METHOD FOR ESTIMATING THE PROGNOSIS AND PREDICTING THE RESPONDENCE TO IMMUNE THERAPY IN PATIENTS WITH MALIGNANT DISEASES

Description

Reference to previous applications

[0001] This patent application claims priority from German patent application DE 10 2016 005 947.8, the disclosure of which is hereby incorporated by reference. Sequence protocol

[0002] The application includes an electronic sequence listing in txt format according to WIPO ST.25 standard with 485 sequences as part of the description, the contents of which are hereby incorporated by reference. Field of the invention

[0003] The invention relates to molecular diagnostic methods in the field of oncology, with which the prognosis of a patient with a malignant disease can be estimated or the response of a patient with a malignant disease to immunotherapy can be predicted. The invention further relates to molecular diagnostic methods that enable the individualized selection of a suitable active ingredient for the immunotherapeutic treatment of these patients. Accordingly, the invention also relates to the use of such methods for prognosis determination, prediction, or individualized active ingredient selection, as well as prognostic and / or predictive biomarkers or biomarkers for the individualized selection of an active ingredient. Furthermore, the invention relates to kits for carrying out the specified methods or for the specified uses. In particular, the invention is limited to in vitro Processes and uses. Background of the invention

[0004] Choosing the right treatment for a patient is a central concern in modern medicine. Selecting the right treatment requires, firstly, the most precise assessment possible of the patient's most likely disease progression, independent of any potential therapy. Based on this prognosis, the clinician can decide whether, for example, a more conservative treatment is appropriate in the case of a favorable outcome or a more radical treatment in the case of an unfavorable outcome. A reliable assessment of the disease progression would therefore enable optimized treatment. In particular, patients could benefit from more targeted therapy.

[0005] Estimating the prognosis of a patient with a malignant disease (cancer) is currently inadequate. Typically, the patient's prognosis is estimated based on the degree of progression of the malignant disease. Parameters such as the presence of metastases, tumor size, and infiltration into lymph and blood vessels are taken into account. These procedures for determining tumor staging vary for each tumor entity. For a few tumors, additional prognostic factors exist, such as determining the Gleason growth pattern in patients with prostate cancer. Overall, this determination of the prognosis for patients with malignant diseases is insufficiently reliable and difficult to standardize.

[0006] Prognostic biomarkers have the potential to solve this problem. From US 2014 / 0011702 A1 it is known that a methylation analysis of the genes DGKI, MGMT and SDPR can be used for prognosis or prediction in glioblastoma patients. US 2006 / 0121467 A1 describes a method that measures the methylation of genes STMN1, SFN, S100A2, TGFBR2, TP53, PTGS2, FGFR1, SYK, PITX2, GRIN2D, PSA, CGA, CYP2D6, MSMB, COX7A2L, VTN, PRKCD, ONECUT2, WBP11, DAG1, ERBB2, TFF1, TMEFF2, ESR1, RASSF1, PITX2, PSAT1 and PCAF for the prediction of response to hormone therapy in breast cancer. From US 2015 / 0051084 A1 it is known that the methylation of the gene HSPB1 used to determine the prognosis of prostate cancer patients. WO 2011 / 051414 A1 describes the use of gene methylation CLK3, MTMR4, NFE2L1, PERLD1, AKAP2, ANAPC1, ANKRD47, ATF7, ATP5G1, BCL2, C9orf3, COIL, CSF2, DMP1, E2F3, ELMO2, ERBB2, GABRG2, GPC5, HSPA6, KISS1, MARCH3, KIAA0100, MRO, MSX1, NCOA6, PAPOLA, PDGFRA, RPL23A, RUFY3, SOX3, TP53, TSPYL1, UBXD3 and ZNF420 on the prognosis and prediction of response to platinum therapy in patients with ovarian cancer.

[0007] Kleffel et al. (Cell 2015, 162, 1242-1256) show that the PD-1:PD-L1 axis in melanoma cells is responsible for tumor growth and that blocking PD-1 in melanoma could contribute to the clinical efficacy of anti-PD-1 therapy.

[0008] Secondly, a reliable prediction of a patient's response to immunotherapy is of great clinical and economic benefit. Immunotherapies with so-called immune checkpoint inhibitors currently represent a breakthrough in oncology, showing outstanding results even in advanced tumors. However, only a relatively small proportion of patients respond to these therapies. A predictive biomarker that could predict response to these therapies would therefore be of particular clinical value. Immunohistological procedures using antibodies are currently being tested. These should indicate the presence of the corresponding immune checkpoint proteins based on a tissue section in order to predict treatment response. However, these tests show only moderate reliability.

[0009] In many cases, the treatment of patients with malignant diseases is still not optimal, as the course of the disease in individual patients can often only be estimated imprecisely, leaving clinicians with insufficient information for individualized selection or adaptation of therapy. Consequently, there is a lack of robust and cost-effective prognostic and predictive methods based on objectively measurable parameters that enable an accurate assessment of the disease course and a reliable prediction of response to therapy in patients with various malignant diseases. Summary of the invention

[0010] Against this background, the present invention provides, in a first aspect, a method for predicting the response of a patient with a malignant disease to immunotherapy, wherein the malignant disease is a cancer. The method is characterized in that a DNA methylation analysis of at least one immunoregulatory gene of cells of the malignant disease and / or of T lymphocytes interacting with the cells of the malignant disease is carried out, wherein the immunoregulatory gene encodes an immune checkpoint selected from B7 proteins and their receptors and is selected from the group consisting of the genes encoding B7 proteins and their receptors CD274, PDCD1LG2 and PDCD1, the genes encoding the B7 protein and its receptor CD80 and CTLA4, the gene encoding the receptor of a B7 protein ICOS and the genes encoding the B7 proteins CD276, C10orf54 and HHLA2, and any combinations thereof, wherein the immunotherapy comprises at least one active ingredient which is an immune checkpoint inhibitor designed to alter the immunoregulatory effect of the immune checkpoint, and the result of the DNA methylation analysis is compared with a reference value in order to determine the patient's response to the immunotherapy (prediction).

[0011] As a reference, according to a second aspect of the invention, a method for the individualized selection of an active ingredient for the immunotherapeutic treatment of a patient with a malignant disease is disclosed, wherein a DNA methylation analysis of at least one immunoregulatory gene of cells of the malignant disease and / or of T lymphocytes interacting with the cells of the malignant disease is carried out and the active ingredient is selected based on the result.

[0012] A third aspect of the invention relates to the use of a DNA methylation analysis of at least one immunoregulatory gene of cells of a malignant disease of a patient and / or of T lymphocytes interacting with the cells of the malignant disease, wherein the malignant disease is a cancer, wherein the immunoregulatory gene encodes an immune checkpoint selected from B7 proteins and their receptors and is selected from the group consisting of the genes encoding B7 proteins and their receptors CD274, PDCD1LG2 and PDCD1, the genes encoding the B7 protein and its receptor CD80 and CTLA4, the gene encoding the receptor of a B7 protein ICOS and the genes encoding the B7 proteins CD276, C10orf54 and HHLA2,and any combinations thereof, for predicting the patient's response to immunotherapy and / or individualized selection of an active ingredient for immunotherapeutic treatment of the patient, wherein the immunotherapy and / or the immunotherapeutic treatment comprises at least one active ingredient which is an immune checkpoint inhibitor designed to alter the immunoregulatory effect of the immune checkpoint, wherein the active ingredient is selected if the DNA methylation analysis indicates expression of the immune checkpoint in the cells of the malignant disease and / or in the T lymphocytes.

[0013] A fourth aspect of the invention relates to the use of the presence, absence or extent of methylation of at least one CpG dinucleotide of an immunoregulatory gene of cells of a malignant disease and / or of T lymphocytes interacting with the cells of the malignant disease, wherein the malignant disease is a cancer, wherein the immunoregulatory gene encodes an immune checkpoint selected from B7 proteins and their receptors and is selected from the group consisting of the genes encoding B7 proteins and their receptors CD274, PDCD1LG2 and PDCD1, the genes encoding the B7 protein and its receptor CD80 and CTLA4, the gene encoding the receptor of a B7 protein ICOS and the genes encoding the B7 proteins CD276, C10orf54 and HHLA2,and any combinations thereof, as a predictive biomarker for predicting the response of the malignant disease to immunotherapy and / or as a biomarker for the individualized selection of an active ingredient for the immunotherapeutic treatment of a patient with the malignant disease, wherein the immunotherapy and / or the immunotherapeutic treatment comprises at least one active ingredient which is an immune checkpoint inhibitor which is designed to alter the immunoregulatory effect of the immune checkpoint, wherein the active ingredient is selected if the methylation of the at least one CpG dinucleotide indicates an expression of the immune checkpoint in the cells of the malignant disease and / or in the T lymphocytes.

[0014] Finally, in a fifth aspect, the invention provides a use of a kit for carrying out the method of the aforementioned first aspect. The kit contains at least one oligonucleotide pair for DNA methylation analysis, wherein the oligonucleotide pair is designed to hybridize to a sequence of the immunoregulatory gene in DNA from the cells of the malignant disease and / or from the T lymphocytes after cytosines contained in the DNA have been converted to uracil or another base with a base pairing behavior and / or molecular weight distinguishable from cytosine, in order to amplify and / or detect the sequence.

[0015] Preferred variants of these aspects arise from the description and the dependent claims. Definitions and general explanations

[0016] Various documents are cited in this description to provide a general technical background related to the present invention. The disclosure and teachings of these documents are incorporated by reference in their entirety in the following description to avoid repetition.

[0017] The following definitions and general explanations are intended to guide and assist the skilled reader in understanding, interpreting, and practicing the present invention. Unless otherwise specified, all technical and scientific terms shall have the meaning commonly understood by one of ordinary skill in the art to which this invention pertains.

[0018] The various aspects and variants of the invention involve techniques and methods from routine molecular biology practice. In particular, DNA methylation analysis to determine the methylation of a CpG dinucleotide is within the expertise of a molecular biologist or geneticist. Convenient laboratory manuals for these techniques and methods are readily available to the skilled person, for example, "Molecular Cloning, A Laboratory Manual" by M.R. Green and J. Sambrook, 4th Edition, 2012, Cold Spring Harbor Laboratory Press.

[0019] As used here, indefinite articles such as "a" or "an" include the possibility that two or more of these features may be present.

[0020] As used here, "malignant diseases" or "malignant disorders" encompasses those diseases characterized by a progressively destructive disease course, potentially leading to the patient's death. Malignant disorders include malignant tissue formations, such as neoplasms or tumors, where malignancy may be characterized by uncontrolled, space-occupying, displacing, infiltrative, and / or invasive growth. Malignant tumors are generally capable of forming metastases. Examples of malignant tumors include carcinomas, sarcomas, melanomas, gliomas, blastomas, seminomas, and teratomas. Malignant disorders also include hematological malignancies, i.e., malignant diseases that affect the blood system or the blood-forming system, such as leukemias, lymphomas, myeloproliferative disorders, and myelodysplastic syndromes.Leukemias comprise a group of malignant diseases in which immature hematopoietic cells have undergone malignant transformation, proliferate excessively, and lead to an accumulation of cells in the peripheral blood. Lymphomas comprise diseases in which cells of the lymphatic system have degenerated. Myeloproliferative diseases comprise a group of diseases in which one or more blood-forming cell lines are greatly increased. Myelodysplastic syndromes involve a clonal expansion of precursor cells of all blood-forming cell lines, with a chronic disorder of differentiation of the blood-forming stem cells underlying them.

[0021] As used herein, "prognosis" refers to the prediction of the health status of a patient with a malignant disease or the future change in the patient's health status. This can include both the health status of patients in the absence of therapeutic intervention and the health status of patients who are already receiving or have received therapy. Prognosis, within the meaning of the present invention, also includes the prediction of the health status or the change in the health status if the patient receives therapy in the future. The therapy can be palliative, curative, neoadjuvant, or adjuvant.In particular, "prognosis" includes the statement about the occurrence of one or more of the various health conditions: death, life, occurrence of a recurrence, occurrence of lymph node metastases, occurrence of distant metastases, progression of the malignant disease, regression of the malignant disease, unchanged malignant disease, increase or decrease of a parameter specific to the malignant disease.

[0022] "Prediction" here refers to a prediction of the response of a malignant disease to a specific therapy. Response to a therapy can be characterized by the extent of the malignant disease decreasing, remaining constant, or increasing at a slower rate during therapy. Lack of response can be characterized by the extent of the malignant disease remaining constant, increasing, or increasing at an accelerated rate. The extent of the malignant disease prior to therapy or a patient who does not receive therapy can serve as a comparison. The extent of the disease can be characterized by the number of malignant cells or the size of the malignant tumor.In particular, a response to therapy may be characterized by a delay in the onset of death, the occurrence of recurrence, the occurrence of lymph node metastases, the occurrence of distant metastases, the progression of the malignant disease, and / or the increase in another parameter specific to the malignant disease.

[0023] In particular, prognosis and prediction refer to deductive steps in connection with a previous in vitro Process such that no technical step essential to the invention takes place on the human or animal body.

[0024] A "gene" is defined here as a section of DNA that comprises regulatory, transcribed, and / or functional sequence regions and thus contains the basic information for the production of biologically active RNA. In particular, a gene comprises those elements, such as the promoter, transcription factor binding sites, CpG islands, open chromatin, enhancers and silencers, and CTCF binding sites, which fulfill a regulatory function in the transcription of the gene.

[0025] The nomenclature for the designation of genes and their nucleotides is based on the recommendation of the Human Genome Organisation Gene Nomenclature Committee (HGNC) as of April 30, 2016. A gene strain, for example, is designated with italic Latin capital letters (e.g. PDCD1, CD274 ) .

[0026] The genes described here are publicly available via the "GenBank" of the National Institute of Health, USA, as of April 30, 2016 (Benson DA et al., Nucleic Acids Research, 2013, 41, D36-42).

[0027] When reference is made to specific DNA sequences (SEQ ID NOs) in the following description, this always includes sequence variants with at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity with the specified DNA sequence. The sequence identity of two nucleic acid sequences can be determined, for example, using the ClustalW algorithm (Thompson et al., Nucleic Acids Research, 1994, 22, 4673-4680).

[0028] A "CpG dinucleotide" is a DNA motif that, in the generally accepted reading direction from 5' to 3', has the nucleoside sequence cytidine-phosphate-guanosine. Guanosine consists of the nucleic base guanine and the sugar β-D-ribose. Cytidine consists of the nucleic base cytosine and the sugar β-D-ribose.

[0029] "DNA methylation" refers to the biochemical or chemical coupling of methyl groups to specific DNA nucleotides. For the purposes of this invention, "DNA methylation" refers to the presence of a methyl group at the fifth carbon atom of a cytosine (5-methylcytosine) located in the CpG dinucleotide context.

[0030] A "DNA methylation analysis" within the meaning of the present invention therefore comprises the determination of the methylation state of one or more CpG dinucleotides from a specific sequence context. In various variants of the invention, "DNA methylation analysis" is understood to mean the determination of whether the cytosine in the CpG dinucleotide(s) is methylated. The DNA methylation analysis can encompass a single copy of the CpG dinucleotide. The DNA methylation analysis can also encompass multiple copies of the CpG dinucleotide, for example, when the DNA is present from multiple cells. In this case, the DNA methylation analysis can provide a methylation status or a methylation value of the CpG dinucleotide, i.e., a cross-sectional value that expresses the extent of methylation in the multiple copies of the CpG dinucleotide.

[0031] An "immune checkpoint" is a protein that modulates the immune response, i.e., it is either immunosuppressive or anti-inflammatory (anti-inflammatory) or immunostimulatory or pro-inflammatory (promoting inflammation). Immune checkpoints serve to monitor the correct function of the immune response, for example, by co-stimulatory (positively regulating) or co-inhibitory (negatively regulating) the strength and intensity of antigen-specific T cell activation or T cell effector function. In some malignant diseases, for example, anti-inflammatory or co-inhibitory immune checkpoints are upregulated during immune evasion.

[0032] For the purposes of the present invention, "T lymphocytes interacting with the cells of the malignant disease" encompasses those T lymphocytes that are specifically in contact with or can come into contact with cells of the malignant disease via a ligand-receptor bond. The ligand can be located on the surface of cells of the malignant disease, for example, an MHC:peptide complex or an MHC II:antigen complex. The receptor can be located on the surface of the T lymphocytes, for example, a T cell receptor (TCR). Alternatively, the ligand can be located on the surface of the T lymphocytes and the receptor can be located on the surface of cells of the malignant disease."T lymphocytes interacting with the cells of the malignant disease" therefore also includes those T lymphocytes that have been activated by contact with antigen-presenting cells to specifically interact with cells of the malignant disease via one of the aforementioned ligand-receptor bonds, without having previously come into contact with the corresponding antigen. An antigen-presenting cell can be, for example, a dendritic cell or a macrophage. Activation occurs, for example, via a ligand-receptor bond between T lymphocytes and an antigen-presenting cell, which presents an antigen originating from a cell of the malignant disease. The ligand, for example, an MHC II:antigen complex, is located on the surface of the antigen-presenting cells, and the T cell receptor (TCR) is located on the surface of the T lymphocyte.Another form of interaction between T lymphocytes and cells of the malignant disease within the meaning of the invention involves the binding of adenosine produced by the cells of the malignant disease by a receptor on the surface of the T lymphocytes. It is also possible that the adenosine is produced by T lymphocytes and the receptor is located on the surface of cells of the malignant disease.

[0033] "Immunotherapy" or "immunotherapeutic treatment" is used here as a collective term for all treatment approaches intended to influence the activity of the immune system. Immunotherapy can be aimed at strengthening or weakening the effect of the immune system. In certain variants of the invention, immunotherapy comprises treatment with active substances to enhance the organism's own immune response against the malignant disease. Such active substances include immune checkpoint inhibitors, such as monoclonal antibodies, which specifically bind to immune checkpoints and thus prevent their (particularly anti-inflammatory) signaling. Another possibility is active substances that already inhibit the expression of the immune checkpoints, for example, through RNA interference or CRISPR interference.However, the active ingredients can also be immune checkpoint agonists, which enhance the signaling of (particularly pro-inflammatory) immune checkpoints.

[0034] As used here, "inhibiting" an immune checkpoint means slowing, inhibiting, or preventing one or more chemical, biological, and / or physical reactions mediated by the immune checkpoint.

[0035] "Biomarkers" are characteristic indicators and / or biological characteristics that can be objectively measured and allow conclusions to be drawn about the status of a normal biological or pathological process in an organism, or the response of a normal or pathological process to an intervention, such as surgery, radiation, or drug treatment. Biomarkers are often (bio)chemical substances, such as proteins, hormones, metabolites, sugars, and nucleic acids, as well as modifications thereof.

[0036] Palliative therapy refers to medical treatment that does not aim to cure a disease, but rather to alleviate symptoms or reduce other adverse consequences (palliation). This contrasts with curative therapies, which aim to cure the disease. Palliative medicine often aims to slow the progression of progressive, incurable diseases and reduce symptoms such as nausea, pain, or (reactive) depression.

[0037] "Curative therapy" is a treatment that aims to completely restore a patient's health while simultaneously preventing deterioration. The term is particularly used when a complete cure is not foreseeable (there is a low percentage chance).

[0038] "Neoadjuvant therapy" refers to a treatment used to reduce tumor mass before a planned surgical procedure. It is often performed when a tumor is initially inoperable. Neoadjuvant therapy can achieve tumor shrinkage, thus making surgical removal possible. In this case, it is often the only curative treatment for a malignant tumor.

[0039] "Adjuvant therapy" refers to a complementary or supportive therapy measure that is applied after complete removal of all detectable tumor parts in order to combat possible, but as yet undetectable, tumor metastases (micrometastases) and thereby improve the long-term prospects of recovery.

[0040] Both the foregoing general description and the following detailed description are to be considered as exemplary and explanatory of the invention as claimed. Other advantages and features of the invention will be apparent from the following description, drawings, and claims. While the invention has been described in terms of its preferred embodiments, many other variations may be made without departing from the scope of the present invention. Therefore, it is intended that the appended claims cover variations and combinations of features that are included within the true scope of the invention even if not expressly recited in the claims. Short description of the characters

[0041] Figure 1shows the Kaplan-Meier analysis of relapse-free survival of 417 prostate cancer patients after radical prostatectomy. The patients were identified using the DNA methylation analysis of the PDCD1 Gene locus stratified in the tumor. Figure 2 shows the Kaplan-Meier analysis of relapse-free survival of 417 (A) and an independent cohort of 259 (B) prostate cancer patients after radical prostatectomy. The patients were identified using the DNA methylation analysis of the CD274 Gene locus stratified in the tumor. DNA methylation analysis was performed using the Infinium HumanMethylation450 BeadChip technology (A) and quantitative methylation-specific real-time PCR (B). Figure 3 shows the Kaplan-Meier analysis of the overall survival of 470 patients with malignant melanomas, which was determined using the DNA methylation analysis of the immunoregulatory genes according to the invention PDCD1 (A), CD274 (B) and PDCD1LG2 (C) in the tumor. Group I: Patients with DNA methylation in the tumor below the threshold. Group II: Patients with DNA methylation in the tumor above the threshold. The following methylation values ​​were set as thresholds for grouping the patients: 12.84% for PDCD1LG2; 13.11% for CD274 and 20.54% for PDCD1. Figure 4 shows the Kaplan-Meier analysis of the overall survival of 470 patients with malignant melanomas, which was determined using the DNA methylation analysis of the immunoregulatory genes according to the invention TIGIT (A), TNFRSF9 (B), LAG3 (C), BTLA (D), CTLA4 (E) and CD40 (F) were stratified. Group I: Patients with tumor methylation below the threshold. Group II: Patients with tumor methylation above the threshold. The following methylation values ​​were set as thresholds for grouping the patients: 86.41% for TIGIT;57.80% for TNFRSF9; 66.94% for LAG3; 91.31% for BTLA; 13.10% for CTLA4 and 17.16% for CD40. Figure 5 shows the Kaplan-Meier analysis of the overall survival of 528 patients with squamous cell carcinoma of the head and neck area, which was determined using the DNA methylation analysis of the PDCD1 Gene locus was stratified. Patients were grouped based on the median methylation of all patients, with patients with methylation above the median being assigned to one group and patients with methylation below the median being assigned to the other group. Figure 6 shows the inventive correlation of DNA methylation analysis and mRNA expression analysis of the genes for the study of 470 patients with malignant melanomas BTLA (A) and CD27 (C) and the results of the Kaplan-Meier analysis of the overall survival of the genes BTLA (B) and CD27(D) stratified patients. Figure 7 shows the inventive correlation of mRNA expression analysis and DNA methylation analysis of the immunoregulatory gene for the study of 470 patients with malignant melanomas CD274 (A) and the Kaplan-Meier analysis of overall survival of patients stratified by the DNA methylation analysis according to the invention (B), by mRNA expression analysis (C) and by the combination of mRNA expression analysis and DNA methylation analysis (D). Figure 8 shows the Kaplan-Meier analysis of overall survival of 470 patients with malignant melanomas, stratified by the DNA methylation analysis according to the invention and the mRNA expression analysis of the immunoregulatory genes CD274 (A) (as in Figure 7 ), PDCD1LG2 (B), PDCD1 (C), BTLA (D), LAG3 (E), TIGIT (F), CD40 (G), CTLA4 (Dog TNFRSF9(I). Group II (high-risk group): high DNA methylation and low mRNA expression; Group IV (low-risk group): low methylation and high mRNA expression; Group I+III (intermediate-risk group): high mRNA expression and high DNA methylation or low mRNA expression and low DNA methylation. Figure 9 shows the Kaplan-Meier analysis of overall survival of 470 patients with malignant melanomas, stratified according to the invention based on mRNA expression analysis (A), DNA methylation analysis (B) and the combined mRNA expression analysis and DNA methylation analysis (C) of the nine immunoregulatory genes CD274, PDCD1, PDCD1LG2, TIGIT, TNFRSF9, LAG3, BTLA, CTLA4 and CD40.Group I: Patients with increased mRNA expression in at least eight of the nine genes; Group II: Patients with increased mRNA expression in fewer than eight of the nine genes; Group III: Patients with increased DNA methylation in a maximum of four of the nine genes; Group IV: Patients with increased DNA methylation in at least five of the nine genes; Group V: Patients belonging to both groups I and III; Group VI: Patients belonging to both groups II and IV; Group VII: Patients belonging to either groups I and IV or groups II and III. Figure 10 shows for the immunoregulatory gene LAG3 the correlation of mRNA expression with DNA methylation of the promoter region (A) and the intragenic region (B); for the immunoregulatory gene CD40the correlation of mRNA expression with DNA methylation of the promoter region (C) and the intragenic region (D); and the Kaplan-Meier analysis of overall survival of 470 patients with malignant melanomas, stratified according to the invention based on DNA methylation analysis of the promoter (E, H), the intragenic region (F, I), and the combined analysis of both regions (G, J) of the immunoregulatory genes LAG3 (E, F, G) and CD40 (H, I, J). Groups I and IV: DNA methylation below the threshold; Groups II and III: DNA methylation above the threshold; Group V: patients from groups II and IV; Group VI: patients from groups II and III or I and IV; Group VII: patients from groups I and III. Figure 11shows the Kaplan-Meier analysis of the overall survival of 182 patients with acute myeloid leukemia, whereby the patients were classified according to the invention using DNA methylation analysis (A, D, G), mRNA expression analysis (B, E, H) and the combination of mRNA expression analysis and DNA methylation analysis (C, F, I) of the immunoregulatory genes CD274 (A, B, C), PDCD1 (D, E, F) and PDCD1LG2 (G, H, I). Group I: DNA methylation below the threshold; Group II: DNA methylation above the threshold; Group III: mRNA expression above the threshold; Group IV: mRNA expression below the threshold. Thresholds: 17.00% for CD274 DNA methylation; 56.95% for PDCD1 DNA methylation; 62.82% for PDCD1LG2 DNA methylation; 4.7319 for CD274 mRNA expression; 23.7933 for PDCD1 mRNA expression and 12.9895 for PDCD1LG2 mRNA expression. Figure 12shows the Kaplan-Meier analysis of the overall survival of 182 patients with acute myeloid leukemia, whereby the patients were classified according to the invention based on the DNA methylation analysis of the immunoregulatory genes TNFRSF9 (A), TIGIT (B), BTLA (C), HAVCR2 (D), CD80 (E), CTLA4 (F), ICOS (G), C10orf54 (H), HHLA2 (I), CD160 (J), KIR2DL4 (K) and KIR3DL1 (L) were stratified. Dichotomization was performed using optimized thresholds: Group I: DNA methylation below the threshold; Group II: DNA methylation above the threshold. Figure 13 shows the Kaplan-Meier analysis of overall survival of 510 patients with low-grade gliomas, stratified according to the invention based on DNA methylation analysis (A, D, G), mRNA expression analysis (B, E, H) and the combination of DNA methylation analysis and mRNA expression analysis (C, F, I) of the immunoregulatory genes CD274(A, B, C), PDCD1 (D, E, F) and PDCD1LG2 (G, H, I). The following thresholds were used for dichotomization of DNA methylation: 32.00% for CD274; 33.79% for PDCD1 and 59.89% for PDCD1LG2. mRNA expression was dichotomized based on the median mRNA expression of the respective gene from all tumors in the patient group. Group I: DNA methylation below the threshold; Group II: DNA methylation above the threshold; Group III: mRNA expression above the median; Group IV: mRNA expression below the median; Group I+III: patients from groups I and III; Group II+IV: patients from groups II and IV; Group I+IV / II+III: patients from groups II and III or I and IV. Figure 14shows the Kaplan-Meier analysis of overall survival of 510 patients with low-grade gliomas, stratified according to the invention based on DNA methylation analysis (A, D, G, J), mRNA expression analysis (B, E, H, K) and the combined DNA methylation analysis and mRNA expression analysis (C, F, I, L) of the immunoregulatory genes CD80 (A, B, C), CTLA4 (D, E, F), ICOS (G, H, I), CD276 (J, K, L). For dichotomization based on DNA methylation, the following thresholds were chosen: 90.76% ( CD80 ); 86.43% (CTLA4); 84.75% ( ICOS ) and 30.08% ( CD276). mRNA expression was dichotomized based on the median mRNA expression of the respective gene from all tumors in the patient group. Group I: DNA methylation below the threshold; Group II: DNA methylation above the threshold; Group III: mRNA expression above the median; Group IV: mRNA expression below the median; Group I+III: patients from groups I and III; Group II+IV: patients from groups II and IV; Group I+IV / II+III: patients from groups II and III or I and IV. Figure 15 shows the Kaplan-Meier analysis of overall survival of 510 patients with low-grade gliomas, stratified according to the invention based on DNA methylation analysis of the genes TNFRSF25 (A), TNFRSF9 (B), CD40 (C), TIGIT (D), BTLA (E), HAVCR2 (F), C10orf54 (G), HHLA2 (H), LAG3 (I), CD160 (J), KIR2DL4 (K) and KIR3DL1(L). The dichotomization was carried out using the following thresholds: 61.88% for TNFRSF25; 79.24% for TNFRSF9; 43.24% for CD40; 80.66% for TIGIT; 84.64% for BTLA; 2.495% for HAVCR2; 66.52% for C10orf54; 77.14% for HHLA2; 74.34% for LAG3; 92.89% for CD160; 90.85% for KIR2DL4 and 52.28% for KIR3DL1. Group I: Methylation below the threshold; Group II: Methylation above the threshold. Figure 16 shows the Kaplan-Meier analysis of overall survival of 318 patients with clear cell renal cell carcinoma, stratified according to the invention based on DNA methylation analysis of the immunoregulatory genes TIGIT (A), TNFRSF9 (B), CD274 (C), CD80 (D), CTLA4 (E), CD276 (F) and HHLA2(G). Dichotomization was performed based on the median DNA methylation of the respective gene from the tumors of all patients. Group I: methylation below the median; Group II: methylation above the median. Figure 17 shows the Kaplan-Meier analysis of overall survival of 318 patients with clear cell renal cell carcinoma, stratified by the inventive combination of DNA methylation analysis and mRNA expression analysis of the immunoregulatory genes TIGIT (A), TNFRSF9 (B), CD80 (C), CTLA4 (D), CD276 (E) and HHLA2(F). Dichotomization was performed based on the median DNA methylation or mRNA expression of the respective gene from the tumors of all patients. Group I: DNA methylation below and mRNA expression above the median; Group III: DNA methylation above and mRNA expression below the median; Group II: DNA methylation above and mRNA expression above the median or DNA methylation below and mRNA expression below the median. Figure 18 shows the inventive DNA methylation analysis of the immunoregulatory gene CD274 to determine the response of patients to immunotherapy with the active ingredient pembrolizumab, which inhibits the immunoregulatory effect of the gene PDCD1 encoded immune checkpoints. Shown are the methylation levels (%) of the gene in metastases from 23 patients with malignant melanomas that were surgically removed before the start of immunotherapy. Figure 19shows the inventive DNA methylation analysis of the immunoregulatory gene PDCD1 to determine the response of patients to immunotherapy with the active ingredient pembrolizumab, which inhibits the immunoregulatory effect of the gene PDCD1 encoded immune checkpoints. Shown are the methylation levels (%) of the gene in metastases from 23 patients with malignant melanomas that were surgically removed before the start of immunotherapy. Short description of the sequences

[0042] SEQ ID NO:1 to SEQ ID NO:485 as defined under the numerical code <213> or <223> of the sequence listing. Description of the invention

[0043] The first aspect of the present invention relates to a method for predicting the response of a patient with a malignant disease to immunotherapy, wherein the malignant disease is a cancer. A DNA methylation analysis of at least one immunoregulatory gene of cells of the malignant disease and / or of T lymphocytes interacting with the cells of the malignant disease is performed. The immunoregulatory gene encodes an immune checkpoint selected from B7 proteins and their receptors, i.e., receptors that bind a B7 protein as a ligand, and is selected from the group consisting of genes encoding B7 proteins and their receptors. CD274, PDCD1LG2 and PDCD1, the genes encoding the B7 protein and its receptor CD80 and CTLA4, the gene encoding the receptor of a B7 protein ICOS and the genes encoding the B7 proteins CD276, C10orf54 and HHLA2.It is also possible to perform DNA methylation analyses of multiple immunoregulatory genes, which may include any combination of these immune checkpoints. In this case, DNA methylation analyses of multiple immunoregulatory genes involved in the same ligand-receptor binding are preferably performed. The immunotherapy comprises at least one active ingredient, which is an immune checkpoint inhibitor designed to alter the immunoregulatory effect of the immune checkpoint. The result of the DNA methylation analysis is compared with a reference value to determine the patient's response to the immunotherapy (prediction).

[0044] The present invention was preceded by the realization that malignant diseases exhibit complex genetic and epigenetic alterations and can therefore vary greatly from individual to individual. Even malignant diseases of the same organ and at the same stage of disease can therefore lead to a different prognosis for the patient. In the course of his research, the inventor discovered that there are immunological subtypes of malignant diseases that are better or worse controlled by the body's immune system, typically resulting in a more or less favorable disease course. This property could be attributed to the fact that these subtypes exhibit particular signatures of DNA methylation and mRNA expression of immunoregulatory genes.

[0045] According to the inventor's research, in patients with favorable outcomes, the cells of the malignant disease or the T lymphocytes interacting with the cells of the malignant disease, or both cell types, are characterized, among other things, by the characteristic expression of one or more B7 proteins and their receptors, one or more MHC:peptide complex-binding co-receptors, one or more of TNFRSF9, CD40, TNFRSF4, TNFRSF18, and CD27, one or more of TIGIT, BTLA HAVCR2, BTNL2, CD48, and / or the adenosine-binding adenosine 2A receptor. In this context, the inventor was able to demonstrate for the first time that the expression of these immunoregulatory genes is regulated by methylation. Thus, through appropriate DNA methylation analysis, an immunological phenotype can be described, which is defined by the methylation of the immunoregulatory genes and is objectively measurable.

[0046] Within the scope of the invention, it was found that the methylation of the immunoregulatory genes according to the invention significantly correlates with the prognosis of the patient's malignant disease. A significant and extremely surprising finding of the inventor is that this is a general phenomenon of malignant diseases, which is not limited to specific entities. Thus, the present invention provides, for the first time, prognostic tests which, based on DNA methylation analysis of the same genes, are universally suitable for predicting the course of a multitude of different malignant disease types, whereas methylation analyses of genes previously known as biomarkers only allowed a prognosis with regard to individual or a few entities. In this regard, reference is also made to the following exemplary embodiments.

[0047] A further unique feature and particular advantage of the invention is that the immunoregulatory genes identified by the inventor, whose DNA methylation is prognostic for the progression of a malignant disease, also simultaneously encode immune checkpoints, which are key targets of immunotherapies. Knowledge of the degree of presence or absence of the corresponding gene products is therefore highly relevant for planning effective immunotherapeutic treatment. Since it has been shown that DNA methylation of the immunoregulatory genes is directly correlated with the expression of the corresponding immune checkpoints, the method according to the invention also enables, as an alternative or in addition to estimating the prognosis, a prediction of the patient's response to a corresponding immunotherapy. For example, response is therefore likely if DNA methylation analysis indicates expression of the immune checkpoint.Thus, for the first time, a simultaneous determination of the patient's prognosis and the prediction of response to immunotherapy can be carried out within the same test, whereas conventional methods based on conventional marker genes have so far only allowed either a prognosis or only a prediction.

[0048] DNA methylation analysis can essentially be performed using all common methods known to the skilled person from the relevant literature. A suitable method comprises, for example, the following steps: A) Providing DNA from the malignant cells or T lymphocytes; B) Converting at least a portion of the cytosines contained in the DNA to uracil or another base with a base pairing behavior and / or molecular weight that is distinguishable from cytosine; C) Examining the DNA methylation of the immunoregulatory gene using the DNA obtained from step B).

[0049] The DNA to be analyzed in step A) can come from various sources, for example from cells of the malignant disease or infiltrating T lymphocytes from tissue removed surgically or by biopsy. The cells can also come from smears and aspirates such as lavage fluids, fine needle aspirates, or sputum. The DNA can also come from blood, blood serum, and blood plasma, for example in the form of freely circulating DNA, exosomal DNA, or in the form of freely circulating cells from which the DNA is obtained. The DNA can also come from other body fluids such as urine, pleural effusions, or ascites, for example in the form of free DNA or in the form of cells from which the DNA is obtained. The DNA can also be obtained from non-preserved (fresh) cells, tissues, and body fluids, as well as from fixed cells, tissues, and body fluids.The fixation of cells, tissues, and body fluids can be achieved using precipitating fixatives such as ethanol and other alcohols, or using cross-linking fixatives such as formaldehyde. The DNA can also come from any combination of these sources. It can also be extracted DNA from the aforementioned sources. It is also possible to enrich the DNA, for example, by precipitation or extraction. This can be useful, for example, for freely circulating DNA from the aforementioned body fluids. It is also possible to enrich the cells, for example, by size filtration or using magnetic particles bearing antibodies on their surface whose antigens are present on the surface of the cells to be enriched. This can be useful, for example, for freely circulating cells of the malignant disease or T lymphocytes from the aforementioned body fluids.Other suitable sources for the DNA to be analyzed are homogenates of fresh tissues and lysates of fixed tissues.

[0050] In a preferred variant, the DNA comprises freely circulating DNA, DNA from exosomes, and / or DNA from freely circulating cells from a body fluid, so-called liquid biopsies. Liquid biopsies currently represent a central area of ​​oncology research. Rather than analyzing the suspicious tissue itself, such as tumor tissue, they analyze a sample of a body fluid, such as a blood sample. Various substances originating from the tumor can be examined in this sample, since freely circulating genomic DNA, exosomal DNA, or freely circulating cells are released from the tumor into the bloodstream. The method according to the invention is advantageously used for analyzing liquid biopsies when the tumor or metastasis cannot be biopsied or when a biopsy would pose too great a risk to the patient in the late tumor stage.What distinguishes the present invention is that DNA methylation of immunoregulatory genes can be measured very well in body fluids, whereas conventional detection of expression of immunoregulatory genes using mRNA or immunohistochemistry is difficult or even impossible.

[0051] The conversion of the DNA in step B) can, in principle, be carried out using any suitable method known in the art for this purpose. Typically, it involves a chemical or enzymatic conversion, for example, by contacting the DNA with bisulfite, such as sodium bisulfite or ammonium bisulfite. If necessary, the DNA can be purified after the conversion in step B) and before the DNA methylation analysis in step C). Suitable purification methods and protocols are known to the person skilled in the art and may include, for example, DNA extraction, precipitation, or polymer-mediated enrichment.

[0052] DNA methylation analysis can comprise determining the presence, absence, or extent of methylation of at least one CpG dinucleotide contained in the immunoregulatory gene. Multiple CpG dinucleotides of the immunoregulatory gene can also be examined. These can also be distributed across different subregions of the immunoregulatory gene to be examined. In preferred variants, the methylation of at least one CpG dinucleotide each from at least two different immunoregulatory genes is determined. In this way, the invention solves the problem that CpG dinucleotides in the DNA of a malignant disease can sometimes be heterogeneously methylated. False-negative and false-positive measurement results are thus avoided, so that a particularly reliable and, moreover, particularly differentiated prognosis and / or prediction is achieved, as can be seen from the following exemplary embodiments.

[0053] The investigation of DNA methylation of the immunoregulatory gene in step C) is not subject to any particular restrictions. Suitable methods can easily be determined by a person skilled in the art on the basis of this disclosure. In this regard, reference is also made to the above-mentioned laboratory manuals. In a preferred variant, a polymerase chain reaction (PCR) is first performed using oligonucleotides, so-called primers, which are designed to amplify a section of the converted DNA that comprises the at least one CpG dinucleotide to be analyzed. This is preferably followed by sequencing of at least part of the amplification product, for example, Sanger sequencing, pyrosequencing, mass spectrometric sequencing, or second- or third-generation sequencing, which are also referred to as "massive parallel sequencing," "next-generation sequencing" (NGS), or nanopore sequencing.It is also possible to perform hybridization with mutation-specific oligonucleotides (probes) following PCR, for example, in the form of a DNA microarray. Methylation can also be determined by quantitative real-time PCR (qPCR), optionally followed by melting curve analysis. In particular, quantitative real-time PCR can be performed with methylation-specific primers as in WO 1997 / 046705 A1 and / or methylation-specific blocker oligonucleotides as in WO 2002 / 072880 A2. In a preferred variant, methylation-specific detection probes are used.

[0054] In other preferred variants, PCR can be omitted, for example, with whole genome shotgun bisulfite sequencing (WGSBS) or direct nanopore sequencing. In WGSBS, the DNA is fragmented, and adapters are then ligated to the DNA fragments. The adapters then allow for amplification and sequencing. It is also possible to omit the fragmentation step in WGSBS, as the DNA may already be fragmented, for example, due to conversion through bisulfite treatment. Protocols for performing WGSBS are readily available to those skilled in the art (Johnson, MD et al., Curr. Protoc. Mol. Biol., 2012, 99, 21.23.1-21.23.28; Lister, R. et al., Nature, 2009, 462, 315-322; Berman, BP et al., Nat. Genet., 2011, 44, 40-46).

[0055] In another preferred variant, hybridization with specific oligonucleotides (probes) can be performed prior to PCR amplification. These probes are ligated upon binding and subsequently amplified by PCR. Suitable methods and protocols, such as multiplex ligation-dependent probe amplification (MLPA), are readily available to those skilled in the art, for example, in "PCR Mutation Detection Protocols" by BDM Theophilus and R. Rapley, 2nd Edition, 2011, Springer.

[0056] In another preferred variant, the methylation analysis is performed using the Infinium HumanMethylation450 BeadChip. Suitable protocols can be found, for example, in the chapter "Determination of DNA Methylation Levels Using Illumina HumanMethylation450 BeadChips" by MA Carless, which can be found in the book "Chromatin Protocols" by SP Chellappan, Volume 1288, 2015, of the book series "Methods in Molecular Biology," Springer Science+Business Media, New York. Further suitable protocols are evident from the following examples.

[0057] DNA methylation analysis is preferably performed under conditions that allow a quantitative determination of the methylation of at least one CpG dinucleotide. The quantitative determination can also include the methylation status of several CpG dinucleotides in one or more genes. The obtained quantities can then be averaged, for example, to obtain a particularly robust value. In this way, a particularly high level of robustness and precision of the method is achieved. Quantitative methylation analysis is suitable, for example, for determining relative methylation by relating the number of methylated copies of a locus to the total number of copies of the same locus.

[0058] The result of the DNA methylation analysis, or the methylation of the immunoregulatory gene, can then be compared, for example, with a reference value to determine the patient's prognosis and / or likely response to immunotherapy. Establishing suitable reference values ​​is part of routine laboratory practice. One option is to perform DNA methylation analysis of the immunoregulatory gene in a group of patients with a comparable malignant disease, particularly the same entity, whose prognosis and / or response to immunotherapy is already known in a retrospective analysis. On this basis, the DNA methylation of the immunoregulatory gene can then be correlated with the prognosis and / or likely response to immunotherapy.For example, the retrospectively analyzed group is divided into two or more subgroups whose prognosis or response can be differentiated based on the DNA methylation of the immunoregulatory gene. A suitable reference value is then, for example, a DNA methylation value that separates two subgroups from each other, hereinafter also referred to as a threshold value. Depending on whether the result of the DNA methylation analysis in the method according to the invention is above or below the threshold value, an individual patient can be assigned to one of the groups with a known prognosis or known treatment response.

[0059] It is also possible to correlate the result of the DNA methylation analysis with the mRNA expression of the immunoregulatory gene. If, for example, the expression of this gene is an indicator of the patient's prognosis, then the patient's prognosis can be determined by determining the mRNA expression based on the DNA methylation of this gene. Since the expression of the immunoregulatory genes according to the invention also provides the target for immunotherapeutic treatment, the response to a corresponding immunotherapy that uses the immune checkpoint encoded by the immunoregulatory gene as a target can also be reliably predicted in this way.

[0060] A particular feature of the present invention is that DNA methylation analysis of the immunoregulatory genes according to the invention is universally suitable for prognosticating the course of disease or predicting the response to immunotherapy in many different tumor entities, in a manner unforeseen by the person skilled in the art. The malignant disease can include, in particular, a carcinoma, a melanoma, a sarcoma, a glioma, a lymphoma, and / or a leukemia.The carcinoma may include, for example, adenocarcinoma, squamous cell carcinoma, small cell carcinoma, neuroendocrine carcinoma, renal cell carcinoma, urothelial carcinoma, hepatocellular carcinoma, anal carcinoma, bronchial carcinoma, endometrial carcinoma, cholangiocellular carcinoma, hepatocellular carcinoma, testicular carcinoma, colorectal carcinoma, head and neck carcinoma, esophageal carcinoma, gastric carcinoma, breast carcinoma, kidney carcinoma, ovarian carcinoma, pancreatic carcinoma, prostate carcinoma, thyroid carcinoma and / or cervical carcinoma. A sarcoma can be, for example, an angiosarcoma, a chondrosarcoma, an Ewing's sarcoma, a fibrosarcoma, a Kaposi's sarcoma, a liposarcoma, a leiomyosarcoma, a malignant fibrous histiocytoma, a neurogenic sarcoma, an osteosarcoma or a rhabdomyosarcoma.A leukemia can be, for example, acute myeloid leukemia (AML), acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), or chronic myeloid leukemia (CML). A lymphoma can be Hodgkin's lymphoma or non-Hodgkin's lymphoma. A non-Hodgkin's lymphoma can be B-cell lymphoma or T-cell lymphoma. Corresponding evidence can be found in the examples.

[0061] In a further variant of the method, in addition to the DNA methylation analysis, an mRNA expression analysis of the immunoregulatory gene is performed. This can, for example, include determining the mRNA expression level of at least one transcript variant of the immunoregulatory gene or of at least one transcript variant of at least two different immunoregulatory genes. Preferably, the mRNA expression analysis is also carried out under conditions that allow a quantitative determination of the mRNA expression level.

[0062] Suitable protocols for mRNA expression analysis are well known to those skilled in the art and typically comprise the following steps: a) providing mRNA from the malignant cells or T lymphocytes; b) transcribing the mRNA into cDNA; c) detecting cDNA from the immunoregulatory gene. However, it is also possible to analyze the mRNA directly without prior transcribing into cDNA. Nanostring technology is particularly suitable for this purpose. mRNA expression analysis can also be performed using RNA-Seq (RNA sequencing) or whole transcriptome shotgun sequencing (WTSS). Next-Generation Sequencing (NGS) methods can be used to determine the presence and / or quantity of an mRNA. For example, the number of mRNA molecules of a specific sequence can be determined and compared to the number of all mRNA molecules to obtain a normalized number ( normalized count) as used in the examples. Suitable RNA-Seq techniques and complete solutions are available, for example, from Illumina, San Diego, CA, USA.

[0063] Surprisingly, the inventive combination of DNA methylation analysis and mRNA expression analysis leads to an additionally improved assessment of the prognosis, enabling an even more precise stratification of prognostic groups. The analyses can be combined in such a way that a prognostic group is initially determined for a patient based on both the DNA methylation analysis and the mRNA expression analysis of an immunoregulatory gene, for example, using the reference values ​​described above. These prognostic groups can then be combined to result in further subgroups for the prognosis. A subgroup with a poor prognosis can be formed, for example, if both the mRNA expression analysis and the DNA methylation analysis of the immunoregulatory gene indicate a poor prognosis.A subgroup with a good prognosis can be formed, for example, if the patient has a good prognosis based on both the mRNA expression analysis and the DNA methylation analysis of the immunoregulatory gene. Two intermediate prognosis groups, for example, include those patients for whom either the DNA methylation analysis or the mRNA expression analysis of the immunoregulatory gene indicates a good prognosis and the other analysis indicates a poor prognosis. An even more differentiated stratification is possible if several immunoregulatory genes are analyzed, which can be used to make a multifactorial classification into prognosis groups. On this basis, the present invention can be used to tailor clinical decision-making to the individual patient even more precisely and reliably in a previously unattainable manner using objectively measurable parameters.

[0064] Altered DNA methylation in cells of a malignant disease affects a multitude of CpG dinucleotides within and around a gene. For the inventive method, CpG dinucleotides in promoters and in the sequence coding for the transcript of the immunoregulatory gene in the cells of the malignant disease or the T lymphocytes are particularly suitable. However, the methylation of CpG dinucleotides outside the gene body of the immunoregulatory genes in the cells of the malignant disease or the T lymphocytes can also be altered and suitable for the inventive method. Preferred regions for the inventive DNA methylation analysis include regulatory gene regions, in particular transcription factor binding sites, promoters, CpG islands, silencers, enhancers, CTCF binding sites, and combinations thereof. Enhancers can, for example, be present at a distance from the gene as distal enhancers.Enhancers can also be located near the gene and are then called proximal enhancers. Regulatory gene regions are well known to the person skilled in the art and are described, for example, in "Gene Control" by DS Latchman, 2nd Edition, 2015, Garyland Science, Taylor & Francis Group, LLC. For the method according to the invention, those CpG dinucleotides whose methylation state correlates with the transcriptional activity or expression of a gene are also suitable. Transcriptional activity can be detected, for example, by an altered chromatin structure. So-called "open chromatin" can be associated with high transcriptional activity of a gene, as described, for example, in "Genetik" by W. Janning and E. Kunst, 2004, Georg Thieme Verlag, Stuttgart and New York. Regions of "open chromatin" are therefore suitable for the DNA methylation analysis according to the invention.

[0065] The identification of regulatory gene elements is readily possible for the skilled person using suitable databases. For example, the "Ensembl" database annotates such regulatory elements, as described in "The Ensembl Regulatory Build" by DR Zerbino, SP Wilder, N. Johnson, T. Juettemann, and PR Flicek, 2015, Genome Biology, Issue 16, doi:10.1186 / s13059-015-0621-5.

[0066] A suitable primary sequence of the human genome, based on which suitable and preferred regions and sequences of the immunoregulatory genes for the DNA methylation analysis according to the invention can be determined, is, for example, the human genome version of the Genome Reference Consortium Human Build 38 (GRCh38) or Reference Consortium Human Build 38 patch release 7 (GRCh38.p7) as of March 21, 2016. Regions of the genome are referred to below in the notation "chromosome number: position of the first base of the region - position of the last base of the region", for example "2:241849881-241858908" for the region from base 241849881 to base 241858908 of chromosome 2.

[0067] In one variant, the immunoregulatory gene is selected from the genes encoding B7 proteins and their receptor CD274, PDCD1LG2 and PDCD1.In another variant, the immunoregulatory gene is selected from the genes encoding the B7 protein and its receptor CD80 and CTLA4. In yet another variant, the immunoregulatory gene is selected from the gene encoding the receptor of a B7 protein ICOS and the genes encoding the B7 proteins CD276, C10orf54 and HHLA2. In yet another variant, the immunoregulatory gene is selected from the genes encoding the MHC:peptide complex binding co-receptors LAG3, CD160, KIR2DL4, KIR3DL1. Any combination of immunoregulatory genes from these variants is also possible.

[0068] The gene PDCD1 or Programmed Cell Death 1 is also known by synonyms CD279, PD1, hPD-1, SLEB2, PD-1, hSLE1 and hPD-1 known. PDCD1 is an immunoregulatory gene and encodes a protein that is an immune checkpoint within the meaning of the invention. The protein PDCD1 is a receptor and can, for example, bind the two ligands belonging to the B7 proteins, which are encoded by the genes CD274 and PDCD1LG2 are coded. PDCD1 belongs to the immunoglobulin superfamily. Preferred areas for DNA methylation analysis of PDCD1 are in regions encoding the transcripts (2:241849881-241858908, SEQ ID NO:17), regions with open chromatin (2:241849051-241853001, SEQ ID NO:18 and 2:241861820-241862593, SEQ ID NO:19), the enhancer (2:241852997-241855201, SEQ ID NO:2), a CTCF binding site (2:241859081-241860074, SEQ ID NO:3) and / or promoters (2:241856912-241861429, SEQ ID NO:29 and 2:241862929-241865230, SEQ ID NO:30). Other preferred regions for DNA methylation analysis of PDCD1 are selected from those which are described for this purpose in the embodiments 1, 3, 5, 7, 8, 10, 12 and 17. For the mRNA expression analysis according to the invention, the detection of at least part of the sequence of transcripts whose cDNA at least partially corresponds to SEQ ID NO:73, SEQ ID NO:74 and / or SEQ ID NO:75 is suitable. The prediction of the response to an immunotherapy includes in particular active substances which are suitable for PDCD1 encoded receptor and / or its ligands, wherein the ligands are particularly encoded by the immunoregulatory genes PDCD1LG2 and CD274 are encoded. Examples are Nivolumab (trade name: Opdivo, manufacturer: Bristol-Myers Squibb), Pembrolizumab (trade name: Keytruda; manufacturer: Merck / MSD Sharp & Dohme), Pidilizumab (CT-011, manufacturer: CureTech Ltd.), MGD013 (Macrogenics), AMP-224 (manufacturer: GlaxoSmithKline), MEDI0680 (AMP-514, manufacturer: MedImmune LLC), AUNP-12 (manufacturer: Aurigene Discovery Technologies Ltd.), BMS935559 (MDX1105, manufacturer: Bristol-Myers Squibb), CA-170 (manufacturer: Curis Inc.), MPDL3280A (manufacturer: Roche), MEDI4736 (manufacturer: AstraZeneca), Avelumab (MSB0010718C, manufacturer: Pfizer) and rHIgM12B7 (B7-DC cross-linking antibody rHIgM12B7, Mayo Clinic).

[0069] The gene CD274 or CD274 molecule is also known by synonyms PDCD1L1, B7-H, B7-H1, PDCD1LG1, PD-L1, PDL1 and B7H1 known. CD274 is an immunoregulatory gene and encodes a B7 protein, which is an immune checkpoint within the meaning of the invention. CD274 encodes a protein which is a ligand of the PDCD1 encoded receptor. CD274 belongs to the immunoglobulin superfamily. Preferred areas for DNA methylation analysis of CD274 are in the region encoding the transcripts (9:5450503-5470566, SEQ ID NO:1) as well as the promoters (9:5445402-5456799, SEQ ID NO:76 and 9:5458041-5461360, SEQ ID NO:77), enhancers (9:5457122-5457702, SEQ ID NO:78; 9:5463574-5468340, SEQ ID NO:79; 9:5440647-5441785, SEQ ID NO:80 and 9:5472191-5473149, SEQ ID NO:81) and / or CTCF binding sites (9:5440970-5441435, SEQ ID NO:82 and 9:5446325-5446870, SEQ ID NO:83). Further preferred regions for DNA methylation analysis include the coding region of CD274, which contains an alternative promoter and a CTCF binding site (9:5451072-5481072, SEQ ID NO:371), the alternative promoter (9:5451072-5461819, SEQ ID NO:375), the regulatory region upstream of the promoter (9:5433159-5449887, SEQ ID NO:370), the region upstream of the promoter which contains a CTCF binding site (9:5439290-5449887, SEQ ID NO:376), the coding region downstream of the promoter (9:5451072-5470566, SEQ ID NO:372), and the region downstream of the coding region which includes a CTCF binding site and an enhancer (9:5470566-5496357, SEQ ID NO:373). Other preferred areas for DNA methylation analysis of CD274 are selected from those which are described for this purpose in the embodiments 2, 3, 7, 8, 10, 12, 15 and 17. For the mRNA expression analysis according to the invention, the detection of at least part of the sequence of transcripts whose cDNA at least partially corresponds to SEQ ID NO:28, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103 and / or SEQ ID NO:104 is suitable. The prediction of the response to an immunotherapy includes in particular active substances which CD274 encoded B7 protein and / or the corresponding PDCD1 encoded receptor and / or the PDCD1LG2 encoded ligand, which also binds to the PDCD1 encoded receptor. Examples include nivolumab, pembrolizumab, pidilizumab, AMP-224, AMP-514, AUNP-12, BMS935559, CA-170 (Curis), atezolizumab (MPDL3280A, Roche, Genentech), MGD013 (Macrogenics), MEDI4736, CA-170 (Curis, Inc.), and avelumab.

[0070] The gene PDCD1LG2 or programmed cell death 1 ligand 2 is also known by synonyms Btdc, PDL2, CD273, PDCD1L2, B7-DC, bA574F11.2, PD-L2 and B7DC known. PDCD1LG2 is an immunoregulatory gene and encodes a B7 protein, which is an immune checkpoint within the meaning of the invention. PDCD1LG2 encodes a B7 protein, which is a ligand of the PDCD1 encoded receptor. PDCD1LG2 belongs to the immunoglobulin superfamily. Preferred areas for DNA methylation analysis of PDCD1LG2 are in the region encoding the transcript (9:5510570-5571254) as well as the promoters (9:5507688-5523442, SEQ ID NO:84; 9:5491444-5503289, SEQ ID NO:85; 9:5528150-5534251, SEQ ID NO:86 and 9:5547972-5571492, SEQ ID NO:87) and / or the enhancer (9:5479110-5491616, SEQ ID NO:88; 9:5522642-5528253, SEQ ID NO:89; 9:5534822-5547690, SEQ ID NO:90; 9:5572730-5580962, SEQ ID NO:91), as well as the region before the coding sequence (9:5496357-5510570, SEQ ID NO:374). Particularly preferably, the DNA methylation analysis comprises one or more of the CpG dinucleotides located in the sequence region 9:5433159-5603325, which is responsible for the transcripts of the neighboring genes. CD274 and PDCD1LG2 and includes enhancers and promoters of these genes, in particular the region between the coding regions of CD274 and PDCD1LG2 (9:5470566-5510570; SEQ ID NO:485). Other preferred areas for methylation analysis of PDCD1LG2 are selected from those which are described for this purpose in the embodiments 3, 5, 7, 8, 10 and 13. For the mRNA expression analysis according to the invention, the detection of at least part of the sequence of the transcript, the cDNA of which corresponds at least partially to SEQ ID NO:105, is suitable. The prediction of the response to an immunotherapy includes in particular active substances which PDCD1LG2 encoded B7 protein and / or the corresponding PDCD1 encoded receptor. Suitable inhibitors include nivolumab, pembrolizumab, pidilizumab, AMP-224, AMP-514, AUNP-12, CA-170 (Curis, Inc.), and rHIgM12B7.

[0071] The gene ICOS or inducible T-cell co-stimulator is also known by synonyms CD278, CVID1 and AILIM known. ICOS is an immunoregulatory gene and encodes a protein that is an immune checkpoint within the meaning of the invention. ICOS is a receptor and can, for example, bind the ligand belonging to the B7 proteins, which is encoded by the gene ICOSLG is coded. ICOSLG or inducible T-cell co-stimulator ligand is an immunoregulatory gene and encodes a ligand that is an immune checkpoint within the meaning of the invention. ICOSLG is also known as ICOSL, B7RP1, LICOS, B7RP-1, KIAA0653, GL50, ICOS-L, CD275, B7H2 and B7-H2 designated. ICOS and ICOSLG belong to the immunoglobulin superfamily. Preferred areas for DNA methylation analysis of ICOS are contained in the transcript encoding region (2:203936748-203961577, SEQ ID NO:92) as well as the promoters (2:203934590-203941036, SEQ ID NO:93 and 2:203948548-203953636, SEQ ID NO:94) and / or the enhancer (2:203931099-203937863, SEQ ID NO:95 and 2:203940518-203949061, SEQ ID NO:96). Preferred regions for DNA methylation analysis of ICOSLG are contained in the region encoding the transcripts (21:44222991-44240966, SEQ ID NO:97) as well as a CpG island (21:44240132-44243380, SEQ ID NO:98), the promoter (21:44238919-44247988, SEQ ID NO:99) and / or an enhancer region (21:44214476-44227512, SEQ ID NO:100). Other preferred regions for DNA methylation analysis of ICOS and ICOSLG are selected from those described for this purpose in Examples 11, 13 and 17. For the mRNA expression analysis of ICOS or from ICOSLG The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:106, SEQ ID NO:107, SEQ ID NO:108, SEQ ID NO:109, SEQ ID NO:110 and / or SEQ ID NO:111 is suitable. A preferred embodiment of the method comprises the application of DNA methylation analysis of the ICOS and / or the ICOSLG gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of ICOS and / or ICOSLG to change, in particular the ICOS encoded receptor. A suitable activator is JTX-2011 (Jounce Therapeutics).

[0072] The gene CTLA4 or cytotoxic T-lymphocyte-associated protein 4 is also known by synonyms CELIAC3, GRD4, CTLA-4, IDDM12, CD, CD28, GSE, CD152 and ALPS5 known. CTLA4 is an immunoregulatory gene and encodes a protein which is an immune checkpoint within the meaning of the invention. CTLA4 codes for a receptor that can bind, for example, the ligand belonging to the B7 proteins, which is encoded by the gene CD80 is coded. CD80 or CD80 molecule is an immunoregulatory gene and encodes a ligand that is an immune checkpoint within the meaning of the invention. CD80 is also, for example, B7, CD28LG1, B7.1, B7-1, LAB7, BB1 und CD28LG named. CTLA4 and CD80 belong to the immunoglobulin superfamily. Preferred areas for methylation analysis of CTLA4 are in the region encoding the transcripts (2:203867786-203873960, SEQ ID NO:160) as well as the promoters (2:203866174-203868926, SEQ ID NO:162 and 2:203869477-203874095, SEQ ID NO:164), the enhancer (2:203874152-203875266, SEQ ID NO:165; 2:203876672-203878051, SEQ ID NO:166 and 2:203879313-203881585, SEQ ID NO:167) and / or the region between CTLA4 and the neighboring immunoregulatory gene ICOS (2:203872383-203939876). Other preferred areas for DNA methylation analysis of CTLA4 are selected from those described for this purpose in Examples 4, 7, 8, 11, 13, 15 and 16. Preferred areas for DNA methylation analysis of CD80 are contained, for example, in the region encoding the transcripts (3:119524293-119559602) as well as the promoters (3:119554042-119563668, SEQ ID NO:163 and 3:119568227-119573274, SEQ ID NO:177) and / or the enhancer (3:119563379-119568778, SEQ ID NO:178; 3:119538188-119543511, SEQ ID NO:179 and 3:119545840-119554325, SEQ ID NO:180). Other preferred regions for methylation analysis of CD80 are selected from those described for this purpose in Examples 11, 13, 15 and 16. For the mRNA expression analysis of CTLA4 or from CD80 suitable for the detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:168, SEQ ID NO:169, SEQ ID NO:170, SEQ ID NO:171, SEQ ID NO:172 and SEQ ID NO:173 or SEQ ID NO:174, SEQ ID NO:175 and / or SEQ ID NO:176. A preferred embodiment of the method involves the application of DNA methylation analysis of the CTLA4 and / or the CD80 Gene to predict response to drugs that are capable of altering the immunoregulatory effect of CTLA4 and / or CD80 to modify, particularly to inhibit. Suitable active ingredients include ipilimumab (MDX-010; trade name Yervoy, Bristol-Myers Squibb), tremelimumab (ticilimumab, CP-675,206, Pfizer, MedImmune, AstraZeneca).

[0073] Further immunoregulatory genes encoding B7 proteins which are preferred for the method according to the invention are the genes CD276, C10orf54, HHLA2, NCR3LG1, CD86 and VTCN1. The genes CD276, C10orf54, HHLA2, NCR3LG1, CD86 and VTCN1 encode immune checkpoints within the meaning of the invention, which also belong to the immunoglobulin superfamily. The gene CD276 or CD276 molecule is also known as B7-H3, B7H3, 4Ig-B7-H3 and B7RP-2 Preferred areas for DNA methylation analysis of CD276 are, for example, in the region coding for the transcripts (15:73683966-73714518, SEQ ID NO:181) as well as promoters (15:73679515-73692168, SEQ ID NO:182 and 15:73693817-73698829, SEQ ID NO:184), a CpG island (15:73683810-73685004, SEQ ID NO:183), a CTCF and transcription factor binding site (15:73699041-73706032, SEQ ID NO:185) and enhancers (15:73674927-73679619, SEQ ID NO:186 and 15:73710442-73723236, SEQ ID NO:187). Other preferred areas for methylation analysis of CD276 are selected from those described for this purpose in Examples 13, 15, 16 and 17. For the mRNA expression analysis of CD276 suitable for the detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to the sequences SEQ ID NO:241, SEQ ID NO:242, SEQ ID NO:243, SEQ ID NO:244, SEQ ID NO:245, SEQ ID NO:246, SEQ ID NO:247, SEQ ID NO:248, SEQ ID NO:249, SEQ ID NO:250, SEQ ID NO:251, SEQ ID NO:251 and / or SEQ ID NO:253. A preferred embodiment of the method comprises the application of the DNA methylation analysis of the CD276 gene to determine the response to an active substance that is capable of inhibiting the immunoregulatory effect of CD276 to modify, especially to inhibit. Suitable active ingredients include enoblituzumab (MGA271, Macrogenics) and MGD009 (Macrogenics).

[0074] The gene C10orf54 or chromosome 10 open reading frame 54 is also known as B7H5, GI24, PP2135, VISTA, B7-H5, DD1alpha and SISP1 Preferred areas for DNA methylation analysis of C10orf54 are contained, for example, in the region encoding the transcripts (10:71747559-71773498, SEQ ID NO:188) as well as promoters (10:71759805-71784691, SEQ ID NO:189 and 10:71723534-71753287, SEQ ID NO:191) and / or a CTCF binding site (10:71738936-71750940, SEQ ID NO:190). Other preferred regions for DNA methylation analysis of C10orf54 are selected from those described for this purpose in Examples 11, 14 and 17. For the mRNA expression analysis of C10orf54 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to the sequences SEQ ID NO:192, SEQ ID NO:193 and / or SEQ ID NO:194 is suitable. A preferred embodiment of the method comprises the application of DNA methylation analysis of the C10orf54 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of C10orf54 to modify, especially to inhibit. Suitable inhibitors include CA-170 (Curis) and JNJ-61610588 (Janssen Biotech, Inc.).

[0075] The gene HHLA2 or HERV-H LTR-associating 2 is also known as B7-H7, B7y, B7H7 and B7-H5 Preferred areas for DNA methylation analysis of HHLA2 are contained, for example, in the region encoding the transcripts (3:108296490-108378285) as well as a regulatory region with enhancers, promoter and CTCF binding sites (3:108291804-108313328, SEQ ID NO:36) and two further promoters (3:108310655-108313476, SEQ ID NO:195 and 3:108341108-108352706, SEQ ID NO:196). Other preferred regions for DNA methylation analysis of HHLA2 are selected from those described for this purpose in Examples 11, 14, 15 and 16. For the mRNA expression analysis of HHLA2 suitable for the detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to the sequences SEQ ID NO:197, SEQ ID NO:198, SEQ ID NO:199, SEQ ID NO:200, SEQ ID NO:201, SEQ ID NO:202, SEQ ID NO:203, SEQ ID NO:204, SEQ ID NO:205, SEQ ID NO:206 and / or SEQ ID NO:207. A preferred embodiment of the method comprises the application of DNA methylation analysis of the HHLA2 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of HHLA2 to change, especially to inhibit.

[0076] The gene NCR3LG1 or natural killer cell cytotoxicity receptor 3 ligand 1 is also known as B7H6, DKFZp686024166 and B7-H6 known. NCR3LG1 is an immunoregulatory gene whose product belongs to the B7 proteins and is an immune checkpoint according to the invention. Preferred regions for DNA methylation analysis of NCR3LG1 are contained, for example, in the region encoding the transcripts (11:17351726-17377341, SEQ ID NO:208) and the promoter (11:17347524-17359073, SEQ ID NO:209) and / or a CpG island (11:17351068-17354459, SEQ ID NO:210). For the mRNA expression analysis of NCR3LG1 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:254 and / or SEQ ID NO:255 is suitable. A preferred embodiment of the method comprises the application of DNA methylation analysis of the NCR3LG1 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of NCR3LG1 and / or the NCR3 to change the encoded receptor.

[0077] The gene CD86 or CD86 molecule is also known as B7-2, B70, LAB72, B7.2 and CD28LG2 known. The CD86 The encoded ligand belongs to the B7 proteins and is an immune checkpoint according to the invention, which, for example, is linked to the CD28 and CTLA4 encoded receptors. Preferred regions for DNA methylation analysis of CD86 are contained, for example, in the region encoding the transcripts (3:122055366-122121139), the promoters (3:122071817-122084231, SEQ ID NO:211; 3:122053987-122061537, SEQ ID NO:212; 3:122081939-122095873, SEQ ID NO:213; 3:122096457-122110191, SEQ ID NO:214) and / or the enhancer (3:122119408-122125888, SEQ ID NO:215). Further preferred regions for the methylation analysis of CD86 are selected from those described for this purpose in the embodiment 17. For the mRNA expression analysis of CD86 suitable for the detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:256, SEQ ID NO:257, SEQ ID NO:258, SEQ ID NO:259, SEQ ID NO:260, SEQ ID NO:261, SEQ ID NO:262, SEQ ID NO:263 and / or SEQ ID NO:264. A preferred embodiment of the method comprises the application of DNA methylation analysis of the CD86 Gene to determine the response to an active substance which is capable of altering the immunoregulatory effect of the CD86 encoded ligands and / or those by CTLA4 and / or CD28 to inhibit encoded receptors. Suitable active substances include ipilimumab (MDX-010; trade name Yervoy, Bristol-Myers Squibb), tremelimumab (ticilimumab, CP-675,206, Pfizer, MedImmune, AstraZeneca).

[0078] The gene VTCN1 or V-set domain containing T-cell activation inhibitor 1 is also known as B7-H4, B7H4, B7S1, B7X, B7h.5, PRO1291 and VCTN1 known. The VTCN1 The protein encoded belongs to the B7 proteins and is an immune checkpoint according to the invention. Preferred areas for DNA methylation analysis of VTCN1 are, for example, in the region encoding the transcripts (1:117143587-117210960) and / or the promoter (1:117193851-117226364). For the mRNA expression analysis of VTCN1 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:377, SEQ ID NO:378 and / or SEQ ID NO:379 is suitable. A preferred embodiment of the method comprises the application of DNA methylation analysis of the VTCN1 Gene to determine the response to an active substance which is capable of altering the immunoregulatory effect of the VTCN1 encoded ligand and / or its receptor.

[0079] The immunoregulatory genes KIR2DL4, KIR3DL1, KIR3DL3, LAG3 and CD160 encode proteins that are MHC:peptide complex binding co-receptors. KIR2DL4, KIR3DL1, KIR3DL3, LAG3 and CD160 are immune checkpoints within the meaning of the invention and belong to the immunoglobulin superfamily. The KIR genes KIR2DL4, KIR3DL1 and KIR3DL3 code for killer cell immunoglobulin-like receptors (KIR or KIR receptors for short). The gene KIR2DL4 or killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 4 is also known as KIR103, KIR103AS, G9P, CD158D, 15.212, KIR-103AS and 103AS Preferred areas for DNA methylation analysis of KIR2DL4 are contained, for example, in the region encoding the transcripts (19:54803535-54814517, SEQ ID NO:216), in the promoters (19:54799627-54807083, SEQ ID NO:217; 19:54791651-54800236, SEQ ID NO:229) and / or two enhancers (19:54803283-54804114, SEQ ID NO:218 and 19:54812823-54819942, SEQ ID NO:219). Other preferred regions for the methylation analysis of KIR2DL4 are selected from those described for this purpose in Examples 11, 14 and 17. For the mRNA expression analysis of KIR2DL4 suitable for the detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:220, SEQ ID NO:221, SEQ ID NO:222, SEQ ID NO:223, SEQ ID NO:224, SEQ ID NO:225, SEQ ID NO:226, SEQ ID NO:227 and / or SEQ ID NO:228. A preferred embodiment of the method comprises the application of DNA methylation analysis of the KIR2DL4 Gene to determine the response to an active substance which is capable of altering the immunoregulatory effect of the KIR2DL4 encoded receptor. One suitable drug is lirilumab (Bristol-Myers Squibb).

[0080] The gene KIR3DL1 or killer cell immunoglobulin-like receptor, three domains, long cytoplasmic tail, 1 is also known as NKB1, CD158E, cl-11, cl-2, CD158e2, NKAT3, AMB11, NKB1B, KIR, KIR3DL2, CD158E1, CD158e1 / 2, NKAT-3, nkat3 and KIR3DL1 / S1Preferred areas for DNA methylation analysis of KIR3DL1 are contained, for example, in the region encoding the transcripts (19:54816468-54866993) as well as the promoter (19:54808180-54831092, SEQ ID NO:230) and / or two enhancers (19:54812749-54821037, SEQ ID NO:231 and 19:54835785-54863422, SEQ ID NO:232). Other preferred regions for DNA methylation analysis of KIR3DL1 are selected from those described for this purpose in Examples 11 and 14. For the mRNA expression analysis of KIR3DL1 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:233, SEQ ID NO:234, SEQ ID NO:235 and / or SEQ ID NO:236 is suitable. A preferred embodiment of the method comprises the application of methylation analysis of the KIR3DL1 Gene to determine the response to an active substance which is capable of altering the immunoregulatory effect of the KIR3DL1 encoded receptor, for example lirilumab (also known as IPH2102 / BMS-986015 from Bristol-Myers Squibb and Innate Pharma) and IPH4102 (Innate Pharma).

[0081] The gene KIR3DL3 or killer cell immunoglobulin-like receptor, three domains, long cytoplasmic tail, 3 is also known as KIR2DS2, CD158z, KIR3DL7, KIR44, CD158Z and KIRC1 Preferred areas for DNA methylation analysis of KIR3DL3 are contained in the region encoding the transcripts (19:54724479-54736536, SEQ ID NO:237) as well as in the promoter (19:54723156-54728908, SEQ ID NO:238) and / or two enhancers (19:54723473-54725230, SEQ ID NO:239 and 19:54735023-54744608, SEQ ID NO:240). For the mRNA expression analysis of KIR3DL3 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:265 is suitable. A preferred method embodiment comprises the application of DNA methylation analysis of the KIR3DL3 Gene to determine the response to an active substance which is capable of altering the immunoregulatory effect of the KIR3DL3 encoded receptor, for example lirilumab (also known as IPH2102 / BMS-986015 from Bristol-Myers Squibb and Innate Pharma).

[0082] The gene LAG3 or lymphocyte-activation gene 3 is also, for example, FDC and CD223 Preferred areas for methylation analysis of LAG3 Examples include the transcript coding region (12:6772512-6778455, SEQ ID NO:161) and the promoter (12:6770333-6774801, SEQ ID NO:349), an alternative promoter with CTCF binding site (12:6777768-6781320, SEQ ID NO:350) and an enhancer (12:6774685-6778062, SEQ ID NO:351). Other preferred regions for DNA methylation analysis of LAG3 are selected from those described for this purpose in the working examples 4, 7, 8, 9 and 14. For the inventive determination of the mRNA of LAG3 For example, the sequences or parts of the sequences SEQ ID NO:352, SEQ ID NO:353, SEQ ID NO:354 and SEQ ID NO:355 are preferred. A preferred method embodiment is the application of the methylation analysis of the LAG3 gene to determine the response to an inhibitor that is capable of inhibiting the immunoregulatory effect of the LAG3 encoded receptor. Suitable inhibitors include MGD013 (Macrogenics) and BMS-986016 (Bristol-Myers Squibb).

[0083] The gene CD160 or CD160 molecule is also known as BY55, NK28 and NK1 Preferred areas for DNA methylation analysis of CD160 are contained, for example, in the region encoding the transcripts (1:145719471-145739288, SEQ ID NO:361) as well as the promoter (1:145718367-145722818, SEQ ID NO:362), an alternative promoter (1:145735798-145741393, SEQ ID NO:364), a CTCF binding site (1:145720204-145722255, SEQ ID NO:363) and / or two enhancers (1:145727546-145735069, SEQ ID NO:365 and 1:145708511-145719786, SEQ ID NO:366). Other preferred areas for DNA methylation analysis of CD160 are selected from those described for this purpose in Examples 11, 14 and 17. For the mRNA expression analysis of CD160 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:356, SEQ ID NO:357, SEQ ID NO:358, SEQ ID NO:359 and / or SEQ ID NO:360 is suitable. A preferred embodiment of the method comprises the application of DNA methylation analysis of the CD160 Gene to determine the response to an active substance which is capable of altering the immunoregulatory effect of the CD160 to change, in particular to inhibit, the encoded receptor.

[0084] The immunoregulatory genes TIGIT, BTLA, HAVCR2, BTNL2 and CD48 encode the proteins TIGIT, BTLA, HAVCR2, BTLN2 and CD48, which belong to the members of the immunoglobulin superfamily and are immune checkpoints within the meaning of the invention.

[0085] The gene TIGIT or T-cell immunoreceptor with Ig and ITIM domains is also, for example, DKFZp667A205, VSIG9, VSTM3, FLJ39873 and WUCAM Preferred areas for DNA methylation analysis of TIGIT are encompassed, for example, by the region encoding the transcripts (3:114276913-114310288) as well as three regulatory regions with enhancers and CTCF binding sites (3:114273873-114278448, SEQ ID NO:266; 3:114306113-114321848, SEQ ID NO:267; 3:114288458-114302904, SEQ ID NO:268). Other preferred regions for DNA methylation analysis of TIGIT are selected from those described for this purpose in Examples 4, 7, 8, 11, 14, 15, 16 and 17. For the mRNA expression analysis of TIGIT suitable for the detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:269, SEQ ID NO:270, SEQ ID NO:271, SEQ ID NO:272, SEQ ID NO:273, SEQ ID NO:274 and / or SEQ ID NO:275. A preferred embodiment of the method involves the application of methylation analysis of the TIGIT gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of TIGIT to change, especially to inhibit.

[0086] The gene BTLA or B and T lymphocyte associated is also known as CD272 and BTLA1 Preferred areas for DNA methylation analysis of BTLA are, for example, in the region encoding the transcripts (3:112463968-112499561) as well as the promoter (3:112494210-112503447, SEQ ID NO:276), an alternative promoter (3:112460682-112487674, SEQ ID NO:279) and / or the enhancer (3:112501618-112513121, SEQ ID NO:277; 3:112486680-112495554, SEQ ID NO:278; 3:112451208-112463106, SEQ ID NO:280; 3:112508297-112522457, SEQ ID NO:281). Other preferred areas for DNA methylation analysis of BTLA are selected from those described for this purpose in Examples 4, 6, 7, 8, 11 and 14. For the mRNA expression analysis of BTLA The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:282, SEQ ID NO:283, SEQ ID NO:284 and / or SEQ ID NO:285 is suitable. A preferred embodiment of the method comprises the application of DNA methylation analysis of the BTLA gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of BTLA to change, especially to inhibit.

[0087] The gene HAVCR2 or hepatitis A virus cellular receptor 2 is also known as TIMD-3, CD366, Tim-3, TIMD3, FLJ14428, TIM3, HAVcr-2 and KIM-3 Preferred areas for DNA methylation analysis of HAVCR2 are comprised of the region encoding the transcripts (5:157085832-157142869) as well as the promoter (5:157138356-157147783, SEQ ID NO:286), an alternative promoter (5:157097106-157121624, SEQ ID NO:287) and three enhancers (5:157144698-157161450, SEQ ID NO:288; 5:157116666-157139926, SEQ ID NO:289 and 5:157080879-157096259, SEQ ID NO:290). Further preferred regions for DNA methylation analysis of HAVCR2 are selected from those described for this purpose in Examples 11 and 14. For the mRNA expression analysis of HAVCR2 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:291, SEQ ID NO:292, SEQ ID NO:293, SEQ ID NO:294, SEQ ID NO:295 and / or SEQ ID NO:296 is suitable. A preferred embodiment of the method involves the application of DNA methylation analysis of the HAVCR2 Gene to determine the response to a drug, which is suitable, the immunoregulatory effect of HAVCR2 to modify, for example, to inhibit. A suitable inhibitor is Anti-TIM-3 Antibody TSR-022 (Tesaro, Inc).

[0088] The gene BTNL2 or butyrophilin like 2 is also known as BTL-II, BTN7, HSBLMHC1 and SS2 Preferred areas for DNA methylation analysis of BTNL2 include the region coding for the transcripts (6:32393963-32407128, SEQ ID NO:387) as well as the region containing the promoter and regulatory elements (6:32387381-32413712, SEQ ID NO:387). Other preferred regions for DNA methylation analysis of BTNL2 are selected from those described for this purpose in Example 17. For the mRNA expression analysis of BTNL2 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:389 is suitable. A preferred embodiment comprises the application of DNA methylation analysis of the BTNL2 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of BTNL2 This includes in particular an antagonist which is capable of altering the BTNL2 encoded immune checkpoint.

[0089] The gene CD48 or CD48 molecule is also known as BCM1, BLAST, BLAST1, MEM-102, SLAMF2, hCD48 and mCD48 Preferred areas for DNA methylation analysis of CD48 include the promoter and regulatory elements (1:160676386-160722581) as well as the promoter (1:160703189-160716911, SEQ ID NO:390). Other preferred regions for DNA methylation analysis of CD48 are selected from those described for this purpose in Example 17. For the mRNA expression analysis of CD48 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:389 is suitable. A preferred embodiment comprises the application of DNA methylation analysis of the CD48 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of CD48 to change.

[0090] CD40, TNFRSF9, TNFRSF18, TNFRSF4 and CD27 are immunoregulatory genes that encode the proteins CD40, TNFRSF9, TNFRSF18, TNFRSF4, and CD27. CD40, TNFRSF9, TNFRSF18, TNFRSF4, and CD27 are receptors of the tumor necrosis factor receptor superfamily, which are immune checkpoints within the meaning of the invention.

[0091] The gene CD40 or CD40 molecule, TNF receptor superfamily member 5 is also known as TNFRSF5, p50, Bp50 and CDW40 Preferred areas for DNA methylation analysis of CD40 are from the region encoding the transcripts (20:46118272-46129863, SEQ ID NO:297) as well as the promoter (20:46115599-46122305, SEQ ID NO:298), an alternative promoter (20:46106713-46115878, SEQ ID NO:299) and / or the enhancer (20:46121274-46143091, SEQ ID NO:300). Further preferred regions for DNA methylation analysis of CD40 are selected from those described for this purpose in Examples 4, 7, 8, 9 and 14. For the mRNA expression analysis of CD40 suitable for the detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:301, SEQ ID NO:302, SEQ ID NO:303, SEQ ID NO:304, SEQ ID NO:305, SEQ ID NO:306 and / or SEQ ID NO:307. A preferred embodiment comprises the application of DNA methylation analysis of the CD40 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of CD40 This includes in particular an agonist which is suitable for altering the CD40 to activate the encoded receptor, such as CP-870,893 (Pfizer, VLST), dacetuzumab (Seattle Genetics), or Chi Lob 7 / 4 (University of Southampton). In particular, the active ingredient can also be selected to inhibit the immunoregulatory effect of CD40. A suitable active ingredient is, for example, lucatumumab (Novartis).

[0092] The gene TNFRSF9 or tumor necrosis factor receptor superfamily member 9 is also known as 4-1BB, CDw137, CD137 and ILA Preferred areas for DNA methylation analysis of TNFRSF9 are contained in the region encoding the transcripts (1:7915894-7943165, SEQ ID NO:308) and the promoter (1:7937419-7950341, SEQ ID NO:309), a regulatory element with CTCF binding site, enhancer and alternative promoter (1:7910016-7923298, SEQ ID NO:312), the enhancer (1:7922095-7939183, SEQ ID NO:310) and / or a CpG island (1:7949284-7956816, SEQ ID NO:311). Further preferred regions for DNA methylation analysis of TNFRSF9 are selected from those described for this purpose in Examples 4, 7, 8, 11, 14, 15, 16 and 17. For the mRNA expression analysis of TNFRSF9 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:313, SEQ ID NO:314, SEQ ID NO:315 and / or SEQ ID NO:316 is suitable. A preferred embodiment of the method comprises the application of DNA methylation analysis of the TNFRSF9 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of TNFRSF9 to change, in particular an agonist which is capable of reducing the TNFRSF9 to activate the encoded receptor. Suitable drugs are PF-05082566 (PF-566, Pfizer) and urelumab (BMS-663513, Bristol-Myers Squibb).

[0093] The gene TNFRSF25 or tumor necrosis factor receptor superfamily member 25 is also known as WSL-LR, WSL, APO3, TNFRSF12, TR3, LARD, DDR3, TRAMP, WSL-1, DR3, WSL1 and APO-3 Preferred areas for DNA methylation analysis of TNFRSF25 are from the region encoding the transcripts (1:6461151-6466195, SEQ ID NO:317) and an alternative promoter (1:6458470-6463974, SEQ ID NO:318), two CpG islands (1:6459713-6463353, SEQ ID NO:319; 1:6465602-6466846, SEQ ID NO:320) and another promoter (1:6464008-6468536, SEQ ID NO:321) and / or an enhancer (1:6468908-6478190, SEQ ID NO:322). Further preferred regions for DNA methylation analysis of TNFRSF25 are selected from those described for this purpose in Examples 14 and 17. For the mRNA expression analysis of TNFRSF25 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:323, SEQ ID NO:324, SEQ ID NO:325, SEQ ID NO:326, SEQ ID NO:327 and / or SEQ ID NO:328 is suitable. A preferred embodiment of the method comprises the application of DNA methylation analysis of the TNFRSF25 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of TNFRSF25 Particularly preferably, this is an agonist which is suitable for altering the TNFRSF25 to activate the encoded receptor.

[0094] The gene TNFRSF18 or tumor necrosis factor receptor superfamily member 18 is also known as GITR, AITR, CD357 and GITR-D Preferred areas for DNA methylation analysis of TNFRSF18 are contained in the region encoding the transcripts (1:1203508-1206691, SEQ ID NO:329) and / or the promoter (1:1195867-1210392, SEQ ID NO:330). For the mRNA expression analysis of TNFRSF18 suitable for the detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:331, SEQ ID NO:332, SEQ ID NO:333 and / or SEQ ID NO:334. Other preferred areas for the methylation analysis of TNFRSF18 are selected from those described for this purpose in the embodiment 17. A preferred method embodiment involves the application of DNA methylation analysis of the TNFRSF18 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of TNFRSF18 This particularly applies to an agonist which is capable of altering the TNFRSF18 encoded receptor, for example TRX518 (GITR Inc.).

[0095] The gene TNFRSF4 or tumor necrosis factor receptor superfamily member 4 is also known as IMD16, OX40, TXGP1L, CD134 and ACT35 Preferred areas for DNA methylation analysis of TNFRSF4 are contained, for example, in the region encoding the transcripts (1:1211326-1214138, SEQ ID NO:335), the promoter (1:1209894-1215938, SEQ ID NO:336) and / or a CpG island (1:1213505-1214288, SEQ ID NO:337). For the mRNA expression analysis of TNFRSF4 suitable for the detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:338, SEQ ID NO:339 and / or SEQ ID NO:340. Other preferred areas for the methylation analysis of TNFRSF4 are selected from those described for this purpose in the embodiment 17. A preferred method embodiment comprises the application of DNA methylation analysis of the TNFRSF4 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of TNFRSF4 Particularly preferred is an agonist which is suitable for altering the TNFRSF4 encoded receptor, such as MEDI6469 (9B12, MedImmune LLC, Astra Zeneca, AgonOx), Hu106-22 and Hu 119-122 (UTMDACC).

[0096] The gene CD27 or CD27 molecule is also known as S152, T14, TNFRSF7, S152, LPFS2 oder Tp55 Preferred areas for DNA methylation analysis of CD27 For example, the region encoding the transcripts (12:6444867-6451718, SEQ ID NO:341) comprises two promoters (12:6442585-6448021, SEQ ID NO:342 and 12:6448982-6453895, SEQ ID NO:343) and / or three enhancers (12:6453736-6456010, SEQ ID NO:344; 12:6447474-6449430, SEQ ID NO:345 and 12:6439173-6442795, SEQ ID NO:346). Further preferred regions for DNA methylation analysis of CD27 are selected from those described for this purpose in Examples 6 and 17. For the mRNA expression analysis of CD27 The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:347 and / or SEQ ID NO:348 is suitable. A preferred embodiment of the method involves the application of DNA methylation analysis of the CD27 gene to determine the response to an active substance which is capable of modulating the immunoregulatory effect of CD27 This can be an agonist, which is suitable for altering the CD27 encoded receptor, for example Varlilumab (Celldex Therapeutics).

[0097] The gene ADORA2A is an immunoregulatory gene that encodes the adenosine 2A receptor. The adenosine 2A receptor is an immune checkpoint within the meaning of the invention that exerts immunoregulatory effects via binding to the metabolite adenosine. The gene ADORA2A or adenosine A2a receptor is also known as A2aR, ADORA2 or RDC8 Preferred areas for DNA methylation analysis of ADORA2A are, for example, in the region coding for the transcripts (22:24417879-24442360, SEQ ID NO:380), in the promoter with a CpG island (22:24422358-24434469, SEQ ID NO:381), in an enhancer (22:24418577-24423493, SEQ ID NO:382) and in the region of the ADORA2A and its antisense RNA (22:24420336-24501503). Other preferred regions for DNA methylation analysis of ADORA2A are selected from those described for this purpose in the embodiment 17. For the mRNA expression analysis of ADORA2A The detection of at least part of the sequence of transcripts whose cDNA corresponds at least partially to SEQ ID NO:383, SEQ ID NO:384, SEQ ID NO:385 and / or SEQ ID NO:386 is suitable. A preferred embodiment of the method comprises the application of DNA methylation analysis of the ADORA2A gene to determine the response to an active substance which is suitable for altering the immunoregulatory effect of the adenosine 2A receptor. Particularly preferred is an antagonist which is suitable for altering the ADORA2A encoded receptor, such as PBF-509, Istradefylline, ST1535, ST4206, Tozadenant, V81444, CPI-444, Preladenant, Vipadenant, SCH58261, ATL801.

[0098] Alternatively, it is possible to perform the DNA methylation analysis of an immunoregulatory gene according to the invention in combination with other prognostic and / or predictive biomarkers. Mutations of the genes are particularly suitable for this purpose. BRAF and EGFR, which can be used to predict response to BRAF and EGFR inhibitors. It is also possible to perform methylation analysis of immunoregulatory genes in combination with methylation analysis and / or mutation analysis of chemokines and chemokine receptors, for example, the chemokine families CXC and CX3C. For example, this can be used to predict response to therapy with ulocuplumab. The inventive methylation analysis of an immunoregulatory gene can also be combined with methylation analysis of the genes IL2RB, CXCL12, CXCR4 and CXCR7 be combined.

[0099] In principle, the prognosis assessment can also be performed before, during, or after immunotherapy treatment of the patient with at least one agent designed to alter the immunoregulatory effect of the immune checkpoint. The prediction can also include the response of the patient's malignant disease to immunotherapy with at least one such agent. The change in the immunoregulatory effect can result from the agent interacting with the analyzed immune checkpoint itself or its receptor or ligand.For example, the immune checkpoint can comprise the ligand CD274 and / or PDCD1LG2, and the active ingredient can be designed to modify, in particular inhibit, the immunoregulatory effect of CD274 and / or PDCD1LG2 through interaction with the corresponding PDCD1 receptor, for example, by blocking the binding of CD274 or PDCD1LG2 to the PDCD1 receptor. In particular, so-called immune checkpoint inhibitors, which are capable of reducing or preventing the immunoregulatory effect of a co-inhibitory or anti-inflammatory immune checkpoint, are suitable as active ingredients. Examples of such immune checkpoint inhibitors are nivolumab, ipilimumab, pembrolizumab, and the other active ingredients already mentioned above. Another possibility are active ingredients that activate the immunoregulatory effect of the immune checkpoints according to the invention.Suitable activating agents include the active substances already mentioned above. It is also possible to select active substances that demonstrate a good response to immunotherapy based on DNA methylation analysis of various immunoregulatory genes of the invention. Such combination therapies are often particularly effective, but due to the associated high costs, they require very careful consideration and therefore a particularly reliable prediction of the response to the individual active substances. The method according to the invention can now achieve this for the first time.

[0100] The second aspect of the invention also relates to this, according to which a method for the individualized selection of an active ingredient for the immunotherapeutic treatment of a patient with a malignant disease is disclosed.The method is in turn characterized in that a DNA methylation analysis of at least one immunoregulatory gene of cells of the malignant disease and / or of T lymphocytes interacting with the cells of the malignant disease is carried out, wherein the immunoregulatory gene encodes an immune checkpoint selected from B7 proteins and their receptors (i.e. receptors that bind B7 proteins), MHC:peptide complex binding co-receptors, the members of the tumor necrosis factor receptor superfamily TNFRSF9, CD40, TNFRSF4, TNFRSF18 and CD27, the members of the immunoglobulin superfamily TIGIT, BTLA, HAVCR2, BTNL2 and CD48 and the adenosine-binding adenosine 2A receptor, and the active ingredient is selected on the basis of the result of the DNA methylation analysis.

[0101] The active ingredient can in turn be selected so that it can change the immunoregulatory effect of the immune checkpoint, for example as an immune checkpoint inhibitor it is able to reduce or prevent the immunoregulatory effect of a co-inhibitory or anti-inflammatory immune checkpoint, or as an immune checkpoint agonist it is able to enhance the immunoregulatory effect of a co-stimulatory or pro-inflammatory immune checkpoint.

[0102] In particular, the active substance is selected if the DNA methylation analysis indicates an expression of the corresponding immune checkpoint in the cells of the malignant disease and / or in the T lymphocytes, since its expression provides the point of attack for immunotherapeutic treatment with the active substance and consequently the response to a corresponding immunotherapy is sufficiently likely.

[0103] The third aspect of the invention therefore relates to the use of a DNA methylation analysis of at least one immunoregulatory gene of cells of a malignant disease of a patient and / or of T lymphocytes interacting with the cells of the malignant disease, wherein the malignant disease is a cancer, for predicting the patient's response to immunotherapy and / or for the individualized selection of an active ingredient for the immunotherapeutic treatment of the patient, wherein the immunotherapy and / or the immunotherapeutic treatment comprises at least one active ingredient which is an immune checkpoint inhibitor which is designed to change the immunoregulatory effect of the immune checkpoint.Here too, the immunoregulatory gene encodes an immune checkpoint selected from B7 proteins and their receptors (i.e. receptors that bind B7 proteins) and is selected from the group consisting of genes encoding B7 proteins and their receptors. CD274, PDCD1LG2 and PDCD1, the genes encoding the B7 protein and its receptor CD80 and CTLA4, the gene encoding the receptor of a B7 protein ICOS and the genes encoding the B7 proteins CD276, C10orf54 and HHLA2, and any combinations thereof, wherein the active ingredient is selected if DNA methylation analysis indicates expression of the immune checkpoint in the cells of the malignant disease and / or in the T lymphocytes.

[0104] The fourth aspect of the invention relates to the use of the presence, absence or extent of methylation of at least one CpG dinucleotide of an immunoregulatory gene of cells of a malignant disease and / or of T lymphocytes interacting with the cells of the malignant disease, wherein the malignant disease is a cancer, as a predictive biomarker for predicting the response of the malignant disease to immunotherapy or as a biomarker for the individualized selection of an active ingredient for the immunotherapeutic treatment of the patient, wherein the immunotherapy and / or the immunotherapeutic treatment comprises at least one active ingredient which is an immune checkpoint inhibitor, which is designed to change the immunoregulatory effect of the immune checkpoint, wherein the immunoregulatory gene in turn encodes an immune checkpoint selected from B7 proteins and their receptors (i.e. receptors,which bind B7 proteins) and is selected from the group consisting of genes encoding B7 proteins and their receptor, CD274, PDCD1LG2 and PDCD1, the genes encoding the B7 protein and its receptor CD80 and CTLA4, the gene encoding the receptor of a B7 protein ICOS and the genes encoding the B7 proteins CD276, C10orf54 and HHLA2, and any combinations thereof, wherein the active ingredient is selected if the methylation of the at least one CpG dinucleotide indicates an expression of the immune checkpoint in the cells of the malignant disease and / or in the T lymphocytes.

[0105] Otherwise, the further features and preferred embodiments of the second, third and fourth aspects correspond, as applicable, to those of the first aspect.

[0106] According to the fifth aspect of the invention, a use of a kit for performing the method according to the first or second aspect is provided. The kit comprises at least one oligonucleotide pair for DNA methylation analysis, wherein the oligonucleotide pair is designed to hybridize to a sequence of the immunoregulatory gene in DNA from the cells of the malignant disease and / or from the T lymphocytes after cytosines contained in the DNA have been converted to uracil or another base with a base pairing behavior and / or molecular weight distinguishable from cytosine, in order to amplify and / or detect the sequence.

[0107] The kit may also contain one or more additional oligonucleotide pairs, each designed to hybridize to additional sequences of the same immunoregulatory gene or other immunoregulatory genes in the converted DNA in order to amplify and / or detect the additional sequences for DNA methylation analysis.

[0108] Furthermore, the kit may comprise at least one oligonucleotide pair for mRNA expression analysis, wherein the oligonucleotide pair is designed to hybridize to a sequence of the mRNA or a cDNA generated from the mRNA of at least one transcript variant of the immunoregulatory gene from the cells of the malignant disease and / or from the T lymphocytes in order to amplify and / or detect the sequence.

[0109] Preferred regions and sequences of the immunoregulatory gene to be amplified and / or detected using the oligonucleotide pairs correspond to those of the first aspect.

[0110] The kit preferably comprises instructions for carrying out the method according to the first and / or second aspect and / or for use according to the third or fourth aspect. Detailed description of implementation examples

[0111] The invention is described in more detail below using exemplary embodiments and experimental results. These exemplary embodiments serve to illustrate the invention and are not intended to limit it to specific details. Example 1: Determination of the prognosis of a patient with prostate cancer after radical resection using DNA methylation analysis of the immunoregulatory gene PDCD1

[0112] Within the scope of the method according to the invention, the prognosis of a patient with a locally confined prostate carcinoma can be determined, for example after a radical removal (radical ectomy) of the prostate and the tumor located in the prostate has been performed.

[0113] First, DNA was extracted from the tumor. Lysis of tumor tissue using proteinase K, followed by DNA extraction using silica spin columns, was suitable for this purpose. Commercially available kits, such as the QIAamp DNA Mini Kit (Qiagen NV, Hilden, Germany), are suitable for extracting DNA from tumors. The extracted DNA was then converted using bisulfite, so that essentially all cytosines were deaminated to uracil, while methylated cytosines remained unchanged. Subsequently, genome-wide DNA methylation analysis was performed using the Infinium HumanMethylation450 BeadChip (Illumina, Inc., San Diego, CA, USA), for example, according to the manufacturer's instructions. The HumanMethylation450 BeadChip raw data were generated as described by the TCGA Research Network (http: / / cancergenome.nih.gov / ).In total, raw tumor data from 417 prostate cancer patients with known prognosis were generated and retrospectively analyzed.

[0114] For the DNA methylation analysis of the PDCD1 A relative methylation was calculated using the HumanMethylation450 BeadChip raw data. For this purpose, the CpG dinucleotides in the PDCD1Genes covered by the bead pairs cg00795812, cg27051683, cg03889044, cg17322655, and cg20805133 of the Infinium HumanMethylation450 BeadChip were used. These bead pairs bind to the bisulfite-converted DNA, which had the sequences SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, and SEQ ID NO: 24 before conversion. First, a methylation value was calculated from the raw data for each of the bead pairs and for each patient sample. This was done by comparing the signal of the bead in a pair that binds to the methylated variant (S_M) with the signal of the bead in a pair that binds to the unmethylated DNA (S_U). A bead contains a bound oligonucleotide and is also referred to here as a probe. DNA methylation was calculated based on the ratio: Methylation = (Probe Intensity S_M) / ((Probe Intensity S_M) + (Probe Intensity S_U)).The methylation values ​​thus determined for each of the five loci with the sequences SEQ ID NO:20, SEQ ID NO:21, SEQ ID NO:22, SEQ ID NO:23, and SEQ ID NO:24 were then arithmetically averaged. Based on the methylation values ​​thus determined, the patients were then retrospectively divided into two groups depending on their known prognosis, resulting in a threshold value for methylation at the . PDCD1 gene locus of 57.5%. One group had methylation at PDCD1 gene locus below the threshold of 57.5% and is referred to as the low PDCD1 Methylation. Accordingly, the group which is PDCD1 gene locus showed a methylation of over 57.5%, as a group with high PDCD1 Methylation.

[0115] Figure 1shows that by applying the method according to the invention it is possible to divide the patients into two groups, of which the patients in the group with low PDCD1 Methylation have a significantly better prognosis than the patients in the group with the high PDCD1 Methylation, whereby the prognosis is defined here, for example, based on the expected time until a biochemical recurrence occurs, i.e. an increase in the blood parameter PSA, which is specific for prostate cancer. The group with low PDCD1 Methylation showed a median PSA relapse-free survival of 92 months, while the group of patients with high PDCD1 Methylation showed a 13-month shorter PSA relapse-free survival rate, averaging 79 months. The inventive application of the method thus enabled the classification of patients into different risk groups for PSA relapse.

[0116] It is therefore possible to determine the prognosis of a patient whose tumor has just been removed and whose future course of the disease is not yet known. For this purpose, the DNA methylation of the PDCD1 gene in this patient's tumor can be determined, for example, using the Infinium HumanMethylation450 BeadChip. If this methylation is below the threshold of 57.5%, then this patient can be assigned to the group of patients with a better prognosis, with an average relapse-free survival of 92 months. If the methylation of the PDCD1For example, if the expression of a gene in a patient's tumor is above the threshold, then PSA relapse is likely to occur earlier in that patient. This patient could then benefit from additional adjuvant therapy. For example, this patient could be monitored more closely during follow-up, so that any PSA relapse can be detected early and appropriate treatment measures can be initiated. Example 2: Determination of the prognosis of a patient with prostate cancer after radical resection using DNA methylation analysis of the immunoregulatory gene CD274

[0117] By carrying out the method according to the invention, it is also possible to determine the prognosis of a patient with malignant disease based on the DNA methylation analysis of CpG dinucleotides within the CD274 For example, determining the prognosis of a patient with localized prostate cancer can be done based on the methylation of the CD274gene in the tumor after the patient has been treated by radical removal (radical ectomy) of the prostate and the tumor located in the prostate.

[0118] Tumor samples from a total of 417 patients with a known prognosis were retrospectively examined. DNA methylation analysis was performed as described in Example 1. For the calculation of the relative methylation of the CD274 Gene locus based on the raw data of the Infinium HumanMethylation450 BeadChip, the five bead pairs cg15837913, cg02823866, cg14305799, cg13474877 and cg19724470 were used, which enable the CpG dinucleotide methylation analysis in the sequences SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11 and SEQ ID NO:12 in the genome. As in Example 1, analogous for the gene PDCD1As described above, a relative methylation was calculated for each patient for each of the five bead pairs by comparing the beads specific for the unmethylated and methylated states of a pair as described in Example 1. These five methylation values ​​were then averaged to yield a relative methylation value per patient sample. The patients were then divided into two groups based on the methylation value thus determined, depending on their retrospectively known prognosis, resulting in a threshold value of 52.75% for the relative methylation at CD274 gene locus. A group of 337 patients had methylation at CD274 gene locus below the threshold of 52.75% and is referred to as the low CD274 Methylation. Accordingly, the group that is CD274gene locus showed a methylation of over 52.75%, as a group with high CD274 Methylation. This group consisted of 80 patients.

[0119] Figure 2A shows that by applying the method according to the invention it is possible to divide the patients into two groups, of which the group with low CD274 Methylation has a significantly better prognosis than the group with the high CD274 Methylation. In this exemplary application, the prognosis is defined as the expected time until the occurrence of a biochemical recurrence, i.e. an increase in the blood parameter PSA, which is specific for prostate cancer. The group with low CD274 Methylation showed a median PSA relapse-free survival of 2822 days, while the group of patients with high PDCD1Methylation showed a PSA relapse-free survival rate of 1244 days, averaging 1578 days. The inventive application of the method thus enabled the classification of patients into different risk groups for PSA relapse.

[0120] It is also possible to perform DNA methylation analysis using real-time PCR to determine the prognosis of patients with malignant diseases according to the invention. In this example, 259 patients with prostate tumors and a known prognosis were retrospectively studied. This was a different patient cohort than the 417 patients described previously.

[0121] For DNA methylation analysis using real-time PCR, thin tissue sections of 10 µm thickness were prepared and mounted on glass slides. Tumor areas were identified by pathological examination based on a HE section. These tumor areas were then scraped from the glass slides using a scalpel. Bisulfite-converted DNA from the tumor areas was prepared using the innuCONVERT Bisulfite All-In-One Kit (Analytik Jena, Jena, Germany) according to the manufacturer's instructions. The amount of converted DNA was then quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). DNA methylation analysis of the prostate tumors was performed by CD274A gene locus was amplified in a methylation-specific manner using quantitative real-time PCR (qPCR) and simultaneously quantified. A duplex or multiplex PCR was used, in which both methylation and total DNA were determined within the same reaction. The determination of total DNA can be performed, for example, by using primers and probes whose target sequence does not contain CpG dinucleotides and whose target sequence is thus amplified independently of methylation. In this case, a locus in the ACTBGene was amplified for the determination of total DNA. This locus in the genome has the sequence SEQ ID NO:32 and, after bisulfite conversion, the sequence SEQ ID NO:7. This sequence was amplified using primers with the sequences SEQ ID NO:4 and SEQ ID NO:5. Sequence-specific detection of the amplification product was performed using the probe with the sequence SEQ ID NO:6, which carried the fluorescent dye Atto 647N at 5' and the quencher BHQ-2 at 3'. Methylation-specific amplification of the CD274Locus was methylated using primers with the sequences SEQ ID NO:13 and SEQ ID NO:14. These primers amplify the sequence resulting from bisulfite conversion of SEQ ID NO:33. In the case of complete methylation, this converted region in the genome has the sequence SEQ ID NO:16. Methylation-specific detection was performed using a probe with the sequence SEQ ID NO:15, which carried the fluorescent dye 6-FAM at 5' and the quencher BHQ-1 at 3'. The methylation status in the converted prostate tumor DNA was calculated using the DeltaDelta-CT method and expressed as a percentage compared to a standard DNA with 100% methylation. Artificially methylated DNA (CpGenome™ Universal Methylated DNA; Merck Millipore, Darmstadt, Germany) was used as the standard DNA; this DNA had previously been converted using the innuCONVERT Bisulfite All-In-One Kit according to the manufacturer's instructions. In this example, real-time PCR quantification of the CD274Methylation was carried out in 20 µl PCR reactions in three independent measurements each, using the following reaction composition: 35 mM Tris-HCl, pH 8.4, 6 mM MgCl 2 , 50 mM KCl, 4% glycerol, 0.25 mM each dNTP (dTTP, dATP, dGTP, dCTP), 2 U FastStart Taq DNA polymerase (Roche Applied Science, Penzberg, Germany), 0.4 µM of each primer, and 0.2 µM of each detection probe. The qPCR was performed using an AB 7500 Fast Real-Time PCR System (Life Technologies Corporation, Carlsbad, CA, USA). A suitable temperature profile included, for example, the following steps: 20 min at 95 °C followed by 45 cycles of 45 s at 56 °C and 15 s at 95 °C.

[0122] Figure 2B shows that the application of the method according to the invention is suitable for the determination of the methylation of the CD274Gene locus to determine the prognosis of patients with prostate cancer. Based on DNA methylation analysis, the 259 patients were divided into two groups with different prognosis. Patients whose tumors showed methylation above 0.975% had an average PSA relapse-free survival of 93 months, while patients whose tumors showed methylation below 0.975% had a 21-month longer average PSA relapse-free survival (114 months).

[0123] For example, it is possible to methylate the CD274Locus in the tumor sample of a patient with an unknown prognosis can be determined analogously to the real-time PCR described above. For example, if this methylation is above 0.975%, the patient may have a poor prognosis, which can average 93 months of relapse-free survival. If, however, tumor methylation is below this threshold, the patient likely has a more favorable prognosis, with an average of 114 months of relapse-free survival.

[0124] In this example, the method according to the invention was carried out using two different technologies: real-time PCR and the Infinium HumanMethylation450 BeadChip. The methylation threshold value used to classify patients into a good and a poor prognosis group was 52.75% (Infinium HumanMethylation450 BeadChip) and 0.975% (real-time PCR). It is apparent to those skilled in the art that suitable threshold values ​​for classifying patients into prognosis groups can be determined depending on the method used for DNA methylation analysis. Another possibility is to calibrate different methods against each other so that the results of the respective DNA methylation analysis can be directly compared.For example, for calibration purposes, a DNA methylation analysis of one or more standard DNA samples with a defined percentage of methylation can be performed using different methods. By relating the results of the DNA methylation analysis of the immunoregulatory genes under investigation to the results of the DNA methylation analysis of the standard DNA samples for each method, the measurement results can be calibrated, resulting in comparability of the calibrated measurement results from different methods. Example 3: Determination of the prognosis of patients with malignant melanomas using DNA methylation analysis of immunoregulatory genes PDCD1, PDCD1LG2 and CD274

[0125] This example shows the determination of the prognosis of 470 patients with malignant melanomas according to the invention. The results of the DNA methylation analysis were generated using the Infinium HumanMethylation450 BeadChip as described in Example 1. To determine the relative methylation of the respective gene, a relative methylation was first determined for each selected bead pair of the Infinium HumanMethylation450 BeadChip as described in Example 1. This relative methylation of up to six bead pairs per gene was then averaged to obtain a representative methylation value for the respective gene. For the methylation analysis of CD274The bead pairs cg15837913 (SEQ ID NO:8), cg02823866 (SEQ ID NO:9), cg14305799 (SEQ ID NO:10), cg13474877 (SEQ ID NO:11), and cg19724470 (SEQ ID NO:12) were used. These beads allow, for example, the determination of the methylation of CpG dinucleotides in the sequence regions SEQ ID NO:1, SEQ ID NO:33, and SEQ ID NO:76 of the CD274 Gens. For the methylation analysis of PDCD1 The bead pairs cg00795812 (SEQ ID NO:20), cg03889044 (SEQ ID NO:22), cg17322655 (SEQ ID NO:23), cg20805133 (SEQ ID NO:24) and cg27051683 (SEQ ID NO:369) were used. These beads allow, for example, the determination of the methylation of CpG dinucleotides in the sequence regions SEQ ID NO:3 and SEQ ID NO:29 of the PDCD1 Gens. For the methylation analysis of PDCD1LG2 The bead pair cg07211259 (SEQ ID NO:31) was used. This bead allows, for example, the determination of the methylation of CpG dinucleotides of the sequence region SEQ ID NO:84 of the PDCD1LG2 Gens.

[0126] Figure 3 shows the retrospective classification of 470 patients with malignant melanoma into prognosis groups based on DNA methylation analyses. The Kaplan-Meier survival analyses of the patients are shown, which were based on the methylation of the genes PDCD1 ( Figure 3A ), CD274 ( Figure 3B ) and PDCD1LG2 ( Figure 3C ) in the tumor were divided into groups depending on their prognosis. Group I includes patients whose tumors exhibited low methylation below a threshold at the corresponding gene locus. Group II contains those patients whose tumors exhibited increased methylation above the threshold of the respective gene. The following methylation values ​​were set as threshold values ​​for grouping the patients: 12.84% for PDCD1LG2, 13.11% for CD274 and 20.54% for PDCD1.For all three genes analyzed, DNA methylation analysis showed that patients with increased tumor methylation had a less favorable disease course. The inventive application of the method thus enabled the determination of prognosis groups of patients with malignant melanoma based on DNA methylation of the immune checkpoint gene. PDCD1 and the gene encoding the corresponding ligand CD274. It was also shown that PDCD1LG2 another gene encoding a ligand of the immune checkpoint PDCD1 could be used to determine the prognosis according to the invention.

[0127] This example demonstrates that the method according to the invention is not limited to a single malignant disease, prostate cancer, as in Examples 1 and 2, but that it is also possible to determine the prognosis for other malignant diseases, such as malignant melanoma. Therefore, determining the prognosis based on DNA methylation analysis of these genes is not limited to individual entities but can be applied to other malignancies. Thus, the present invention solves the problem of the limited applicability of conventional prognosis methods to individual entities.

[0128] Furthermore, this example demonstrates that the inventive implementation of the method makes it possible to determine the prognosis for various future health conditions. In Examples 1 and 2, the occurrence of a PSA relapse was correctly predicted. A PSA relapse is a characteristic limited to prostate tumors. In the present Example 3, however, the onset of the patient's death was predicted. Example 4: Determination of the prognosis of patients with malignant melanomas using DNA methylation analysis of immunoregulatory genes TIGIT, TNFRSF9, LAG3, BTLA, CTLA4 and CD40

[0129] The method according to the invention also enables the estimation of the prognosis of patients with malignant melanomas based on the methylation of the immune checkpoint genes TIGIT, TNFRSF9, LAG3, BTLA, CTLA4 and CD40.In the present example, the results of the DNA methylation analysis for the determination of the relative methylation of the genes were generated using the Infinium HumanMethylation450 BeadChip (Illumina, Inc., San Diego, CA, USA) as described in Example 1. Subsequently, for the genes TIGIT, TNFRSF9, LAG3, BTLA, CTLA4 and CD40 A relative methylation was determined for each bead. For this purpose, a ratio of the beads specific for the methylated and the beads specific for the unmethylated state was calculated as described in Example 1. For the gene BTLAThe bead pairs cg24157392 and cg19281794 were used. These beads bind to the human DNA sequence that had the sequence with SEQ ID NO:37 or SEQ ID NO:37 before bisulfite conversion, respectively, and thus enable, for example, the determination of methylation for the sequence region SEQ ID NO:276. Both bead pairs consist of a pair of beads, one of which binds to the sequence that was unmethylated before bisulfite conversion and one of which binds to the sequence that was methylated before the conversion. The ratio was calculated from these two bead pairs, as shown below for the bead pair cg24157392: Methylation = (Intensity cg24157392_M) / ((Intensity cg24157392_M) + (Intensity cg24157392_U)), where cg24157392_M is the bead that binds to the methylated variant and cg24157392_U is the bead that binds to the unmethylated variant. The same procedure was applied for the bead pair cg19281794. Subsequently, the relative methylation value for BTLA calculated by arithmetically averaging the relative methylation of the bead pairs cg24157392 and cg19281794.

[0130] Accordingly, the relative methylation of the genes TIGIT, TNFRSF9, LAG3, BTLA, CTLA4 and CD40 For the determination of the relative methylation of TNFRSF9 The bead pairs cg14614416 (SEQ ID NO:51), cg14153654 (SEQ ID NO:54), cg17123655 (SEQ ID NO:55), cg18025409 (SEQ ID NO:57), cg06956444 (SEQ ID NO:58) and cg08840010 (SEQ ID NO:59) were used, which allow, for example, the determination of the methylation of the sequence regions SEQ ID NO:308, SEQ ID NO:309 and SEQ ID NO:321 in the genome. When determining the methylation of CD40 The bead pairs cg19785066 (SEQ ID NO:47), cg09053081 (SEQ ID NO:48) and cg21601405 (SEQ ID NO:49) were used, which are suitable, for example, for determining the methylation of the sequence regions SEQ ID NO:297 and SEQ ID NO:298. For the determination of the methylation of TIGITThe bead pairs cg02771886 (SEQ ID NO:61), cg08723913 (SEQ ID NO:62), cg17164827 (SEQ ID NO:63), cg04885775 (SEQ ID NO:64), cg23637607 (SEQ ID NO:65) and cg16358924 (SEQ ID NO:66) were used, which are suitable for determining the methylation of the sequence region SEQ ID NO:267. For the determination of the methylation of the sequence region SEQ ID NO:162 of the CTLA4 The bead pair cg08460026 (SEQ ID NO:39) was used to determine the relative methylation of LAG3 The bead pairs cg04153135 (SEQ ID NO:40), cg20652042 (SEQ ID NO:41) and cg01820374 (SEQ ID NO:42) were used, which are suitable for determining methylation in the sequence regions SEQ ID NO:161, SEQ ID NO:349 and SEQ ID NO:351.

[0131] Figure 4shows the retrospective classification of 470 patients with malignant melanoma into prognosis groups based on the DNA methylation analyses of the invention. The Kaplan-Meier survival analyses of the patients are shown, which were based on the methylation of the genes TIGIT ( Figure 4A ), TNFRSF9 ( Figure 4B ), LAG3 ( Figure 4C ), BTLA ( Figure 4D ), CTLA4 ( Figure 4E ) and CD40 ( Figure 4F ) in the tumor were divided into groups. Group I includes patients whose tumors showed low methylation below a threshold at the corresponding gene locus. Group II contains those patients whose tumors showed increased methylation above the threshold of the respective gene. The following methylation values ​​were set as threshold values ​​for grouping the patients: 86.41% for TIGIT, 57.80% for TNFRSF9, 66.94% for LAG3, 91.31% for BTLA, 13.10% for CTLA4 and 17.16% for CD40. For all immunoregulatory genes examined in this example, it was shown that patients whose tumors showed increased methylation above the threshold showed a significantly worse disease course than patients whose tumors showed methylation below the threshold. Example 5: Determination of the prognosis of patients with squamous cell carcinoma of the head and neck using DNA methylation analysis of the immunoregulatory Gens PDCD1

[0132] Figure 5 shows the retrospective classification of 528 patients with squamous cell carcinoma of the head and neck region into prognosis groups using the method of the invention. Death is defined as the clinical endpoint. DNA methylation analysis of the immune checkpoint gene PDCD1 was measured in DNA from tumor tissue. Patients with high PDCD1 Methylation show a worse course than the patients with low methylation of the PDCD1Gene locus. Patients were grouped by dichotomization using the median as the threshold. This means that half (n = 264 patients) of the patient samples had a methylation level above the threshold and the other half (n = 264 patients) had a methylation level below the threshold.

[0133] It was also possible to perform DNA methylation analysis of PDCD1by real-time PCR. Suitable conditions and compositions for the real-time PCR correspond to those of Example 2. The following oligonucleotides were used for the PCR: SEQ ID NO: 25 (forward primer), SEQ ID NO: 26 (reverse primer), and SEQ ID NO: 27 detection probe. The detection probe was labeled with FAM and BHQ-1. The primers used enable the amplification of the bisulfite-converted DNA with the sequence SEQ ID NO: 368. This sequence had the sequence SEQ ID NO: 367 before the bisulfite conversion. Tumor samples from a total of 120 patients with squamous cell carcinoma of the head and neck region and a known prognosis were retrospectively examined. Based on DNA methylation analysis and depending on their prognosis, the patients could be divided into a group of 40 patients with low methylation and a group of 80 patients with high methylation.The group of patients with high methylation showed a significantly worse overall survival compared to the group with low methylation (p-value < 0.05).

[0134] In Example 1 it was shown that the method according to the invention enables the determination of the prognosis in patients with prostate cancer based on the methylation of the gene PDCD1These prostate carcinomas were carcinomas that originated from the glandular epithelium of the prostate. These carcinomas therefore belong to the adenocarcinomas. Example 3 also demonstrated that the method according to the invention enables prognosis in patients with malignant melanomas. Melanomas are not carcinomas because they arise from melanocytes, not epithelial cells. Melanomas therefore represent a fundamentally different malignant disease than carcinomas. Example 5 additionally demonstrated that the prognosis determination according to the invention is also possible in patients with squamous cell carcinomas of the head and neck region. These squamous cell carcinomas, like adenocarcinomas, belong to the carcinomas, meaning they arise from epithelial cells. Unlike adenocarcinomas, squamous cell carcinomas do not arise from the glandular epithelia, but from the squamous epithelium.It was thus demonstrated that the method according to the invention is capable of determining the prognosis not only for carcinomas of various origins, but also for malignant diseases that are not carcinomas. These results further demonstrate the universal applicability of the method according to the invention for prognosis and / or prediction in a wide variety of malignant diseases. Example 6: Determination of mRNA expression of Immune- Checkpoint genes based on DNA methylation analysis

[0135] The inventive embodiment of the method also enables the determination of the expression of the immune checkpoints encoded by the immunoregulatory genes, as described below based on the mRNA expression of the genes BTLA and CD27 is shown as an example.

[0136] The mRNA can be determined, for example, after mRNA extraction, preparation of a cDNA library of total RNA, and ultimately a next-generation sequencing-based analysis. For example, the RNA can be extracted from the tumor tissue using the RNeasy Mini Kit (Qiagen, Hilden) according to the manufacturer's instructions. A library can then be prepared, for example, according to the manufacturer's instructions using the TruSeq RNA Library Preparation Kit v2 (Illumina) and analyzed using the HiSeq PE (Paired-End) Cluster Kit v4 cBot (Illumina) on a HiSeq 2500 next-generation sequencer (Illumina). The mRNA measurement, or the determination of the number of mRNA molecules of a specific sequence in relation to the number of total mRNA molecules, was carried out as described by the TCGA Research Network (http: / / cancergenome.nih.gov / ).

[0137] Figure 6shows the correlation of DNA methylation in the tissue of malignant melanomas from 470 patients with the mRNA expression for BTLA ( Figure 6A ) and CD27 ( Figure 6C ). It is evident that all tumors that showed a methylation of the respective loci of less than 50% had an mRNA expression of more than 10 ( BTLA, Figure 6A ) or over 100 ( CD27, Figure 6C ) showed. Figure 6B shows that melanoma patients whose tumors have BTLA mRNA expression of >10 had a significantly better prognosis than patients whose tumors had lower mRNA expression. Analogous to BTLA is in Figure 6D shown that melanoma patients whose tumors have a CD27 mRNA expression of >100 had a significantly better prognosis than patients whose tumors had lower mRNA expression.

[0138] The expression of genes BTLA and CD27can not only be used to determine the patient's prognosis, but is also indicative of the likely response to therapy, for example if the therapy is directed against the corresponding gene product. This example shows that the methylation of the gene loci in the tumor can be used to determine mRNA expression and can be used to determine the patient's prognosis. This solves the problem that direct determination of the mRNA itself is often only possible to a limited extent in clinical routine due to the low stability of mRNA. Furthermore, this example shows that by determining the patient's prognosis based on DNA methylation analysis, it is also possible to simultaneously estimate the expression of the analyzed immunoregulatory gene.This allows the patient to qualify for a therapy that is likely to be effective or unlikely to be effective depending on the expression of the gene product, i.e., the immune checkpoint. Therapies that directly target the immune checkpoint or its interaction partners are particularly suitable for this purpose. Immunotherapies with monoclonal antibodies directed against these gene products are particularly suitable for this purpose. Example 7: Determination of the prognosis of a patient with malignant melanoma using combined DNA methylation analysis and mRNA expression analysis

[0139] A DNA methylation analysis and an mRNA expression analysis of the CD274 gene in tumors from 470 patients with malignant melanomas and a known prognosis. Determination of mRNA expression and DNA methylation was performed as described in Examples 1, 3, and 6.

[0140] Figure 7A shows a scattergram of the determined mRNA expression and DNA methylation of the CD274Gene with marked threshold values, based on which the retrospective classification into prognostic groups with high and low mRNA expression or DNA methylation was carried out. Figure 7A shows the significant negative correlation (Spearman's ρ = -0.546, p < 0.001) between DNA methylation and mRNA expression. Based on the introduced threshold for methylation at 13.11%, the patients can be divided into a group with higher (group II+III) and a group with lower DNA methylation (group I+IV). Analogously, the Figure 7A The introduction of a vertical threshold for mRNA expression at 18.23, as shown, resulted in the formation of two groups with high (group III+IV) and low (group I+II) mRNA expression. Figure 7B shows a Kaplan-Meier analysis of the overall survival of patients with high CD274Tumor methylation (groups II and III) compared to patients with low methylation (groups I and IV). Patients in groups II and III have a poorer prognosis compared to patients in groups I and IV. Figure 7C illustrates the Kaplan-Meier analysis of overall survival of patients with high CD274 mRNA expression in the tumor (groups III and IV) compared to patients with low methylation (groups I and II). Patients whose tumors showed increased mRNA expression of the gene CD274 in the tumor (group III+IV) have a more favorable clinical course compared to the patients from group I+II, whose tumors show a lower mRNA expression of CD274 had. Figure 7D shows the Kaplan-Meier analysis of overall survival of patients with high CD274mRNA expression and low methylation in the tumor (Group IV) compared to patients with high methylation and low mRNA expression (Group II), as well as patients with high methylation and high mRNA expression and patients with low methylation and low mRNA expression (Groups I+III). It is evident that patients who are assigned to the group with a good prognosis based on both mRNA expression analysis and DNA methylation analysis (Group IV: high mRNA expression and low methylation) have a significantly more favorable course compared to patients whose tumors are assigned to the group with a poor prognosis based on mRNA expression analysis and DNA methylation analysis (Group II: high methylation and low expression). All other patients (Groups I+III) show a prognosis that lies between that of Groups II and IV ( Figure 7D ).

[0141] Figure 7describes in detail the method according to the invention for combining mRNA expression analysis and DNA methylation analysis for improved determination of the prognosis of a patient with malignant melanoma using the example of the gene CD274. Applying this method to other genes also allows for an improved prognosis determination. Compared to CD274 ( Figure 8A and Figure 7D ) this could also be observed for the genes PDCD1LG2 ( Figure 8B ), PDCD1 ( Figure 8C ), BTLA ( Figure 8D ), LAG3 ( Figure 8E ), TIGIT ( Figure 8F ), CD40 ( Figure 8G ), CTLA4 ( Figure 8H ) and TNFRSF9 ( Figure 8I ) will be shown. Figure 8shows the retrospective classification of 470 patients with malignant melanomas and a known prognosis based on the combined analysis of mRNA expression and DNA methylation of various immunoregulatory genes of the present invention. The grouping of patients was analogous to Figure 7 according to individually adjusted thresholds for each gene. The disease progression of patients with high DNA methylation and low mRNA expression in the tumor (Group II), patients with low DNA methylation and high mRNA expression in the tumor (Group IV), and the remaining patients (Groups I and III) were compared. For all genes examined, it was shown that Group II had an unfavorable disease progression and Group IV a favorable disease progression, while the remaining patients (Groups I and III) had a moderate risk of death. Group II thus represents a high-risk group and Group IV a low-risk group. Example 8: Determination of the prognosis of a patient with malignant melanoma using combined DNA methylation analysis and mRNA expression analysis of immunoregulatory genes PDCD1LG2, CD274, PDCD1, BTLA, LAG3, TTGTT, CD40, CTLA4 and TNFRSF9

[0142] In Examples 3 and 4, the determination of the prognosis of patients with malignant melanomas according to the invention was demonstrated using DNA methylation analysis of a total of nine different immune checkpoint genes ( PDCD1LG2, CD274, PDCD1, BTLA, LAG3, TIGIT, CD40, CTLA4 and TNFRSF9). The determination of the prognosis of patients with malignant disease can be further improved by combining DNA methylation analyses and mRNA expression analyses of various genes. The determination of mRNA expression and DNA methylation was carried out as described in Examples 1 and 6. A quantitative methylation value was calculated for each gene as described in Examples 3 and 4. The patients were initially divided into prognosis groups based on the mRNA expression of the nine genes. Group I is the group to which a favorable prognosis could be assigned based on the mRNA expression of the nine genes. Group I includes patients whose tumor showed increased mRNA expression above the threshold of at least eight of the nine genes. Group II is the group to which an unfavorable prognosis could be assigned based on the mRNA expression of the nine genes.Group II includes those patients whose tumors showed mRNA expression above the threshold for fewer than eight of the nine genes analyzed. The patients were then classified based on the DNA methylation of the nine genes. Group III is the group to which a favorable prognosis could be assigned based on the DNA methylation of the nine genes. Group III includes patients whose tumors show increased DNA methylation above the threshold for a maximum of four of the nine genes. Group IV is the group to which an unfavorable prognosis could be assigned based on the DNA methylation of the nine genes. Group IV includes those patients whose tumors showed methylation above the threshold for at least five of the nine genes analyzed. Finally, the patients were classified based on DNA methylation and mRNA expression of the nine genes.Group V included patients who could be assigned to the groups with a favorable prognosis based on both mRNA expression and DNA methylation. Group V included patients who were in both Group I and Group III. Group VI included patients who were in the groups with an unfavorable prognosis based on both mRNA expression and DNA methylation. Group VI included patients who were in both Group II and Group IV. Group VII included patients who were in the group with a favorable prognosis based on either mRNA expression or DNA methylation. Group VII included patients who were in both Group I and Group IV or both Group II and Group III. Figure 9Bshows that those patients who were assigned to a poorer prognosis group based on DNA methylation of a maximum of four of the nine genes analyzed, i.e., those with methylation levels above the threshold for the respective gene, had a very favorable prognosis overall (Group III). Patients who were assigned to a poorer prognosis group based on methylation analysis for at least five of the nine genes (Group IV), in contrast, also had a significantly poorer prognosis overall. Figure 9Ashows that patients who, based on mRNA expression analysis, could be assigned to the group with the better prognosis for at least eight of the nine genes (Group I), had a significantly better overall prognosis than patients who, based on mRNA expression analysis, could be assigned to the group with the good prognosis for fewer than eight of the nine genes (Group II). The following methylation values, for example, were set as threshold values ​​for grouping patients based on DNA methylation analysis: 12.84% for PDCD1LG2, 13.11% for CD274, 20.54% for PDCD1, 86.41% for TIGIT, 57.80% for TNFRSF9, 66.94% for LAG3, 91.31% for BTLA, 13.10% for CTLA4 and 17.16% for CD40. For example, the threshold values ​​for grouping patients based on mRNA expression analysis were set as follows: 5.61 for PDCD1LG2, 18.23 for CD274, 7.29 for PDCD1, 12.38 for TIGIT, 0.935 for TNFRSF9, 9.65 for LAG3, 3.33 for BTLA, 13.12 for CTLA4 and 30.14 for CD40.

[0143] In Example 7 ( Figure 8 ) It has already been shown that the inventive combination of DNA methylation analysis and mRNA expression analysis enables an even more differentiated determination of the prognosis of patients with malignant disease than determining the prognosis based on either DNA methylation analysis or mRNA expression analysis alone. This advantageous effect of the invention can therefore be further enhanced, as shown here, if the combination of DNA methylation analysis and mRNA expression analysis includes multiple immunoregulatory genes. Figure 9C shows that patients whose tumors are classified as having a good prognosis based on both DNA methylation analysis and mRNA expression analysis of the nine genes examined here ( Figure 9B , Group III and Figure 9A , Group I), have an overall very good prognosis ( Figure 9C , Group V). Patients who were in the poor prognosis group based on both DNA methylation analysis and mRNA expression analysis of the nine genes examined here ( Figure 9 C , Group VI) had an unfavorable prognosis. All other patients ( Figure 9C , Group VII) showed a course that was more favorable than that of Group VI and less favorable than that of Group V. Example 9: Determination of the prognosis of a patient with malignant melanoma based on promoter methylation and intragenic methylation using immunoregulatory genes as an example CD40 and LAG3

[0144] It is a widespread assumption that the DNA methylation of a gene correlates inversely with mRNA expression. This means that high methylation of the gene is associated with low mRNA expression. In this exemplary embodiment of the invention, it was shown that a significant positive correlation can also occur in intragenic regions. This was demonstrated using the example of the genes LAG3 and CD40 The determination of mRNA expression and DNA methylation was performed as described in Examples 1 and 6. The calculation of the relative methylation of the promoters of the two genes LAG3 and CD40was carried out as described in Example 4. For the determination of the intragenic regions, the bead pairs listed below were used and compared to each other as described in Example 1 to obtain a relative methylation value for each gene locus. For the intragenic DNA methylation analysis of LAG3 The bead pairs cg11429292 (SEQ ID NO:43) and cg14292870 (SEQ ID NO:44) were used, which enable, for example, DNA methylation analysis in the regions SEQ ID NO:161 and SEQ ID NO:350. For the intragenic DNA methylation analysis of CD40 The bead pair cg06218285 (SEQ ID NO:50) was used, which enables, for example, DNA methylation analysis in the region SEQ ID NO:297. Figure 10shows the results of the inventive prognosis determination of 470 patients with malignant melanomas. Patients were classified retrospectively based on the analysis of methylation of the promoter and intragenic regions, each individually and in combination. Figure 10A and Figure 10C show that the relative methylation of CpG dinucleotides located in the promoter region is negatively correlated with the mRNA expression of the respective gene. Furthermore, it was shown that the relative methylation of the intragenic regions of these genes, in contrast to the promoter regions, is positively and significantly correlated with mRNA expression ( Figure 10B and Figure 10D). Figure 10E and 10H show the results already presented in Example 4 for the DNA methylation analysis of the genes LAG3 and CD40.

[0145] Those patients with increased DNA methylation (Group II) in LAG3 and CD40 Promoter show an unfavorable course ( Figures 10E and 10H). These relationships are surprisingly reversed in the intragenic region. Group III in Figure 10F includes those patients whose tumors have intragenic methylation of the LAG3 gene over 20.67%. Group IV includes those patients whose tumors showed methylation below 20.67%. Those patients with reduced intragenic methylation of the LAG3 The Kaplan-Meier survival analysis of the patient outcome depending on the combined intragenic and promoter methylation of the LAG3 Gens showed improved prognosis determination ( Figure 10G). Group V includes those patients who are in both group II and group IV. Group VII includes those patients who are in both group I and group III. Group VI includes those patients who are in either group II and III or I and IV. The patients who are classified according to both intragenic methylation and promoter methylation of the LAG3 gene showed an unfavorable prognosis, formed a group with a particularly unfavorable course ( Figure 10G , Group V) .

[0146] The Kaplan-Meier survival analysis of the course of the patients depending on the promoter and intragenic methylation of the CD40 Gens and the combined analysis of both analyses ( Figure 10H, 10I, 10J ) led to comparable results to those obtained with LAG3 were achieved. Group I ( Figure 10H) includes those patients whose tumors showed methylation below 17.16%. Group II includes those patients whose tumors showed methylation above 17.16% ( Figure 10H ). Those patients with increased methylation in CD40 promoter showed an unfavorable course. Group III ( Figure 10I ) includes those patients whose tumors showed methylation above 35%. Group IV ( Figure 10I ) includes those patients whose tumors showed methylation below 35%. Those patients with reduced intragenic methylation of the CD40 Gens showed an unfavorable course. Group V ( Figure 10J ) includes those patients who are in both Group II and Group IV. Group VII ( Figure 10J) includes those patients who are in both group I and group III. Group VI includes those patients who are in either groups II and III or I and IV. It has been shown that the patients who were in both the DNA methylation analysis of the CD40 promoter and the intragenic region in the groups with a good prognosis now formed a group with a particularly good prognosis. Thus, the combined DNA methylation analysis of the promoter and the intragenic region of the CD40 Gens, the determination of the prognosis can be improved compared to the individual analyses in the sense of a more differentiated risk assessment. Example 10: Determination of the prognosis of patients with acute myeloid leukemia using DNA methylation analysis and mRNA expression analysis of CD274, PDCD1 and PDCD1LG2

[0147] The method of the invention also enables the determination of the prognosis of patients with malignant hematological diseases. The mRNA expression analysis and DNA methylation analysis of malignant cells from 182 patients with acute myeloid leukemia were performed as described in Examples 1 and 6. The calculation of the relative DNA methylation of the genes CD274, PDCD1 and PDCD1LG2 based on the results of the DNA methylation analysis was carried out as described in Example 3.

[0148] Figure 11 Kaplan-Meier analyses the overall survival of 182 patients with acute myeloid leukemia. Surprisingly, the patients with gene methylation CD274, PDCD1 and PDCD1LG2 below the threshold (Group I, Figures 11A, 11D, 11G) an unfavorable prognosis, whereas in the previous examples, patients with increased methylation of these immunoregulatory genes had a more unfavorable prognosis. It is therefore a surprising finding that the method according to the invention also enables the determination of the prognosis of patients with malignant hematological diseases. The analysis of the mRNA expression of the genes CD274, PDCD1 and PDCD1LG2 ( Figures 11B, 11E, 11H ) also made it possible to determine the patients' prognosis. It is also surprising that, in the analysis of mRNA expression, those patients with reduced expression (Group IV) had a more favorable prognosis, in contrast to the previously described results from Examples 7 and 8. Figures 11C, 11F and 11Ishows that the combined analysis of DNA methylation and mRNA expression leads to a more differentiated risk assessment compared to the respective individual analyses of mRNA expression or DNA methylation. Example 11: Determination of the prognosis of patients with acute myeloid leukemia using DNA methylation analysis of TNFRSF9, TIGIT, BTLA, HAVCR2, CD80, CTLA4, ICOS, C10orf54, HHLA2, CD160, KIR2DL4 and KIR3DL1

[0149] The method according to the invention enables the determination of the prognosis of patients with malignant hematological diseases, also using other immunoregulatory genes. For DNA methylation analysis, the data collected in Example 10 were used. The determination of the relative methylation of TNFRSF9, TIGIT, BTLA and CTLA4 based on the data for the bead pairs was carried out as described in Example 4. For the determination of the relative methylation of the genes HAVCR2, CD80, ICOS, C10orf54, HHLA2, CD160, KIR2DL4 and KIR3DL1 The bead pairs listed below were used to calculate the relative methylation as described in Example 1. For HAVCR2The bead pair cg09574807 (SEQ ID NO:46) was used, which enabled the determination of the methylation of the gene region SEQ ID NO:286. The determination of the methylation of CD80 was carried out using the bead pairs cg12978275 (SEQ ID NO:126) and cg13458803 (SEQ ID NO:127), which enabled the methylation analysis of the sequence region SEQ ID NO:163. The DNA methylation analysis of the gene ICOS was based on the bead pairs cg18561976 (SEQ ID NO:112) and cg15344028 (SEQ ID NO:113), which allow the methylation analysis of the sequence regions SEQ ID NO:92, SEQ ID NO:93 and SEQ ID NO:95. The DNA methylation analysis of HHLA2was performed using the four bead pairs cg00915092 (SEQ ID NO:122), cg14703454 (SEQ ID NO:123), cg11326415 (SEQ ID NO:124), and cg22926869 (SEQ ID NO:125), which allow the analysis of the sequence regions SEQ ID NO:36, SEQ ID NO:195, and SEQ ID NO:196. For the DNA methylation analysis of the sequence regions SEQ ID NO:190 and SEQ ID NO:191 of the gene C10orf54 The bead pair ch.10.1529706R (SEQ ID NO:35) was used. DNA methylation analysis of the sequence regions SEQ ID NO:361, SEQ ID NO:362 and SEQ ID NO:366 in CD160 The gene was analyzed using the bead pairs cg12832565 (SEQ ID NO:150), cg10798745 (SEQ ID NO:151), and cg15892497 (SEQ ID NO:152). For DNA methylation analysis of the sequence regions SEQ ID NO:217 and SEQ ID NO:218 of the gene, KIR2DL4 The bead pair cg08326410 (SEQ ID NO:143) was used. The DNA methylation analysis of KIR3DL1was performed using the bead pairs cg15588997 (SEQ ID NO:137), cg08129658 (SEQ ID NO:138), cg02469067 (SEQ ID NO:139), cg19689800 (SEQ ID NO:140), cg06494497 (SEQ ID NO:141) and cg05720980 (SEQ ID NO:142), which enable the methylation analysis of the sequence regions SEQ ID NO:230, SEQ ID NO:231 and SEQ ID NO:232.

[0150] Figure 12 Kaplan-Meier analysis of overall survival of 182 patients with acute myeloid leukemia. Patients were stratified based on DNA methylation analysis of immunoregulatory genes TNFRSF9 ( Figure 12A ), TIGIT ( Figure 12B ), BTLA ( Figure 12C ), HAVCR2 ( Figure 12D ), CD80 ( Figure 12E ), CTLA4 ( Figure 12F ), ICOS ( Figure 12G ), C10orf54 ( Figure 12H ), HHLA2 ( Figure 12I ), CD160 ( Figure 12J ), KIR2DL4 ( Figure 12K ) and KIR3DL1 ( Figure 12L). Dichotomization was performed using optimized thresholds. The threshold was optimized to ensure optimal separation—i.e., the lowest possible p-value of the log-rank test—of patients with good and poor prognosis. For all genes analyzed, it was shown that patients whose malignant disease exhibited methylation below the threshold (Group I) exhibited an unfavorable course compared to patients with methylation above the threshold. Example 12: Determination of the prognosis of patients with low-grade gliomas using DNA methylation analysis and mRNA expression analysis of CD274, PDCD1 and PDCD1LG2

[0151] The method according to the invention also enables the determination of the prognosis in patients with gliomas. For example, DNA methylation analysis and mRNA expression analysis of the immunoregulatory genes CD274, PDCD1 and PDCD1LG2in 510 patients with low-grade gliomas. DNA and RNA preparation, mRNA expression analysis, and DNA methylation analysis, as well as their evaluation, were performed as described in Examples 1 and 6. The calculation of the relative methylation of the genes CD274, PDCD1 and PDCD1LG2 based on the data generated using the Illumina HumanMethylation450 BeadChip was carried out as described in Example 3.

[0152] As already shown in Example 11 for malignant hematological diseases, the prognosis in patients with gliomas can also be determined using DNA methylation analysis, mRNA expression analysis, and the combination of both analyses. Figure 13shows that those patients who had methylation of the immunoregulatory genes in the glioma cells below the threshold (Group I) had a significantly worse prognosis than patients whose tumors had methylation above the threshold (Group II). This could be seen for the three genes CD274 ( Figure 13A ), PDCD1 ( Figure 13D ) and PDCD1LG2 ( Figure 13G) were equally shown. When analyzing the corresponding mRNA expression of the immunoregulatory genes, patients with a poor prognosis could also be identified based on increased gene expression (Group III). The inventive combination of mRNA expression analysis and DNA methylation analysis proved to be particularly advantageous, enabling the formation of three prognosis groups, which had a high risk (Group I+III), a medium risk (Group I+IV / II+III), and a low risk (Group II+IV) for death. As already shown for malignant hematological diseases in Examples 10 and 11, it is also a surprising finding in gliomas that, in contrast to the previous analyses of malignant diseases, reduced mRNA expression and increased DNA methylation were associated with a good prognosis.This underlines the power of the present invention in determining the prognosis in patients with a wide variety of malignant diseases. Example 13: Determination of the prognosis of patients with low-grade gliomas using DNA methylation analysis and mRNA expression analysis of CD80, CTLA4, ICOS and CD276

[0153] The inventive determination of the prognosis of patients with gliomas is also based on the DNA methylation and mRNA expression of the immunoregulatory genes CD80, CTLA4, ICOS and CD276 possible. The present analysis is based on the methylation and expression data collected as described in Example 12. The determination of the relative DNA methylation of the genes CD80, CTLA4 and ICOS was carried out as described in Examples 11 and 4. For the determination of the relative DNA methylation of the CD276The bead pairs cg24688248 (SEQ ID NO:119), cg14910296 (SEQ ID NO:120), and cg12524179 (SEQ ID NO:121) were used and computationally evaluated as described in Example 1. These bead pairs allow the determination of DNA methylation for the sequence regions SEQ ID NO:181, SEQ ID NO:182, and SEQ ID NO:183.

[0154] Figure 14 shows the Kaplan-Meier analysis of overall survival of the 510 patients with low-grade gliomas analyzed. The patients were stratified based on DNA methylation analysis ( Figure 14A, 14D, 14G, 14J ), mRNA expression analysis ( Figure 14B, 14E, 14H, 14K ) and the combined DNA methylation and mRNA expression analysis ( Figure 14C, 14F, 14I, 14L ) of the immunoregulatory genes CD80 ( Figure 14 A , B, C), CTLA4 ( Figure 14 D , E, F), ICOS ( Figure 14G, 14H, 14I ), CD276 (14J, 14K, 14L). The following thresholds were chosen for dichotomization based on DNA methylation: 90.76% (CD80), 86.43% (CTLA4), 84.75% (ICOS) and 30.08% ( CD276 ). For the dichotomization of mRNA expression, the median was used as the threshold. Group I includes patients with methylation below the threshold, Group II consists of patients with methylation above the threshold. Group III includes patients whose tumors showed mRNA expression above the median, while patients from Group IV showed mRNA expression in the tumor below the median. As already described in Example 12 and Figure 13 for CD274, PDCD1 and PDCD1LG2 was also shown by DNA methylation and mRNA expression of the genes CD80, CTLA4, ICOS and CD276and the combination of both analyses to determine the patient's prognosis. In this case, too, it was a surprising finding that high DNA methylation and low mRNA expression of the immunoregulatory genes of the invention were associated with a more favorable disease course. Example 14: Determination of the prognosis of patients with low-grade gliomas using DNA methylation analysis from TNFRSF25, TNFRSF9, CD40, TIGIT, BTLA, HAVCR2, C10orf54, HHLA2, LAG3, CD160, KIR2DL4 and KIR3DL1

[0155] It is also possible to determine the prognosis of patients with low-grade gliomas based on DNA methylation analysis of other immunoregulatory genes. Figure 15 The results presented are based on an analysis of the DNA methylation data generated as described in Example 12. The methods used to determine the relative DNA methylation of the genes TNFRSF9, CD40, TIGIT, BTLA, HAVCR2, C10orf54, HHLA2, LAG3, CD160, KIR2DL4 and KIR3DL1 used bead pairs and the calculation of DNA methylation is described in Examples 1, 4 and 11. For the determination of DNA methylation of the gene TNFRSF25For example, the bead pairs cg27224823 (SEQ ID NO:153), cg13331246 (SEQ ID NO:154), cg23588699 (SEQ ID NO:155) and cg10982045 (SEQ ID NO:156) used in this example can be used, which enable the DNA methylation analysis of the sequence regions SEQ ID NO:317, SEQ ID NO:318 and SEQ ID NO:319. The bead pairs cg00087884 (SEQ ID NO:157), cg10059687 (SEQ ID NO:158) and cg11756870 (SEQ ID NO:159) can also be used for DNA methylation analysis of the sequence regions SEQ ID NO:317, SEQ ID NO:320 and SEQ ID NO:321.

[0156] Figure 15 shows the Kaplan-Meier analysis of the overall survival of 510 patients with low-grade gliomas, which were diagnosed using DNA methylation analysis of the genes TNFRSF25 (A), TNFRSF9 (B), CD40 (C), TIGIT (D), BTLA (E), HAVCR2 (F), C10orf54 (G), HHLA2 (H), LAG3 (I), CD160 (J), KIR2DL4 (K) and KIR3DL1(L) were retrospectively stratified. Patients were stratified based on dichotomized relative gene methylation, using the following thresholds: 61.88% (TNFRSF25), 79.24% (TNFRSF9), 43.24% (CD40), 80.66% (TIGIT), 84.64% ( BTLA ), 2.495% (HAVCR2), 66.52% (C10orf54), 77.14% (HHLA2), 74.34% (LAG3), 92.89% (CD160), 90.85% (KIR2DL4) and 52.28% (KIR3DL1). Group I includes patients with DNA methylation below the threshold; Group II represents those patients whose tumors showed DNA methylation above the threshold. For all immunoregulatory genes, low DNA methylation has been shown to be associated with a poor prognosis. Example 15: Determination of the prognosis of patients with clear cell renal cell carcinoma using DNA methylation analysis of CD274, TNFRSF9, TIGIT, CD80, CTLA4, CD276 and HHLA2

[0157] In the preceding embodiments, it has already been demonstrated that the method according to the invention enables the determination of the prognosis of patients with various malignant diseases, for example, adenocarcinomas, squamous cell carcinomas, melanomas, leukemias, and gliomas. In this example, the method according to the invention was applied to patients with clear cell renal cell carcinomas. The DNA methylation data were generated as described in Example 1. The calculation of the relative DNA methylation based on the data from the bead pairs for the genes CD274, TNFRSF9, TIGIT, CD80, CTLA4, CD276 and HHLA2 was carried out as described in Examples 3, 4, 11 and 13.

[0158] Figure 16 shows the Kaplan-Meier analyses of the overall survival of 318 patients with clear cell renal cell carcinoma, which were assessed based on the DNA methylation of the immunoregulatory genes TIGIT (A), TNFRSF9 (B), CD274 (C), CD80 (D), CTLA4 (E), CD276(F) and HHLA2 (G) were retrospectively stratified. The relative DNA methylation values ​​were dichotomized for each gene based on the respective median DNA methylation of all patients. Group I includes patients whose tumors had DNA methylation below the median, while Group II includes those patients whose tumors had DNA methylation above the median. Figure 16 shows that in six of the seven genes analyzed, increased DNA methylation (Group I) significantly correlated with a worse prognosis. For the gene HHLA2 Surprisingly, it was shown that the prognosis of patients with clear cell renal cell carcinoma is unfavorable if the immunoregulatory gene had methylation below the threshold value ( Figure 16G). The results demonstrate that the method according to the invention is applicable to other carcinomas besides the adenocarcinomas and squamous cell carcinomas described in the preceding examples. Example 16: Determination of the prognosis of patients with clear cell renal cell carcinoma using DNA methylation analysis in combination with mRNA expression analysis of TNFRSF9, TIGIT, CD80, CTLA4, CD276 and HHLA2

[0159] The universal applicability of the method according to the invention in a wide variety of malignant diseases was further demonstrated in a combined analysis of mRNA expression and DNA methylation for the genes TNFRSF9, TIGIT, CD80, CTLA4, CD276 and HHLA2 in 318 patients with clear cell renal cell carcinoma. DNA methylation and mRNA expression analysis were performed and evaluated as described in Examples 1 and 6. The calculation of the relative methylation of the genes based on the bead pairs used is described in Examples 4, 11, and 13.

[0160] Figure 17shows the Kaplan-Meier analysis of the overall survival of the 318 patients with clear cell renal cell carcinoma, which was determined using the combined analysis of DNA methylation and mRNA expression of the genes TIGIT (A), TNFRSF9 (B), CD80 (C), CTLA4 (D), CD276 (E) and HHLA2 (F) were stratified. Dichotomization was performed based on the median methylation or mRNA expression of all patients. Group I: methylation below and mRNA above the median; Group III: methylation above and mRNA below the median; Group II: methylation above and mRNA above the median or methylation below and mRNA below the median. For the six genes analyzed in this example, it was shown that patients could be divided into three groups, each with a high, medium, and low risk of death.

[0161] While genes TIGIT (A), TNFRSF9(B), CD80 (C), CTLA4 (D) and CD276 (E) high DNA methylation and lower mRNA expression (Group I) was associated with a particularly poor prognosis, for the gene HHLA2 (F) an exactly opposite relationship, which also confirms the surprising result already described in Example 15 for the DNA methylation analysis of this gene alone.

[0162] As already shown in the embodiments 13, 12, 10, 8 and 7, the combined analysis of DNA methylation and mRNA expression of immunoregulatory genes according to the invention represents a particularly advantageous method design, as it enables a more differentiated prognosis compared to the individual analyses. Example 17: Determination of the response of patients with malignant melanoma to immunotherapy using DNA methylation analysis of PDCD1 and CD274

[0163] The inventive application of DNA methylation analysis of an immunoregulatory gene to predict response to immunotherapy was tested in a cohort of 23 patients with malignant melanoma. The patients received immunotherapy with pembrolizumab. This drug is a monoclonal antibody directed against the antibody encoded by the immunoregulatory gene. PDCD1 encoded receptor PDCD1. The antibody prevents the binding of the corresponding CD274 and PDCD1LG2encoded ligands, thereby altering the immunoregulatory effects of both PDCD1 and CD274 and / or PDCD1LG2. Of the 23 patients, 11 showed disease progression (patients 13 to 23), while in 11 patients (patients 2 to 12) the disease remained stable or the patients showed a shrinkage of tumor mass. In one patient (patient 1), the tumor shrank to the point where it was no longer detectable.

[0164] For DNA methylation analysis of immunoregulatory genes PDCD1 and CD274DNA was extracted from cells of cutaneous metastases, lymph node metastases, and distant metastases of the tumor patients. The metastases were surgically removed before the start of immunotherapy. Conversion into bisulfite-converted DNA was carried out as described in Example 2. The DNA methylation analysis of the immunoregulatory genes according to the invention was carried out using quantitative real-time PCR as in Examples 2 and 5 for each CD274 and PDCD1 described.

[0165] Figure 18 shows the patient-dependent DNA methylation of the immunoregulatory gene CD274, which encodes the ligand CD274, which is involved in the immunoregulatory gene PDCD1encoded receptor PDCD1. Patients 13 to 23, who did not respond to pembrolizumab therapy, had an average tumor DNA methylation of 18.5%, while the group of patients who responded partially or completely (patients 1 to 12) had an average tumor DNA methylation of only 6.2%.

[0166] The results show, using the example of DNA methylation analysis of the CD274 gene and the active ingredient pembrolizumab, that the method according to the invention can be used to predict the response to immunotherapy. In particular, the results show that it is possible to select patients based on DNA methylation, for example, in such a way that only those patients with low DNA methylation, for example, below 10%, of the CD274gene, since patients with high DNA methylation are less likely to respond to therapy in the present example. Of the patients 1 to 12 with partial or complete response, ten (83%) have DNA methylation of CD274 less than 10% and could thus be correctly identified in this example as patients with an expected response to therapy. A total of eight patients showed a CD274 Methylation of over 10%. For six of these eight patients (75%), a lack of response to immunotherapy was correctly predicted.

[0167] This example demonstrates that the inventive determination of DNA methylation of an immunoregulatory gene is fundamentally suitable for predicting the response to immunotherapy. This is therefore also possible when the immunoregulatory gene encodes a ligand as an immune checkpoint and an active substance is used that is intended to alter the immunoregulatory effect of the ligand by inhibiting the corresponding receptor of the ligand. This also demonstrates that the inventive DNA methylation analysis of an immunoregulatory gene is also suitable for predicting the response to an active substance that interacts antagonistically with an immune checkpoint encoded by another immunoregulatory gene.

[0168] Figure 19 shows the corresponding determination of DNA methylation of the immunoregulatory gene according to the invention PDCD1.In the group of patients 1 to 12 who showed a response, an average of 59% methylation of the PDCD1 gene was found in the tumor samples. This methylation was significantly higher, with a t-test p-value of 0.008, than the methylation in the tumors of patients who showed disease progression, where the mean methylation was 22%. The level of DNA methylation of the PDCD1 gene also correlates with the response to immunotherapy and allows the DNA methylation of PDCD1 to identify patients who are likely to respond to therapy before starting therapy. For example, the tumors of nine of the twelve (75%) responding patients (patients 1 to 12) showed DNA methylation of the PDCD1gene of over 25% and were thus correctly identified as responding patients. The patient with complete tumor regression due to immunotherapy (Patient 1) had the highest methylation of all examined samples at 98% and was thus identified with particular reliability. In contrast, nine of the eleven (82%) patients (Patients 13-23) who did not respond to therapy showed PDCD1Methylation of less than 25% and were correctly identified as patients who did not respond to the selected immunotherapy with pembrolizumab. The application of the method according to the invention could, for example, spare such patients immunotherapy with pembrolizumab and its side effects in the future. At the same time, valuable time could be gained by administering another therapy to these patients earlier that is more likely to be effective. For example, an individualized selection of the alternative therapy could have been made through the inventive application of the method for determining the DNA methylation of additional immunoregulatory genes encoding immune checkpoints in order to predict the response to therapy with active substances capable of altering the immunoregulatory effect of these additional immune checkpoints.

[0169] The Figure 19also shows, by way of example, that considering the quantitative methylation of an immunoregulatory gene can enable the prediction of treatment response. This allows a patient's treatment response to be predicted even without applying a threshold. In the present example, patient 1, in whom the tumor completely regressed, shows a higher methylation of the PDCD1 Gens than patients 2 to 12, who only partially responded to therapy.

[0170] It is also possible to combine the inventive application of DNA methylation analysis of various immunoregulatory genes in order to predict the response to immunotherapy even more precisely. Figures 18 and 19 It is evident that patient 1, who showed a complete regression of the tumor, had both a low CD274 DNA methylation (3.6%), as well as high methylation of PDCD1 (98%) in the tumor. CD274encodes the ligand that binds to the PDCD1 encoded receptor. Pembrolizumab is an active ingredient that alters the immunoregulatory effect of the two immune checkpoints encoded by these genes by interrupting or inhibiting this ligand-receptor binding. This finding demonstrates that the inventive analysis of multiple immune checkpoints, particularly those involved in the same ligand-receptor binding, can further improve the prediction of response to immunotherapy. This can also be demonstrated in the present example by the fact that of the 12 patients (patients 1 to 12) who responded at least partially to the therapy, all (100%) responded either by a CD274 Methylation below 10% or by a PDCD1Methylation above 25%. Thus, response to immunotherapy could be correctly predicted for these patients. Of the 11 patients with no response to therapy (patients 13-23), 5 (45%) showed CD274 Methylation over 10% and a PDCD1 Methylation less than 25% (patients 14 to 16, 19 and 21). For these patients, the lack of treatment response could be determined using the combined DNA methylation analysis of PDCD1 and CD274 can be predicted particularly reliably. Example 18: Determination of the prognosis of patients with squamous cell carcinoma of the head and neck using DNA methylation analysis and mRNA expression analysis of various immunoregulatory genes

[0171] DNA methylation analysis was performed as described in Example 1. To calculate the relative methylation of the individual immunoregulatory genes described in Table 1, the bead pairs of the Infinium HumanMethylation450 BeadChip (Illumina, Inc., San Diego, CA, USA) listed in Table 1 were used. The target sequence of these bead pairs in the genome is also identified in Table 1 by a corresponding SEQ ID NO. For each bead pair, a relative methylation was calculated for each patient by comparing the beads specific for the unmethylated and methylated states of a pair as described in Example 1. The overall survival of 528 patients with squamous cell carcinoma of the head and neck region was investigated as in Example 5.

[0172] The mRNA expression was determined as described in Example 6. The mRNAs listed in Table 2 were examined.

[0173] The results summarized in Table 1 show that the inventive determination of DNA methylation of immunoregulatory genes is significantly associated with patient survival and thus allows for the determination of patient prognosis. The hazard ratio 1 was determined using the Cox proportional hazard model and describes the extent to which the patient's risk of death increases with an increase in the DNA methylation level of the corresponding immunoregulatory gene. Hazard ratios greater than one mean that the patient's risk of death increases with higher DNA methylation levels. A hazard ratio less than one decreases this risk.

[0174] This example demonstrates, firstly, that the DNA methylation analysis of the immunoregulatory genes listed in Table 1 according to the invention allows the determination of the patient's prognosis. Furthermore, this example demonstrates that the determination of the prognosis is possible not only by classifying patients into groups with different prognosis based on methylation thresholds, but also by using statistical models, such as the Cox Proportional Hazards Model, which can be used to determine the extent to which a patient's prognosis changes depending on the respective methylation value.

[0175] Using the gene examples ADORA2A, BTNL2, C10orf54, CD160, CD276, CD48, CD80, CD86, PDCD1, TIGIT and TNFRSF18Table 1 also shows that different regions within a gene can be suitable for the method application according to the invention. This is evident from the fact that, for these genes, DNA methylation analysis of a large number of regions, which were examined using the different bead pairs, each allows for a successful determination of the prognosis.

[0176] Hazard ratio 2 in Table 1 describes the extent to which patients with tumors whose methylation level is above a threshold is higher than those with values ​​below the threshold. For example, in the case of the immunoregulatory gene CD274 Surprisingly, for the two regions with SEQ ID NO:425 and SEQ ID NO:426, it was shown that increased methylation is associated with a low risk of death, while in Example 3 it was shown that increased methylation of other regions of the CD274 gene is associated with poorer survival in malignant melanoma. A similar finding was found for CD276. The DNA methylation analysis of the areas from CD276, covered by SEQ ID NO:427, SEQ ID NO:428, and SEQ ID NO:429 correlate inversely with survival, meaning increased methylation of these regions is associated with a lower risk of death. In contrast, methylation of the region with SEQ ID NO:430 correlates positively with the risk of death. A particular advantage of the DNA methylation analysis according to the invention is that by selecting one or more regions in the gene to be examined, an even more differentiated prognosis can be achieved than is possible, for example, by considering mRNA or protein expression alone.

[0177] Table 2 shows, using the example of the immunoregulatory genes mentioned, that their mRNA expression analysis also enables the prediction of patient survival and can thus complement the DNA methylation analysis according to the invention. Patients were grouped based on an exemplary threshold. This threshold is the number of mRNA molecules of the immune checkpoint in relation to all mRNA molecules analyzed by RNA-Seq ( normalized count ), which allowed patients to be divided particularly well into a group with a good and a group with a poor prognosis. Patients who had an mRNA expression of CD274 or CD276 above the respective threshold value had a significantly worse course of the disease than patients whose tumors expressed these genes below the respective threshold value. ADORA2a, BTNL2, C10orf54, CD160, CD27, CD48, PDCD1, TIGIT, TNFRSF18, TNFRSF25 and TNFRSF4In contrast, patients whose tumors expressed the gene above the respective threshold had a more favorable prognosis. This example demonstrates that mRNA expression analysis can be reliably used as an additional indicator for determining prognosis according to the invention. Table 2: Results of the analysis of overall survival in 528 patients with squamous cell carcinoma of the head and neck. The association of mRNA expression of the aforementioned immunoregulatory genes with patient survival was investigated. The hazard ratio was determined using the mRNA expression value dichotomized using a threshold. gene Threshold p-value Hazard ratio ADORA2A 87,2 0,003 0,60 BTNL2 0,45 0,049 0,58 C10orf54 1015 0,096 0,73 CD160 11,78 0,002 0,42 CD27 108,1 <0,001 0,54 CD274 127,0 0,015 1,50 CD276 3944 0,004 1,59 CD48 270 0,018 0,63 PDCD1 48,2 0,021 0,68 TIGIT 100 0,010 0,65 TNFRSF18 250 0,018 0,68 TNFRSF25 250 0,041 0,69 TNFRSF4 57,32 <0,001 0,48 Example 19: Determination of DNA methylation and mRNA expression based on co-methylation and co-expression of immunoregulatory genes

[0178] As already shown in Example 8, immunoregulatory genes are often co-expressed. This means that cells of a malignant disease and / or immune cells such as T lymphocytes do not only express a single immunoregulatory gene, but rather there is simultaneous expression of several immunoregulatory genes. In this experiment, the mRNA expression and DNA methylation of various immunoregulatory genes were determined as described in Examples 1 and 6. The mRNA expression data were generated for 520 and the DNA methylation data for 528 head and neck tumor patient samples.

[0179] Table 3 shows that for the immunoregulatory genes listed as examples, there is a statistically significant and positive correlation between the mRNA expression of various immunoregulatory genes in the tumors. Thus, by determining the mRNA expression of one or more immunoregulatory genes, the mRNA expression of other immunoregulatory genes can be inferred.

[0180] PDCD1LG2 and CD274 encode ligands that both bind to the receptor encoded by the gene PDCD1 The correlation of DNA methylation was investigated for these genes. For the DNA methylation analysis of PDCD1LG2 the bead pair cg07211259 (SEQ ID NO:31) and for CD274The bead pairs cg15837913 (SEQ ID NO:8), cg13474877 (SEQ ID NO:11), and cg19724470 (SEQ ID NO:12) were used. The correlation was determined using a Spearman rank correlation, and the correlation coefficient is expressed as Spearman's ρ. Statistical significance is expressed as a p-value. The result for bead pair cg07211259 correlated with bead pair cg15837913 (ρ=0.33, p<0.001), cg07211259 with cg13474877 (ρ=0.24, p<0.001), and cg07211259 with cg19724470 (ρ=0.42, p<0.001). This shows that, for example, DNA methylation analysis of PDCD1LG2 Based on the correlation found, a statement about the methylation of CD274 In this way, for example, DNA methylation analysis of PDCD1LG2 the prediction of response to therapy with an active substance that reduces the CD274 or PDCD1encoded immune checkpoint is inhibited, for example when DNA methylation analysis of PDCD1LG2 Due to the correlation shown, a specific DNA methylation is representative of CD274 which makes the response to therapy with the active substance in question likely.

Claims

1. A method for predicting response of a patient with a malignant disease to immunotherapy, wherein said malignant disease is a cancer disease, wherein a DNA methylation analysis of at least one immunoregulatory gene of cells of the malignant disease and / or of T lymphocytes interacting with said cells of the malignant disease is performed, wherein said immunoregulatory gene encodes an immune checkpoint selected from B7 proteins and their receptors and selected from the group consisting of the B7 proteins and their receptor encoding genes CD274, PDCD1LG2 and PDCD1, the B7 protein and its receptor encoding genes CD80 and CTLA4, the gene ICOS encoding the receptor of a B7 protein and the B7 proteins encoding genes CD276, C10orf54 and HHLA2, and any combinations thereof, wherein said immunotherapy comprises at least one pharmaceutical compound which is an immune checkpoint inhibitor intended to alter the immunoregulatory effect of said immune checkpoint, and the result of the DNA methylation analysis is compared to a reference value to determine the patient's response to the immunotherapy.

2. The method according to claim 1, wherein the malignant disease comprises a carcinoma, in particular a squamous cell carcinoma or an adenocarcinoma, a melanoma, a leukemia, a glioma, a sarcoma and / or a lymphoma.

3. The method according to claim 1, wherein said immune checkpoint comprises CD274 and / or PDCD1LG2 and said pharmaceutical compound is designed to alter, in particular inhibit, the immunoregulatory effect of CD274 and / or PDCD1LG2 by interaction with PDCD1.

4. The method according to any one of claims 1 to 3, wherein the DNA methylation analysis comprises determining the presence, absence or level of methylation of at least one CpG dinucleotide contained in said immunoregulatory gene.

5. The method according to claim 4, wherein the methylation of at least one CpG dinucleotide from each of at least two different immunoregulatory genes is determined.

6. The method according to claim 4 or 5, wherein the DNA methylation analysis is performed under conditions which allow for a quantitative determination of said methylation of the at least one CpG dinucleotide.

7. The method according to one of claims 1 to 6, wherein an additional mRNA expression analysis of said immunoregulatory gene is performed.

8. Use of a DNA methylation analysis of at least one immunoregulatory gene of cells of a malignant disease of a patient and / or of T lymphocytes interacting with the cells of the malignant disease, wherein said malignant disease is a cancer disease, wherein said immunoregulatory gene encodes an immune checkpoint consisting of B7 proteins and their receptors and is selected from the group consisting of the B7 proteins and their receptor encoding genes CD274, PDCD1LG2 and PDCD1, the B7 protein and its receptor encoding genes CD80 and CTLA4, the gene ICOS encoding the receptor of a B7 protein and the B7 proteins encoding genes CD276, C10orf54 and HHLA2, and any combinations thereof, for predicting the patient's response to an immunotherapy and / or for the individualised selection of a pharmaceutical compound for immunotherapeutic treatment of the patient, wherein said immunotherapy and / or said immunotherapeutic treatment comprises at least one pharmaceutical compound which is an immune checkpoint inhibitor designed to alter an immunoregulatory effect of said immune checkpoint, wherein said pharmaceutical compound is selected when said DNA methylation analysis indicates expression of said immune checkpoint in said cells of the malignant disease and / or in said T lymphocytes.

9. Use of presence, absence or level of methylation of an immunoregulatory gene of cells of a malignant disease and / or of T lymphocytes interacting with said cells of the malignant disease, wherein said malignant disease is a cancer disease, wherein said immunoregulatory gene encodes an immune checkpoint consisting of B7 proteins and their receptors and is selected from the group consisting of the B7 proteins and their receptor encoding genes CD274, PDCD1LG2 and PDCD1, the B7 protein and its receptor encoding genes CD80 and CTLA4, the gene ICOS encoding the receptor of a B7 protein and the B7 proteins encoding genes CD276, C10orf54 and HHLA2, and any combinations thereof, as predictive biomarker for predicting response of said malignant disease to an immunotherapy and / or for individualized selection of a pharmaceutical compound for immunotherapeutic treatment of a patient afflicted with said malignant disease, wherein said immunotherapy and / or said immunotherapeutic treatment comprises at least one pharmaceutical compound which is an immune checkpoint inhibitor designed to alter an immunoregulatory effect of said immune checkpoint, wherein said pharmaceutical compound is selected when said DNA methylation analysis indicates expression of said immune checkpoint in said cells of the malignant disease and / or in said T lymphocytes.

10. Use of a kit for carrying out the method according to any one of claims 1 to 7, the kit comprising at least one pair of oligonucleotides for said DNA methylation analysis, said oligonucleotide pair being designed to hybridize to a sequence of the immunoregulatory gene in DNA from said cells of the malignant disease and / or from said T lymphocytes after cytosines contained in said DNA have been converted into uracil or another base having a base pairing behaviour and / or molecular weight distinguishable from that of cytosine in order to amplify and / or detect said sequence.