WO2019048588A1

WO2019048588A1 - Mixed protein and autoantibody biomarker panel for diagnosing colorectal cancer

Info

Publication number: WO2019048588A1
Application number: PCT/EP2018/074087
Authority: WO
Inventors: Hermann Brenner; Hongda Chen
Original assignee: Deutsches Krebsforschungszentrum Stiftung des öffentlichen Rechts
Priority date: 2017-09-07
Filing date: 2018-09-07
Publication date: 2019-03-14

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, in a patient. The method is based on the determination of the level of a panel of least four biomarker selected from the group consisting of the protein biomarker AREG, GDF-15, FasL, Flt3L, and the autoantibody biomarker TP53 -autoantibody. 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.

Description

MIXED PROTEIN AND AUTOANTIBODY BIOMARKER PANEL FOR

DIAGNOSING COLORECTAL CANCER

FIELD OF THE INVENTION

DESCRIPTION

A major step in many aspects of research related to diseases such as cancer is the identification of specific and sensitive biomarkers suitable for the development of effective and improved diagnostic, prognostic and therapeutic modalities. An aim of the present invention is to provide novel biomarkers and biomarker panels for use as novel diagnostic and/or prognostic markers and/or for use in the development of novel therapeutics. Whilst mass spectrometry, shot gun proteomics and DNA/RNA microarray analyses, and deep sequencing have resulted in an increasing list of reported potential tumor biomarkers, very few have found their way into the clinical validation phase and even fewer are used as reliable therapeutic targets or diagnostic markers.

With over 1.4 million new cancer cases and 693,900 deaths estimated to have occurred in 2012, colorectal cancer (CRC) is the third most commonly diagnosed cancer in males and the second in females. Randomized controlled trials and observational studies have shown that screening by endoscopic examinations or stool-based tests yield a reduction of CRC incidence and mortality. However, the participation rates in endoscopy and stool-based screening offers are often relatively low. For example, in an ongoing randomized clinical trial (NordlCC study) conducted in Poland, Norway, the Netherlands and Sweden, the participation rates of screening colonoscopy varied from 22.9% to 60.7%. The participation rates of fecal occult blood tests (FOBTs) varied from 7.0% to 67.7% across CRC screening programs worldwide. The relatively low compliance of endoscopic examinations and stool tests might limit the screening efficacy on a population level.

Due to their non-invasive nature and ease of application in routine medical practice, blood- based tests could ensure high levels of adherence when applied as primary screening tools in population-based CRC screening, especially for individuals who don't prefer stool sampling, and search for blood-based screening tests is a very active research area. However, most previous studies aiming to discover and validate novel blood-based screening markers recruited participants directly from hospitals. In such clinical settings, the CRC cases typically include a higher proportion of cases in advanced tumor stage than in screening settings. Furthermore, cases may have undertaken some diagnostic or early therapeutic procedures, which may influence potential bio markers and might lead to overestimation of differences from biomarker levels in healthy controls and hence of diagnostic performance. Additionally, confounding may result from non-comparability of cases and controls with respect to other factors, such as other medical conditions, setting of recruitment, or pre-analytical handling of blood samples. Therefore, it is a critical issue to identify bio markers and to evaluate their diagnostic performance in a true screening setting.

To date, plasma methylated septin 9 is the first and only blood-based test approved by the US Food and Drug Administration (FDA) for CRC screening. However, the diagnostic performance of this test is not optimal, with sensitivity for detecting CRC and advanced adenoma of 48.2% and 11.2%, respectively, at 91.5% specificity. Other alleged promising blood biomarkers, such as autoantibodies and microRNAs, were rarely validated in the targeted screening populations.

Due to the continuing need for quick, but sensitive and specific cancer diagnostics the present invention seeks to provide a novel approach for a simple and minimal invasive but specific and sensitive test system for the diagnosis or monitoring various cancer diseases.

The above problem is solved in a first aspect by 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 four biomarker selected from the group consisting of the protein biomarker AREG, GDF-15, FasL, Flt3L, and the autoantibody biomarker TP53 -autoantibody, in the biological sample, wherein a differential level of the at least four 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.

The term protein biomarker shall refer in context of the invention to a marker which is a protein molecule, preferably a protein which is not an antibody. On the other hand, the term "autoantibody biomarker" shall refer to a specific species of autoantibody used as biomarker. In this case the autoantibody is an indirect sign of an increase of protein expression of a protein biomarker to which the patient or host mounts an immune response reflected in the concentration of level of autoantibodies against said protein.

A "diagnosis" or the term "diagnostic" in context of the present invention means identifying the presence or nature of a pathologic condition. Diagnostic methods differ in their sensitivity and specificity. The "sensitivity" of a diagnostic assay is the percentage of diseased individuals who test positive (percent of "true positives"). Diseased individuals not detected by the assay are "false negatives." Subjects who are not diseased and who test negative in the assay, are termed "true negatives." The "specificity" of a diagnostic assay is 1 minus the false positive rate, where the "false positive" rate is defined as the proportion of those without the disease who test positive. While a particular diagnostic method may not provide a definitive diagnosis of a condition, it suffices if the method provides a positive indication that aids in diagnosis.

The term "prognosis" refers to a forecast as to the probable outcome of the disease as well as the prospect of recovery from the disease as indicated by the nature and symptoms of the case. Accordingly, a negative or poor prognosis is defined by a lower post-treatment survival term or survival rate. Conversely, a positive or good prognosis is defined by an elevated post- treatment survival term or survival rate. Usually prognosis is provided as the time of progression free survival or overall survival.

The term "stratification" for the purposes of this invention refers to the advantage that the method according to the invention renders possible decisions for the treatment and therapy of the patient, whether it is the hospitalization of the patient, the use, effect and/or dosage of one or more drugs, a therapeutic measure or the monitoring of a course of the disease and the course of therapy or etiology or classification of a disease, e.g., into a new or existing subtype or the differentiation of diseases and the patients thereof. Particularly with regard to colorectal cancer, "stratification" means in this context a classification of a colorectal cancer as early or late stage colorectal cancer.

The term "monitoring a therapy" means for the purpose of the present invention to observe disease progression in a subject who receives a cancer therapy. In other words, the subject during the therapy is regularly monitored for the effect of the applied therapy, which allows the medical practitioner to estimate at an early stage during the therapy whether the prescribed treatment is effective or not, and therefore to adjust the treatment regime accordingly.

As used herein, the term "subject" or "patient" refers to any animal (e.g., a mammal), including, but not limited to, humans, non-human primates, rodents, and the like, which is to be the recipient of a particular treatment. Typically, the terms "subject" and "patient" are used interchangeably herein in reference to a human subject. As used herein, the term "subject suspected of having cancer" refers to a subject that presents one or more symptoms indicative of a cancer (e.g., a noticeable lump or mass). A subject suspected of having cancer may also have one or more risk factors. A subject suspected of having cancer has generally not been tested for cancer. However, a "subject suspected of having cancer" encompasses an individual who has received an initial diagnosis (e.g., a CT scan showing a mass) but for whom the subtype or stage of cancer is not known. The term further includes people who once had cancer (e.g., an individual in remission), and people who have cancer and are suspected to have a metastatic spread of the primary tumor. In this regard the present invention is also applicable as follow-up care for monitoring a subject for a reoccurrence of the cancer.

The term "cancer" and "cancer cells" refers to any cells that exhibit uncontrolled growth in a tissue or organ of a multicellular organism. Particular preferred cancers in context of the present invention are selected from colorectal cancer, pancreatic cancer, gastric cancer, breast cancer, lung cancer, prostate cancer, hepatocellular cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, leukemia or brain cancer.

As used herein, the term "colorectal cancer" includes the well-accepted medical definition that defines colorectal cancer as a medical condition characterized by cancer of cells of the intestinal tract below the small intestine (i.e., the large intestine (colon), including the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum). Additionally, as used herein, the term "colorectal cancer" also further includes medical conditions, which are characterized by cancer of cells of the duodenum and small intestine (jejunum and ileum).

As used herein, the terms "gastric cancer" or "stomach cancer" refer to cancers of the stomach. The most common types of gastric cancer are carcinomas, such as but not limited to, adenocarcinomas, affecting the epithelial cells of the stomach. Stomach cancers may additionally include, for example, sarcomas affecting the connective tissue of the stomach and blastomas affecting the blast tissue of the stomach.

The term "pancreatic cancer" encompasses benign or malignant forms of pancreatic cancer, as well as any particular type of cancer arising from cells of the pancreas (e.g., duct cell carcinoma, acinar cell carcinoma, papillary carcinoma, adenosquamous carcinoma, undifferentiated carcinoma, mucinous carcinoma, giant cell carcinoma, mixed type pancreatic cancer, small cell carcinoma, cystadenocarcinoma, unclassified pancreatic cancers, pancreatoblastoma, and papillary-cystic neoplasm, and the like.).

The term "biological sample" as used herein refers to a sample that was obtained and may be assayed for any one of the biomarkers as disclosed with the present invention, or their gene expression. The biological sample can include a biological fluid (e.g., blood, cerebrospinal fluid, urine, plasma, serum), tissue biopsy, and the like. In some embodiments, the sample is a tissue sample, for example, tumor tissue, and may be fresh, frozen, or archival paraffin embedded tissue. Preferred samples for the purposes of the present invention are bodily fluids, in particular blood or plasma samples.

A "biomarker" or "marker" in the context of the present invention refers to an organic biomolecule, particularly a polypeptide, which is differentially present in a sample taken from subjects having a certain condition as compared to a comparable sample taken from subjects who do not have said condition (e.g., negative diagnosis, normal or healthy subject, or non- cancer patients, depending on whether the patient is tested for cancer, or metastatic cancer). For examples, a marker can be a polypeptide or polysaccharide (having a particular apparent molecular weight) which is present at an elevated or decreased level in samples of cancer patients compared to samples of patients with a negative diagnosis. The term "determining the level of a biomarker in a sample, control or reference, as described herein shall refer to the quantification of the presence of said biomarkers in the testes sample. For example the concentration of the biomarkers in said samples may be directly quantified via measuring the amount of protein/polypeptide/polysaccharide as present in the tested sample. However, also possible is to quantify the amount of biomarker indirectly via assessing the gene expression of the encoding gene of the biomarker, for example by quantification of the expressed mR A encoding for the respective biomarker. The present invention shall not be restricted to any particular method for determining the level of a given biomarker, but shall encompass all means that allow for a quantification, or estimation, of the level of said biomarker, either directly or indirectly. "Level" in the context of the present invention is therefore a parameter describing the absolute amount of a biomarker in a given sample, for example as absolute weight, volume, or molar amounts; or alternatively "level" pertains to the relative amounts, for example and preferably the concentration of said biomarker in the tested sample, for example mo 1/1, g/1, g/mol etc. In preferred embodiments the "level" refers to the concentration of the tested biomarkers in g/1.

"Increase" of the level of a biomarker in a sample compared to a control shall in preferred embodiments refer to statistically significant increase in preferred aspects of the invention.

In alternative embodiments of the invention, certain biomarkers as disclosed herein may also be significantly decreased in the event of a cancer disease in a subject.

In a preferred embodiment the method of the herein disclosed invention is performed noninvasive, such as an in vitro or ex vivo method. So far the herein described diagnostic methods are non-invasive the term "providing a biological" sample shall preferably not be interpreted to include a surgical procedure conducted at the subject.

Preferred embodiments of the present invention pertain to panels of a plurality of biomarkers as identified herein for the diagnostic purposes as described. The advantage of combing the biomarkers, in particular the combination of protein biomarkers and autoantibody biomarkers, as disclosed herein, is an increased sensitivity and/or specificity of the diagnostic assays. Hence a preferred embodiment of the invention pertains to the herein disclosed method wherein step (b) comprises determining the level of at least four or preferably all five, biomarkers in the biological sample. Most preferred is that at least five biomarkers are used. In one embodiment of the herein disclosed invention the level of at least CEA, AREG, and GDF-15, in the biological sample, is determined. In addition one or more additional biomarkers known to be useful in the diagnosis of the disease may be tested.

In this regard it is preferred that the analysis of the marker panel in step (b) of the diagnostic method of the invention is characterized in that the tested marker panel has an apparent area under the curve (AUC) at 95% confidence interval (CI) of at least 60%, preferably at least 65% or more preferably at least 70%. How to determine the AUC is known to the skilled artisan. Alternatively or additionally the panel of the invention may be characterized by a sensitivity of at least 75%, preferably at least 80%>, and a specificity of at least 40%>, preferably at least 50%, more preferably at least 60%. Preferably the CI and specificity/sensitivity of the marker panels are as disclosed in the example section.

To date, no single blood biomarker qualifying for mass screening has been identified. The combination of multiple markers might be a more promising approach to achieve the necessary sensitivity and specificity for application in mass screening. Although other marker panels were tested in the prior art, the apparent differences to the panel as provided herein can be explained by the fact that those prior art studies were done in a clinical setting and did not apply any adjustment for over-optimism. The above mentioned limitations were also shared by many other studies regarding blood biomarkers for CRC detection. For reasons outlined in detail in the introduction, it is a critical issue to identify and evaluate biomarkers in samples from screening settings in order to obtain valid performance characteristics under screening conditions. Furthermore, as demonstrated herein, correction for overfitting (cross-validation, bootstrap techniques) and/or external validation are also indispensable to adjust for potential overestimation of diagnostic performance. Hence, the marker panel of the present invention is advantageous over previous prior art panels.

The biomarkers of the invention are preferably protein biomarkers and/or autoantibody biomarkers.

The biomarker panel as disclosed herein is particular useful in a cancer screening setting. Cancer screening in the herein disclosed invention shall refer to a procedure where a subject is for which not diagnosis was established is tested for the presence of the cancer disease. This shall not be interpreted to exclude the use of the biomarker of the invention for a diagnostic of a subject that was already diagnosed to suffer from a cancer disease. Non limiting examples for such an application are confirmation of a diagnosis, monitoring or treatment success or monitoring reoccurrence of a cancer in a subject that already received a treatment and wherein cancer is in remission or was cured.

The skilled artisan will understand that numerous methods may be used to select a threshold or reference value for a particular marker or a plurality of markers. In diagnostic aspects, a threshold value may be obtained by performing the assay method on samples obtained from a population of patients having a certain type of cancer, and from a second population of subjects that do not have cancer. For prognostic or treatment monitoring applications, a population of patients, all of which have, for example, ovarian cancer, may be followed for the time period of interest (e.g., six months following diagnosis or treatment, respectively), and then dividing the population into two groups: a first group of subjects that progress to an endpoint (e.g., recurrence of disease, death); and a second group of subjects that did not progress to the end point. These are used to establish "low risk" and "high risk" population values for the marker(s) measured, respectively. Other suitable endpoints include, but are not limited to, 5-year mortality rates or progression to metastatic disease.

Once these groups are established, one or more thresholds may be selected that provide an acceptable ability to predict diagnosis, prognostic risk, treatment success, etc. In practice, Receiver Operating Characteristic curves, or "ROC" curves, are typically calculated by plotting the value of a variable versus its relative frequency in two populations (called arbitrarily "disease" and "normal" or "low risk" and "high risk" for example). For any particular marker, a distribution of marker levels for subjects with and without a disease may overlap. Under such conditions, a test does not absolutely distinguish "disease" and "normal" with 100% accuracy, and the area of overlap indicates where the test cannot distinguish "disease" and "normal." A threshold is selected, above which (or below which, depending on how a marker changes with the disease) the test is considered to be "positive" and below which the test is considered to be "negative." The area under the ROC curve (AUC) is a measure of the probability that the perceived measurement may allow correct identification of a condition.

Additionally, thresholds may be established by obtaining an earlier marker result from the same patient, to which later results may be compared. In some aspects, the individuals act as their own "control group." In markers that increase with disease severity or prognostic risk, an increase over time in the same patient can indicate a worsening of disease or a failure of a treatment regimen, while a decrease over time can indicate remission of disease or success of a treatment regimen.

In some embodiments, multiple thresholds or reference values may be determined. This can be the case in so-called "tertile," "quartile," or "quintile" analyses. In these methods, the "disease" and "normal" groups (or "low risk" and "high risk") groups can be considered together as a single population, and are divided into 3, 4, or 5 (or more) "bins" having equal numbers of individuals. The boundary between two of these "bins" may be considered "thresholds." A risk (of a particular diagnosis or prognosis for example) can be assigned based on which "bin" a test subject falls into.

All numeric values are herein assumed to be modified by the term "about," whether or not explicitly indicated. The term "about" generally refers to a range of numbers that one of skill in the art would consider equivalent to the recited value (i.e., having the same function or result). In many instances, the terms "about" may include numbers that are rounded to the nearest significant figure. In particularly preferred embodiments of the invention the term "about" may refer to a deviation of the respective numeric value of a maximum of 20% of the numerical value, however more preferred is 15%, 10%>, 5% and most preferred is 4%, 3%, 2%, and most preferred is 1%.

In a preferred embodiment said sample is selected from the group consisting of body fluids or tissue, preferably wherein said body fluid sample is a blood sample, more preferably a plasma or serum sample.

In all aspects and embodiments of the present invention in may be preferred that the level of said at least one biomarker in said sample is determined by means of a nucleic acid detection method or a protein detection method. However, nucleic acid detection methods are only applicable where an expressed protein is the biomarker. Generally all means shall be comprised by the present invention which allow for a quantification of the expression of any one of the herein disclosed biomarker. Therefore also promoter analysis and procedures assessing the epigenetic status of a gene locus encoding a protein biomarker of the invention are comprised by the herein described invention. Detection methods that are preferred in context of the herein described invention the level of said at least one biomarker in said sample is determined by means of a detection method selected from the group consisting of mass spectrometry, mass spectrometry immunoassay (MSIA), antibody-based protein chips, 2-dimensional gel electrophoresis, stable isotope standard capture with anti-peptide antibodies (SISCAPA), high-performance liquid chromatography (HPLC), western blot, cytometry bead array (CBA), protein immuno- precipitation, radio immunoassay, ligand binding assay, and enzyme-linked immunosorbent assay (ELISA), preferably wherein said protein detection method is ELISA. Suitable alternative detection methods for quantification of a biomarker of the invention are known to the skilled artisan.

With regard to autoantibody biomarkers, an immunological capture assay using a protein or protein fragment is preferred. In these assay the autoantibody is detected by detecting the binding of the autoantibody to its respective antigen, or to a fragment of the antigen which contains the binding epitope. Preferably the autoantibody biomarker is TP53 -autoantibody, and said capturing protein is TP53 protein, or an antigenic fragment thereof, preferably wherein the antigenic fragment comprises an epitope to which said TP53 -autoantibody is capable to bind.

In yet another aspect, the invention provides kits for aiding a diagnosis of cancer, wherein the kits can be used to detect the biomarkers of the present invention. For example, the kits can be used to detect any one or combination of biomarkers described above, which biomarkers are differentially present in samples of a patient having the cancer and healthy patients. The kits of the invention have many applications. For example, the kits can be used to differentiate if a subject has the cancer, or has a negative diagnosis, thus aiding a cancer diagnosis. In another example, the kits can be used to identify compounds that modulate expression of the biomarkers in in vitro cancer cells or in vivo animal models for cancer.

Optionally, the kit can further comprise instructions for suitable operational parameters in the form of a label or a separate insert. For example, the kit may have standard instructions informing a consumer how to wash the probe after a sample of plasma is contacted on the probe.

In another embodiment, a kit comprises (a) an antibody that specifically binds to a marker; and (b) a detection reagent. Such kits can be prepared from the materials, and the previous discussion regarding the materials (e.g., antibodies, detection reagents, immobilized supports, etc.) is fully applicable to this section and need not be repeated.

In either embodiment, the kit may optionally further comprise a standard or control information so that the test sample can be compared with the control information standard to determine if the test amount of a marker detected in a sample is a diagnostic amount consistent with a diagnosis of cancer.

Preferably the kit of the invention is a diagnostic kit for performing a method in accordance with the present invention comprising means for quantifying the level of said at least one biomarker. Preferably the kit of the invention comprises means for quantifying a protein biomarker selected from AREG, GDF-15, FasL, and Flt3L. Such means for quantifying is for example at least one antibody, preferably wherein the antibody is a monoclonal antibody, such as a monoclonal antibody that specifically binds to any of the aforementioned biomarkers. Such antibodies are known in the art and commercially available. Alternatively or additionally the diagnostic kit of the invention may contain means for detection of the presence or absence, and quantification thereof, of the autoantibody biomarker TP53 autoantibody.

The present invention will now be further described in the following examples with reference to the accompanying figures and sequences, nevertheless, without being limited thereto. For the purposes of the present invention, all references as cited herein are incorporated by reference in their entireties. In the Figures and Sequences:

Figure 1: STAandards for the Reporting of Diagnostic accuracy studies (STARD) diagram showing the selection of study participants enrolled in the BLITZ study in 2005-2015 and the analysis scheme.

Figure 2: Comparison of receiver operating characteristics curves of the four-marker panel and five-marker panel for detecting: a) colorectal cancer vs. controls free of neoplasm; b) advanced adenomas vs. controls free of neoplasm.

Figure 3: Comparison of the receiver operating characteristics curves of the (a) four- marker panel and (b) five-marker panel for detecting early stage and late stage CRCs. EXAMPLES

Materials and Methods

Study design and study population

In the present analyses, a two-step approach with selection of biomarkers and construction of multi-marker algorithms in a discovery set, and validation of the findings in an independent validation set was adopted. Samples from two study populations were used, including clinically detected CRC cases recruited at hospitals (used for marker discovery only) and participants with CRC or advanced adenomas, as well as control participants without colorectal neoplasms recruited in a true screening setting (BLITZ study).

The prediagnostic samples from the BLITZ study

The BLITZ study (Begleitende Evaluierung innovativer Testverfahren zur Darmkrebsfruerkennung) is an ongoing cohort of participants attending screening colonoscopy in Germany. Detailed information on the BLITZ study has been reported elsewhere (Chen H, Zucknick M, Werner S, Knebel P, Brenner H. Head-to-Head Comparison and Evaluation of 92 Plasma Protein Biomarkers for Early Detection of Colorectal Cancer in a True Screening Setting. Clin Cancer Res 2015;21 :3318-26; Brenner H, Tao S, Haug U. Low-dose aspirin use and performance of immunochemical fecal occult blood tests. JAMA 2010;304:2513-20.).

Briefly, this study is conducted in collaboration with 20 gastroenterology practices in southern Germany since November 2005. Participants are recruited at a preparatory visit at practices typically one week before screening colonoscopy and are invited to donate prediagnostic blood and stool samples. Self-administrated questionnaires regarding potential risk factors known or suspected to be related to CRC, such as family history, smoking, diet and physical activity information, are collected from all participants. The German screening colonoscopy program, introduced in October 2002, offers up to two screening colonoscopies at least 10 years apart to men and women aged 55 years or older. The high quality of screening colonoscopy in Germany is reflected in high adenoma detection rates which have steadily increased since the introduction of the screening program.

By September 2014, a total number of 7,197 participants have been recruited (see Figure 1). For this analysis, the following exclusion criteria were applied: 1) missing plasma samples; 2) blood taken after screening colonoscopy; 3) inflammatory bowel disease or previous colorectal cancer; 4) insufficient bowel preparation (only for individuals with no significant findings at screening colonoscopy); 5) incomplete colonoscopy (only for individuals with no significant findings at screening colonoscopy). From the remaining participants of the BLITZ study recruited in 2005-2015 (N=6018), all 43 screening detected CRC cases, as well as random samples of individuals with advanced colorectal adenomas (N=113) or with no colorectal neoplasms (N=233) were included in this analysis. Because this study was conducted in a true screening population in which patients with colorectal cancer are expected to be on average slightly older and to include a somewhat large proportion of men, the inventors did not match for these factors as this might lead to biased estimates of specificity in such a setting.

Clinically selected CRC samples

Given the limited number of CRC cases identified in the screening setting even in a study as large as BLITZ, the inventors additionally included 239 CRC patients recruited after diagnosis but before treatment at four hospitals in Southern Germany for the discovery phase of this study. These patients were different from the much smaller number of such patients (n=54) included in a previous study. The same questionnaire data and medical records were collected, and blood samples were obtained according to identical SOPs.

Classification of colorectal cancer and advanced adenoma

CRC stage was classified according to the UICC (Union for International Cancer Control) TNM (tumor-node-metastasis) stage classification (7th version). Participants of screening colonoscopy were classified according to the most advanced finding reported in the colonoscopy and/or histology report. Advanced adenomas were defined as adenomas with at least one of the following features: 1) high grade dysplasia (HGD); 2) villous or tubular- villous architecture; 3) size >10 mm. Relevant information was extracted from colonoscopy and hospital records by two research assistants independently who were blinded with respect to the blood test results.

Sample preparation

Blood samples were collected in Sarstedt S-Monovette K3 EDTA or BD Vacutainer K3 EDTA tubes prior to bowel preparation to bowel colonoscopy (BLITZ study), or prior to large bowel surgery or neoadjuvant therapy (239 CRC cases from the clinical setting). The samples were transported to the laboratory while preserving a cold chain and were centrifuged at 2000-2500g for 10 minutes, aliquotted and stored at -80°C until further use. Details on the standard operating procedure have also been described previously.

Protein profiling (Protein Biomarker)

Ninety-two predefined human tumor-associated protein biomarkers were measured in 628 samples using Proseek Multiplex Oncology I v296x96 (Olink Bioscience, Uppsala, Sweden). The panel of 92 protein biomarkers reflects various biologic mechanisms involved in carcinogenesis, such as angiogenesis, cell-cell signaling, growth control, and inflammation. All laboratory operations were conducted according to the Proseek Multiplex Oncology 196x96 User Manual at Olink Bioscience (Uppsala, Sweden). In short, the Proseek reagents are based on the Proximity Extension Assay (PEA) technology, where 92 oligonucleotide labeled antibody probe pairs are allowed to bind to their respective target present in the sample. A PCR reporter sequence is formed by a proximity dependent DNA polymerization event and is subsequently detected and quantified using real-time PCR. The laboratory operators were blinded with respect to any information regarding the study participants.

Antibodies against TP53 (Autoantibody Biomarker)

For the majority of study participants, including 239 cases with clinically identified CRC, 39 cases with screening detected CRC, 82 participants with advanced adenomas and 208 controls free of neoplasm, measurements of antibodies against TP53 were available from a previous study, which has been reported on elsewhere (Chen H, Werner S, Butt J, et al. Prospective evaluation of 64 serum autoantibodies as biomarkers for early detection of colorectal cancer in a true screening setting. Oncotarget 2016;7: 16420-32.). Briefly, antibodies against TP53 were measured by multiplex serology, a fluorescent bead-based GST capture immunosorbent assay, as described previously (Zornig I, Halama N, Lorenzo Bermejo J, et al. Prognostic significance of spontaneous antibody responses against tumor-associated antigens in malignant melanoma patients. Int J Cancer 2015;136: 138-51; Waterboer T, Sehr P, Michael KM, et al. Multiplex human papillomavirus serology based on in situ-purified glutathione s- transferase fusion proteins. Clin Chem 2005;51 : 1845-53).

Data normalization of protein profiling

The raw data of the protein profiling were firstly normalized following the standard protocol from the manufacturer and using the Olink Wizard of GenEx software (MultiD, Goteborg, Sweden). For each data point, the raw quantification cycle value (Cq-value, in log2 scale) was exported from the Fluidigm Real-Time PCR Analysis Software. The Cq-value is defined as the calculated cycle number at which the PCR product crosses a threshold of detection and is used to represent the expression levels of respective proteins in the present study. The first step of normalization was to subtract the raw Cq-value for the extension control for the corresponding sample in order to correct for technical variation. The calculated Cq-values (dCq-value) were further normalized against the negative control determined in the measurement, which yielded ddCq-values (hereafter: Cq-value, in log2 scale) and could be used for further analyses. Limit of detection (LOD) was defined as the mean value of the three negative controls plus 3 calculated standard deviations. 30 samples with invalid test results were excluded from this analysis. Missing data and data with a value lower than LOD were replaced with LOD in the following statistical analyses.

Statistical analyses

In the analysis, the discovery set samples included CRC cases recruited in the clinical setting and 118 randomly selected controls free of neoplasm from the BLITZ study (Figure 1). The validation set was defined in such a way that it represents a true screening setting, i.e., only participants from the BLITZ study were included.

The plasma protein levels (Cq-value) were first compared between CRC cases and neoplasm- free controls in the discovery set samples and validation set samples using Wilcoxon Rank Sum Test (hereafter: Wilcoxon test). The Benjamini & Hochberg method was additionally employed to correct for multiple testing.

A multi-marker algorithm was derived by applying the Lasso logistic regression model based on significant bio markers identified in the discovery set samples. A second prediction algorithm was built by combining the measurements of the selected protein biomarkers from the Lasso logistic regression model with TP53 autoantibody measurements using logistic regression. Both prediction algorithms were further validated using receiver operating characteristics (ROC) curves in the validation set. Areas under the curve (AUCs) and sensitivities at cutoffs yielding 80% and 90%> specificity, respectively, and their 95% CIs of the multi-marker algorithms were calculated and reported. In addition, the inventors conducted subgroup analyses on the diagnostic performance of the multi-marker algorithms according to sex and age (<65 years vs. >65 years) and cancer stage in the validation set. Statistical analyses were performed with the statistical software R version 3.0.3.23 All tests were two-sided and p- values of 0.05 or less were considered to be statistically significant.

Results

Figure 1 provides the STAandards for the Reporting of Diagnostic accuracy studies (STARD) diagram showing the selection of study participants enrolled in the BLITZ study in 2005- 2015 and also the scheme of analysis. The discovery set included 226 clinically recruited CRC cases and 118 controls free of colorectal neoplasms. The validation set included 41 CRC cases, 106 participants with advanced adenomas and 107 controls free of colorectal neoplasms all of whom were recruited in the screening setting.

Table 1. Characteristics of the study population in the discovery set and the validation set

Discovery set Validation set

Group CRC CRC Adv. adenoma Control

Control (N, %)

(N, %) (N, %) <N, %) (N, %)

Total 226 118 41 106 107

Age (years)

<60 ^' 50 (23.1) ^' 49 (42.6) 7 (19.4)^" " ^' 37 (36.3) 46 (43.8)

60-64 32 (14.8) 29 (25.2) 1 1 (30.6) 31 (30.4) 22 (21.0)

65-69 30 ( 13.9) 17 (14.8) 5 (13.9) 10 (9.8) 17 (16.2)

>70 104 (48.1) 20 (17.4) 13 (36.1) 24 (23.5) 20 (19.0)

Mean ± SD 67.8 ± 12.0 62.0 ± 7.3 66.8 ± 6.9 63.5 ± 7.1 62.2 + 6.3

Male 129 (57.1) 52 (44.1) ^" 29 ( 70.7) ^" 56 (52.8) 44 (41.1 )

Female 97 (42.9) 66 (55.9) 12 (29.3) _{, r}50 (47.2)_f 62 (57.9)

I ^'ICC I NN i tumor staee

I 67 (29.6) _ ^' 14 (34.1)^' - _

II 64 (28.3) - 3 (7.3) - -

III 67 (29.6) - 21 (51.2) - -

1Y 28 ( 12.4) - 3 (7.3) - -

CRC location

Colon ^"l39 (6l^'.5)^{" '} - ^{" "}23 (56.1) ^{" "} - -

Rectum 87 (38.5) - 17 (41.5) - -

Unknown - - 1 (2.4) - -

Advanced adenoma subclass

HGD - - - 14 ( 13.2) -

Villous - - - 56 (34.0) -

Adenoma > 1cm - - - 36 (52.8) -

Abbreviations: CRC, colorectal cancer; HGD, high-grade dysplasia; UICC TNM, Union for International

Cancer Control (UICC) tumor-node-metastasis.

Table 1 shows the distribution of study population characteristics of the discovery set and the validation set. In both sets, CRC cases were on average a few years older than controls free of neoplasm and advanced adenomas. In addition, the proportion of men was somewhat higher in the CRC groups and in the advanced adenoma group than in the control groups. Approximately half of the CRC cases were diagnosed in early (I or II) stages. A slightly higher proportion of CRCs was diagnosed at early stage (stage I/II) for the discovery set than for the validation set (57.9% vs. 41.4%). More cancer patients had their tumor located in the colon than in the rectum.

Overall, 39 proteins showed statistically significant different expression levels between CRC cases and controls free of neoplasm in the discovery set (adjusted p-values <0.05). Twelve of them were successfully replicated in the validation set even though included a much lower number of CRC cases (n=41). The respective results are shown in Table 2. All 12 proteins showed statistically significant higher expression levels in CRC cases than in controls. Two of them, i.e. GDF-15 and AREG individually predicted presence of CRC with an AUC>0.70.

Table 2. Diagnostic performance of 12 significant protein markers

Discovery set Validation set

Protein

marker Fold change Fold change

p-value' p-value (CRC vs. free of neoplasm) (CRC vs. free of neoplasm)

GDF-15 1.68 <0.001 1.42 <0.001

AREG 1.34 <0.001 1.32 <0.001

TRAILR-2 1.23 «3.001 1.28 <0.001

IL-6 1.56 <0.001 1.48 0.014

AM 1.23 <0.001 1.28 0.004

HE4 1.24 <0.001 1.19 0.014

TNFR-2 1.21 <0.001 1.16 0.014

ILT3 1.21 <0.001 1.14 0.043

CEA 1.29 <0.001 1.86 <0.001

CXCL9 1.27 0.001 1.42 0.040

TNFR-1 1.17 0.001 1.24 0.014

HGF 1.14 0.007 1.27 0.032

Abbreviation: CRC, colorectal cancer; GDF-15, Growth differentiation factor 15; AREG, Amphiregulin;

TRAILR-2,TNF-related apoptosis-inducing ligand receptor-2; IL-6, Interleukin-6; AM, Adrenomedullin;

HE4, WAP four-disulfide core domain protein 2; TNF-R2, Tumor necrosis factor receptor-2; ILT3,

Immunoglobulin-like transcript 3; CEA, Carcinoembryonic antigen; CXCL9, C-X-C motif chemokine 9;

TNFR-1, Tumor necrosis factor receptor-1; HGF, Hepatocyte growth factor

* p-values were adjusted for multiple testing (Benjamini-Hochberg method)

The inventors used the lasso logistic regression models to construct a multi-marker prediction algorithm based on the 39 significant bio markers identified in the discovery set. The following 4 proteins were selected in the algorithm: growth differentiation factor 15 (GDF- 15), am hiregulin (AREG), Fas antigen ligand (FasL) and Fms-related tyrosine kinase 3 ligand (Flt3L). The four biomarker panel constitutes Example 1 of the present invention. Another prediction model combining these 4 proteins with TP53 autoantibody was further constructed. The five biomarker panel constitutes Example 2 of the present invention. In the discovery set, the apparent AUCs of the 4-marker algorithm and 5-marker algorithm for discriminating CRC vs. controls free of neoplasm were 0.87 (95% CI, 0.83-0.90) and 0.89 (95% CI, 0.85-0.92), respectively.

Table 3. Diagnostic performance of multi-marker panels for detecting colorectal cancer in the validation set

Marker panel AUC (95% CI) Sensitivity (95% CI)

at 90% specificity at 80% specificity

(a) CRC vs. controls free of neoplasm

4 protein panel 0.81 (0.73-0.88) 53.6 (26.8-70.7) 63.4 (48.8-82.9)

4-protein panel + TP53 autoantibody 0.82 (0.74-0.90) 56.4 (38.4-71.8) 66.7 (48.7-82.1)

(b) advanced adenomas vs. controls free of neoplasm

4 protein panel 0.58 (0.51-0.65) 18.9 (8.5-27.4) 23.6 (15.1-38.7)

4-protein panel + TP53 autoantibody 0.60 (0.52-0.69) 22.0 (13.4-35.4) 31.7 ( 18.3-45.1)

Abbreviation: AUC, area under the curve; CRC, colorectal cancer; 95% CI, 95% confidence interval

4 protein panel including GDF-15, AREG, Fas and Flt3L.

Table 3 and Figure 2 show the comparison of the two prediction algorithms in detecting CRC and its precursors in the validation set. The AUC of the 4-protein panel for discriminating CRC versus controls free of neoplasm was 0.81 (95% CI, 0.73-0.88). Adding TP53 autoantibody to the four-protein panel conferred a modest improvement in terms of AUC (0.82, 95%) CI, 0.74-0.90), but suprisingly strong improvement could be observed at the left side of the ROC curve. When defining cutoffs yielding 90%> specificity, the sensitivity of the four- and the five-marker algorithm for detecting CRC were 53.6%> (95%> CI, 26.8-70.7%)) and 56.4% (95% CI, 38.4-71.8%), respectively, and at cutoffs yielding 80% specificity, the sensitivity of the four- and the five-marker algorithm for detecting CRC were 63.4% (95% CI, 48.8-82.9%) and 66.7% (95% CI, 48.7-82.1%), respectively.

Both algorithms also showed modest diagnostic efficacy for detecting advanced adenomas, with AUCs of 0.58 (95% CI, 0.51-0.65) and 0.60 (95% CI, 0.52-0.69) for the 4-protein algorithm and the 5-marker algorithm, respectively. At cutoff yielding 90% specificity, the sensitivity for detecting advanced adenomas were 18.9% (95% CI, 8.5-27.4%>) and 22.0% (95% CI, 13.4-35.4%), respectively.

Both prediction algorithms showed similar overall diagnostic performance for detecting early stage CRC (TNM stage I/II) and late stage CRC (TNM stage III/IV), as presented in Figure 3. For instance, the AUCs of the 5-marker algorithm for detecting early stage CRC and late stage CRC were 0.84 (95% CI, 0.73-0.92) and 0.81 (95% CI, 0.70-0.91), respectively. The differences were not statistically significant (p=0.77).

Both panels showed slightly higher AUCs for detecting CRC among women and in the younger age group (< 65 years), but subgroup specific confidence intervals were wide and overlapping (not shown). Furthermore, adding age and sex to the five-marker panel in the regression models did not further improve the diagnostic performance for detecting CRC and its precursors (not shown).

Discussion

In this study, the inventors evaluated the diagnostic performance of 92 plasma proteins and serum TP53 autoantibodies for detecting CRC and its precursors in a head-to-head manner using a large set of samples. Twelve protein biomarkers showed significantly higher expression levels in CRC patients than in controls free of neoplasms in both the discovery set and a validation set that was entirely derived from a true screening setting. Moreover, a five- marker panel including GDF-15, AREG, FasL, Flt3L and TP53 autoantibody (Example 2) was constructed and validated. In the validation set, the AUCs of the five-marker panel for detecting CRC and advanced adenomas were 0.82 (95% CI, 0.74-0.90) and 0.60 (95% CI, 0.74-0.90), respectively. Of note, the panel showed similar diagnostic performance for detecting early and late stage CRCs.

Of the five markers included in the final panel, GDF-15 and AREG exhibited very good diagnostic performance for detecting CRC, with AUCs higher than 0.70 even when used as single prediction markers. GDF-15 (also known as macrophage inhibitory cytokine- 1) is a divergent member of the human TGF-beta superfamily and a mediator of systemic inflammatory response and has been reported to be related to various types of cancer.

The vast majority of studies evaluating blood-based biomarkers for CRC screening have been exclusively conducted in clinical settings. Some of these studies have reported higher levels of sensitivity and specificity, which typically could not be confirmed in rigorous validation in screening settings though. In clinical settings, cases are typically symptomatic and have undergone a variety of diagnostic procedures (such as colonoscopy) leading to the diagnosis. Some patients may even have had even initial therapeutic intervention before collection of blood samples. Controls often consist of or include patients with other diseases, and there may be differences in sample collection and processing procedures between cases and controls. All of these factors can influence apparent diagnostic performance and easily lead to false- positive findings. In context of the disclosed invention the utmost attention was paid to avoid such bias by a rigorous study design in which independent validation of biomarkers identified in the discovery set was done in a validation set that exclusively relied on participants recruited prior to screening colonoscopy in a true screening setting.

Advanced adenoma is the most important precursor of CRC, which a substantial risk of development into CRC in the long run. Early detection and removal of these precancerous lesions could therefore reduce the risk of CRC occurrence. To date, it is still a major challenge to detect advanced adenomas using blood-based tests, and most studies found very poor diagnostic performance for this outcome. Although some candidates, such as miRNA- 135b and a panel of BAG4, IL6ST and CD44, were reported in some studies to present good sensitivity for detecting advanced adenomas, these findings were either derived from studies having limited sample size or using clinically identified cases, thus requiring further independently validation in larger screening populations.

It should also be noted that the panel of Example 2 exhibited comparable diagnostic performance compared to the plasma methylated septin9, the only US FDA approved blood based test for CRC screening and therefore presents another diagnostic blood based option for patients in the future. The sensitivity of methylated septin 9 for detecting CRC and advanced adenomas were reported to be 48.2% and 11.2%, respectively, at a specificity of 91.5%>.When adjusting the cutoff yielding the identical specificity as reported by Church and colleagues (see references No. 10), the sensitivities of the five-marker panel for detecting CRC and advanced adenomas were 56.4% and 20.7%, respectively (not reported in the results section), demonstrating better diagnostic performance.

In particular advantageous aspects of the invention include the adopted a two-step approach, with biomarker discovery and subsequent validation in an independent sample set. Of note, the validation set consisted of prediagnostic samples from a large cohort of participants attending screening colonoscopy, therefore representing the target population for CRC screening. Moreover, both CRC and its precursors were included in the validation set, therefore rendering a thorough overview of the diagnostic potential of all examined biomarkers and the multi-marker panels. In addition, a large number of markers were tested simultaneously using state-of-the-art techniques, making a direct comparison of the diagnostic performance of all tested markers possible.

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 four biomarker selected from the group consisting of the protein biomarker AREG, GDF-15, FasL, Flt3L, and the autoantibody biomarker TP53 -autoantibody, in the biological sample, wherein a differential level of the at least four 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 the level of at least the protein biomarker AREG, GDF-15, FasL, and Flt3L, in the biological sample.

3. The method according to claim 1, wherein step (b) comprises determining the level of the protein biomarkers AREG, GDF-15, FasL, and Flt3L, and the autoantibody biomarker TP53 -autoantibody, in the biological sample.

4. The method according to any of claims 1 to 3, wherein the biological sample is a tissue sample or body liquid sample, preferably a blood sample, most preferably a plasma sample.

5. The method according to any of claims 1 to 4, wherein the method is a non- invasive method, preferably an ex vivo method or in vitro method.

6. 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.

7. The method according to any of claims 1 to 6, wherein the cancer is colorectal cancer, pancreatic cancer, gastric cancer, breast cancer, lung cancer, prostate cancer, hepatocellular cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, leukemia or brain cancer.

8. The method according to any of the preceding claims wherein the cancer is colorectal cancer, gastric cancer or pancreatic cancer.

9. The method according to any one of claims 1 to 8, wherein a differential level of a biomarker is a higher (or increased) level or higher (or increased) concentration of said protein biomarker or autoantibody biomarker, respectively, in the sample compared to the control.

10. The method according to any one of the preceding claims, wherein the biomarker is detected using one or more antibodies in case the biomarker is a protein biomarker, preferably wherein the protein biomarker is detected by western blot, ELISA, Proximity Extension Assay, or mass-spectrometrically.

11. The method according to any one of the preceding claims, wherein the biomarker is detected using a capture assay using an protein or protein fragment in case the biomarker is an autoantibody biomarker, preferably wherein the autoantibody biomarker is TP53 -autoantibody, and said capturing protein is TP53 protein, or an antigenic fragment thereof, preferably wherein the antigenic fragment comprises an epitope to which said TP53 -autoantibody is capable to bind.

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

13. The diagnostic kit of claim 12, comprising one or more antibodies for the detection of the at least 4 biomarkers, and/or comprising one or more TP53 capturing protein or antigenic fragment thereof.

14. Use of an antibody, or derivative thereof, directed to any one of the protein biomarkers selected from AREG, GDF-15, FasL, and Flt3L, in the performance of a method according to any of claims 1 to 11.

15. Use of a TP53 protein, or an antigenic fragment thereof, the fragment being capable of being bound by TP53 -autoantibodies, in the performance of a method according to any of claims 1 to 11.