-Sensitivity; Sp-Specificity. All proteins abbreviations: AREG-Amphiregulin; CEA-Carcinoembryonic antigen; IGFBP2-Insulin like growth factor binding protein 2; KRT19-Keratin, type I cytoskeletal 19; MASP1-Mannan binding lectin serine protease 1; OPN-Osteopontin; PON3-Serum paraoxonase lactonase 3; TR-Transferrin receptor protein 1.

[0082] FIG. 8 shows table 6: Diagnostic performance of multi-marker signatures for detecting CRC (all stages/early/late) and AA. Abbreviations: AA-Advanced Adenomas; AUC-Area under the Receiver Operating Curve; AUCBS-0.632+ bootstrap estimates of AUC; AUC*-apparent AUC; CRC-Colorectal Cancer; 95% CI-95% Confidence Interval; Se-Sensitivity; Sp-Specificity. All proteins abbreviations: AREG-Amphiregulin; CEA-Carcinoembryonic antigen; IGFBP2-Insulin like growth factor binding protein 2; KRT19-Keratin, type I cytoskeletal 19; MASP1-Mannan binding lectin serine protease 1; OPN-Osteopontin; PON3-Serum paraoxonase lactonase 3; TR-Transferrin receptor protein 1.

EXAMPLES

[0083] Certain aspects and embodiments of the invention will now be illustrated by way of example and with reference to the description, figures and tables set out herein. Such examples of the methods, uses and other aspects of the present invention are representative only, and should not be taken to limit the scope of the present invention to only such representative examples.

[0084] The examples show:

Example 1: Characteristics of Study Population

[0085] The STARD diagrams displaying selection of study participants enrolled in iDa, ASTER and BLITZ are provided in FIG. 1, respectively. The discovery set A used for LC-MRM/MS consisted of 100 clinically recruited CRC cases and 100 controls free of neoplasms and for discovery set B using PEA of 98 CRC cases and wo controls free of neoplasms, from iDa and ASTER studies, respectively. The three stage specific prediction algorithms were then externally evaluated and validated in a validation set comprising CRC cases and sex and age matched controls without colorectal neoplasms selected from participants of screening colonoscopy. Because the sex and age distribution of participants with AA or those of controls free of neoplasm actually differs from the corresponding distributions among CRC cases in true screening practice, observations from participants with AA and controls without neoplasms were weighted in the analysis in such a way that their sex and age distribution reflects the sex and age distribution of all participants with AA and controls without neoplasms among the participants of screening colonoscopy, respectively, in order to provide valid estimates of the algorithms' performance in a true screening setting. The validation set that consisted of participants of screening colonoscopy consisted of 58 CRC, 106 AA and 106 controls free of neoplasms from the BLITZ study. The characteristics of populations from all three sets are shown in Table 1 in FIG. 3. The distribution of characteristics was largely similar across all three sets with males representing ≥60% of population and median age being around 65 years in all sets. The validation set included a higher proportion of stage I CRCs and a lower proportion of stage IV CRCs than the discovery sets A and B.

Example 2: Individual Markers

[0086] The diagnostic performances of all the above mentioned protein markers across the three different sets are listed in Table 2 in FIG. 4. After adjustment for multiple corrections, the number of proteins with significant differences between CRC and controls were (N=6, 7 and 1) in discovery sets A and B and validation set, respectively. The AUCs ≥0.60 were observed for (N=6, 7 and 3) and sensitivities >25% at 90% specificity for (N=6, 7 and 3) protein biomarkers in discovery sets A and B and validation set, respectively. Of the eleven protein biomarkers commonly quantified by both platforms the best individual diagnostic performance was observed for PON3 in discovery set A with an AUCBS of 0.72 (95% CI, 0.63-0.81), in discovery set B with an AUCBS of 0.74 (95% CI, 0.65-0.84) and in validation set for TR with an AUCBS of 0.72 (95% CI, 0.64-0.85). As seen from Table 2 in FIG. 4 the diagnostic performance of nine out of eleven markers was similar in both discovery sets A and B that had an overlap of 96 CRC cases and 94 controls free of colorectal neoplasms.

Example 3: Correlation Analysis

[0087] The results of the Pearson's product-moment correlation analysis for protein biomarkers measured across the same sample from discovery sets A and B consisting of 190 participants (CRC=96 and controls=94) revealed that the correlation coefficient was highest for PON3 (0.79) and was 0.6 for eight out of eleven biomarkers. The good concordance observed for protein biomarkers not only confirms the diagnostic potential of these markers, but also indicates the robustness of the findings.

Example 4: Multi-Marker Signatures

[0088] To assess whether the diagnostic performance of individuals protein biomarkers can be improved further, the inventors derived a multi-marker signatures for CRC detection. Table 3 in FIG. 5 and FIG. 2 present an overview upon the diagnostic performance indicators across three sets. For a five marker signature consisting of biomarkers IGFBP2, MASP1, OPN, PON3 and TR, he AUCsBS for all stage CRC detection were 0.80 and 0.84 in the discovery set A and B, respectively, and in the validation set an AUC of 0.72 (95% CI, 0.64-0.80), was observed. For early stage CRC detection, the five marker signature yielded AUCsBS of 0.74, 0.78 and an AUC of 0.72 (95% CI, 0.60-0.82) in discovery sets A and B and validation set, respectively. For late stage CRC detection the AUCsBS were 0.85 and 0.90 in discovery sets A and B, respectively and an AUC of 0.69 (95% CI, 0.59-0.79) was observed in the independent validation set.

[0089] When the diagnostic performance of the proteins from the five marker signature was analyzed in combination with three additional biomarkers AREG, CEA and KRT19 the performance improved (FIG. 5). In the discovery set B for the eight marker signature (AREG, CEA, IGFBP2, KRT19, MASP1, OPN, PON3 and TR), the AUCsBS improved to 0.88, 0.84 and 0.91 for all, early and late CRC detection. In an independent validation set comprising of participants of screening colonoscopy AUCs of 0.82 (95% CI, 0.75-0.89), 0.80 (95% CI, 0.71-0.89) and 0.79 (95% CI, 0.69-0.87) were observed for all, early and late stage CRC detection comparison, respectively. For the eight marker signature in the validation set, the sensitivities at 80% specificity were 69%, 57% and 66% and sensitivities at cutoffs yielding 90% specificity were 50%, 43% and 51% for all, early and late stage CRC detection, respectively. When the five and eight marker signatures were applied for detection of AA cases the same AUC of 0.57 (95% CI, 0.49-0.65) was observed for both signatures. The incorporation of age and gender into signatures did not result in any change in the diagnostic performance. As described in Table 4 in FIG. 6, all the eight biomarkers selected in both signatures had a wide variety of biological and molecular functions. Additionally, when Ingenuity Pathway Analysis (QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis, version 01-10; Ingenuity Systems, Redwood City, USA) was used to understand the interaction between these proteins, it was observed that though functionally different these proteins interact at cellular or extracellular level in development of carcinoma.

[0090] Additional preferably diagnostic algorithms could be calculated with similarly advantageous AUC as shown in FIGS. 7 and 8.

[0091] In FIG. 8, the diagnostic performance of a four marker signature comprising the combination of MASP1, OPN, PON3 and TR was analysed and compared to the five marker signature comprising the combination of AREG, MASP1, OPN, PON3 and TR. In the discovery set analysed by LC-MRM/MS measurements, the AUCs.sup.BS for the four marker signature (MASP1, OPN, PON3 and TR) measured were 0.80, 0.75, and 0.84 for all, early and late CRC detection. The discovery set was additionally analysed by PEA measurements. When the discovery set was analysed by PEA measurements, the AUCs.sup.BS measured for the four marker signature (MASP1, OPN, PON3 and TR), were 0.84, 0.79, and 0.90 for all, early and late CRC detection, compared to AUCs.sup.BS for the five marker signature (AREG, MASP1, OPN, PON3 and TR) of 0.88, 0.84 and 0.92 for all, early and late CRC detection.

[0092] In an independent validation set comprising participants of screening colonoscopy, AUCs of 0.76 (95% CI, 0.67-0.85), 0.78 (95% CI, 0.66-0.88) and 0.71 (95% CI, 0.59-0.83) were observed for the four marker signature (MASP1, OPN, PON3 and TR) for all, early and late stage CRC detection comparison, when analysed by LC-MRM/MS measurements. When the independent validation set comprising participants of screening colonoscopy was analysed by PEA measurements, AUCs of 0.75 (95% CI, 0.65-0.84), 0.75 (95% CI, 0.63-0.86) and 0.72 (95% CI, 0.59-0.83) were observed for the four marker signature (MASP1, OPN, PON3 and TR) for all, early and late stage CRC detection comparison, compared to AUCs.sup.BS for the five marker signature (AREG, MASP1, OPN, PON3 and TR) of 0.82 (95% CI, 0.74-0.89), 0.86 (95% CI, 0.77-0.92) and 0.76 (95% CI, 0.64-0.86) for all, early and late CRC detection.

[0093] For the four marker signature (MASP1, OPN, PON3 and TR) in the validation set, the sensitivities at 80% specificity were 46%, 43% and 48% when analysed by LC-MRM/MS measurements. When analysed by PEA measurements, the sensitivities for the four marker signature (MASP1, OPN, PON3 and TR) at 80% specificity were 46%, 52% and 55%, compared to 71%, 83% and 58% as analysed for the five marker signature (AREG, MASP1, OPN, PON3 and TR). Moreover, sensitivities at cutoffs yielding 90% specificity were 36%, 30% and 21% for all, early and late stage CRC detection for the four marker signature (MASP1, OPN, PON3 and TR) when analysed by LC-MRM/MS measurements, and 36%, 35% and 33% for all, early and late stage CRC detection for the four marker signature (MASP1, OPN, PON3 and TR) when analysed by PEA measurements. As a comparison, the sensitivities at cutoffs yielding 90% specificity analysed for the five marker signature (AREG, MASP1, OPN, PON3 and TR) were 50%, 43% and 45% for all, early and late stage CRC detection, when analysed by PEA measurements. The data derived from the independent validation set comprising participants of screening colonoscopy AUCs is a representation of how the markers would perform in the general screening population, where the cases and controls are not matched by age and gender.

[0094] Conclusion: In recent years several studies have identified blood based protein marker panels and signatures, which have shown the potential to yield AUCs higher than 0.8 for CRC detection. However, the participants in these studies were not recruited in screening settings [15, 16]. Two previous research studies from the inventors have identified eight and six marker panels with AUCs for CRC of 0.76 [13] and 0.84 [27], respectively. However unlike the current study the CRC cases were validation sets had clinically recruited in those studies. Two other publications where external validation comprised of only preclinical samples yielded AUC for CRC detection of 0.59 [28] and 0.82 [14] for two individual 5-marker signatures. In the current study of the invention it could be included that CRC and AA cases from participants undergoing screening colonoscopy and the number of CRC cases in the validation set is higher than in the previous studies.

[0095] Additionally, the proteins in the current study were separately measured with two highly target specific proteomic technologies. Previous research on blood based biomarkers other than proteins like COLOX (gene expression of 29 genes), COLODETECT (4 proteins+3 phages) and CANCERSEEK (16 genes+8 proteins) had CRC cases that were recruited in partial or complete clinical settings and the diagnostic performance of these tests for early detection of CRC in screening setting samples is not known. For COLOSENTRY (7 genes) when the performance was evaluated in screening setting sensitivity of 61% at 77% specificity was observed for all stages. The eight marker algorithm identified according to the invention yielded 57% sensitivity at cutoff yielding 80% specificity. The blood based test Epi proColon 2.0 based on Sept9gene methylation, showed 59% sensitivity at 79% specificity for early stages CRC [29], which is comparable with the diagnostic performance displayed by the eight marker signature from the current study. Therefore, the diagnostic performance of the current signature is in line with results of handful studies validating diagnostic performance of blood-based tests in true screening-setting like the PRESEPT clinical trial on Sept9 gene methylation [30].

[0096] The eight proteins identified from both signatures as demonstrated Table 4 in FIG. 6 are involved in different biological processes and also have diverse molecular function. Cytokines AREG and OPN and glycoprotein CEA have been identified previously as biomarkers with diagnostic potential for CRC [13, 14, 27, 29, 31-33]. Protein biomarkers KRT19 and TR which are both involved in biological processes of host-virus interaction and hydrolases PON3 and MASP1 have been recently associated with CRC detection. IGFBP2 a growth factor binding protein is involved in growth regulation. Therefore, the current panel of the invention is nonobvious and surprising and shows the potential of these eight biomarkers as a signature for early detection of CRC with end validation in an independent set consisting of samples from participants of colonoscopy.

[0097] The detection and quantitation of low abundant biomarkers with low sample volume has been possible because of advancement in the field of proteomics. The peptides selected for the LC-MRM/MS had good mass spectrometer (MS) responses and uniquely identified the target protein. Moreover, using the triple-quadrupole MS high specificity is achieved firstly by only allowing a selected peptide to pass through the first quadrupole and enter the collision cell where the peptide dissociates into fragments specific to the amino acid sequence of the precursor peptide. Another second stage of technical specificity is added in the second MS, and only a specific fragment is allowed to pass through and strike the detector. Similarly, for PEA the pair of oligonucleotide labeled antibodies or probes have to be in close proximity and only this dual recognition of the target protein leads to initiation of an amplified signal detection. On account of these factors both LC-MRM/MS and PEA are very target specific method. The technical assay sensitivity of the LC-MRM/MS is in mid-high nanogram/ml range and it was obvious practical to use detection method with same ability. Often it is recommended to use ELISA for repeat measurements but individual ELISAs would have required more sample volume than any multiplex platform. Therefore, PEA with analytical sensitivity in picogram/ml was used for repeat measurements. Since both the technologies are highly sensitive and the performance of the biomarkers was almost similar in both discovery sets A and B, it can be certainly determined that the observed diagnostic performances were not simply a matter of chance. Apart from advanced technology used for detection and quantitation, the pre-analytical processing of samples has been shown to influence the measurements in the protein biomarker research [34-36]. In the current study, even though the participants were selected from three different studies the collection, handling, processing and storage of the samples across the three studies were performed with similar standardized operating procedure (SOPs).

[0098] This is the first study that identifies, evaluates and validates biomarkers across two different platforms using a three stage design. In the current study the inventors not only performed correction of over-optimism with 0.632+ bootstarp method but also externally validated the findings in an independent validation set that consisted of participants with CRC, AA and no colorectal neoplasms at all at screening colonoscopy. Furthermore, the inventors have re-measured the identified protein biomarkers on two different independent detection methods (LC-MRM/MS and PEA), which are both highly sensitive, target specific technologies and possess the ability of detecting even low abundant markers using very low volumes of plasma. The diagnostic performance of the eight marker signature was fairly good for a blood-based test, with an AUC of 0.82 (95% CI 0.75-0.89) for all stage CRC detection. Therefore, the identified plasma protein biomarkers are potential candidates for further research on blood-based test for CRC screening and early detection.

[0099] Utilizing two competitive target specific protein detection methods the invention identified a promising eight marker signature with diagnostic potential for early detection of CRC. The protein biomarkers AREG, CEA, IGFBP2, KRT19, MASP1, OPN, PONS and TR exhibited diagnostic performance competitive with all existing tests comprising of only protein or any other biomarkers validated in true screening setting samples. The biomarkers identified constitute a promising blood-based test for population based screening and early detection of CRC and its premalignant lesions.

Materials and Methods

Study Design

[0100] The protein biomarkers were assessed in a three-step approach, with first measurement performed in discovery set A using LC-MRM/MS. This was followed by re-evaluating the performance in samples from the same study population in discovery set B using PEA and lastly the algorithm was validated in an independent study population of participants of screening colonoscopy in validation set using PEA again.

Study Population: Discovery Sets A and B

[0101] The discovery set A included 100 CRC cases recruited prior to any therapeutic intervention from the iDa (“Durch innovative Testverfahren Darmkrebs früher erkennen”) study in hospitals in southwestern Germany between 2013 and 2016. As controls the inventors included 100 participants of screening colonoscopy who were recruited in the ASTER (“Mit ASS Darmtumore früher erkennen”) study and were free of colorectal neoplasms. ASTER is a multicenter prospective randomized controlled trial (EudraCT No. 2011-005603-32). Participants of ASTER were recruited and blood samples were taken at recruitment from gastrointestinal practices in Germany from 2013 to 2016 [17]. The discovery set B consisted of 98 CRC cases from the iDa study and 100 controls free of neoplasm from the ASTER study. The study population was nearly the same between both discovery sets (96 CRC cases and 94 controls overlapped between discovery sets A and B) and the difference of ten participants was on account of limited sample volume. The use of samples for early detection of CRC has been approved by the ethics committees of the Medical Faculty Heidelberg and from the responsible state medical boards, for both iDa and ASTER studies.

Study Population: Validation Set

[0102] Blood samples for independent external validation of the algorithm were selected from participants of screening colonoscopy collected in the BLITZ (“Begleitende Evaluierung innovativer Testverfahren zur Darmkrebs-Früherkennung”) study. Details of the BLITZ study design have been reported previously [13, 14, 18-20], briefly, BLITZ is an ongoing prospective screening study of participants of the German screening colonoscopy program that is offered to average risk population. Participants are recruited in 20 gastroenterology practices since end of the year 2005. By the end of June 2016, out of 9425 participants in BLITZ, CRC and AA had been detected in 64 and 633 participants, respectively. In the current study, validation of signatures identified and evaluated in discovery sets A and B, respectively, were carried out in blood samples from 58 participants with CRC and 106 participants that were free of colorectal neoplasm at screening colonoscopy. Additionally, the inventors enriched the study population with 106 participants with AA (defined as adenoma with >1 cm in diameter, tubulovillous or villous components, or highgrade dysplasia [21]. The controls free of neoplasms and also AA participants were frequency matched to the CRC cases by sex and age. The use of samples from the BLITZ study for evaluation of early detection markers for CRC has been approved by the ethics committees of the Medical Faculty Heidelberg (S-178/2005), and of the physicians' boards of Baden-Wuerttemberg (M118-05-f), Rheinland-Palatinate (837.047.06(5145)), and Saarland (217/13). The STARD diagram showing selection of study participants from the BLITZ study is presented in FIG. 1.

Sample Collection and Storage

[0103] Ethylenediaminetetraacetic acid (EDTA) plasma samples were collected before screening colonoscopy in ASTER and BLITZ and at first diagnosis of CRC before any treatment for cancer in iDa. After blood draw, plasma samples were centrifuged between 2000-2500 g for 10 minutes at4° C. Then they were transported to the biobank at German Cancer Research Centre (DKFZ) in a cold chain, centrifuged again, aliquoted, and stored at −80° C. until the protein measurements. All the laboratory analyses were performed blinded with respect to disease status or findings at colonoscopy.

Laboratory Assays

[0104] Plasma samples were analyzed in the discovery set A for the targeted quantitation by peptide based analysis using LC-MRM/MS for eleven proteins that overlapped between both methods, namely, Cadherin 5 (CDH5), Galectin 3 (Gal 3), Insulin like growth factor binding protein 2 (IGFBP2), Mannan binding lectin serine protease 1 (MASP1), Matrix metalloproteinase 9 (MMP9), Myeloperoxidase (MPO), Osteopontin (OPN), Serum paraoxonase lactonase 3 (PON3), Myeloblastin (PRTN3), SPARC protein (SPARC) and Transferrin receptor protein 1 (TR). These peptides had been previously validated for their use in experiments following the Clinical Proteome Tumor Analysis Consortium (CPTAC) guidelines for assay development (“https://assays.cancer.gov”) and the details of the LC-MRM/MS has been published elsewhere [11, 22].

[0105] In the discovery set B and validation set protein concentrations in plasma samples were measured utilizing the PEA offered by Olink [23]. Apart from the aforementioned overlapping eleven proteins that were quantified by LC-MRM/MS assay, additionally, three proteins that have been identified promising for early detection of CRC from previous research [13, 14] (also submitted Bhardwaj et al., 2019) namely Amphiregulin, (AREG), Carcinoembryonic antigen (CEA) and Keratin, type I cytoskeletal 19 (KRT19) were analyzed.

Statistical Analysis

[0106] The data from LC-MRM/MS was visualized and examined with Skyline Quantitative Analysis software and the standard curve was used to calculate the peptide concentration in fmol/μl of plasma in the samples. The protein concentrations obtained from PEA were presented in from normalized protein expression (NPX).

[0107] The protein values obtained from both LC-MRM/MS and PEA were first compared for each individual biomarker between CRC and controls using Wilcoxon rank-sum test and correction for multiple testing by the Benjamini and Hochberg method [24]. For each individual protein area under the receiver operating characteristic (ROC) curves (AUCs) and their 95% confidence intervals (95% CI) and sensitivity at 8o% and 90% specificities were calculated. Additionally, in order to see concordance between the proteins measured on exactly same samples in discovery sets A and B the Pearson's product-moment correlation was calculated.

[0108] In order to measure the diagnostic performance of multi-markers combinations for detection of CRC, LASSO (Least absolute shrinkage and selection operator) regression models with 0.632+ bootstrap [25] to adjust for overfitting, were applied to protein biomarkers in the discovery set A. The biomarkers obtained from the LASSO logistic regression model were further re-evaluated using logistic regression in the discovery set B. Another prediction algorithm was evaluated by combining biomarkers from LASSO regression and AREG, CEA and KRT19. Both the prediction algorithms were externally evaluated in the validation set that exclusively included participants of screening colonoscopy. The diagnostic performance was evaluated by calculating sensitivity at 80% and 90% specificities and apparent AUC i.e. the AUC not adjusted for overfitting (AUC*) with 95% CI, as well as 0.632+ bootstrap adjusted AUC (AUC.sup.BS) in discovery sets A and B. All statistical analyses were performed with statistical software R language and environment (version 3.5.0, R core team) [26]. For all tests two-sided p-values of 0.05 or less were considered to be statistically significant.

REFERENCES

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