WO2018202666A1

WO2018202666A1 - Cpg-site methylation markers in colorectal cancer

Info

Publication number: WO2018202666A1
Application number: PCT/EP2018/061118
Authority: WO
Inventors: Min JIA; Michael Hoffmeister
Original assignee: Deutsches Krebsforschungszentrum
Priority date: 2017-05-03
Filing date: 2018-05-02
Publication date: 2018-11-08

Abstract

The present invention relates to a method for determining a survival probability of a subject suffering from colorectal cancer comprising a) detecting the methylation status of at least two CpG sites selected from the CpG sites of Table 1 in a sample of said subject and, b) based on the methylation status detected in step a), determining the survival probability of said subject. The present invention further relates to a use of the methylation status of genomic DNA or means for the determination thereof in a sample of a subject suffering from colorectal cancer for determining a survival probability of said subject, preferably for predicting the mortality risk of said subject, and to a data collection, a kit, and a device related thereto.

Description

CpG-site methylation markers in colorectal cancer

Colorectal cancer (CRC) is one of the most common cancers worldwide with a five-year survival rate of more than 60% (Siegel et al. (2017), CA Cancer J Clin, 2017). Survival after CRC is largely dependent on the disease stage at diagnosis, but there is accumulating evidence that somatic mutations and epigenetic changes play a significant role in the progression of CRC and already guide clinical decision-making (Lievre et al. (2006), Cancer Res 66:3992; Lievre et al. (2008), J Clin Oncol 26:374).

As an important regulation approach in epigenetics, aberrant DNA methylation was extensively observed in various cancers including CRC (Naumov et al. (2013), Epigenetics 8:921). High-level methylation of CpG islands in the promoter region (CpG island methylator phenotype (CIMP)) can induce the silencing of tumor- suppressor genes, which can impact tumor onset and progression (Herman & Baylin (2003), N Engl J Med 349:2042; Kim & Deng (2007), Gut Liver 1 : 1; Toyota et al. (1999), Proc Natl Acad Sci U S A 96:8681). Colorectal tumors showing such methylation were consistently associated with specific patient and tumor characteristics, such as older age, proximal location, and microsatellite instability (MSI-H)(Weisenberger et al. (2006), Nat Genet 38:787). However, studies on the association of CIMP status and survival after CRC were inconsistent (Bae et al. (2013), Br J Cancer 109: 1004; Ogino et al. (2009), Gut 58:90). In previous studies, genes were selected for CIMP panels because they were cancer suppressor genes silenced by methylation in the promoter region or were differentially methylated in tumor tissue and normal tissue (Toyota et al, loc. cit.; Weisenberger et al, loc. cit.; Ogino et al. (2007), J Mol Diagn 9:305). Although silencing of these genes is reasoned by high methylation of CpG islands in the promoter region, other aberrant methylation on these genes may also influence their activity. In addition to methodological issues in some of the studies, studies mostly used different marker sets to define CIMP, which likely contributed to the inconsistency of results (Cleven et al. (2014), Clin Cancer Res 20:3261; Hokazono et al. (2014), Oncol Lett 8: 1937).

There is, thus, a need for improved methods for determining prognosis of colorectal cancer patients, and in particular for improved genetic and/or epigenetic markers for such determination.

This problem is solved by the methods and means of the present invention with the features of the independent claims. Preferred embodiments, which may be realized in an isolated fashion or in arbitrary combination, are listed in the dependent claims.

Accordingly, the present invention relates to a method for determining a survival probability of a subject suffering from cancer, preferably from colorectal cancer, comprising

a) detecting the methylation status of at least two CpG sites selected from the CpG sites of Table 1 in a sample of said subject and,

b) based on the methylation status detected in step a), determining the survival probability of said subject. .

As used in the following, the terms "have", "comprise" or "include" or any arbitrary grammatical variations thereof are used in a non-exclusive way. Thus, these terms may both refer to a situation in which, besides the feature introduced by these terms, no further features are present in the entity described in this context and to a situation in which one or more further features are present. As an example, the expressions "A has B", "A comprises B" and "A includes B" may both refer to a situation in which, besides B, no other element is present in A (i.e. a situation in which A solely and exclusively consists of B) and to a situation in which, besides B, one or more further elements are present in entity A, such as element C, elements C and D or even further elements.

Further, as used in the following, the terms "preferably", "more preferably", "most preferably", "particularly", "more particularly", "specifically", "more specifically" or similar terms are used in conjunction with optional features, without restricting further possibilities. Thus, features introduced by these terms are optional features and are not intended to restrict the scope of the claims in any way. The invention may, as the skilled person will recognize, be performed by using alternative features. Similarly, features introduced by "in an embodiment of the invention" or similar expressions are intended to be optional features, without any restriction regarding further embodiments of the invention, without any restrictions regarding the scope of the invention and without any restriction regarding the possibility of combining the features introduced in such way with other optional or non- optional features of the invention. Moreover, if not otherwise indicated, the term "about" relates to the indicated value with the commonly accepted technical precision in the relevant field, preferably relates to the indicated value ± 20%, more preferably ± 10%, most preferably ± 5%.

The method for determining a survival probability of the present invention, preferably, is an in vitro method. Moreover, it may comprise steps in addition to those explicitly mentioned above. For example, further steps may relate, e.g., to obtaining a sample for step a), deriving recommendations for further proceeding and/or providing treatment after obtaining the result of step b), and/or further steps as specified herein below. Moreover, one or more of said steps may be performed by automated equipment.

As used herein, the term "survival probability" relates to the probability that a subject will be alive during certain period of time. As will be understood by the skilled person, a survival probability is also a measure for a mortality risk, i.e. for the probability that said subject dies within the indicated period of time. Preferably, said period of time is at most 5 years, more preferably at most 4 years, more preferably at most 40 months, even more preferably at most 35 months, most preferably at most 30 months. Thus, preferably, the survival probability may be a favorable survival probability, i.e. a survival probability indicating a low probability for dying within one of the aforesaid time frames. Preferably, the survival probability for the aforesaid time frames in case a favorable survival probability is determined is at least 0.8, more preferably at least 0.9, most preferably at least 0.95. Also preferably, the survival probability may be an unfavorable survival probability, i.e. a survival probability indicating a decreased probability for surviving one of the aforesaid time frames. Preferably, the survival probability for the aforesaid time frames in case an unfavorable survival probability is determined is at most 0.8, more preferably at most 0.75, even more preferably at most 0.7, still more preferably at most 0.65, most preferably at most 0.6. In accordance with the above, "determining a survival probability" of a subject, as used herein, relates to determining the probability according to which the subject will survive the aforesaid time frame, which also is a measure for the probability die within one of the aforesaid time frames. Preferably, survival probability (p_s) and mortality risk (p_m) are correlated according to equation p_s = 1 - p_m; preferably, said the mortality risk is a probability to die from a specific disease, most preferably from colorectal cancer. Preferably, the aforesaid time frames are calculated from the time, preferably the day, the sample is obtained from the subject. As specified elsewhere herein, the subject, preferably, is a subject known to suffer from cancer, preferably colorectal cancer. Thus, the method of the present invention, preferably, does not provide diagnosis that a subject is, at the time of assessment, afflicted with disease, in particular colorectal cancer. Thus, in an embodiment, determining a survival probability is not diagnosing a specific disease, more preferably is not diagnosing disease. Thus, preferably, the method for determining a survival probability is not required to be performed by a medical practitioner, more preferably is not performed by a medical practitioner. Preferably, the result of the method of the present invention is not a diagnosis of disease. As will be understood by the skilled person, in case an unfavorable survival probability is determined according to the method of the present invention, the subject and/or the counseling medical practitioner may decide to or recommend to perform life-style changes in order to improve its survival probability; also, treatment methods, in particular aggressive treatment methods, may be recommended, e.g. surgery, high-dose chemotherapy and/or high-dose radiotherapy. In an embodiment, detecting an unfavorable survival probability, preferably, provides an indication that a subject has an increased probability to, preferably within the time frames as specified above, experience severe aggravation of disease, preferably at least of one of the diseases as specified herein. In a further embodiment, detecting an unfavorable survival probability, preferably, provides an indication that a subject has an increased probability to, preferably within the time frames as specified above, die from disease, preferably at least one of the diseases as specified herein.

The term "subject", as used herein, relates to an animal, preferably a mammal, and, more preferably, a human. Preferably, the subject according to the present invention is a subject of at least 40 years of age, more preferably at least 50 years of age, even more preferably at least 60 years of age, most preferably at least 65 years of age. Preferably, the subject has been diagnosed with colorectal cancer. In contrast, the term "apparently healthy subject" relates to a subject not known to suffer from colorectal cancer, preferably not suspected to suffer from colorectal cancer based on physical examination, more preferably not showing any symptom of disease. As will be understood by the skilled person, the subject and the apparently healthy subject preferably are corresponding subjects from the same species, preferably from the same race.

The term "sample", as used herein, refers to a sample of a body fluid, to a sample of separated cells or to a sample from tissue of the subject; preferably, the term refers to a tumor cell- comprising sample of a body fluid, to a sample of separated tumor cells or to a sample from tumor tissue of the subject. Samples of body fluids can be obtained by well known techniques and include, preferably, samples of blood, plasma, serum, or urine. Tissue or organ samples may be obtained from any tissue or organ by, e.g., biopsy. Separated cells may be obtained from the body fluids or the tissues or organs by separating techniques such as centrifugation or cell sorting. Preferably, cell-, tissue- or organ samples are obtained from tumor tissues. Preferably, the sample is a sample comprising tumor cells, more preferably a tumor sample, preferably a formalin- fixed tumor sample, more preferably a formalin- fixed, paraffin embedded tumor sample. Preferably, the sample is a sample comprising colorectal cancer cells, more preferably a tumor sample of a colorectal cancer, preferably a formalin-fixed tumor sample of a colorectal cancer, more preferably a formalin-fixed, paraffin embedded tumor sample of a colorectal cancer.

The term "colorectal cancer" is, in principle, known to the skilled person as relating to a cancer originating in the colon (colon cancer) or in the rectum (rectal cancer). As will be understood by the skilled person, metastases of cancers having the primary tumor in other parts of the body are not colorectal cancer, even if said metastases are situated in the colon or rectum. Preferably, the colorectal cancer is an adenocarcinoma, a carcinoid tumor, a gastrointestinal stromal tumor, a lymphoma, or a sarcoma. More preferably, the colorectal cancer is an adenocarcinoma. As used herein, colorectal cancer may be of any of cancer stages I to IV.

The terms "CpG" and "CpG site" are known to the skilled person. Preferably, the terms relate to a site in DNA, preferably chromosomal DNA of a subject, having the nucleotide sequence 5'-CG-3'. As is also known to the skilled person, CpG sites can be methylated by DNA methyltransferases at the cytosine residue to yield a 5-methylcytosine residue, and methylation at a specific CpG site may be inherited or may be a de novo methylation acquired during life time of the subject. The CpG sites as referred to herein are those of Table 1. The CpG site locations indicated in Table 1 refer to the positions in the human reference genome GRCh37 as provided by the Genome Reference Consortium (www.ncbi.nlm.nih.gov/grc) on 2009/02/27. This assembly is also referred to as hg 19.

Table 1 : CpG sites of the invention; positions on human chromosome and nucleotide number of the CpG sites refer to the human genome sequence assembly GRCh37/hgl9.

Preferably, the CpG sites analyzed according to the method of the present invention comprise sites selected from list consisting of cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052, i.e. are selected from the first seven CpG sites of Table 1.

As used herein, the term "methylation status" relates to a state of a specific CpG site in a cell being methylated or not, more preferably relates to the extent to which a specific CpG site is methylated in a population of cells, or not. As is understood by the skilled person, in a diploid cell, there are four occurrences of a specific CpG site, i.e. two alleles, with each allele comprising the two strands of DNA making up double-stranded DNA; thus, the methylation status of a single CpG site may be all four CpGs non-methylated; one CpG methylated; two, three, or four CpGs methylated. Preferably, in the method of the present invention, the methylation of only one strand of a given DNA is analyzed, e.g. by hybridizing a primer upstream of said CpG site as specified herein below. Also, in a population of cells, in particular in a mixed population of cells, the methylation status of a CpG site is not necessarily identical for all cells of said population. Thus, preferably, the methylation status is detected as the number of cells comprising a specific CpG site at least one, preferably at least twice, in methylated form in a given number of cells; or the methylation status is detected as the number of methylated forms of a specific CpG site detected in a given number of cells. Preferably, the methylation status of at least 10, more preferably at least 25, most preferably at least 100 cells is detected in such case. More preferably, the methylation status is detected as a relative methylation status, e.g. in comparison to a population of a corresponding cell population obtained from one or more apparently healthy subjects. Most preferably, the methylation status is detected as a ratio of the number of individual CpG sites at a given position found to be methylated to the total number of individual CpG sites at said given position analyzed, which is known as the methylation beta value (i.e., preferably, β = methylated signal / (unmethylated signal + methylated signal)), or as a figure derivable therefrom by standard mathematical operations. In accordance with the above, beta-values range from 0 to 1 , with 0 representing completely unmethylated and 1 represents completely methylated. A known parameter derivable from the beta- value is the methylation M value (methylated/unmethylated) which can be calculated from β = 2M/(2M+1); thus M=log2[P /(l- β)]· Thus, the methylation status of a CpG site in a population of cells, preferably, is the average degree of methylation of said CpG site in a population of at least 10, preferably at least 25, more preferably at least 100 cells. In an embodiment, the methylation status may also be expressed as a ratio of the number of individual CpG sites at a given position found to be unmethylated to the total number of individual CpG sites at said given position analyzed, i.e. as a non-methylation status. More preferably, the methylation status is expressed as a ratio of the number of individual CpG sites at a given position found to be methylated to the total number of individual CpG sites at said given position analyzed.

Methods for determining the methylation status of a CpG site are known in the art. Preferably, the method comprises isolating genomic DNA from said sample, preferably from cells comprised in said sample. Preferably, the method comprises contacting said DNA with a methylation-sensitive restriction enzyme having a nucleic acid sequence comprising the sequence 5'-CG-3' as a recognition sequence; preferably, the method further comprises contacting a further aliquot of said DNA with a corresponding non-methylation-sensitive restriction enzyme having the same nucleic acid sequence comprising the sequence 5'-CG-3' as a recognition sequence. More preferably, the method comprises treating said DNA, before or after isolation, with a bisulfite, preferably sodium bisulfite. Preferably, the method further comprises annealing an oligonucleotide specifically annealing to a sequence immediately upstream of said CpG site and comprising a 3'-terminal sequence 5'-CG-3' and/or an oligonucleotide specifically annealing to a sequence immediately upstream of said CpG site and comprising a 3 '-terminal sequence 5'-CA-3' to said genomic DNA, preferably to said bisulfite-treated genomic DNA, per CpG site. Preferably, the method further comprises performing a one-nucleotide extension reaction after said annealing in such case. Also preferably, the method comprises annealing per CpG site an oligonucleotide specifically annealing to a sequence immediately upstream of said CpG site and having a C as the terminal nucleotide, and performing pyrosequencing using said oligonucleotide as a sequencing primer. Preferably, in the method comprising annealing of an oligonucleotide, methylation of only the strand complementary to said oligonucleotide is analyzed. Also preferably, for a given CpG site, methylation of the CpG site of only one strand of DNA is analyzed, namely the CpG site as indicated above in Table 1 ; thus, preferably, for each CpG site indicated in Table 1, only one oligonucleotide is used in analysis.

According to the method for determining a survival probability, the methylation status of at least two CpG sites selected from Table 1 is determined. As is understood by the skilled person, accuracy of prediction may be increased by determining the methylation status of an increased number of CpG sites; thus, preferably, the methylation status of at least three, preferably at least four, more preferably at least five, most preferably at least six of said CpG sites of Table 1 is determined. More preferably, the methylation status of at least three, preferably at least four, more preferably at least five, most preferably at least six CpG sites selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and eg 15659052 is detected. Most preferably, the methylation status of all seven aforesaid methylation sites is determined.

Preferably, an unfavorable survival probability is determined if a methylation status deviating from a reference is detected, preferably is detected for at least two, more preferably at least three, even more preferably at least four, most preferably at least five CpG sites. Thus, preferably, the method for determining a survival probability comprises comparing the methylation status determined for a CpG site in a sample to a reference. Thus, preferably, the method comprises further step al) comparing the methylation status of said at least two CpG sites of step a) to references; and wherein in step b) the determining is based on the comparison of step al). As used herein, the term "reference" relates to a reference value or a reference range, preferably derived from a population of subjects, preferably a population of apparently healthy subjects as specified herein above. More preferably, a reference value or reference range is obtained by sub-classifying a population of subjects suffering from colorectal cancer, more preferably by sub-classifying a population of subjects suffering from colorectal cancer into tertiles for each CpG site determined, most preferably as shown herein in the Examples. In such case, preferably, the value range of the higher two tertiles is used as a reference range, and an unfavorable survival is determined in case the value determined for the subject under investigation is lower than the reference range. As is understood by the skilled person, the lower limit of the middle tertile of values may be used as a reference value in such case, an unfavorable survival being determined in case the value determined for the subject under investigation is lower than the reference value. Also preferably, reference values or reference ranges are predetermined references, which may, e.g. be provided in the form of a database, a list, or the like.

Methods for determining a, preferably significant, more preferably statistically significant, deviation of a methylation status from a reference are known to the skilled person; preferably, a value and a reference value are determined to be essentially identical if the difference between two values is, preferably, not significant and shall be characterized in that the value is within at least the interval between 1st and 99th percentile, 5th and 95th percentile, 10th and 90th percentile, 20th and 80th percentile, 30th and 70th percentile, 40th and 60th percentile of the reference value, preferably, the 50th, 60th, 70th, 80th, 90th or 95th percentile of the reference value. Statistical test for determining whether two amounts are essentially identical are well known in the art and are also described elsewhere herein. Conversely, an observed difference for two values, on the other hand, shall preferably be statistically significant. A difference in value is, preferably, significant outside of the interval between 45th and 55th percentile, 40th and 60th percentile, 30th and 70th percentile, 20th and 80th percentile, 10th and 90th percentile, 5th and 95th percentile, 1st and 99th percentile of the reference value. Preferably, in particular for CpG sites cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052, a methylation status value lying in the first (i.e. lowest) tertile of reference values of a population of subjects, preferably a population of subjects suffering from colorectal cancer, is considered to be significantly different. More preferably, a methylation status value lying in the first (i.e. lowest) quartile of reference values of a population of subjects, preferably a population of subjects suffering from colorectal cancer, is considered to be significantly different. More preferably, the reference value or reference range is a cut-off value of an average degree of methylation of < 0.73 for cgl6935707, < 0.62 for cg05481217, < 0.58 for cg08044454, < 0.60 for cg01552551, < 0.54 for cg24311416, < 0.53 for cg02425108, and/or < 0.41 for cgl5659052. Most preferably, the reference value or reference range is a cut-off value of an average degree of methylation of < 0.72736 for cgl6935707, < 0.61265 for cg05481217, < 0.57145 for cg08044454, < 0.59834 for cg01552551, < 0.53430 for cg24311416, < 0.52523 for cg02425108, and/or < 0.40182 for cgl5659052. Whether a difference is statistically significant can be determined without further ado by the person skilled in the art using various well known statistic evaluation tools, e.g., determination of confidence intervals, p-value determination, Student^'s t-test, Mann- Whitney test etc.. Details are found in Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983. Preferred confidence intervals are at least 90%, at least 95%, at least 97%, at least 98% or at least 99 %. The p- values are preferably < 0.1, more preferably < 0.05, still more preferably < 0.01, even more preferably < 0.005, or, most preferably < 0.0001. Preferably, the probability envisaged by the present invention allows that the determination will be correct for at least 60%, more preferably at least 70%>, still more preferably at least 80%>, or, most preferably, at least 90%> of the subjects of a given cohort or population. Further methods of evaluating statistical significance of differences in methylation are described herein below in the Examples.

As indicated above, at least two CpG sites are evaluated according to the present invention. As is understood by the skilled person, the value detected for a specific CpG site is compared to a reference for a corresponding CpG site, i.e. to a reference value or reference range pertaining to the CpG site having the same position in the genome. Thus, in case e.g. the average degree of methylation is determined for the seven first CpG sites of Table 1, each of these values is compared to a corresponding reference value, respectively. As is also understood by the skilled person, values are compared to corresponding values, i.e. average degree of methylation values are compared to average degree of methylation values, numbers of cells comprising the CpG site in methylated form are compared to numbers of cells comprising the CpG site in methylated form, and the like. Preferably, an unfavorable health state is determined if at least one of said CpG sites deviates from, preferably significantly deviates from, more preferably is lower than, most preferably is significantly lower than, the reference value. More preferably, an unfavorable survival probability is determined if a methylation status deviating from, preferably significantly deviating from, more preferably being lower than, most preferably significantly lower than, the reference is detected for at least two, more preferably at least three, even more preferably at least four, still more preferably at least five, most preferably more than five CpG sites. Most preferably, an unfavorable survival probability is determined if a methylation status deviating from, preferably significantly deviating from, more preferably being lower than, most preferably being significantly lower than, the reference is detected for at least two, more preferably at least three, even more preferably at least four, still more preferably at least five, most preferably more than five CpG sites selected from the first seven CpG sites of Table 1.

Preferably, determining a survival probability comprises calculating a score from the values detected for the CpG sites, which may, preferably, include a weighting of the CpG sites analyzed. Preferably, said score is calculated by counting the number of CpG sites deviating from the reference, preferably as specified above; preferably, a high score is indicative of an unfavorable survival probability in such case. More preferably, said score is calculated as a tertile score as specified herein in the Examples, i.e. the methylation status for each CpG site is determined for a population of subjects suffering from colorectal cancer and the values obtained are grouped into tertiles. The methylation status for the corresponding CpG sites is determined for the subject under investigation, and it is established whether the respective value falls in the first, second, or third of the aforesaid tertiles. For each CpG site, a value corresponding to the number of the tertile which the value of the subject under investigation falls into is included into, preferably added to, the score. Thus, preferably, the tertile with the lowest values is assigned number 0, the middle tertile is assigned number 1, and the third tertile with the highest values is assigned number 2. Thus, if seven CpG sites are tested, the tertile score for a subject can assume a value from 0 (all values of methylation status falling in the respective first tertile with the lowest values) to 14 (all values of methylation status falling in the respective third tertile with the highest values). As will be appreciated by the skilled person, according to the invention, survival probability, preferably, is low if a low tertile score is determined. Preferably, in such case, a tertile score of 9, more preferably of 5, is indicative of an unfavorable survival probability.

Also more preferably, a coefficient score is calculated by summing up values of methylation status of the CpG sites determined, optionally assigning weighting factors to the values of methylation status, and said coefficient score is compared to a corresponding reference coefficient score. Preferably, a reference coefficient score is provided by calculating coefficient scores for values obtained form a reference population, preferably a reference population suffering from colorectal cancer, and sub-dividing the coefficient scores into tertiles. Preferably, the weighting factor is (-1.24524) for cgl6935707, (-1.32717) for cg05481217, (-1.23953) for cg08044454, (-1.15191) for cg01552551, (-0.62350) for cg24311416, (-0.53487) for cg02425108 and/or (-0.17845) for cgl5659052). More preferably, the coefficient score is calculated as: coefficient score = ((-1.24524) * cgl6935707) + ((-1.32717) * cg05481217) + ((-1.23953) * cg08044454) + ((-1.15191) * cg01552551) + ((-0.62350) * cg24311416) + ((-0.53487) * cg02425108) + ((-0.17845) * cgl5659052); preferably, the reference coefficient score is a cutoff-value of -4.3 or more, more preferably -4.1 or more, even more preferably -4.0 or more, even more preferably -3.9 or more, most preferably -3.93, in such case. Preferably, references and/or evaluation algorithms are stored on a suitable data storage medium, preferably in the form of a database and are, thus, also available for future assessments.

As will be understood by the skilled person, other methods of establishing a reference and/or a score according to the invention can be envisaged. E.g., a reference value may be established by determining the mean methylation status of a population of apparently healthy subjects or of a population of subjects suffering from colorectal cancer, and may be used as a cutoff value; or the lower limit of normal (LLN) may be used as a cutoff. Also, values derived from one of said populations may be divided in halves, quartiles, pentiles, or the like. Also, as shown in Fig. 3, the lower two tertiles of values (instead of the lowest tertile) may be regarded as being indicative of unfavorable survival probability. The specific choice of reference and/or score will mainly be governed on the specific sensitivity and specificity required, but also by other parameters such as the particular population of interest. The skilled person has means and methods at hand enabling appropriate election.

Advantageously, it was found in the work underlying the present invention that the methylation status of the indicated CpG sites, in particular the first seven CpG sites of Table 1, is an independent indicator of the overall and disease-specific mortality risks of a subject suffering from colorectal cancer. Surprisingly, the methylation status was found to be an indicator independent of other parameters traditionally used in predicting a mortality risk, such as cancer stage, lymph node count, Kirsten rat sarcoma viral oncogene homo log (KRAS) mutation, B-Raf proto-oncogene (BRAF) mutation, and microsatellite instability. The definitions made above apply mutatis mutandis to the following. Additional definitions and explanations made further below also apply for all embodiments described in this specification mutatis mutandis.

The present invention further relates to a method of treating a subject suffering from colorectal cancer comprising the steps of the method for determining a survival probability and, depending on the result of said method, providing close monitoring and/or lifestyle recommendations and/or treatment methods to said subject.

The method of treating a subject of the present invention, preferably, is an in vivo method. Moreover, it may comprise steps in addition to those explicitly mentioned above. For example, further steps may relate, e.g., to diagnosing colorectal cancer before the steps of the method for determining a survival probability, providing further therapeutic options, or administering one or more therapeutic measures to said subject, depending on the result of said method. Moreover, one or more of said steps may be performed by automated equipment.

According to the invention, the possibility to establish a survival probability for a subject enables the medical practitioner to better select an appropriate therapy. Preferably, in the method of treating a subject, the primary tumor is removed by at least one of surgery, ablation (e.g. radio frequency ablation), and cryotherapy (cryosurgery). Thus, preferably, the method of treating a subject of the present invention, preferably, comprises the step of removal of the primary tumor, more preferably before or after further treatment is administered. Preferably, the method of treating a subject further comprises providing at least one of close monitoring and/or lifestyle recommendations and/or treatment methods to said subject.

The term "close monitoring", as used herein, relates to medically examining a subject for signs of relapse and/or metastasis at least once within 3 months, preferably within two months, more preferably within one month for a period of at least 12 months, more preferably at least 18 months, still more preferably at least 24 months, most preferably at least 35 months. The term "lifestyle recommendations", as used herein, relates to recommendations decreasing the probability of relapse and/or metastasis. Preferably, such recommendations are recommendations to reduce or quit alcohol consumption, to reduce or quit smoking, to reduce body weight, to increase exercise and/or to use healthy nutrition.

Treatment methods for a patient suffering from colorectal cancer, in particular after removal of the primary tumor, preferably include chemotherapy, radiotherapy, targeted therapy, and immunotherapy. As used herein, the term "chemotherapy" relates to treatment of a subject with an antineoplastic drug. Preferably, chemotherapy is a treatment including alkylating agents (e.g. cyclophosphamide), platinum (e.g. carboplatin), antimetabolites (e.g. 5-Fluorouracil), anthracyclines (e.g. doxorubicin, epirubicin, idarubicin, or daunorubicin), topoisomerase II inhibitors (e.g. etoposide, irinotecan, topotecan, camptothecin, or VP 16), anaplastic lymphoma kinase (ALK)-inhibitors (e.g. Crizotinib or AP26130), aurora kinase inhibitors (e.g. N-[4-[4-(4-Methylpiperazin- 1 -yl)-6- [(5 -methyl- 1 H-pyrazol-3-yl)amino]pyrimidin-2- yl]sulfanylphenyl]cyclopropanecarboxamide (VX-680)), or Iodinel31-l-(3- iodobenzyl)guanidine (therapeutic metaiodobenzylguanidine), or histone deacetylase (HDAC) inhibitors, alone or any suitable combination thereof. It is to be understood that chemotherapy, preferably, relates to a complete cycle of treatment, i.e. a series of several (e.g. four, six, or eight) doses of antineoplastic drug or drugs applied to a subject, which may be separated by several days or weeks without such application.

The terms "radiation therapy" and "radiotherapy" are known to the skilled artisan. The term relates to the use of ionizing radiation to treat or control cancer.

The term "targeted therapy", as used herein, relates to application to a patient a chemical substance known to block growth of cancer cells by interfering with specific molecules known to be necessary for tumorigenesis or cancer or cancer cell growth. Examples known to the skilled artisan are small molecules like, e.g. PARP-inhibitors (e.g. Iniparib), antiangiogenic agents (e.g. Bevacizumab, Ramucirumab, Ziv-aflibercept), signalling inhibitors (e.g. cetuximab or panitumumab), or kinase inhibitors (e.g. Regorafenib). The term "immunotherapy" as used herein relates to the treatment of cancer by modulation of the immune response of a subject. Said modulation may be inducing, enhancing, or suppressing said immune response, e.g. by administration of at least one cytokine, and/or of at least one antibody specifically recognizing cancer cells. The term "cell based immunotherapy" relates to a cancer therapy comprising application of immune cells, e.g. T- cells, preferably tumor-specific NK cells, to a subject.

The present invention also relates to the use of the methylation status of genomic DNA or means for the determination thereof in a sample of a subject suffering from colorectal cancer for determining a survival probability of said subject, preferably for predicting the mortality risk of said subject.

The present invention also relates to a data collection, preferably comprised on a data carrier, comprising the positions of at least two, preferably at least three, more preferably at least four, even more preferably at least five, most preferably at least six CpG sites selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052; preferably of from three to all, more preferably of from five to all, even more preferably of from 6 to 7, most preferably of the first 7 CpG sites selected from Table 1. The term "data collection" refers to a collection of data which may be physically and/or logically grouped together. Accordingly, the data collection may be implemented in a single data storage medium or in physically separated data storage media being operatively linked to each other. Preferably, the data collection is implemented by means of a database. Thus, a database as used herein comprises the data collection on a suitable storage medium. Moreover, the database, preferably, further comprises a database management system. The database management system is, preferably, a network-based, hierarchical or object-oriented database management system. Furthermore, the database may be a federal or integrated database. More preferably, the database will be implemented as a distributed (federal) system, e.g. as a Client-Server-System. More preferably, the database is structured as to allow a search algorithm to compare a test data set with the data sets comprised by the data collection. Specifically, by using such an algorithm, the database can be searched for similar or identical data sets being indicative for a survival probability as set forth above (e.g. a query search). Consequently, the information obtained from the data collection can be used, e.g., as a reference for the methods of the present invention described above. The term "data storage medium" as used herein encompasses data storage media which are based on single physical entities such as a CD, a CD-ROM, a hard disk, optical storage media, a diskette, or a sheet of paper. Moreover, the term further includes data storage media consisting of physically separated entities which are operatively linked to each other in a manner as to provide the aforementioned data collection, preferably, in a suitable way for a query search.

The present invention further relates to a kit comprising means for determining the methylation status of at least two CpG sites selected from the CpG sites of Table 1 in a sample of a subject suffering from colorectal cancer, and a data collection according to the present invention. Preferably, the CpG sites are selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052. Means for determining the methylation status of CpG sites are, in principle, known to the skilled person and are described herein above. Preferred means are oligonucleotides specifically hybridizing upstream of at least one CpG site, buffers, trinucleotide solutions, precipitation means, and the like. Preferably, the kit comprises at least two non-identical oligonucleotides, each of said oligonucleotides specifically hybridizing upstream of at least one CpG site of the invention.

The present invention also relates to a device comprising an analysis unit comprising means for determining the methylation status of at least two CpG sites selected from the CpG sites of Table 1, preferably selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052 in a sample of a subject suffering from colorectal cancer, and an evaluation unit comprising a data collection according to the present invention.

A device as used herein shall comprise at least the aforementioned means. The means of the device are, preferably, operatively linked to each other. How to link the means in an operating manner will depend on the type of means included into the device. For example, where means for automatically determining the methylation status are applied, the data obtained by said automatically operating means can be processed by, e.g., a computer program in order to facilitate the assessment. Preferably, the means are comprised by a single device in such a case. Said device may accordingly include an analyzing unit for determining the methylation status and an evaluation unit, e.g. a computer unit, for processing the resulting data for the assessment. Preferred devices are those which can be applied without the particular knowledge of a specialized clinician, e.g., electronic devices which merely require loading with a sample.

Alternatively, the methods for the determination of a survival probability can be implemented into a system comprising several devices which are, preferably, operatively linked to each other. Specifically, the means must be linked in a manner as to allow carrying out the method of the present invention as described in detail above. Therefore, operatively linked, as used herein, preferably, means functionally linked. Depending on the means to be used for the system of the present invention, said means may be functionally linked by connecting each means with the other by means which allow data transport in between said means, e.g., glass fiber cables or other cables for high throughput data transport. Nevertheless, wireless data transfer between the means is also envisaged by the present invention, e.g., via LAN (Wireless LAN, W-LAN). A preferred system comprises means for determining a methylation status. Means for determining a methylation status are described herein elsewhere. The means for analyzing the results may comprise at least one database, preferably as specified above, and an implemented computer program for comparison of the results to references. Preferably, the computer program code is capable of executing step of the method of the present invention as specified elsewhere herein in detail.

In view of the above, the following embodiments are particularly preferred:

1. A method for determining a survival probability of a subject suffering from cancer, preferably suffering from colorectal cancer, comprising

b) based on the methylation status detected in step a), determining the survival probability of said subject.

2. The method of embodiment 1, wherein said at least two CpG sites are selected from the list consisting of cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052.

3. The method of embodiment 1 or 2, wherein the methylation status of at least three, preferably at least four, more preferably at least five, most preferably at least six of said CpG sites is detected. 4. The method of any one of embodiments 1 to 3, wherein the methylation status of seven CpG sites is determined.

5. The method of any one of embodiments 1 to 4, wherein said detecting the methylation status of a CpG site is detecting the average degree of methylation of said site from at least 10, preferably at least 25, more preferably at least 100 cells.

6. The method of any one of embodiments 1 to 5, wherein a decreased average degree of methylation compared to a population of subjects suffering from colorectal cancer population is indicative of an unfavorable survival probability.

7. The method of any one of embodiments 1 to 6, wherein said method comprises further step al) comparing the methylation status of said at least two CpG sites of step a) to a reference or to references; and wherein in step b) the determining is based on the comparison of step al).

8. The method of embodiment 7, wherein said reference is a reference value or a reference range.

9. The method of embodiment 7 or 8, wherein said reference is obtained from a population of apparently healthy subjects or is obtained from a population of subjects suffering from colorectal cancer.

10. The method of any one of embodiment 1 to 9, wherein determining said survival probability comprises calculating a tertile score, said calculating a tertile score preferably comprising

bl) comparing the methylation status detected in a) for each of said CpG sites to tertiles of average degree of methylation of corresponding CpG sites in a population of subjects suffering from colorectal cancer; and

b2) calculating a tertile score based on the comparison of bl).

11. The method of any one of embodiments 1 to 10, wherein determining said survival probability comprises calculating a coefficient score, said calculating a coefficient score preferably comprising

bl) calculating a weighted methylation status by assigning a weighting coefficient to each methylation status determined in step a); and

b2) calculating a coefficient score based on the weighted methylation status values of bl); preferably, wherein the absolute value of the weighting coefficient is about 1.25 for cgl6935707, about 1.33 for cg05481217, about 1.24 for cg08044454, about 1.15 for cg01552551, about 0.62 for cg24311416, about 0.53 for cg02425108 and/or about 0.18 for cgl5659052.

12. The method of any one of embodiments 1 to 9, wherein an average degree of methylation of < 0.73 for cgl6935707, < 0.62 for cg05481217, < 0.58 for cg08044454, < 0.60 for cg01552551, < 0.54 for cg24311416, < 0.53 for cg02425108, and/or < 0.41 for cgl5659052, more preferably an average degree of methylation of < 0.727 for cgl6935707, < 0.613 for cg05481217, < 0.571 for cg08044454, < 0.598 for cg01552551, < 0.534 for cg24311416, < 0.525 for cg02425108, and/or < 0.402 for cgl5659052 is indicative of an unfavorable survival probability.

13. The method of any one of embodiments 1 to 12, wherein determining said survival probability comprises determining a mortality risk

14. The method of embodiment 13, wherein said mortality risk is a disease-specific mortality risk.

15. The method of a embodiment 13 or 14, wherein said mortality risk not a non-disease mortality risk.

16. The method of any one of embodiments 1 to 15, wherein said sample is a tissue sample, preferably is a tumor sample, more preferably is a sample of tumor cells.

17. The method of any one of embodiments 1 or 16, wherein said methylation status is detected in cells of said subject, preferably is detected in tumor cells.

18. The method of any one of embodiments 1 to 17, wherein said subject is a human. 19. The method of any one of embodiments 1 to 18, wherein said method comprises isolating genomic DNA from said sample.

20. The method of embodiment 19, wherein said method comprises treating said genomic DNA with a bisulfite.

21. The method of embodiment 19 or 20, wherein said method comprises annealing an oligonucleotide specifically annealing to a sequence immediately upstream of said CpG site and comprising a 3'-terminal sequence 5'-CG-3' and/or an oligonucleotide specifically annealing to a sequence immediately upstream of said CpG site and comprising a 3 '-terminal sequence 5'-CA-3' to said genomic DNA, preferably to said bisulfite-treated genomic DNA, per CpG site.

22. The method of embodiment 21, wherein said method comprises performing a one- nucleotide extension reaction after said annealing.

23. The method of any one of embodiments 1 to 22, wherein said method comprises annealing per CpG site an oligonucleotide specifically annealing to a sequence immediately upstream of said CpG site and having a C as the terminal nucleotide and performing pyrosequencing using said oligonucleotide as a sequencing primer.

24. The method of any one of embodiments 1 to 23, wherein said determining a survival probability is not diagnosing disease.

25. The method of any one of embodiments 1 to 24, wherein the CpG sites correlate with positions in the human genome as shown in Table 1.

26. A method of treating a subject suffering from colorectal cancer comprising the steps of the method according to any one of embodiments 1 to 25 and, depending on the result of said method, providing close monitoring and/or lifestyle recommendations and/or treatment methods to said subject.

27. Use of the methylation status of genomic DNA or means for the determination thereof in a sample of a subject suffering from colorectal cancer for determining a survival probability of said subject, preferably for predicting the mortality risk of said subject.

28. The use of embodiment 27, wherein said use comprises detecting the methylation status of at least two CpG sites selected from the CpG sites of Table 1, preferably selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052.

29. A data collection, preferably comprised on a data carrier, comprising the positions of at least two, preferably at least three, more preferably at least four, even more preferably at least five, most preferably at least six CpG sites selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052; preferably of from three to all, more preferably of from five to all, even more preferably of from 6 to 7, most preferably of the first 7 CpG sites selected from Table 1.

30. The data collection of embodiment 29, further comprising reference values or reference ranges for the methylation status of said CpG sites.

31. A kit comprising means for determining the methylation status of at least two CpG sites selected from the CpG sites of Table 1, preferably selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052 in a sample of a subject suffering from colorectal cancer, and a data collection according to embodiment 29 or 30.

32. A device comprising an analysis unit comprising means for determining the methylation status of at least two CpG sites selected from the CpG sites of Table 1, preferably selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052 in a sample of a subject suffering from colorectal cancer, and an evaluation unit comprising a data collection according to embodiment 29 or 30.

All references cited in this specification are herewith incorporated by reference with respect to their entire disclosure content and the disclosure content specifically mentioned in this specification.

Figure Legends Fig. 1 : Dose-response association of methylation levels and disease-specific survival in the study cohort and the validation cohort. Restricted cubic splines analyses with adjustment for age, sex, tumor location, tumor stage, chemotherapy, BRAF mutation and microsatellite instability.

Fig. 2: Direct survival curves of the prognostic score (tertile score) with disease-specific survival and non-disease-specific survival in the validation cohort (adjusted for age, sex, tumor location, tumor stage, chemotherapy, BRAF mutation and microsatellite instability).

Fig. 3: Unadjusted Kaplan-Meier curves of the association of the prognostic score (tertile score) with disease-specific survival and non-disease-specific survival in the validation cohort.

The following Examples shall merely illustrate the invention. They shall not be construed, whatsoever, to limit the scope of the invention. Example 1 : Methods

1.1 Study population

Patients with CRC were recruited into the DACHS (Darmkrebs: Chancen der Verhutung durch Screening) study, a large ongoing population-based cohort study on CRC with long- term follow-up (Jia et al. (2016), Br J Cancer 115: 1359) Patients with histologically confirmed CRC who were at least 30 years old and physically and mentally able to participate in a personal interview were enrolled from 22 hospitals in the Rhine-Neckar-Odenwald region of southwestern Germany. Sociodemographic and lifestyle information was collected by trained interviewers using a standardized questionnaire during face-to-face interviews. Additionally, discharge letters, pathology reports and endoscopy reports were collected at baseline. After three years, information on the patients' therapy was requested from the patients' physicians. After five years, survivors were contacted to complete a standardized questionnaire. In addition, at both follow-ups, disease recurrence was assessed. Also, data on vital status were obtained from the population registries and causes of death were verified by death certificates from the health authorities. More details on the study design, data collection and follow-up have been reported previously (Hoffmeister et al. (2015), J Natl Cancer Inst 107:djv045; Brenner et al. (2014), Gastroenterology 146:709). In the present study, only patients recruited between 2003 and 2007 with available information on DNA methylation, important clinical characteristics (such as age, sex, cancer stage, tumor location and survival data) and molecular features (such as BRAF mutation and microsatellite instability (MSI)) were included. The study was approved by the ethics committees of the Medical Faculty of the University of Heidelberg and of the Medical Chambers of Baden- Wuerttemberg and Rhineland-Palatinate.

1.2 Tumor Sample Analyses

DNA was extracted from formalin-fixed, paraffin-embedded (FFPE) tumor samples of the patients under microscopic control of unstained slides and was prepared using the DNeasy tissue kit (Qiagen, Hilden, Germany)(Warth et al. (2011), Mod Pathol 24:564). Methylation of tissue DNA was analyzed using the Illumina Human Methylation 450 BeadChip covering 485,577 CpGs of 99% of RefSeq genes (Illumina, San Diego, CA, USA) following the manufacturer's instructions. Tumors of patients examined in the pathology institutes in Heilbronn, Ludwigshafen, Mannheim, and Speyer (study cohort) were analyzed more than one year apart from tumors of patients examined in the pathology institute in Heidelberg (validation cohort). Methylation signals at CpG sites were converted into β-values (methylated signal / (unmethylated signal + methylated signal)), β-values ranged from 0 to 1 : 0 represents totally unmethylated and 1 represents totally methylated. In data pre-processing, the following probes were excluded: probes targeting the X and Y chromosomes, probes containing a single-nucleotide polymorphism (dbSNP132 Common) within five base pairs, probes not mapping uniquely to the human reference genome (hgl9) allowing for one mismatch, and probes that have failed in more than 10% of the samples based on the detection p-value (detection p-value > 0.01). Data was normalized by pre-processing in GenomeStudio (Pidsley et al. (2013), BMC Genomics 14:293).

MSI-H was determined using a mononucleotide marker panel (BAT25, BAT26 and CAT25)(Findeisen et al. (2005), Cancer Res 65:8072) KRAS mutation was determined by single- stranded conformational polymorphism technique using the same DNA sample (Blaker et al. (2004), Scand J Gastroenterol 39:748). The expression of BRAF V600E was determined by immunohistochemical analyses in sections of tissue microarray blocks and evaluated by two pathologists independently.

1.3 Statistical Analyses Genes used to define CIMP in previous studies were identified by a literature review (Jia et al. (2016), Clin Epigenetics 8:25; Ashktorab et al. (2013), Epigenetics 8:807). In a split-sample approach, we used a larger study cohort to investigate associations of CpG sites on CIMP- related genes with disease-specific survival (DSS), and a smaller validation cohort to validate our findings. Hazard ratios (HR) and 95% confidence intervals (95% CI) were calculated by multivariable Cox proportional hazards regression with adjustment for age, sex, tumor stage, tumor location, chemotherapy, MSI status and BRAF mutation status. In addition, a correction for late entry defined as the potentially delayed time between date of diagnosis and date of enrolment was included in the adjusted model, as well as a time-dependent variable of age and chemotherapy. We used three different sets of CpGs on the CIMP genes to test for associations: CpG set 1 consisted of CpGs located in the CpG islands of promoter region, CpG set 2 included any CpG in CpG islands, and CpG set 3 included any CpG in any region on the genes. In the study cohort, CpGs were deemed to be associated with CRC prognosis only if they passed the correction for multiple testing by Benjamini-Hochberg (false discovery rates (FDR) < 0.05) and if the β-value range was not too narrow (≥0.1) to ensure reproducibility. The linear association with DSS was calculated using the z-score of the β- value.

The CpGs associated with DSS in the study cohort were then analyzed in the validation cohort using the same adjusted Cox model. The association between tertiles of β-values and DSS analyzed in the study cohort was analyzed in the validation cohort using the tertile cutoffs of the study cohort. Dose-response curves of methylation levels at the identified CpGs were plotted to illustrate the association with DSS by restricted cubic spline regression adjusting for the confounders mentioned above. The selected CpGs confirmed in the validation cohort were used to investigate individual associations and to construct a prognostic score using 2 different approaches (coefficient score and tertile score).

The coefficient score was calculated by summation of multiplying the β-value of each validated CpG with its corresponding coefficient value in the Cox regression. To calculate the tertile score, β-values of validated CpG sites were grouped into tertiles numbered from 3 (highest β-values) to 1 (lowest β-values) in the study cohort and were then added up. Tertiles of the sum were used to build three groups with tertile 3 representing patients with the highest methylation at the selected CpGs sites, which was used as the reference (among the identified CpGs lower methylation was associated with higher mortality). Direct adjusted survival curves (adjusting for the same covariates) and Kaplan-Meier curves were plotted to illustrate the association of the prognostic score with DSS and non-DSS over time. The associations of clinical and molecular features with the tertile score in the study and the validation cohort were analyzed by chi-square test. All statistical analyses were performed with SAS, software version 9.4 (SAS Institute Inc., Cary, NC). Tests of statistical significance were defined as two-sided at an alpha level of 0.05.

Example 2: Results 2.1 Patient cohort

Of 702 patients in the study cohort and 366 patients in the validation cohort, 134 patients and 58 patients, respectively, were excluded because of missing information on important covariates (chemotherapy, MSI status or BRAF mutation status). Finally, 568 patients in the study cohort and 306 patients in the validation cohort were included in the analyses. The median follow-up time was 5.2 years in both cohorts. The characteristics of the patients included in the study cohort and validation cohort were very similar. Slight differences were observed in the distribution of the education level, alcohol consumption, lymph node count, chemotherapy frequency and KRAS mutation (Table 2). Table 2: Characteristics of patients in the study cohort and the validation cohort. (Missing data for (^a) 3 patients of the study set and for 2 patients of the validation set, (^b) 1 patient of the study set and for 2 patients of the validation set, (^c) 3 patients of the study set and for 4 patients of the validation set, (^d) 1 patient of the study set and for 4 patients of the validation set, (^e) 15 patients of the study set and for 9 patients of the validation set, (^f) 2 patients of the study set and for 1 patient of the validation set, (^g) 2 patients of the study set, (^h) 1 patient of the study set and for 1 patient of the validation set, (') 76 patients of the study set and for 18 patients of the validation set.

Study Validation P value for

Characteristics

cohort (n=568) cohort (n=308) heterogeneity

Age, n (%) 0.1316

<=65 y 198 (35) 124 (40)

66-74 y 194 (34) 86 (28)

75+ y 176 (31) 98 (32)

Sex, n (%) 0.1579

Female 268 (47) 130 (42)

Male 300 (53) 178 (58) Education, n (%) 0.0218 Low 380 (67) 190 (62)

Medium 117 (21) 58 (19)

High 71 (13) 60 (19)

Family history of CRC, n (%)^a 81 (14) 47 (15) 0.6839

Lifetime regular active smoking, n 0.5390

(%)^b

None 258 (46) 125 (41)

<20 pack-years 190 (34) 108 (35)

20+ pack-years 119 (21) 73 (24)

Alcohol consumption, mean (g/day) ^c 16.1 20.3 0.0009 Body mass index, mean (kg/m²)^d 26.5 26.4 0.6692 Physical activity, mean (life time

240.7 237.2 0.6983 METs, hr/week)^e

Regular use of NSAIDs, n (%)^f 147 (26) 90 (29) 0.2887 Regular use of statins, n (%)^g 61 (11) 47 (15) 0.0544 Regular use of hormone replacement

82 (14) 38 (12) 0.3927 therapy, n (%)^h

Tumor location, n (%) 0.4279

Proximal colon 210 (37) 108 (35)

Distal colon 176 (31) 88 (29)

Rectum 182 (32) 112 (36)

Cancer stage, n (%) 0.3926

I 105 (18) 54 (18)

II 184 (32) 113 (37)

III 192 (34) 89 (29)

IV 87 (15) 52 (17)

Lymph node count, n (%) 0.0013

<=12 178 (31) 82 (27)

12-20 274 (48) 129 (42)

20+ 116 (20) 97 (31)

Surgery, n (%) 567 (100) 308 (100) 0.4612 Chemotherapy, n (%) 270 (48) 121 (39) 0.0190

KRAS mutation, n (%)' 0.0008

Negative 324 (66) 224 (77)

Positive 168 (34) 66 (23)

BRAF mutation, n (%) 0.2981

Negative 522 (92) 289 (94)

Positive 46 (8) 19 (6)

Microsatellite instability, n (%)

MSS 503 (89) 282 (92) 0.1644

MSI-H 65 (11) 26 (8) 2.2 Selection of CIMP-related genes

Within the 43 CIMP genes investigated in this study, 405 CpGs were located in CpG islands of the promoter region (CpG set 1), 701 CpGs were located in CpG islands of any region (CpG set 2), and 1852 CpGs were located anywhere on the genes (CpG set 3).

2.3 Identification of prognostic CpGs

In adjusted analyses, 42 CpGs from CpG set 1 and 65 CpGs from CpG set 2 were found to be associated with DSS (p-value<0.05). However, after correction for multiple testing none of these CpG sites was associated with DSS anymore. By further extension to CpGs anywhere on the genes (CpG set 3), 10 of the 249 associated CpG sites showed significant associations with DSS after correction for multiple testing.

Two of the 10 CpGs identified in the study cohort were not associated with DSS in the validation cohort (cg05075097, cg24771017). Another CpG site (cg20537325) had β-values from lowest to highest of less than 0.1 which was considered too narrow to define distinct groups. Therefore, seven CpGs (cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108 and cgl5659052) confirmed in the validation cohort were included in the final analyses (Table 3 a).

Table 3a. CpG sites identified in the study cohort that were included in or excluded from validation in the final analyses.

Association with disease-specific Not confirmed

10%~90% range of

CpG site survival¹ (HR, 95%CI) in validation Distance < 0.1 Finally included β value (distance)

Study cohort Validation cohort cohort

cgl6935707 1.43 (1.22-1.67) 1.33 (1.09-1.61) 0.612--0.890 (0.278)

cg05481217 1.43 (1.22-1.67) 1.30 (1.06-1.60) 0.543--0.705 (0.162) V

cg08044454 1.44 (1.22-1.69) 1.39 (1.14-1.70) 0.426-0.790 (0.364) V

cg01552551 1.43 (1.22-1.68) 1.28 (1.04-1.58) 0.451-0.816 (0.365) V

cg20537325 1.34 (1.18-1.55) 1.31 (1.05-1.63) 0.889-0.932 (0.043) X

cg24311416 1.43 (1.21-1.71) 1.32 (1.04-1.66) 0.412-0.759 (0.347) V

cg02425108 1.40 (1.19-1.65) 1.32 (1.04-1.66) 0.440-0.709 (0.269) V

cg05075097 1.51 (1.23-1.86) 1.05 (0.83-1.33) 0.053-0.106 (0.053) X

cgl5659052 1.38 (1.17-1.63) 1.37 (1.09-1.71) 0.293-0.689 (0.396) V

cg24771017 1.32 (1.14-1.54) 1.04 (0.83-1.31) 0.858-0.953 (0.095) X

a: Cox regression ana iyses using the z-score of the β value (per unit, high to low) with adjustment for age, sex, tumor stage, tumor locatio chemotherapy, MSI status and BRAF mutation status.

Table 3b. Association of the coefficient score and the tertile score with disease-specific survival in the study cohort and the validation cohort.

Risk score Tertile groups Study cohort Validation cohort

(cutoff points) N Deaths ι (%) H R (95% CI)^a P value of trend N Deaths (%) H R (95% CI)^a P value for tren

Coefficient score Tertile 3 195 36 (18) Ref. 124 25 (20) Ref.

Tertile 2 (-4.32247) 188 31 (16) 1.31 (0.80-2.15) 89 22 (25) 1.66 (0.91-3.05)

Tertile 1 (-3.92950) 185 69 (37) 3.16 (2.08-4.80) <0.0001 95 35 (37) 2.75 (1.56-4.85) 0.0004

Tertile score Tertile 3 158 28 (18) Ref. 110 23 (21) Ref.

Tertile 2 (9) 216 40 (19) 1.74 (1.06-2.88) 105 24 (23) 1.73 (0.94-3.18)

Tertile 1 (5) 194 68 (35) 3.11 (1.97-4.91) <0.0001 93 35 (38) 3.06 (1.71-5.45) <0.0001

Abbreviations: CI, confidence interval; HR, hazard ratio.

a: Cox regression adjusted for age, sex, tumor stage, tumor location, chemotherapy, MSI status and BRAF mutation statu

2.4 Associations of identified CpG sites with survival

All seven CpGs showed similar linear associations with DSS in the study cohort and the validation cohort (Figure 1). Compared to tertile group 3, tertile group 1 (lowest methylation) showed significantly poorer survival for all the seven CpGs in the study cohort. Very similar results were observed in the validation cohort, except for cg02425108, where the association was somewhat weaker in the validation cohort (Table 2).

2.5 Associations of the prognostic methylation score with survival

Using the tertile approach, low methylation was strongly associated with poorer DSS compared with high methylation in the study cohort (HR = 3.11; 95% CI = 1.97-4.91) and in the validation cohort (HR = 3.06; 95% CI = 1.71-5.45) (Table 3b). Patients with intermediate methylation scores also showed poorer DSS compared to patients with high methylation scores, and the trend was statistically significant in both the study cohort and the validation cohort. Very similar associations were found by using the coefficient approach (coefficient value (-1.24524) for cgl6935707, (-1.32717) for cg05481217, (-1.23953) for cg08044454, (- 1.15191) for cg01552551, (-0.62350) for cg24311416, (-0.53487) for cg02425108 and (- 0.17845) for cgl5659052).

Unadjusted Kaplan-Meier curves of the association of the prognostic score with DSS and non-DSS showed poorer survival in the lowest tertile group only (Figure 2). Using direct adjusted survival curves, the middle and lowest tertile score were increasingly strongly associated with poorer DSS, whereas no association was found for CRC patients who died of other causes (Figure 3). As most of the CRC patients died of CRC, the association of the methylation panel with overall survival was similar with the association for DSS.

In subgroup analyses stratified by age, sex, tumor location, cancer stage and chemotherapy, similar results were observed for the association between the prognostic scores and DSS. The prognostic score was not related to clinical and tumor characteristics of the CRC patients such as age, sex, education level, family history of CRC, lifetime regular active smoking status, tumor location, cancer stage, lymph node count, chemotherapy, KRAS mutation, BRAF mutation and MSI status in both study and validation cohort. Only for cancer stage heterogeneity was observed in the study cohort due to dissimilar stage distribution in the tertile 2 group. However, this was not observed in the validation cohort (Table 4). Table 4. Association of the prognostic score (tertile score) with characteristics of colorectal cancer patients in the study cohort and the validation cohort.

Characteristics Study cohort (n=568) Validation cohort (n=308)

P

Tertilel Tertile2 Tertile3 P value Tertilel Tertile2 Tertile3

value

Age, n (%)

<=65 y 60 (31) 73 (34) 65 (41) 41 (44) 44 (42) 39 (35)

66-74 y 75 (39) 74 (34) 45 (28) 23 (25) 29 (28) 34 (31)

75+ y 59 (30) 69 (32) 48 (30) 0.2429 29 (31) 32 (30) 37 (34) 0.7506

Sex, n (%)

Female 90 (46) 105 (49) 73 (46) 38 (41) 44 (42) 48 (44)

Male 104 (54) 111 (51) 85 (54) 0.8666 55 (59) 61 (58) 62 (56) 0.9207

Education, n (%)

Low 131 (68) 145 (67) 104 (66) 56 (60) 60 (57) 74 (67)

Medium 39 (20) 50 (23) 28 (18) 17 (18) 26 (25) 15 (14)

High 24 (12) 21 (10) 26 (16) 0.3202 20 (22) 19 (18) 21 (19) 0.3083

Family history of CRC, n (%)

No 161 (83) 188 (87) 135 (87) 79 (85) 92 (88) 88 (81)

Yes 32 (17) 28 (13) 21 (13) 0.5433 14 (15) 13 (12) 20 (19) 0.4602

Lifetime regular active smoking,

n (/o)

None 88 (45) 98 (46) 72 (46) 34 (37) 37 (36) 54 (50)

<20 pack-years 61 (31) 72 (33) 57 (36) 35 (38) 38 (37) 35 (32)

20+ pack-years 45 (23) 45 (21) 29 (18) 0.8209 24 (26) 29 (28) 20 (18) 0.2077

Tumor location, n (%)

Proximal colon 74 (38) 91 (42) 45 (28) 40 (43) 32 (30) 36 (33)

Distal colon 54 (38) 63 (29) 59 (37) 20 (22) 33 (31) 35 (32)

Rectum 66 (34) 62 (39) 54 (34) 0.0654 33 (35) 40 (38) 39 (35) 0.2936

Cancer stage, n (%)

1 32 (16) 47 (22) 26 (16) 16 (17) 17 (16) 21 (19)

II 57 (29) 79 (37) 48 (30) 29 (31) 46 (44) 38 (35)

III 67 (34) 72 (33) 53 (34) 32 (34) 26 (25) 31 (28)

IV 38 (20) 18 (8) 31 (20) 0.0197 16 (17) 16 (15) 20 (18) 0.6059

Lymph node count, n (%)

<=12 56 (29) 63 (29) 59 (37) 20 (22) 29 (28) 33 (30)

12-20 93 (48) 110 (51) 71 (45) 36 (39) 47 (45) 46 (42)

20+ 45 (23) 43 (20) 28 (18) 0.3393 37 (40) 29 (28) 31 (28) 0.3117

Chemotherapy, n (%)

No 97 (50) 125 (58) 76 (48) 51 (55) 68 (65) 68 (62)

Yes 97 (50) 91 (42) 82 (52) 0.1219 42 (45) 37 (35) 42 (38) 0.3459

KRAS mutation, n (%)

Negative 107 (61) 129 (69) 88 (67) 66 (73) 83 (83) 75 (75)

Positive 67 (39) 58 (31) 43 (33) 0.3031 24 (27) 17 (17) 25 (25) 0.2283

BRAF mutation, n (%)

Negative 175 (90) 197 (91) 150 (95) 89 (96) 98 (93) 102 (93)

Positive 19 (10) 19 (9) 8 (5) 0.2410 4 (4) 7 (7) 8 (7) 0.6580

Microsatellite instability, n (%)

MSS 178 (92) 184 (85) 141 (89) 86 (92) 94 (90) 102 (93)

MSI-H 16 (8) 32 (15) 17 (11) 0.1080 7 (8) 11 (10) 8 (7) 0.6513

Claims

A method for determining a survival probability of a subject suffering from colorectal cancer comprising

The method of claim 1, wherein said at least two CpG sites are selected from the list consisting of cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052.

The method of claim 1 or 2, wherein the methylation status of at least three, preferably at least four, more preferably at least five, even more preferably at least six of said CpG sites is detected, most preferably wherein the methylation status of seven CpG sites is determined.

The method of any one of claims 1 to 3, wherein said detecting the methylation status of a CpG site is detecting the average degree of methylation of said site from at least 10, preferably at least 25, more preferably at least 100 cells.

The method of any one of claims 1 to 4, wherein a decreased average degree of methylation compared to a population of apparently healthy subjects is indicative of an unfavorable survival probability.

The method of any one of claims 1 to 5, wherein said method comprises further step al) comparing the methylation status of said at least two CpG sites of step a) to a reference or to references; and wherein in step b) the determining is based on the comparison of step al).

The method of claim 6, wherein said reference is obtained from a population of apparently healthy subjects or is obtained from a population of subjects suffering from colorectal cancer. The method of any one of claim 1 to 7, wherein determining said survival probability comprises calculating a tertile score, said calculating a tertile score preferably comprising

b2) calculating a tertile score based on the comparison of bl).

The method of any one of claims 1 to 8, wherein said sample is a tissue sample, preferably is a tumor sample, more preferably is a sample of tumor cells.

The method of any one of claims 1 to 9, wherein said subject is a human.

The method of any one of claims 1 to 10, wherein the CpG sites correlate with positions in the human genome as shown in Table 1.

Use of the methylation status of genomic DNA or means for the determination thereof in a sample of a subject suffering from colorectal cancer for determining a survival probability of said subject, preferably for predicting the mortality risk of said subject.

A data collection comprised on a data carrier, comprising the positions of at least two, preferably at least three, more preferably at least four, even more preferably at least five, most preferably at least six CpG sites selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052; preferably of from three to all, more preferably of from five to all, even more preferably of from 6 to 7, most preferably of the first 7 CpG sites selected from Table 1.

A kit comprising means for determining the methylation status of at least two CpG sites selected from the CpG sites of Table 1, preferably selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052 in a sample of a subject suffering from colorectal cancer, and a data collection according to claim 13. A device comprising an analysis unit comprising means for determining the methylation status of at least two CpG sites selected from the CpG sites of Table 1 , preferably selected from cgl6935707, cg05481217, cg08044454, cg01552551, cg24311416, cg02425108, and cgl5659052 in a sample of a subject suffering from colorectal cancer, and an evaluation unit comprising a data collection according to claim 13.

A method of treating a subject suffering from colorectal cancer comprising the steps of the method according to any one of embodiments 1 to 1 1 and, depending on the result of said method, providing close monitoring and/or lifestyle recommendations and/or treatment methods to said subject.