KR20210033402A - Method for predicting the response to anticancer immunotherapy using dna methylation aberration and tumor mutational burden - Google Patents

Abstract

The present invention relates to an information providing method for predicting immunotherapy responsiveness, comprising the steps of: obtaining information about a global DNA methylation level detected from a sample of a cancer patient; obtaining information about a tumor mutation burden from the sample; and evaluating the responsiveness to immunotherapy based on the information on the global DNA methylation level and the information on the tumor mutation burden.

Description

Method for predicting the reactivity of immunotherapy using DNA methylation mutation and tumor mutation burden{METHOD FOR PREDICTING THE RESPONSE TO ANTICANCER IMMUNOTHERAPY USING DNA METHYLATION ABERRATION AND TUMOR MUTATIONAL BURDEN}

The present invention relates to an information providing method and a predicting device for predicting immune chemotherapy responsiveness using DNA methylation mutation and tumor mutation burden, and more specifically, to a global measured based on LINE-1 factor (Long Interspersed Nuclear Element-1). The present invention relates to a method and a prediction device for providing information for predicting responsiveness to immunotherapy in cancer patients using DNA methylation level information and tumor mutation burden.

Cancer is one of the highest causes of death in the world, and the cure rate is often determined depending on whether or not it is diagnosed early, so early diagnosis of cancer through health check-ups is considered important. In general, a cancer test that is widely used in health checkups is a protein tumor marker test in the blood, and in addition, the occurrence of cancer can be confirmed through an endoscopy, a biopsy, and the like.

There are three types of abnormal mutations in the genome that cause cancer.Epigenetic change, which is a change in chromatin, in addition to changes in the sequence or nucleotide sequence of chromosomes in which a part of the chromosome is changed entirely or the nucleotide sequence is changed in 1-2 places. (Sticker T, Catenacci DV, Seiwert TY. Molecular profiling of cancer-the future of personalized cancer medicine: a primer on cancer biology and the tools necessary to bring molecular testing to the clinic. Semin Oncol 2011 38( 2): 173-85). In general, mutations in which a part or several parts of a chromosome are changed may be a genetic cause, and may occur locally in somatic cells by a mutagenic factor such as radiation. The exact cause of DNA methylation changes in cancer cells, the most representative of epigenetic changes, is thought to be due to dietary habits or environmental factors. In particular, since such epigenetic changes are found in a large number of genomes of cancerous tissues, interest in clinical applications such as the development of diagnostic methods using the same is increasing.

DNA methylation is a phenomenon in which a methyl group (-CH3) is covalently added to the fifth carbon of the cytosine pyrimidine ring. It is playing an important role.

In cancer tissues, two kinds of DNA methylation phenomena appear different from those of normal cells: global hypomethylation across the genome and hypermethylation of CpG islands located in the gene expression regulatory region. Hypomethylation occurs mainly in genes and intergenic regions, which destabilizes chromosomes and is presumed to cause recombination, transfer, deletion, and rearrangement of chromosomes during cell division. In particular, it is known that transposons such as LINE-1 are normally methylated, inhibiting their expression, and then expressed by hypomethylation in cancer and metastasized throughout the genome, causing one cause of chromosomal instability. Here, DNA methylation changes in cancer tissues are considered to be epigenetic.Since these epigenetic changes are maintained even after cell division, hypomethylation and hypermethylation are consistent with the expression of translocation factors and genes located around CpG islands. Will have an effect. In fact, it is known that cancer suppressors, cell cycle regulating genes, DNA repair-related genes, and cell adhesion-related genes are suppressed by DNA methylation in cancer tissues, thereby exhibiting the same effect as when these genes are broken ( McCabe MT, Brandes JC, Vertino PM.Cancer DNA methylation: molecular mechanisms and clinical implications.Clin Cancer Res. 2009 15(12): 3927-37). By suppressing the expression of these genes, the cells proliferate abnormally and cannot maintain genetic stability, causing additional mutations to play an important role in progressing cancer.

On the other hand, immune anticancer drugs such as CTLA-4 and PD-1/PD-L1 immune checkpoint inhibitors help immune cells attack cancer cells by activating the body's immune system unlike existing anticancer drugs that target cancer cells or cancer-related genes. It is important to select a patient group suitable for immunotherapy as only a small number of patients with anticancer drugs have a cure effect compared to their high price, but knowledge of factors predicting treatment responsiveness is very limited, and immunotherapy responsiveness and DNA methylation There is no research on the correlation of mutations.

Republic of Korea Patent Publication No. 10-2018-0054867

It is an object of the present invention to obtain information on a global DNA methylation level detected from a sample of a cancer patient, obtaining information on a tumor mutation burden from the sample, and information on the global DNA methylation level And it provides a method of providing information for predicting the responsiveness of the immune chemotherapy treatment, comprising the step of evaluating the responsiveness to the immuno-chemotherapy based on the information on the tumor mutation burden.

The problem to be solved by the present invention is not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those of ordinary skill in the art from the following description.

According to an embodiment of the present invention, obtaining information on a global DNA methylation level detected from a sample of a cancer patient, obtaining information on a tumor mutation burden from the sample, and the global DNA methylation A method of providing information for predicting responsiveness to immuno-chemo-cancer therapy is provided, comprising the step of evaluating responsiveness to immuno-cancer therapy based on information on the level and information on the burden of tumor mutations.

According to one side, the global DNA methylation level may be measured using an average value of methylation occurring in a number of LINE-1 factors.

According to one side, the LINE-1 factor may be L1HS or L1PA.

According to one side, the LINE-1 factor corresponds to the evolutionary new LINE-1 family, and the criteria for selecting the evolutionary new LINE-1 family are provided by the RepeatMasker database (http://www.repeatmasker.org). DNA sequences that can be used can be used.

According to one side, in the step of evaluating the reactivity to the immuno-chemotherapy, when the global DNA methylation level is low, it may be determined that resistance to the immuno-chemotherapy is high.

According to one side, the sample may be cancer tissue, whole blood, serum, saliva, sputum, cerebrospinal fluid, or urine.

According to one side, the cancer is melanoma, bladder cancer, esophageal cancer, glioma, adrenal cancer, sarcoma, thyroid cancer, colorectal cancer, prostate cancer, head and neck cancer, urinary tract cancer, gastric cancer, pancreatic cancer, liver cancer, testicular cancer, ovarian cancer, endometrial cancer. It may be cancer, cervical cancer, brain cancer, breast cancer, kidney cancer or lung cancer.

According to another embodiment of the present invention, in the immuno-anticancer treatment responsiveness prediction apparatus, comprising a processor, the processor, obtaining information on a global DNA methylation level detected from a sample of a patient, and obtaining a tumor from the sample. An apparatus for predicting responsiveness to immuno-cancer treatment is provided, which acquires information on a mutation burden and evaluates responsiveness to immuno-cancer treatment based on the information on the global DNA methylation level and the tumor mutation burden.

The method and prediction apparatus for providing information for predicting the responsiveness of immune chemotherapy according to the present invention is based on the global DNA methylation information, tumor mutation burden information, chromosomal aneuploidy information, or a combination thereof of the patient's resistance to immune chemotherapy. Can be predicted, and can provide information related to the responsiveness of immune chemotherapy quickly and simply with high reliability.

Therefore, when the information providing method and the predicting device for predicting the responsiveness of the immuno-anticancer treatment of the present invention are used, it can be effectively used to select a patient group predicted to have a good therapeutic effect and prognosis before proceeding with cancer treatment, particularly immunotherapy.

However, the effects of the present invention are not limited to the above effects, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims of the present invention.

Figure 1 shows the correlation between the cell proliferation marker (a), tumor mutation burden (b), chromosomal aneuploidy level (c) and tumor penetrating CD8+ T cell marker (d) and the global DNA methylation level for 21 types of cancer. .
2A and 2B show that global hypomethylation is associated with decreased immunity of cancer regardless of the association with tumor mutation burden or chromosomal aneuploidy. FIG. 2A shows that the global methylation level is related to the activity of various immune cells infiltrating cancer tissues. It shows a positive correlation (red heat map), and Figure 2b shows that the global methylation level is positively correlated with the hallmark immune gene set expressed in cancer cells (red heat map), and the cell division gene (hallmark). proliferation gene sets) have a negative correlation (blue heatmap).
Figure 3a shows how the expression levels of genes in the late-replicating region of DNA replication are different between a patient sample with high global methylation and a patient sample with low global methylation, according to each carcinoma, with low global methylation in several carcinomas. It can be seen that the gene expression is low in the patient.
Figure 3b shows the number of hypermethylated CpG islands present in the promoters of genes in regions where DNA replication is slow compared to normal cells in each carcinoma between patient samples with high global methylation and low patient samples. As a result, it can be confirmed that CpG island hypermethylation occurs more in patients with low global methylation in several carcinomas.
FIG. 3C statistically shows that genes corresponding to cell division are more concentrated in regions where early replication is replicated, and, on the contrary, genes related to immune response are concentrated in regions where genes are reproduced late.
Figure 4a is a view of the DNA methylation pattern according to the global methylation level of the lung cancer cohort sample (n = 141) divided into CpG island (CGI), shore, shelf and open sea sites.
Figure 4b shows how the methylation of the CGI and open sea regions, the burden of tumor mutation, and the level of chromosomal aneuploidy differ quantitatively according to the global methylation level in the samples.
5A shows the survival rate according to the global methylation level analyzed using the Cox proportional hazard model in the lung cancer cohort sample, when the two cohorts are combined (combined cohort), the IDIBELL cohort, and the results for each of the SMC cohorts.
Figure 5b shows the results of the combined cohort, the IDIBELL cohort, and the SMC cohort as the survival rate according to the tumor mutation burden analyzed using the Cox proportional risk model in the lung cancer cohort sample.
6 shows the comparison of the survival rate according to the global methylation level and tumor mutation burden analyzed using the Cox proportional risk model in melanoma cohort samples.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It is to be understood that all changes, equivalents, or substitutes to the embodiments are included in the scope of the rights.

The terms used in the examples are used for illustrative purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof does not preclude in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in the present application. Does not.

In addition, in the description with reference to the accompanying drawings, the same reference numerals are assigned to the same components regardless of the reference numerals, and redundant descriptions thereof will be omitted. In describing the embodiments, when it is determined that a detailed description of related known technologies may unnecessarily obscure the subject matter of the embodiments, the detailed description thereof will be omitted.

The present invention was completed by discovering that global DNA methylation is related to immune evasion response and resistance to immunotherapy of mutated tumors, and as a new marker that can be used to predict the responsiveness and prognosis of immune chemotherapy, I want to provide a use.

Accordingly, according to an embodiment of the present invention, obtaining information on a global DNA methylation level detected from a sample of a cancer patient, obtaining information on a tumor mutation burden from the sample, and the global A method of providing information for predicting responsiveness to immuno-chemo-cancer treatment is provided, comprising the step of evaluating responsiveness to immuno-cancer treatment based on information on the level of DNA methylation and information on the burden of tumor mutations.

In the present invention, the term "method of providing information for predicting immune chemotherapy responsiveness" is a preliminary step for diagnosis and provides objective basic information necessary for the diagnosis of immune chemo-cancer, and clinical judgment or findings of a doctor are excluded.

In the present invention, the term "methylation" refers to a phenomenon in which a methyl group (-CH3) is covalently attached to the 5th carbon of the cytosine pyrimidine ring constituting DNA. More specifically, "global DNA methylation" of the present invention refers to methylation occurring in the cytosine of the LINE-1 CpG site. When methylation occurs, the binding of transcription factors is thereby inhibited, thereby inhibiting the expression of a specific gene. Conversely, when demethylation or hypomethylation occurs, the expression of a specific gene may increase.

In the present invention, the global DNA methylation level is meant to be measured as an average value of methylation occurring in a plurality of LINE-1 factors, and the LINE-1 factor may be L1HS or L1PA corresponding to the evolutionary newborn LINE-1 family. have.

Meanwhile, the average value of methylation is a microarray, methylation-specific polymerase chain reaction (MSP), real time methylation-specific polymerase chain reaction (PCR), and PCR using methylated DNA-specific binding protein. , Pyrosequencing, MS-HRM (Methylation-Sensitive High-Resolution Melting Analysis, methylation specific-high resolution melting curve analysis and methylation determination using methylation-sensitive restriction enzymes, DNA chip and automatic base analysis such as bisulfite sequencing, etc. It can be measured by a method, but is not limited thereto.

On the other hand, the methylation site can be classified into shelf, shore, and open sea according to the distance from CGI. If it is within 2 kb from CGI, up to 2 kb from shore and shore is called shelf, and 4 kb or more away from CGI. The area is represented by the open sea.

In the present invention, in the step of evaluating the responsiveness to the immuno-anticancer treatment, when the global DNA methylation level is low, it may be determined that resistance to the immuno-anti-cancer treatment is high. Specifically, this is because the lower the global DNA methylation level, the more inhibited the activity of immune cells infiltrating cancer tissues, and the expression level of immune response genes expressed in cancer cells decreases. In addition, as shown in the following examples, the survival rate of immunotherapy cohort cancer patients is statistically significantly reduced, which appears as a variable that acts independently of tumor mutation burden and chromosomal aneuploidy information, and is a predictor that is actually widely used. It was confirmed that the accuracy higher than that of the tumor mutation burden was consistently shown in several cohorts, and the responsiveness to cancer treatment can be predicted very effectively through the method of the present invention.

The immune chemotherapy may include an immune checkpoint inhibitor, an immune cell therapy, a therapeutic antibody, and the like. Immune checkpoint inhibitors are drugs that attack cancer cells by activating T cells by blocking the activation of immune checkpoint proteins involved in T cell suppression, and may be CTLA-4, PD-1, PD-L1 inhibitors, etc., but are limited thereto. It is not.

In the present invention, the sample may be tissue, whole blood, serum, saliva, sputum, cerebrospinal fluid, or urine, but is not limited thereto.

In addition, the cancer is melanoma, bladder cancer, esophageal cancer, glioma, adrenal cancer, sarcoma, thyroid cancer, colorectal cancer, prostate cancer, head and neck cancer, urothelial cancer, gastric cancer, pancreatic cancer, liver cancer, testicular cancer, ovarian cancer, endometrial cancer, uterus It may be cervical cancer, brain cancer, breast cancer, kidney cancer, or lung cancer, but is not limited thereto.

Meanwhile, in the present invention, the term "Tumor Mutation Burden (TMB)" is a numerical value expressing the number of mutations quantitatively, and refers to the number of mutations observed per mega base in the sequencing result of cancer tissues.

In the present invention, the term "chromosome aneuploidy" means that the number of chromosomes per cell in a cell, individual or lineage is not an integral multiple of the base number, and 1 to several are in a state in which there are many or fewer, that is, incomplete It refers to the state including the composed dielectric. In the present invention, the chromosomal aneuploidy value can be derived from CNV (Copy number variations), which is at least 10% of the chromosomal arm or chromosomal arm by applying a threshold value ±0.2 (average value of LUAD and LUSC) to a log2 ratio (normal versus normal). It is calculated as the sum of the absolute segment log2 ratio by detecting the redundancy/deletion affecting 5% of.

According to another embodiment of the present invention, in the immuno-anticancer treatment responsiveness prediction apparatus, comprising a processor, the processor, obtaining information on a global DNA methylation level detected from a sample of a patient, and obtaining a tumor from the sample. A device for predicting responsiveness to immuno-chemo-cancer treatment is provided for obtaining information on a mutation burden and evaluating the responsiveness to immuno-cancer treatment based on the global DNA methylation level and the information on the tumor mutation burden.

In the cancer treatment responsiveness prediction device, the measurement of the global DNA methylation level, the LINE-1 factor, the cancer treatment, the sample and the description of the carcinoma are as described above.

Hereinafter, preferred embodiments are presented to aid in understanding the present invention. However, the following examples are provided for easier understanding of the present invention, and the contents of the present invention are not limited by the examples.

Example 1. Derivation of the correlation between global DNA methylation and immune evasion

1-1. Selection of target data

For analysis, DNA methylation (based on Infinium Methylation 450k technology), mRNA expression, and gene mutation data generated by The Cancer Genome Atlas (TCGA) were downloaded ( https://gdc.cancer.gov/about-data/publications/pancanatlas) (https://gdc.cancer.gov/about-data/publications/pancanatlas) ). Bladder cancer (BLCA), breast adenocarcinoma (BRCA), cervical cancer, squamous cell carcinoma, cervical adenocarcinoma (CESC), colorectal cancer (CRC), as cancer types with over 100 patient samples with all molecular data and age information. Esophageal cancer (ESCA), head and neck squamous cell carcinoma (HNSC), clear cell renal cell carcinoma (KIRC), papillary renal cell carcinoma (KIRP), low-grade glioma (LGG), hepatocellular carcinoma (LIHC), lung adenocarcinoma ( LUAD), lung squamous cell carcinoma (LUSC), pancreatic cancer (PAAD), adrenal cancer (PCPG), prostate cancer (PRAD), sarcoma (SARC), cutaneous melanoma (SKCM), gastric adenocarcinoma (STAD), testicular cancer (TGCT) ), thyroid cancer (THCA) and endometrial cancer (UCEC) were selected for 21 types of cancer, which included 6968 samples. Chromosome aneuploidy levels were obtained from plots by Taylor et al. (Cancer Cell 33, 676-689.e3 (2018)).

1-2. Measurement of global methylation levels

To measure global methylation levels, at least 90% (45 bp) of each probe sequence in the Infinium Methylation 450k microarray is the young LINE-1 subfamily (L1HS and L1PA) provided by the RepeatMasker database (www.repeatmasker.org). ) Was selected. The average beta values of the selected probes in each tumor sample were averaged and used as an estimate of the global methylation level.

Example 2. Derivation of global DNA methylation level correlation

Using the values obtained in Example 1, correlations between global DNA methylation levels, cell proliferation markers, tumor mutation burden, chromosomal aneuploidy, and tumor penetrating CD8+ T cell markers were derived for 21 types of cancer, and the results are shown in FIG. Shown in. Cell proliferation markers refer to genes involved in cell division and cell cycle, and CD8+ T cell markers refer to genes specifically expressed in CD8+ T cells. Was used as. The definition of each marker followed the existing literature (Immunity 48, 812-830.e14 (2018)). Median values were calculated for each cancer type, and statistical significance was verified using Separman's correlation.

As can be seen in FIG. 1, as the DNA methylation level increases, cell proliferation marker expression, tumor mutation burden, and chromosomal aneuploidy tend to decrease, and tumor infiltrating CD8+ T cell marker expression tends to increase. Through this, it was confirmed that there is a positive correlation that the level of DNA methylation gradually decreases as cell division occurs (demethylation or methylation loss), and there is a positive correlation between the burden of tumor mutation and the increase in DNA mutations such as chromosomal aneuploidy. However, a new fact identified in the present invention is that there is a correlation between these indicators and the immune response.

In order to solve the entangled correlation, the mRNA expression level for each gene for each cancer type is used as a response variable, and a linear regression is made using global methylation, tumor mutation burden, chromosome aneuploidy, tumor purity, age and tumor base as predictor variables. The model was applied.

A regression model using the following formula was created using R's lm function:

MRNA expression of gene Y ~ β ₁ *Global methylation level+β ₂ *Tumor mutation burden+β ₃ *Chromosomal aneuploidy level+β ₄ *Tumor purity+β ₅ *Age+β ₆ *Tumor base

Gene Set Enrichment Analysis (GSEA; Subramanian, A. et al., Proc. Natl. Acad. Sci. 102, 15545-15550 (2005))) and cells with significant NES (Normalized Enrichment Scores) (FER<0.25) were The GSEA NESs heat map is shown in Figs. 2A and 2B.

As a result, as shown in Figs. 2a and 2b, as the global methylation level is lower, the activity of various immune cells infiltrating cancer tissues independently, regardless of tumor mutation burden and chromosomal aneuploidy, decreases, and is expressed in cancer cells. It was confirmed that the expression level of the immune genes was also lowered.

Example 3. Relationship between global DNA methylation and expression of immune genes

As shown in Example 2, the level of DNA methylation gradually decreases as cell division occurs, and accordingly, the amount of expression of immune genes expressed in cancer cells decreases.However, the decrease in methylation due to cell division is mainly due to DNA replication. It is known to occur in the late-replicating region. In fact, the differences in gene expression levels in regions where DNA replication occurs late between patient samples with high and low global methylation were compared according to carcinoma. As a result, it was confirmed that the gene expression of patients with low global methylation in several carcinomas was low, as shown in FIG. 3A.

In addition, it is known that the overall change in methylation of cancer is hypomethylated in translocation factors such as LINE, whereas hypermethylation is concomitant in the CpG islands present in the gene promoter. In fact, the degree of hypermethylation of CpG islands in the promoters of genes in regions where DNA replication occurs late between patient samples with high and low global methylation was compared according to carcinoma. As a result, it was confirmed that CpG island hypermethylation more occurred in patients with low global methylation in various carcinomas as shown in FIG. 3B.

It can be inferred that the decrease in the expression level of the immune gene due to the decrease in global methylation as a result of Example 2 should be consistent with the change in the late replication region. It could be statistically confirmed that the genes involved were more concentrated in the regions where they were replicated earlier, while the genes involved in the immune response were concentrated in the regions where they were lately replicated.

Example 4. Correlation between global DNA methylation and immunotherapy responsiveness

4-1. Patient Cohort for Lung Cancer Checkpoint Inhibitors

Sixty patients with advanced non-small cell lung cancer who were treated with anti-PD-1/PD-L1 from 2014 to 2017 at Samsung Medical Center (SMC) were enrolled in this study.

Sample ID Mutation burden Global methylation level Aneuploidy level PFS PD_Event.1_Censoring.0 Clinical benefit 3 241 0.462218282 9963319.531 28.1 One DCB 20 119 0.497886714 0 1.133333333 One NDB 258 78 0.414770954 141952144.3 3.966666667 One NDB 325 289 0.357762176 373215792.9 3.866666667 0 na 378 169 0.414296576 102187138.2 13.73333333 0 DCB 488 493 0.434316952 535751134.6 1.6 One NDB 541 240 0.362546225 349091677.2 1.2 One NDB 573 478 0.410597593 114018200.9 1.5 One NDB 618 194 0.349218209 263496773.3 1.366666667 One NDB 658 156 0.403780292 141510150.7 1.066666667 One NDB 678 731 0.396234214 191366708.2 22.43333333 0 DCB 700 102 0.428694023 17433991.68 1.366666667 One NDB 720 461 0.294703592 307380376.1 4.066666667 One NDB 756 831 0.280030877 658988768.2 1.5 One NDB 825 284 0.251712972 784194239.2 1.433333333 One NDB 830 307 0.479584284 0 2.933333333 One NDB 947 548 0.387089023 58799436.69 20.6 0 DCB 990 59 0.325908608 285011126.5 3.166666667 One NDB 1017 122 0.417538488 286678444.4 1.266666667 One NDB 1066 62 0.395929718 48979857.76 0.766666667 One NDB 1079 154 0.392672276 128159753.6 5.8 One NDB 1104 1756 0.293149031 895819215 0.966666667 One NDB 1145 238 0.421131362 5860926.472 2.433333333 One NDB 1155 362 0.335438797 443235770.9 1.133333333 One NDB 1164 236 0.293888515 520968472.1 0.366666667 One NDB 1203 193 0.35971363 437223965.6 0.833333333 One NDB 1208 351 0.349741738 927702954.8 0.933333333 One NDB 1250 511 0.327435945 861780460 0.933333333 One NDB 1297 584 0.416016984 68793176.27 10.5 One DCB 1322 263 0.443427473 227632642 1.1 One NDB 1327 585 0.42678156 99766614.32 6.833333333 0 DCB 1337 578 0.329661704 436129375.2 2.166666667 One NDB 1352 1858 0.22054474 553574113.5 8.333333333 0 DCB 1358 439 0.377759517 649708156.1 8.566666667 One DCB 1401 58 0.436672231 17494258.78 0.933333333 One NDB 1412 294 0.264443355 545902993.4 0.1 One NDB 1425 341 0.303500801 346192818.8 1.966666667 One NDB 1443 423 0.270158864 836459992.2 1.766666667 One NDB 1456 237 0.407939594 76492185.01 7.5 0 DCB 1490 248 0.315326936 403292217.7 0.9 One NDB 1508 22 0.415487136 7542228.411 2.166666667 One NDB 1510 364 0.377090295 465391056.4 10.8 0 DCB 1528 190 0.364790201 374128096.1 9.3 0 DCB 1554 49 0.400425652 5947504.686 1.233333333 One NDB 1589 681 0.328377787 297587719.9 3.966666667 One NDB 1619 15 0.455254226 7041421.973 2.733333333 One NDB 1637 344 0.394335199 265842882.3 2.6 One NDB 1708 365 0.450911862 2076055.089 5.6 One DCB 1711 440 0.421240392 401532642.4 na na na 1751 448 0.390149399 225455169.6 4.833333333 One NDB 1778 473 0.371084292 128523671.2 0.766666667 One NDB 1809 214 0.324772186 400743320.7 1.466666667 One NDB 1873 503 0.307758327 486476347.6 1.233333333 One NDB 1883 335 0.413997537 199412472 4.366666667 One DCB 1960 7030 0.413389522 349177478.5 1.166666667 One NDB 2107 796 0.312709532 716225322 6.466666667 0 DCB 2126 392 0.415388193 145048834.8 2.1 One NDB 2132 285 0.377043232 235992924.3 1.233333333 One NDB 2133 706 0.381138966 93298957.65 3.766666667 One NDB 2317 795 0.323139645 495812412.5 0.766666667 One NDB

Clinical response was assessed through a minimum follow-up of 6 months according to the response evaluation criteria of Solid Tumors (RECIST) version 1.1. Responses to immunotherapy were classified as either a durable clinical benefit (DCB, responder) or a non-durable benefit (NDB, non-responder). Partial response (PR) or stable disease (SD) lasting longer than 6 months was considered DCB/responder. Progressive disease (PD) or SD lasting less than 6 months was considered an NDB/non-responder. Progression-free survival (PFS) was calculated from the start of treatment to the date of progression or death, whichever is earlier. If the patient was alive without progression, it was censored on the date of the last follow-up for PFS.

Methylation data for 81 samples of lung cancer of the same type, designated IDIBELL cohort, were used as provided in the following literature. Davalos, V. et al. Epigenetic prediction of response to anti-PD-1 treatment in non-small-cell lung cancer: a multicentre, retrospective analysis. Lancet Respir. Med. 6, 771-781 (2018).

4-2. Methylation and tumor mutation burden analysis for SMC samples

Tumor samples were obtained before anti-PD1/PD-L1 treatment, and after formalin fixation, they were embedded in paraffin or stored fresh. DNA was prepared using the AllPrep DNA/RNA Mini Kit (Qiagen, 80204), AllPrep DNA/RNA Micro Kit (Qiagen, 80284), or QlAamp DNA FFPE Tissue Kit (Qiagen, 56404) to prepare the library for whole exome sequencing. . Library preparation was performed using SureSelectXT Human All Exon V5 (Agilent, 5190-6209) according to the instructions. The ligated DNA was hybridized using whole exome baits of SureSelectXT Human All Exon V5, then the library was quantified by Qubit and 2200 Tapestation, and the mated ends of 2X100 bp were sequenced on the Illumina HiSeq 2500 platform.

Target range for normal sample is 50X, tumor sample is 100X, Strelka2 54 is used to derive somatic variants, and single nucleotide variants (SNV) and indels covered by at least 10 and 5 reads in the tumor, respectively, are selected. I did. In addition, general germ cell variants present in dbSNP 150 were further filtered, and somatic cell variants were annotated using ANNOVAR.

Next, methylation analysis was performed according to the guidelines of Infinium Methylation EPlC BeadChIP Kit (Illumina, WG-317-1002). The crude methylation value was pretreated with a beta value, and then treated as described in "Measurement of Global Methylation Level" in Example 1-2 above.

4-3. Assessment of the impact of global DNA methylation levels on checkpoint inhibitors

The effect on the checkpoint inhibitor was evaluated by dividing it into high and low groups based on the median global DNA methylation level measured through the above process.

As a result, it can be seen that hypomethylation occurs mainly in the open sea region in the low group, whereas high methylation occurs in the CpG island and the surrounding region (Figs. 4a and 4b), as observed in Fig. 1 of the TCGA example. In the low group, tumor mutation burden and chromosomal aneuploidy were also increased.

Importantly, it was found that the survival rate after checkpoint treatment was lower in the mutant group. As can be seen from a and b of FIG. 5, the burden of tumor mutation did not show a significant correlation with the survival rate, but when using global DNA methylation as a predictor variable, it was confirmed that the survival rate significantly decreased in the low group.

The above results suggest that the global DNA methylation level can be used as a significant marker for predicting the responsiveness to immunotherapy and further prognosis of cancer patients, and that the accuracy as a predictor of cancer treatment responsiveness is higher than the previously known tumor mutation burden. .

Example 5. Correlation between global DNA methylation and immunotherapy responsiveness to melanoma

In addition, progression-free survival data of 15 melanoma patients who received immune checkpoint inhibitors (lpilimumab, Yervoy, or Pembrolizumab) were obtained from Ock et.al ( Nat. Commun. 8, 1050 (2017)). In addition, data of 25 patients who received other types of immunotherapy were obtained from the GDC legacy archive (https://portal.gdc.cancer.gov/legacy-archive). Their methylation and mutation data were obtained from TCGA as in Example 1-1 above. The correlation between global DNA methylation patient survival was evaluated in the same manner as described in Example 3 above.

As a result, as shown in FIG. 6, when the global DNA methylation level is low, progression-free survival rate decreased, but in the case of tumor mutation burden, a significant correlation with progression-free survival rate was not confirmed.

As described above, although the embodiments have been described by the limited drawings, a person of ordinary skill in the art can apply various technical modifications and variations based on the above. For example, even if the described techniques are performed in a different order from the described method, and/or the described components are combined or combined in a form different from the described method, or are replaced or substituted by other components or equivalents. Appropriate results can be achieved.

Therefore, other implementations, other embodiments, and claims and equivalents fall within the scope of the following claims.

Claims (8)

Obtaining information on the level of global DNA methylation detected from the sample of the cancer patient;
Obtaining information on a tumor mutation burden from the sample; And
Evaluating responsiveness to immuno-cancer treatment based on the information on the global DNA methylation level and the burden on tumor mutations;
Comprising, the method for providing information for predicting the responsiveness of immuno-anticancer treatment. The method of claim 1,
The global DNA methylation level is measured as an average value of methylation occurring in a plurality of LINE-1 (Long Interspersed Nuclear Element-1) factors. The method of claim 2,
The LNIE-1 factor is L1HS or L1PA, a method for providing information for predicting immune anticancer treatment responsiveness. The method of claim 1,
The step of evaluating the responsiveness to the immuno-anticancer treatment comprises determining that resistance to the immuno-anti-cancer treatment is high when the global DNA methylation level is low. The method of claim 1,
The sample is tissue, whole blood, serum, saliva, sputum, cerebrospinal fluid or urine, a method of providing information for predicting cancer treatment responsiveness. The method of claim 1,
The cancer is melanoma, bladder cancer, esophageal cancer, glioma, adrenal cancer, sarcoma, thyroid cancer, colorectal cancer, prostate cancer, head and neck cancer, urinary tract cancer, gastric cancer, pancreatic cancer, liver cancer, testicular cancer, ovarian cancer, endometrial cancer, cervical cancer, Brain cancer, breast cancer, kidney cancer or lung cancer, a method of providing information for predicting cancer treatment responsiveness. The method of claim 1,
The cancer is melanoma, bladder cancer, esophageal cancer, glioma, adrenal cancer, sarcoma, thyroid cancer, colorectal cancer, prostate cancer, head and neck cancer, urinary tract cancer, gastric cancer, pancreatic cancer, liver cancer, testicular cancer, ovarian cancer, endometrial cancer, cervical cancer, Brain cancer, breast cancer, kidney cancer or lung cancer, a method of providing information for predicting cancer treatment responsiveness. In the immune chemotherapy responsiveness prediction device,
Including a processor, the processor,
Obtaining information on the level of global DNA methylation detected from the patient's sample,
Obtaining information on the tumor mutation burden from the sample,
Immuno-chemo-treatment response prediction device for evaluating the responsiveness to immuno-cancer treatment based on the information on the global DNA methylation level and tumor mutation burden.

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