ATAC-SEQ DATA NORMALIZATION AND METHOD FOR UTILIZING SAME

Abstract

The present invention relates to ATAC-seq data normalization for utilizing epigenetic information associated with chromatin openness, and a method for utilizing same. According to the present invention, it is possible to readily normalize and quantitatively compare ATAC-seq in various samples and various cohorts, and selected differential peaks can be used in various epigenetic studies, the diagnosis of diseases, and prediction of the prognoses of diseases.

Description

TECHNICAL FIELD

The present invention relates to a method of normalizing ATAC-seq data for utilizing epigenetic information related to chromatin openness and a method for utilizing the same.

BACKGROUND ART

Epigenetics is defined as a field that studies the phenomenon in which specific genes are expressed differently between generations even though the DNA sequence that transmits genetic information between generations does not change. Epigenetic changes may be induced by various factors in cells constituting our body, and these changes may induce different responses in each individual. In particular, epigenetic changes are known to play a very critical role in the development and differentiation of various cells and are found to provide a major cause of most diseases, including cancer. The major mechanisms related to epigenetics that have been revealed until recently include DNA methylation, histone modification, chromatin remodeling, RNA-mediated targeting, and the like.

A large-scale parallel sequencing technique is used for epigenetic research based on the mechanism by which the chromatin structure is altered and access to specific loci by enzymes becomes easier under a specific environment. Formaldehyde-assisted isolation of regulatory elements (FAIRE) and sonication of crosslinked chromatin sequencing (Sono-seq) were developed to observe the nucleosome-depleted, chromatin open state and the nucleosome-occupied, chromatin closed state, respectively. DNase I hypersensitive site sequencing (DNase-seq) can also detect fragments cleaved by DNA degrading enzyme I (DNase I) and thus is used to identify open regions of chromatin. In addition, the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) uses a transposon, known as “jumping gene”, that moves from one location on the genome to another and finds a chromatin open region like the other methods mentioned above. It is used for genome-related research.

It is known that among them, ATAC-seq may analyze the chromatin open region faster and more sensitively compared to the conventionally used micrococcal nuclease (MNase)-seq and DNase-seq. Specifically, ATAC-seq is a technology that identifies ‘accessible DNA regions in chromatin’ with an active-type mutant Tn5 transposase that inserts a sequencing adapter into a chromatin open region. Tn5 transposase cleaves long DNA strands in a process called tagmentation. Tagmentation refers to the action of simultaneously ‘tagging’ and ‘fragmentation’ of DNA with a Tn5 transposase preloaded with a sequencing adapter. Thereafter, the tagged DNA fragment is purified, amplified by polymerase chain reaction (PCR), and sequencing of the amplified product is performed. The sequencing read obtained through this is used to infer an ‘open region’ that is an accessible chromatin region, and regions for transcription factor binding sites and nucleosome locations can be mapped.

However, various studies have not yet been conducted to quantify the “chromatin open region” identified through ATAC-seq and to apply the same to epigenetic studies using the same.

DISCLOSURE

Technical Problem

Accordingly, the present inventors quantified the chromatin open region identified by the ATAC-seq method, studied a method for utilizing the same, and selected a normalization control peak to correct the amount of the sample for quantitative analysis to confirm a method for analyzing the ATAC-seq results. In addition, the present inventors have completed the present invention by confirming a method for selecting and verifying biomarkers that can predict the responsiveness of anti-PD-1 therapy in patients with gastric cancer based on this.

Therefore, an object of the present invention is to provide a method for selecting a normalization control peak for quantification of ATAC-seq, a normalization method using the same, and a method of selecting a differential peak for predicting the responsiveness of anti-PD-1 therapy by reflecting the epigenetic characteristics of chromatin based on this.

Technical Solution

Accordingly, the present invention provides a method for selecting normalization control peak for normalization of ATAC-seq, the method including steps of: a) aligning and peak calling cell-derived ATAC-seq data; b) selecting overlapping peaks between cell samples among the peaks called in step a); c) selecting a peak coincident with the DNase I hypersensitivity consensus peak among the peaks selected in step b); and d) selecting a peak having a coefficient of variation (CV) of less than 0.3 and peak width of less than 500 bp among the peaks selected in step c).

In addition, the present invention provides a normalization control peak selected by the method.

In addition, the present invention provides a method of selecting the ATAC-seq normalization factor.

In addition, the present invention provides a method of normalizing the area value of a peak by using the ATAC-seq normalization factor (F) selected.

In addition, the present invention provides a method of selecting a differential peak for predicting the responsiveness of anti-PD-1 therapy, the method including steps of: a) aligning and peak calling ATAC-seq data from patients who respond to anti-PD-1 therapy and those who do not; b) selecting a peak which is distinct from the responding and non-responding patient samples among the peak calling values of step a); c) normalizing the selected peak area by normalizing the peak area value of the peak selected in step b) by the normalization method; d) selecting a peak having a normalized area value exceeding a mean area value of the normalized peak area values obtained in step c) among the peaks selected in step b); and e) selecting a peak in which the mean difference between normalized peak area values among the anti-PD-1 therapy responding and non-responding patient samples among the peaks selected in step d) satisfies significance p<0.05.

In addition, the present invention provides a differential peak selected by the method.

In addition, the present invention provides a biomarker composition for predicting the responsiveness of anti-PD-1 therapy, the composition including one or more predictive peaks selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299.

In addition, the present invention provides a method of predicting the responsiveness of anti-PD-1 therapy, the method including a step of detecting one or more predictive peaks selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299.

In addition, the present invention provides a use of one or more predictive peaks selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299 for predicting the responsiveness of anti-PD-1 therapy.

Advantageous Effects

According to the present invention, it is possible to provide easy normalization and quantitative comparison of ATAC-seq data in various samples and various cohorts, and the selected differential peak can be utilized for various epigenetic studies, disease diagnosis, and prognosis prediction.

DESCRIPTION OF DRAWINGS

FIG. 1 is a view schematically illustrating a protocol of the present invention for deriving a predictive peak (biomarker) using ATAC-seq.

FIG. 2 is a view illustrating a process of normalization control peak selection, differential peak selection, and predictive peak selection for predicting a prognosis for anti-PD-1 therapy from the normalized differential peaks.

FIG. 3 is a view illustrating a method and a used formula for deriving a normalization factor (F).

FIG. 4 is a view illustrating a normalization control peak selection and normalization process.

FIG. 5 shows the result of confirming the change in the normalization factor according to the increase in the number of normalization control peaks in order to select the optimal number of normalization control peaks (a) and the result of confirming the rank change of 121 differential peaks according to the increase of the normalization control peak (b).

FIG. 6A is a view illustrating a process of selecting 67 differential peaks. FIG. 6B is a view showing nine representative differential peaks (M1, 2, 5, 17, 20, 29, 30, 35, 36) among the selected differential peaks visualized in the complete response (CR)+partial response (PR) and progress disease (PD) groups.

FIG. 7A is a view illustrating the sensitivity and specificity of representative differential peaks, which are selected predictive peaks, through receiver operating characteristics (ROC) curve analysis. FIG. 7B is a view illustrating a threshold, sensitivity, and specificity for determining the responsiveness of anti-PD-1 therapy using differential peaks.

FIG. 8 is a view including the left portion showing the difference of the mean normalized area values in the responder (R) and non-responder (NR) groups of representative differential peaks in the discovery cohort, the center portion showing confirmation of the responsiveness of anti-PD-1 therapy according to ‘mean normalized area value threshold,’ and the right portion showing the responsiveness of anti-PD-1 therapy by the differential peak, that is, results of comparing median progression-free survival (mPFS) based on chromatin openness.

FIG. 9 is a view showing the sensitivity and specificity of a representative differential peak in a discovery cohort.

FIG. 10 is a view showing the results of confirming the responsiveness to anti-PD-1 therapy by the combination of weighted score of predictive peaks in the discovery cohort. FIG. 10A includes a left portion showing the difference in the weighted score values in the responder (R) and non-responder (NR) groups and a right portion showing the responsiveness of anti-PD-1 therapy based on the weighted score minus the threshold. FIG. 10B is a view showing the responsiveness of anti-PD-1 therapy by the differential peak, that is, the median progression-free survival (mPFS) based on chromatin openness.

FIG. 11 is a view showing the results of confirming the sensitivity and specificity for the responsiveness of anti-PD-1 therapy using predictive peaks in the validation cohort.

FIG. 12 is a view showing the results of confirming the responsiveness of anti-PD-1 therapy in the metastatic gastric cancer patient group by the combination of weighted score of predictive peaks in the validation cohort. FIG. 12A includes a left portion showing the difference in the weighted score values in the responder (R) and non-responder (NR) groups and a right portion showing the responsiveness of anti-PD-1 therapy based on the weighted score minus the threshold. FIG. 12B is a view of showing the responsiveness of anti-PD-1 therapy by the differential peak, that is, the median progression-free survival (mPFS) based on chromatin openness.

FIG. 13 is a view showing the results of confirming the normalization control peaks in eight hematopoietic cells.

FIG. 14 is a view showing the results of confirming normalization control peaks in three acute myeloid leukemia cells.

FIG. 15 is a view showing the results of confirming normalization control peaks for normal bronchial epithelial cells, small cell lung cancer cells, normal prostate basal epithelial cells, small cell prostate cancer cells, and epidermal growth factor receptor-negative and positive glioblastoma.

BEST MODE

The present invention provides a method for selecting normalization control peak for normalization of ATAC-seq, the method including steps of: a) aligning and peak calling cell-derived ATAC-seq data; b) selecting overlapping peaks between cell samples among the peaks called in step a); c) selecting a peak coincident with the DNase I hypersensitivity consensus peak among the peaks selected in step b); and d) selecting a peak having a coefficient of variation (CV) of less than 0.3 and peak width of less than 500 bp among the peaks selected in step c).

In the present invention, the term “peak” means a chromatin open region as a chromatin region accessible by Tn5 transposase.

Cell-derived ATAC-seq data of cell samples according to step a) of the present invention can be obtained by directly performing ATAC-seq using a cell sample to be confirmed or obtained from a public database. Alignment of read data may be based on a reference sequence, for example, mouse mm9, mouse mm10, human hg19, or human hg38 genome version of the genomic reference consortium database, and in the present invention, human hg19 version was used in a preferred embodiment.

Alignment of the obtained data and peak calling may be performed through methods known in the art, for example, alignment of the obtained read data may use a BWA, Bowtie, or Bowtie2 program, and peak calling may be performed by Hypergeometric Optimization of Motif EnRichment (HOMER) suite, Model-based Analysis of ChIP-Seq 2 (MACS2) or CisGenome.

In the present invention, step b) is a step of selecting overlapped peaks between cell samples from among the peaks called in step a). In a preferred embodiment of the present invention, common overlapped peaks in various subsets of CD8⁺ T cells were primarily selected.

In the present invention, step c) is a step of selecting a peak coincident with the consensus peak of DNase I hypersensitivity among the peaks selected in step b). This is the step of selecting a peak that 100% matches the DNase I hypersensitivity consensus peak of the ENCODE project, making it suitable for data normalization for analysis of various samples and cohort data. In addition, the peak selected as described above has the characteristic of being located in a transcription start site (TSS) or a 5′ untranslational region (5′ UTR).

In the present invention, step d) is a step of selecting a peak having a coefficient of variation of peak area values less than 0.3 and peak width of less than 500 bp between selected peaks among the peaks selected in step c). Step d) is a step for obtaining more accurate normalization control from the selected consensus ATAC-seq peak. In order to satisfy the ‘consensus’ criterion that can distinguish between samples with small differences in ATAC-seq peaks, peaks are selected based on two criteria: i) the coefficient of variation is less than 0.3 and ii) the peak width is less than 500 bp. That is, this selects with low ‘variability’ between samples. Here, 500 bp means a peak width in a base pair scale including up to two nucleosomes.

In the present invention, the term “normalization control peak” refers to a peak with ‘consensus’ between samples and is used for normalization.

For example, in a preferred embodiment of the present invention, it was obtained by the method including steps of: obtaining peaks from ATAC-seq data of various CD8⁺ T-cell subsets to select overlapped peaks commonly found among all CD8⁺ T-cell subsets; and matching the selected peaks with the consensus peak discovered by the DNase I hypersensitivity assay of the ENCODE project—that is, the ‘DNase I hypersensitivity consensus peak to select the peak located at the transcription start site or 5’ untranslational region while having 100% coincident with these.

More specifically, in the present invention, the above steps were performed in ATAC-seq data of CD8⁺ T cells to identify a total of 32,485 overlapped peaks among all CD8⁺ T-cell subsets, and among them, 4,798 peaks having 100% consistent with the DNase I hypersensitivity consensus peak and located in the transcription start site or the 5′ untranslational region were selected. Thereafter, 533 consensus peaks identically identified in the ATAC-seq data of CD8⁺ T cells used in the study were selected once again, and the peak with a coefficient of variation between the selected peaks of less than 0.3 and peak width between the selected peaks of less than 500 bp was selected, and finally, 232 normalization control peaks for normalization of ATAC-seq were derived.

The normalization control peak obtained by the above method is conserved across various species and can be used universally as a control peak for normalization in various cell populations. Accordingly, the cell may be one selected from the group consisting of cancer cells, stem cells, immune cells, inflammatory cells, epithelial cells, hematopoietic cells, and fibroblasts, and preferably, for example, peripheral blood mononuclear cells (PBMCs), B cells, T cells, CD8⁺ T cells, CD4⁺ T cells, CD8⁺ T cells, CD14⁺ monocytes, acute myeloid leukemia cells, bronchial epithelial cells, small cell lung cancer cells, prostate basal epithelial cells, small cell prostate cancer cells, or epidermal growth factor receptor (EGFR) negative and positive glioblastoma cells can be used for normalization of ATAC-seq data and its analysis. Particularly preferably, the cells may be immune cells.

The normalization control peak obtained through the method of the present invention may consist of sequences represented by SEQ ID NO: 1 to SEQ ID NO: 232. Accordingly, the normalization control peak may be at least one selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 232, and the selected peak may constitute a normalization control group. In addition, the normalization control peak may be at least one selected from the group consisting of SEQ ID NOs: 1 to 20, and the selected peak may constitute a normalization control group. More detailed information about the normalization control peak is shown in Table 4.

All 232 normalization control peaks of the present invention may be used for subsequent normalization factor calculation and differential peak selection, but after determining the order by arranging the mean of the area values of all peaks from SEQ ID NO: 1 to SEQ ID NO: 232 in descending order, 5 or more normalization control peaks may be utilized according to the rank, for example, 5 to 232 peaks, preferably 5 to 50 peaks, preferably 20 to 50 peaks may be used. As shown in FIG. 5A, in order to reflect the effect of adding the number of normalization control peaks, pairwise variations for all series of F_m and F_(m+1) was calculated so that as the number of normalization control peaks increased, the normalization factor (F) gradually converged to one value. That is, when the number of normalization control peaks is 5 or more, the values of F_m and F_(m+1) gradually converge so that the number of normalization control peaks is preferably 5 or more, and from 50, the normalization factor (F) gradually converges to one value without change according to the increase in the number of normalization control peaks (m). Considering the efficiency of data processing, it is desirable to set the number of control peaks to 50 or less. Therefore, in the present invention, the normalization control peaks are arranged in descending order by arranging the mean of the area values of all control peaks from SEQ ID NO: 1 to SEQ ID NO: 232 to determine the order, and then 5 or more peaks may be selected and utilized according to the rank. For example, 5 to 232 peaks, preferably 5 to 50 peaks, 5 to 20 peaks, 20 to 50 peaks can be used. In this case, 20 preferred normalization control peaks that can be used are shown as SEQ ID NOs: 1 to 20.

In addition, the present invention relates to a method for selecting the normalization factor (F) for quantification of ATAC-seq analysis, the method including steps of:

a) deriving the average height (k) of an individual normalization control peak selected from an individual sample by dividing the area of the individual normalization control peak selected by the above method by the peak width in an individual sample in a cohort as shown in the following formula:

Average height (k) of an Individual peak

$k=\frac{\mathrm{area}}{\mathrm{width}}=\frac{\mathrm{the}\mathrm{sum}\mathrm{of}\mathrm{enrichment}\mathrm{within}\mathrm{the}\mathrm{peak}}{\mathrm{the}\mathrm{base}\mathrm{length}\mathrm{of}\mathrm{peak}};$

b) selecting m number of normalization control peaks existing in an individual sample in the cohort, and deriving a mean height (h), which is a mean value of the average heights (k) of the selected m number of normalization control peaks, through the following formula:

Mean Height (h) of Selected Peaks Ach Sample

$h=\frac{{\sum }_{i=1}^m{k}_i}{m},k_i=\frac{\mathrm{the}\mathrm{area}{A}_i\mathrm{of}\mathrm{the}\mathrm{ith}\mathrm{peak}}{\mathrm{the}\mathrm{base}\mathrm{length}\mathrm{of}\mathrm{the}\mathrm{ith}\mathrm{peak}}$ m=the number of selected peaks in a sample; and

c) deriving an ATAC-seq normalization factor (F) through the following formula:

$F_{\mathrm{ab}}=\frac{s_a\left(=\frac{{\sum }_{i=1}^{n_a}{h}_{\mathrm{ai}}}{n_a}\right)}{h_{\mathrm{bj}}}\mathrm{for}1\le a\le b\le 2$ h _ai=the mean height of the fifth sample in the cohort C_a

h _bj=the mean height of the jth sample in the cohort C_b

n _a=the number of samples in the cohort C_a

The ATAC-seq normalization factor (F) normalizes the area of the peak region obtained by ATAC-seq, allowing quantitative comparison.

In the present invention, the term “discovery cohort” refers to a population for selecting a differential peak capable of predicting differentiation with respect to a condition of interest. The condition of interest may include various conditions expected or predicted to show a difference between samples according to the degree of chromatin openness, and the differentiation may be used in the same meaning as terms such as responsiveness and suitability.

In the embodiment of the present invention, a population to distinguish response and non-response, that is, responsiveness, to anti-PD-1 therapy as a condition of interest was used as a discovery cohort.

In the present invention, the term “validation cohort” refers to a population for validating the selected differential peak. In the validation cohort, the differential peak selected from the discovery cohort is applied to verify whether the selected differential peak can accurately predict the differentiation for the desired condition of interest.

In the above equation, when a=b, the discovery cohort and the validation cohort are the same, and the same normalization factor (F) is applied for normalization. In the embodiment of the present invention, the normalization factor (F) derived from the discovery cohort was equally applied to the validation cohort by making the discovery cohort and validation cohort the same.

In the present invention, the term “normalization factor (F)” is a factor for adjusting the openness value of ATAC-seq of each sample for quantitative comparison between samples and means a value obtained by dividing the mean height h for the “average height (k) of the normalization control peak” of all samples in the cohort by the mean height h of the normalization control peak selected from the sample to be analyzed. The normalization factor (F) used in the present invention is simpler and less system load for calculation compared to the conventionally used method. When data is processed by multiplying the normalization factor (F) by the peak area value, the area value can converge to the mean value of each group, allowing the quantitative adjustment. Therefore, ATAC-seq data collected from various samples and various cohorts can be adjusted using normalization factors (F), then utilized in the next analysis.

In the present invention, the mean height h means the mean height with respect to the “average height (k) of the normalization control peak” of the samples in the cohort, which means the chromatin openness of the individual samples. That is, the mean height (h) is the mean of k values present in the sample, and this value generally represents the chromatin openness of the normalization control peak of one sample.

In order to derive the normalization factor (F), 5 to 50 normalization control peaks present in individual samples in a cohort may be selected according to rank. As m, the number of normalization control peaks selected from the sample increases, the normalization value appears constant, so that normalization can be performed better. However, if the number is greater than a certain number, a better normalization effect according to the increase in the number of normalization controls is not confirmed, and the maximum effect is exhibited so that the number of optimal efficient normalization control peaks may be selected. The present inventors confirmed that the desired normalization could be sufficiently achieved by setting the number of normalization control peaks to 5 to 50, 5 to 20, and 20 to 50.

In addition, the present invention provides a method for normalizing the peak area of the ATAC-seq normalization factor (F) by multiplying the selected ATAC-seq normalization factor (F) as in the following formula:

Normalized area(A)=F_ab×A_q

in which F_ab is defined as follows:

$F_{\mathrm{ab}}=\frac{s_a\left(=\frac{{\sum }_{i=1}^{n_a}{h}_{\mathrm{ai}}}{n_a}\right)}{h_{\mathrm{bj}}}\mathrm{for}1\le a\le b\le 2$ h _ai=the mean height of the fth sample in the cohort C_a

h _bj=the mean height of the fth sample M the cohort C_b

n _a=the number of samples M the cohort C_a,

and A_q represents the area value of the q-th peak of the j-th sample of cohort C_b.

In addition, m, the number of normalization control peaks selected in the sample in the method may be 5 to 50.

In the present invention, the “normalized area (A)” is the same as the “peak area of the normalization factor (F),” and the peak area is used to quantitatively compare “chromatin openness.”

In addition, the present invention provides a method of selecting differential peak for predicting the responsiveness of anti-PD-1 therapy, the method including steps of: a) aligning and peak calling ATAC-seq data from patients who respond to anti-PD-1 therapy and those who do not; b) selecting a peak which is distinct from the responding and non-responding patient samples among the peak calling values of step a); c) normalizing the selected peak area by multiplying the peak area value (A_d) of the peak selected in step b) by the ATAC-seq normalization factor (F_ab) selected by the method of normalizing as in the following formula; Normalized area (A)=F_ab×A_d; d) selecting a peak having a normalized area value exceeding a mean area value of the normalized peak area values obtained in step c) from among the peaks selected in step b); and e) selecting a peak in which the mean difference between normalized peak area values from among the anti-PD-1 therapy responding and non-responding patient samples among the peaks selected in step d) satisfies significance p<0.05.

The A_d refers to the peak area value of the individual peaks selected in step b), and the normalized area of the individual peaks can be obtained by multiplying this by a normalization factor (F).

By selecting a peak having a normalized area value exceeding the mean of the normalized peak area values obtained in step c) from among the peaks selected in step d), a peak showing a distinct difference among all peaks can be selected and noise can be removed.

In the present invention, the term “differential peak” means a significant peak that appears differently between individuals as a result of ATAC-seq analysis, reflects different degrees of chromatin openness, and can be selected by the following method. Since the differential peak selected in the present invention is clearly different in responding patients and non-responding patients to anti-PD-1 therapy, it can be used as a biomarker for predicting the responsiveness of anti-PD-1 therapy. In particular, it can use differential peaks selected as highly predictable as “predictive peaks.” Here, “predictive peak” is used interchangeably with “predictive marker” and “biomarker.”

The peak calling of step a) is performed using two or more types of peak callers selected from the group consisting of HOMER suite, MACS2, and CisGenome, and the selection of step b) is characterized by selecting a common distinct peak from the two or more types of peak callers, thereby increasing reliability.

In a preferred embodiment of the present invention, among 2,560 differential peaks in the cohort, a peak exceeding a mean area value of the normalized area values by multiplying by a normalization factor (F) was selected. Thereafter, 121 differential peaks were selected by additionally selecting differential peaks in which the mean difference of normalized peak area values between the responder and non-responder groups was statistically significant (p<0.05). The list of normalized peaks in the normalization control sets of C₅, C₂₀, and C₅₀ using the normalization control peak numbers 5, 20, and 50 (C₅, C₂₀, C₅₀) in the differential peaks were compared. In these sets, 67 differential peaks shown in common without changing the order of the differential peaks were selected as predictive peaks finally confirming the responsiveness of anti-PD-1 therapy.

The present invention relates to a differential peak for predicting the responsiveness of anti-PD-1 therapy selected through the above method.

The differential peak is referred to herein as a predictive peak or a biomarker and may be one or more selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299, preferably one or more selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 241.

SEQ ID NOs: 233 to 241 are referred to as representative differential peaks in the specification of the present invention, and is the peak selected once again among the 67 differential peaks based on i) a large difference in the relative numerical values between the mean area values of the responder and non-responder groups, ii) a higher mean area value in the responder groups, or iii) the low variance between the area values of the peaks in the responder groups. They can provide information on the responsiveness of anti-PD-1 therapy with high sensitivity and specificity based on a more pronounced difference in chromatin openness.

In addition, the present invention provides a biomarker composition for predicting the responsiveness of anti-PD-1 therapy, including one or more predictive peaks selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299, preferably SEQ ID NO: 233, SEQ ID NO: 234, SEQ ID NO: 237, SEQ ID NO: 249, SEQ ID NO: 252, SEQ ID NO: 261, SEQ ID NO: 262, SEQ ID NO: 267, and SEQ ID NO: 268.

In addition, the present invention provides a method of predicting the responsiveness of anti-PD-1 therapy, the method including a step of detecting one or more predictive peaks selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299.

In addition, the present invention provides a use of one or more predictive peaks selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299 for predicting the responsiveness of anti-PD-1 therapy.

The description of the biomarker composition for predicting responsiveness applies equally to the responsiveness prediction method and responsiveness prediction use, and overlapping descriptions are excluded to avoid the complexity of the description.

The predictive peaks can effectively provide information on responsiveness prediction even with a single one, but when they are used in combination, responsiveness can be predicted with improved sensitivity and specificity. In particular, it was confirmed that only the combination of four predictive peaks achieved the optimal saturation point in sensitivity and specificity in the present invention.

Accordingly, the biomarker composition of the present invention may include a combination of four or more predictive peaks selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299, preferably, a combination of four or more predictive peaks selected from the group consisting of SEQ ID NO: 233, SEQ ID NO: 234, SEQ ID NO: 237, SEQ ID NO: 249, SEQ ID NO: 252, SEQ ID NO: 261, SEQ ID NO: 262, SEQ ID NO: 267, and SEQ ID NO: 268, and most preferably, a combination of SEQ ID NO: 268, SEQ ID NO: 249, SEQ ID NO: 252, and SEQ ID NO: 262 (‘M36+M17+M20+M30’).

In the present invention, the predictive peak can be characterized in that it provides information on the responsiveness of anti-PD-1 therapy by distinguishing the difference in chromatin openness between patients who respond to anti-PD-1 therapy and those who do not.

Hereinafter, the present invention will be described in detail through specific examples, but the scope of the present invention is not limited by the following examples.

[Modes of the Invention]

Experimental Method

ATAC-seq library preparation and ultrahigh-speed sequencing

Nuclei isolated from CD8⁺ T cells underwent a transposition reaction using Illumina transposase in 1× Tagment DNA (TD) buffer. Library fragments were amplified using indexing primers from the Nextera kit (Illumina, San Diego, Calif., USA), and quantified. The quantified library was then verified by quantitative PCR (qPCR) using the KAPA Library Quantification Kit (Roche Applied Science, Basel, Switzerland). The pool of indexed libraries was sequenced using Illumina NextSeq500 for 75 single-read base sequences and analyzed using the CASAVA (v. 1.8.2) base sequencing software (Illumina). Reads were checked using the FASTQC software to assess the quality of single-end or paired-end read sequences. For analysis, sequencing data of each sample required a Phred quality score and a total number of reads equal to or greater than 30 (Q30) and 20 million, respectively.

ATAC-Seq Alignment and Peak Calling

FASTQ data were processed using the Trimmomatic software to increase read quality (per-base sequence quality >30) prior to alignment in the human genome and then aligned to the primary assembly of the GRCh37/hg19 human genome (chr1˜22 and chrX) using the Bowtie2 software with the parameters ‘-k 4 -N 1-R 5-end-to-end’. Finally, aligned reads were converted into browser extensible data (BED) format using the ‘bedtools bamtobed’ parameter. The positive strand was offset by +4 bp and the negative strand was offset by −5 bp. Tn5 transposase occupied 9 bp during transposition.

Three peak callers were used to perform peak calling: HOMER suite, MACS2, and CisGenome. The aligned read information was used for peak calling with the HOMER suite using ‘findPeaks’ and the ‘-style factor-tbp 3-region’ option; MACS2 using ‘callpeaks’ and the ‘-nolambda-nomodel-shift 100-extsize 200’ option; and CisGenome using the ‘Read extension length=150 bp’, ‘Bin size=50 bp’, ‘Max gap=50 bp’, and ‘Min peak length=100 bp’ options. During peak calling, according to the guidelines of Encyclopaedia of DNA Elements (ENCODE), which blacklisted regions with a large number of aligned reads (ultra-high signal artifact, UHS), the regions were excluded from the resulting peaks. Signal tracks were generated using the HOMER suite with the ‘makeUCSCfile’ command and the ‘-fragLength 147-tbp 3-style chipseq-raw’ options and visualized in the UCSC genome browser (https://genome.ucsc.edu/).

Peak Area Calculation

There are two methods for identification of accessible chromatin regions (open chromatin) by transposase marked as peaks: a method using peaks of various ranges or a method using peaks of a fixed range. The method using peaks of the fixed range may be beneficial in the analysis of large peaks or large datasets. When the peak size is small, the area excluded from the peak range may affect data analysis. Therefore, a method using peaks of various ranges (‘variable width peak’) was applied and analyzed using the Homer suite.

While comparing the overall chromatin accessibility (i.e., chromatin openness), limiting the range of the actual peak to a fixed range cannot accurately determine the degree of openness within the actual peak range. Thus, all sample data were concatenated into a single set and peak calling was performed using the HOMER suite for ‘variable-width peak’. Peak regions thus obtained corresponded to each peak in each sample. Instead of tag enrichments, peak area, representing the sum of tag enrichment, was calculated using each genome browser file such as bedGraphs. Briefly, bedGraphs were generated using the HOMER suite with the ‘makeUCSCfile’ command and the ‘-fragLength 147-tbp 3-style chipseq-raw’ options. The area of each peak was then calculated by summing height per base pair in the peak range using a genome browser track file. For each sample, the area of the q-th peak region was computed as follows:

$A_q={\sum }_{p=X_q}^{Y_q}{d}_{\mathrm{pq}}.$

In the equation, d_pq refers to the height per base pair at position p of the coordinate from start (X_q) to end (Y_q) of the q-th peak. Finally, the peak areas were used to quantitatively compare “chromatin openness.”

Statistical Analyses

Data were expressed as means±standard errors of the mean (SEM), and the Mann-Whitney U test was used to compare difference between groups. The progression-free survival (PFS) and median PFS (mPFS) were determined using the Kaplan-Meier method. Groups were compared using the log-rank (Mantel-Cox) test. All statistical analyses were performed using GraphPad Prism 6.01 (GraphPad Software, La Jolla, Calif., USA) and R statistical software (R Foundation for Statistical Computing, Vienna, Austria). The Cutoff Finder (http://molpath.charite.de/cutoff/) was used to determine an optimal threshold for chromatin openness to classify responding and non-responding patients who were receiving anti-PD-1 therapy. Through receiver operating characteristics (ROC) curve analysis, the predictive accuracy of selected peaks was calculated. In all analyses, p<0.05 was considered to indicate statistical significance.

Example 1. Patient Group and Experimental Design

In order to predict the responsiveness of gastric cancer patients to anti-PD-1 therapy, characteristics of peripheral blood CD8⁺ T cells of metastatic gastric cancer (mGC) patients being treated with the anti-PD-1 therapy, Keytruda (pembrolizumab; Kenilworth, N.J., USA) were analyzed in this study. CD8⁺ T cells are the main target cells for anti-PD-1 therapy. The response to anti-PD-1 therapy is classified into progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR). It was confirmed that there was no significant difference in the frequencies of potential CD8⁺ T cells and PD-1+ CD8⁺ T cells between the patients in the group.

Chronic stress caused by factors such as pathogens, microbiota, acidity, hypoxia, glucose deprivation, and the tumor microenvironment can cause epigenetic alterations in cells that make up our body, which can also cause subtle alterations in immune cells that make up the immune system. Accordingly, the present inventors studied the epigenetic characteristics of CD8⁺ T cells in the peripheral blood of metastatic gastric cancer patients who received anti-PD-1 therapy, in particular, determined that the treatment prognosis of patients before anti-PD-1 therapy can be predicted by using the chromatin openness that is distinguished between the responder and non-responder groups to anti-PD-1 therapy and intended to develop a biomarker using the same.

This study was approved by the Institutional Review Boards of Seoul National University Hospital (0905-014-280) and Samsung Medical Center (2015-09-053). Patients participating in the clinical trial (clinicaltrials.gov identifier NCT #02589496) were designated as the discovery cohort. Patients with stage IV metastatic gastric cancer mGC (n=32) who were undergoing anti-PD-1 therapy with pembrolizumab were recruited, and blood was collected before treatment and every three weeks thereafter. In addition, from January to May 2018, a new cohort of patients with stage IV mGC (n=52) who were treated with pembrolizumab was recruited as the validation cohort. Patient information for the discovery and validation cohorts is presented in Tables 1 and 2. Written informed consent was obtained from all patients in accordance with the Declaration of Helsinki. Microsatellite instability (MSI) or Epstein-Barr virus (EBV) positive information for tumor specimens was obtained from a report of a previous study (Kim et al., Nat. Med. 2018).

TABLE 1
Discovery Cohort Patient Characteristics
	Number of	Age
Response	patients (n)	(years)
Total	33	56.7 ± 1.9
Complete response (CR)	2	62.5 ± 6.5
Partial response (PR)	8	59.3 ± 4.2
Stable disease (SD)	7	64.3 ± 5.2
Progressive disease (PD)	15	51.0 ± 3.7
Ages are presented as means ± standard errors of the mean (SEMs).

TABLE 2
Validation Cohort Patient Characteristics
		Number of	Age
	Response	patients (n)	(years)
	Total	52	60.9 ± 1.4
	Complete response (CR)	3	69.3 ± 6.6
	Partial response (PR)	15	63.3 ± 2.6
	Stable disease (SD)	12	61.3 ± 3.2
	Progressive disease (PD)	22	58.0 ± 3.7
	Ages are presented as means ± SEMs.

All CD8⁺ T cells were collected from blood samples of patients enrolled in a clinical trial phase II pembrolizumab, anti-PD-1 therapy, and for each sample, the genome-wide assay was performed for Tn5 transposase-accessible chromatin using ATAC-seq.

Through this analysis, in the present invention, biomarkers were derived for distinguishing between responder and non-responder patients before anti-PD-1 therapy, and it was confirmed that the prediction method using these biomarkers could have high accuracy and sensitivity. A protocol schematically illustrating the method is shown in FIG. 1.

The published ATAC-seq data were used for quantitative ATAC-seq analysis, and the data used are shown in Table 3.

TABLE 3
Sample	Total	Aligned	%	%	%				TSS
ID	reads	reads	Aligned	Duplicated	Mitochondria	NRF	PBC	FRIP	enrichment
SS01	79,369,131	72,958,765	91.9%	51.3%	31.4%	0.98	0.86	0.60	16.4
SS02	87,638,376	80,039,100	91.3%	43.0%	21.4%	0.98	0.83	0.47	16.0
SS03	126,685,260	115,837,870	91.4%	45.6%	21.2%	0.98	0.86	0.55	26.8
SS04	66,576,763	60,989,804	91.6%	43.1%	26.0%	0.98	0.89	0.64	28.2
SS05	69,794,733	63,072,918	90.4%	46.4%	26.2%	0.98	0.85	0.54	18.1
SS06	110,916,650	102,146,159	92.1%	46.1%	26.9%	0.98	0.86	0.53	11.9
SS07	87,534,113	80,315,836	91.8%	50.5%	29.0%	0.98	0.83	0.50	14.1
SS08	79,366,311	73,459,580	92.6%	41.6%	23.9%	0.98	0.88	0.53	15.7
SS09	111,528,381	102,440,329	91.9%	47.7%	21.2%	0.98	0.77	0.51	16.9
SS10	106,022,192	97,579,178	92.0%	46.6%	24.4%	0.98	0.83	0.51	21.3
SS11	71,755,642	66,493,486	92.7%	38.3%	20.8%	0.98	0.88	0.46	16.3
SS12	88,065,358	82,845,759	94.1%	47.2%	24.7%	0.98	0.82	0.42	14.3
SS13	64,060,244	60,505,431	94.5%	44.3%	21.5%	0.97	0.83	0.44	19.2
SS14	103,257,981	97,746,391	94.7%	39.3%	13.3%	0.98	0.76	0.51	16.8
SS15	124,158,216	116,490,224	93.8%	53.5%	16.8%	0.97	0.66	0.57	16.3
SS16	73,535,141	69,458,094	94.5%	36.6%	18.5%	0.98	8.83	0.53	21.9
SS17	84,446,831	79,526,537	94.2%	38.0%	14.7%	8.98	0.80	0.38	16.3
SS18	61,138,757	58,835,255	96.2%	36.3%	18.9%	0.99	0.87	0.49	26.6
SS19	74,499,318	71,572,361	96.1%	44.2%	21.4%	0.95	0.81	0.41	28.2
SS20	80,080,760	76,640,704	95.7%	41.4%	17.6%	0.98	0.80	0.48	16.1
SS21	70,826,527	67,917,053	95.9%	40.5%	18.0%	0.98	0.22	0.54	17.2
SS22	89,838,238	86,206,816	96.0%	45.4%	22.9%	0.97	0.83	0.48	14.0
SS23	25,306,813	69,825,407	92.7%	37.3%	18.0%	0.98	0.86	0.35	10.8
SS24	115,656,817	107,321051	92.8%	39.1%	14.7%	0.98	0.79	0.37	12.3
SS25	89,273,685	82,918,784	92.9%	49.1%	24.5%	0.98	0.79	0.42	15.3
SS26	107,117,518	99,277,606	92.7%	32.6%	11.6%	0.98	0.84	0.48	13.4
SS27	52,636,072	48,671,987	92.5%	38.5%	20.4%	0.98	0.87	0.44	15.8
SS28	64,582,832	60,171,173	93.2%	43.7%	23.8%	0.98	0.86	0.42	14.8
SS29	80,226,651	75,580,819	94.2%	57.0%	33.6%	0.97	0.51	0.46	23.0
SS30	101,999,249	95,839,289	94.0%	54.0%	30.3%	0.97	0.81	0.39	13.5
SS31	49,007,643	45,924,143	93.7%	30.1%	14.7%	0.98	0.89	0.61	24.0
SS32	114,145,932	107,358,491	94.1%	53.5%	24.7%	0.97	0.74	0.54	20.5
* NRF, non-redundant fraction;
PBC, polymerase chain reaction (PCR) bottlenecking coefficient;
FRIP, fraction of reads in peaks;
TSS, Transcription start Site

In order to improve the working speed of the computer delayed by the size of the data used for analysis, the Euclidian distance method was used. In the analysis of human genome (hg19)-mapped ATAC-seq data, a two-step selection process was performed to have a normalization control peak selection for normalization as a pre-step to discover a differential peak that is distinguished between responder and non-responder groups and a differential peak selection, and this process and the process of deriving a ‘differential peak’ for finally predicting the responsiveness of anti-PD-1 therapy is shown in FIG. 2.

As shown in FIG. 2, after ATAC-seq library generation, each library pool was quantified using a Bioanalyzer and sequenced on a single lane of the NextSeq 500 system using 75-bp single reads. Sequencing read (fastq) files were mapped using the human genome database (hg19) and Bowtie v. 2.0 software. Further analysis was performed in two stages:

1) Selection of a consensus peak among CD8⁺ T-cell subsets and selection of a control peak by matching it to the consensus peak known as a DNase I hypersensitive site (“normalization control peak” selection step) followed by normalization of peaks from each patient using the normalization control peaks (normalization step); and 2) final selection of predictive peak to distinguish response and non-response to anti-PD-1 therapy after normalization of the selected differential peaks. The normalization control peak and normalization step of step 1) are shown in more detail in FIG. 4.

Example 2. Selection of Normalization Control Peaks and Derivation of Normalization Factors (F)

The first selection step is to find a consensus peak to be applied as a normalization control and select it as a normalization control peak. That is, the normalization control group was determined for normalization from the results of CD8⁺ T cells ATAC-seq by the selection of peaks with low variability across samples. For this purpose, ATAC-seq data derived from the CD8⁺ T-cell subset including naïve and memory CD8⁺ T cells from PBMCs (GSE89308) obtained from healthy volunteers were used to screen control peaks for normalization. For all sample data, peak calling was performed using the HOMER suite to generate peak regions. Next, the area of selected peaks from each CD8⁺ T-cell subset was calculated using bedGraphs. After comparisons of CD8⁺ T-cell subsets, a total of 32,485 overlapped peaks were obtained among all CD8⁺ T-cell subsets using ‘findOverlaps.’ They were matched to the DNase I hypersensitivity consensus peak discovered by the ENCODE project located at the transcription start site (TSS) or 5′ untranslational region (5′ UTR), a transcriptionally active region of the genome, thereby selecting 4,798 peaks that matched 100% with DNase I hypersensitivity consensus peaks and were located on the TSS or 5′ UTR.

Among the selected peaks, 533 consensus ATAC peaks identified in the ATAC-seq data from CD8⁺ T cells used in this study were selected once again. Among the selected consensus ATAC-seq peaks, in order to satisfy the criterion of low diversity and showed high similarity between samples, two criteria with a coefficient of variation of less than 0.3 and a peak width of less than 500 bp were applied to select peaks. The peak width of 500 bp means a base pair size including up to two nucleosomes.

As a result, 232 ATAC peaks were selected, and they are shown in Table 4 below with information designating them. The numbers of the start and end positions of the peaks shown in Table 4 were designated based on hg19.

The 232 selected normalization control peaks shown in Table 4 are evolutionarily conserved among 17 species, and thus they may be useful for genomic studies of other species as well. In addition, it was confirmed that they could be applied to other cells such as CD4⁺ T cells and CD14+ monocytes, and the results are shown in Example 6 below in detail. Thereafter, the normalization control peak selected was applied for normalization in the cohort.

TABLE 4
Selected normalization control peaks
	Chromo-
Control	some	Start	End	Symbol	Annotation
C1	chr2	198,317,581	198,319,004	COQ10B	Promoter, TSS	1
C2	chr10	97,453,104	97,454,237	TCTN3	5′ UTR	2
C3	chr3	93,698,438	93,699,698	ARL138	Promoter, TSS	3
C4	chr7	44,121,720	44,122,668	POLM	Promoter, TSS	4
C5	chr7	124,569,281	124,570,400	POT1	Promoter, TSS	5
C6	chr11	124,980,971	124,982,078	TMEM218	Promoter, TSS	6
C7	chr5	112,311,713	112,313,144	DCP2	Promoter, TSS	7
C8	chr12	49,503,923	49,504,972	LMBR1L	5′ UTR	8
C9	chr11	73,881,841	73,382,698	PPME1	Promoter, TSS	9
C10	chr12	57,039,601	57,040,435	ATP5F1B	Promoter, TSS	10
C11	chr5	149,339,838	149,340,820	SLC26A2	Promoter, TSS	11
C12	chr3	127,842,341	127,843,211	RUVBL1	Promoter, TSS	12
C13	chr16	72,042,308	72,043,066	DHODH	Promoter, TSS	13
C14	chr2	197,503,899	197,504,911	CCDC150	Promoter, TSS	14
C15	chr10	99,446,579	99,447,600	AVPI1	Promoter, TSS	15
C16	chr12	54,712,365	54,719,307	COPZ1	Promoter, TSS	16
C17	ch5	68,462,420	68,463,306	CCNB1	Promoter, TSS	17
C18	chr20	13,619,031	13,620,138	TASP1	Promoter, TSS	18
C19	chr19	33,462,571	33,463,350	CEP89	Promoter, TSS	19
C20	chr19	104,359,038	104,360,317	TDG	Promoter, TSS	20
C21	chr8	74,883,696	74,885,141	ELOC	promoter, TSS	21
C22	chr3	186,287,767	186,289,030	DNAJB11	promoter, TSS	22
C23	chr3	99,979,029	99,980,513	TBC1D23	5′ UTR	23
C24	chr4	141,444,608	141,446,084	ELMOD2	promoter, TSS	24
C25	chr14	45,603,137	45,604,667	FKBP3	promoter, TSS	25
C26	chr7	102,003,917	102,004,669	LOC100630923	promoter, TSS	28
C27	chr9	131,842,917	131,843,849	DQPP1	promoter, TSS	27
C28	chr12	54,120,913	34,121,840	CALCOCO1	promoter, TSS	28
C29	chr14	23,398,267	23,399,495	LOC101926933	promoter, TSS	29
C30	chr16	2,205,033	2,205,927	SNHG19	promoter, TSS	39
C31	chr20	60,813,046	60,813,770	OSBPL2	promoter, TSS	31
C32	chr2	216,176,146	216,177,543	ATIC	5′ UTR	32
C33	chr1	202,317,372	202,318,359	PPP1R128	promoter, TSS	33
C34	chr7	149,535,044	149,536,019	ZNF862	promoter, TSS	34
C35	chr14	23,257,625	53,258,794	GNPNAT1	5′ UTR	35
C36	chr7	23,338,269	23,339,795	MALSU1	promoter, TSS	36
C37	chr17	46,124,877	46,126,026	NFE2L1	promoter, TSS	37
C38	chr15	100,273,047	100,274,120	LYSMD4	promoter, TSS	38
C39	chr1	36,614,610	36,615,677	TRAPPC3	promoter, TSS	39
C40	chr3	66,270,564	66,271,965	SLC25A26	promoter, TSS	48
C41	chrX	119,763,449	119,764,519	C1GALT1C1	promoter, TSS	41
C42	chr14	73,924,904	73,925,758	NUMB	promoter, TSS	42
C43	chr10	104,004,821	104,005,489	GBF1	promoter, TSS	43
C44	chr20	23,338,203	23,339,458	LINC01431	promoter, TSS	44
C45	chr7	56,173,782	56,174,973	CHCHD2	promoter, TSS	45
C48	chr3	127,770,514	127,771,610	SEC61A1	promoter, TSS	46
C47	chr6	170,893,029	170,894,413	PDCD2	promoter, TSS	47
C48	chr2	27,008,384	27,009,106	CENPA	promoter, TSS	48
C49	chr13	25,875,227	25,876,522	NUP58	5′ UTR	49
C50	chr3	14,692,665	14,693,338	CCDC174	promoter, TSS	50
C51	chr20	5,093,144	5,094,245	TMEM230	promoter, TSS	51
C52	chr6	88,299,162	88,300,219	RARS2	promoter, TSS	52
C53	chr12	58,145,785	88,146,823	CDK4	promoter, TSS	53
C54	chr6	111,279,278	111,280,465	GTF3C6	5′ UTR	54
C55	chr1	155,278,181	155,278,987	FDPS	promoter, TSS	55
C56	chr4	185,654,644	185,655,789	CENPU	promoter, TSS	56
C57	chr10	13,203,033	13,203,924	MCM10	promoter, TSS	57
C58	chr2	219,575,276	219,376,173	TTLL4	5′ UTR	58
C59	chr6	170,615,298	170,616,376	FAM120B	promoter, TSS	59
C60	chr17	1,419,528	1,420,391	INPPSK	promoter, TSS	60
C61	chr14	32,030,613	32,031,121	NUBPL	promoter, TSS	61
C62	chr20	43,150,146	43,151,236	SERINC3	promoter, TSS	62
C63	chr19	36,504,997	36,505,620	LOC101927572	promoter, TSS	63
C64	chr7	150,075,855	150,076,741	ZNF775	promoter, TSS	64
C65	chr18	39,534,461	39,535,915	PIK3C3	promoter, TSS	65
C66	chr9	35,111,101	35,111,979	FAM2148	promoter, TSS	66
C67	chr20	49,126,527	49,127,249	PTPN1	promoter, TSS	67
C68	chr20	33,264,551	33,265,369	PIGU	5′ UTR	68
C69	chr9	127,905,442	127,906,255	SCAI	promoter, TSS	69
C70	chr11	61,891,126	61,891,809	INCENP	promoter, TSS	70
C71	chr12	49,463,427	49,464,134	RHEBL1	promoter, TSS	71
C72	chr19	11,456,825	11,457,389	CCDC159	promoter, TSS	72
C73	chr12	110,939,483	110,940,450	VPS29	promoter, TSS	73
C74	chrX	77,150,435	77,151,643	MAGT1	promoter, TSS	74
C75	chr6	49,430,355	49,431,624	MUT	promoter, TSS	75
C76	chr1	203,764,424	203,765,127	ZC3H11A	5′ UTR	76
C77	chr3	128,902,187	126,903,439	CNBP	promoter, TSS	77
C78	chr2	85,822,301	85,823,256	RNF181	promoter, TSS	73
C79	chr9	95,055,786	95,056,422	IARS	promoter, TSS	79
C80	chr7	54,826,380	54,827,704	LOC100996654	promoter, TSS	80
C81	chr14	24,604,884	24,665,759	PSME1	promoter, TSS	81
C82	chr1	156,163433	156,164,073	SLC2544	promoter, TSS	82
C83	chr17	7,835,138	7,835,885	CNTROB	promoter, TSS	33
C84	chr9	115,983,271	115,983,998	FKBP15	promoter, TSS	84
C85	chr18	21,082,699	21,083,898	RMC1	promoter, TSS	85
C86	chr2	69,663,818	69,665,064	NFU1	5′ UTR	86
C87	chr3	50,396,620	50,397,330	TMEM115	promoter, TSS	87
C88	chr10	104,263,341	104,264,065	SUFU	promoter, TSS	88
C89	chr10	105,991,750	105,992,632	CFAP43	promoter, TSS	89
C90	chr6	31,125,807	31,126,453	CCHCR1	promoter, TSS	99
C91	chr16	81,348,085	81,349,123	GAN	promoter, TSS	91
C92	chr6	32,095,827	32,096,372	ATF6B	promoter, TSS	92
C93	chr12	114,403,635	114,404,675	RBM19	promoter, TSS	93
C94	chr2	97,405,416	97,406,226	LMAN2L	promoter, TSS	94
C95	chr1	203,830,373	203,831,113	SNRPE	promoter, TSS	95
C96	chr17	47,492,047	47,492,791	PHB	promoter, TSS	96
C97	chr17	35,715,849	35,716,593	ACACA	promoter, TSS	97
C98	chr19	36,119,454	36,120,170	RBM42	promoter, TSS	98
C99	chr19	39,935,545	39,936,590	SUPTSH	promoter, TSS	99
C100	chr7	128,116,567	123,117,671	METTL28	promoter, TSS	100
C101	chr12	50,419,084	50,419,686	RACGAP1	promoter, TSS	101
C102	chr15	64,679,669	64,680,238	TRIP4	promoter, TSS	M2
C103	chr3	48,481,320	48,482,057	TMA7	promoter, TSS	103
C104	chr19	49,990,391	49,991,067	RPLISA	promoter, TSS	104
C105	chr9	34,178,559	34,179,245	UBAPI	promoter, TSS	105
C106	chr19	11,266,326	11,266,924	SPC24	promoter, TSS	106
C107	chr1	54,665,221	54,666,231	CY35RL	promoter, TSS	M7
C108	chr22	18,586,359	18,560,921	PEX26	promoter, TSS	108
C109	chr12	56,727,306	56,728,289	PAN2	promoter, TSS	109
C110	chr17	41,277,224	41,277,747	BRCA1	promoter, TSS	116
C111	chr19	13,858,361	13,858,934	CCDC130	promoter, TSS	111
C112	chr19	5,977,894	5,978,520	RANBP3	promoter, TSS	112
C113	chr20	37,100,840	37,101,662	RALGAPB	promoter, TSS	113
C114	chr22	30,234,080	30,234,725	ASCC2	promoter, TSS	114
C115	chr17	27,139,070	27,140,040	FAM222B	promoter, TSS	115
C116	chr19	36,138,848	36,139,368	COX661	promoter, TSS	116
C117	chr7	128,095,471	128,096,320	HILPDA	promoter, TSS	117
C118	chr19	11,708,005	11,708,637	ZNF627	promoter, TSS	118
C119	chr17	26,971,797	26,972,686	KIAA0100	promoter, TSS	119
C120	chr2	172,864,231	172,865,374	METAP1D	promoter, TSS	120
C121	chr20	1,098,917	1,099,591	PSMF1	promoter, TSS	121
C122	chr20	37,590,596	37,591,507	DHX35	promoter, TSS	122
C123	chr19	51,611,543	51,612,188	CTU1	promoter, TSS	123
C124	chr12	51,477,051	51,477,666	CSRNP2	promoter, TSS	124
C125	chr7	44,240,271	44,240,926	YKT6	promoter, TSS	125
C126	chr15	59,396,962	59,397,659	CCNB2	promoter, TSS	126
C127	chr2	73,963,997	73,984,995	TPRKB	promoter, TSS	137
C128	chr3	48,754,468	48,754,973	IP6K2	promoter, TSS	128
C129	chr10	105,156,089	105,156,683	PDCD11	promoter, TSS	129
C130	chrX	118,986,796	118,987,387	UPF3B	promoter, TSS	130
C131	chr7	102,937,440	102,938,298	PMPCB	promoter, TSS	131
C132	chr19	54,605,926	54,606,340	NDUFA3	promoter, TSS	132
C133	chr2	55,920,623	55,921,389	PNPT1	promoter, TSS	133
C134	chr20	44,044,468	44,044,977	PIGT	promoter, TSS	134
C135	chr17	57,784,612	37,785,199	PTRH2	promoter, TSS	135
C136	chr1	43,311,917	43,312,416	ZNF691	promoter, TSS	136
C137	chr12	121,018,994	121,019,692	POP5	promoter, TSS	137
C138	chr2	234,762,839	234,763,530	HJURP	promoter, TSS	138
C139	chr19	18,668,397	18,668,838	KXD1	promoter, TSS	139
C140	chr19	53,465,796	53,466,658	ZNF816-ZNF321P	promoter, TSS	140
C141	chr12	57,881,460	57,882,173	MARS	promoter, TSS	141
C142	chr19	45,873,666	45,874,137	ERCC2	promoter, TSS	142
C143	chr19	58,360,995	58,361,445	ZNF587	promoter, TSS	143
C144	chr22	35,795,783	35,796,265	MCM5	promoter, TSS	144
C145	chr12	124,118,029	124,118,558	GTF2H3	promoter, TSS	145
C145	chr19	1,039,781	1,040,256	ABCA7	promoter, TSS	146
C147	chr1	6,259,411	6,259,954	RPL22	promoter, TSS	147
C148	chr18	47,900,933	47,901,758	5KA1	promoter, TSS	148
C149	chr1	44,440,063	44,440,783	ATP6V0B	5' UT	149
C150	chr1	26,758,482	26,759,032	DHDDS	promoter, TSS	136
C151	chr22	39,189,835	39,190,462	DNAL4	promoter, TSS	131
C152	chr17	5,094,839	5,095,522	ZNF594	promoter, TSS	152
C153	chr22	23,483,879	23,484,614	RSPH14	promoter, TSS	153
C154	chr9	131,591,658	131,592,394	SPOUT1	promoter, TSS	154
C155	chr8	37,707,049	37,707,721	BRF2	promoter, TSS	155
C156	chr9	44,079,591	44,080,138	XRCC1	promoter, TSS	156
C157	chr17	56,296,205	56,296,948	MKS1	promoter, TSS	157
C158	chr1	44,435,330	44,435,859	DPH2	promoter, TSS	158
C159	chr18	55,297,133	55,297,928	LOC100505549	promoter, TSS	159
C160	chr22	18,111,305	18,111,995	ATP6V1E1	promoter, TSS	160
C161	chr1	44,172,843	44,173,445	ST3GAL3	promoter, TSS	161
C162	chrX	129,299,437	129,300,425	AIFM1	promoter, TSS	162
C163	chr11	46,721,735	46,722,555	ARHGAP1	promoter, TSS	163
C164	chr19	7,694,281	7,694,827	XAB2	promoter, TSS	164
C165	chr3	124,448,809	124,449,799	UMPS	promoter, TSS	165
C166	chr1	231,376,566	231,377,356	C1orf131	promoter, TSS	166
C167	chr19	2,819,568	2,820,267	ZNF554	promoter, TSS	167
C168	chr1	222,763,083	222,763,717	TAF1A-AS1	promoter, TSS	168
C169	chrX	48,554,647	48,555,337	SUV39H1	promoter, TSS	169
C170	chr20	62,496,100	62,496,814	TPD52L2	promoter, TSS	170
C171	chr16	2,509,755	2,510,317	TEDC2	promoter, TSS	171
C172	chr15	65,117,705	65,118,167	PIF1	promoter, TSS	172
C173	chr9	130,679,067	130,670,693	ST6GALNAC4	promoter, TSS	173
C174	chr19	19,249,083	19,249,488	TMEM161A	promoter, TSS	174
C175	chr7	127,983,505	127,984,235	RBM28	promoter, TSS	175
C176	chr14	100,842,464	100,843,974	WDR25	promoter, TSS	176
C177	chr19	17,858,224	17,858,712	FCHO1	promoter, TSS	177
C178	chr11	68,816,050	68,816,709	TPCN2	promoter, TSS	178
C179	chr1	53,703,996	53,704,524	LOC100507564	promoter, TSS	179
C180	chr9	35,102,785	35,103,665	STOML2	promoter, TSS	186
C181	chr8	145,980,585	145,981,260	ZNF251	promoter, TSS	181
C182	chr16	1,993,129	1,993,666	MSRB1	promoter, TSS	182
C183	chr9	131,133,302	131,133,896	URM1	promoter, TSS	183
C184	chr6	31,774,452	31,774,898	LSM2	promoter, TSS	184
C185	chr20	48,732,309	48,732,751	UBE2V1	promoter, TSS	185
C186	chr1	33,502,358	33,502,787	AK2	promoter, TSS	186
C187	chr22	47,158,220	47,158,853	TBC1D22A	promoter, TSS	187
C188	chr1	151,118,980	151,119,468	SEMA6C	promoter, TSS	188
C189	chr12	57,940,810	57,941,242	DCTN2	promoter, TSS	189
C190	chr17	73,452,363	73,452,863	TMEM94	promoter, TSS	190
C191	chr15	74,753,150	74,753,777	U8L7	promoter, TSS	191
C192	chr15	90,895,226	90,095,796	ZNF774	promoter, TSS	192
C193	chr20	62,526,139	62,526,683	DNAICS	promoter, TSS	193
C194	chrX	9,430,908	9,431,616	TBL1X	promoter, TSS	194
C195	chr9	127,177,528	127,178,022	PSMB7	promoter, TSS	195
C196	chr1	39,456,512	39,457,243	AKIRIN1	promoter, TSS	196
C197	chr3	113,557,417	113,558,074	GRAMD1C	promoter, TSS	197
C198	chr19	57,922,343	57,922,830	ZNF17	promoter, TSS	198
C199	chr15	44,092,539	44,093,175	HYPK	promoter, TSS	199
C200	chr3	50,385,450	50,365,942	TUSC2	promoter, TSS	200
C201	chr6	32,143,699	32,144,130	AGPAT1	promoter, TSS	201
C202	chr11	3,078,509	3,078,932	CARS	promoter, TSS	202
C203	chr11	67,236,528	67,237,017	TMEM134	promoter, TSS	203
C204	chr7	152,372,873	152,373,539	XRCC2	promoter, TSS	204
C205	chr17	3,749,353	3,749,872	NCBP3	promoter, TSS	205
C206	chr20	34,129,573	34,130,131	EBGIC3	promoter, TSS	206
C207	chr17	71,083,638	71,089,155	SLC39A11	promoter, TSS	207
C208	chr20	60,982,173	60,982,776	CABLES2	promoter, TSS	208
C209	chr19	49,954,957	49,955,341	PIH1D1	promoter, TSS	209
C210	chr3	48,646,854	48,647,360	UQCRC1	promoter, TSS	210
C211	chr5	141,258,478	141,259,116	PCDH1	promoter, TSS	211
C212	chr15	75,315,741	75,316,129	PPCDC	promoter, TSS	212
C213	chr22	31,607,972	31,608,374	LIMK2	promoter, TSS	213
C214	chr17	28,804,115	28,804,621	GOSR1	promoter, TSS	214
C215	chr2	239,228,878	239,229,541	TRAF3IP1	promoter, TSS	215
C216	chr10	104,192,104	104,192,680	CUEDC2	promoter, TSS	216
C217	chr6	43,484,463	43,485,031	POLR1C	promoter, TSS	217
C218	chrX	47,863,242	47,863,782	ZNF182	promoter, TSS	218
C219	chr1	226,270,903	226,271,686	UNC01703	promoter, TSS	219
C220	chr6	33,547,877	33,548,329	BAK1	promoter, TSS	220
C221	chr1	27,718,810	27,719,275	GPR3	promoter, TSS	221
C222	chr9	97,021,321	97,021,758	ZNF169	promoter, TSS	222
C223	chr1	40,997,054	40,997,617	ZNF684	5′ UTR	223
C224	chr19	55,574,368	55,574,744	RDH13	promoter, TSS	224
C225	chr8	23,145,369	23,145,952	R3HCC1	promoter, TSS	225
C226	chr14	71,067,168	71,067,834	MED6	promoter, TSS	226
C227	chr2	120,739,834	120,740,290	SIRT4	promoter, TSS	227
C228	chr17	28,443,500	28,443,993	NSRP1	promoter, TSS	228
C229	chr1	38,156,043	38,156,578	C1of109	promoter, TSS	229
C230	chrX	47,049,953	47,050,436	UBA1	promoter, TSS	238
C231	chr17	56,494,850	56,495,237	RNF43	promoter, TSS	231
C232	chr7	73,703,473	73,704,041	CLP2	promoter, TSS	232

Calculation of Normalization Factor (F)

Under the assumption that the shape of the selected control peak is different for each sample, the average height (k) of one peak is calculated by dividing the peak area of the normalization control peak by the peak width. As a result, it was converted into a simple figure as shown in FIG. 3. The mean height (h) of the entire control peak was obtained by dividing the sum of the average peak (k) of each of m number of normalization control peaks selected from a sample of one patient by m. That is, the mean height (h) is the mean of k values in one sample, and this value represents the chromatin openness throughout the normalization control peak of one sample. Finally, a normalization factor (F) was obtained by dividing h of all samples in the cohort by the mean height (h) of the selected peak in the sample to be normalized, that is, each sample in order to adjust the openness value of ATAC-seq peak of each sample for quantitative comparison between samples. A schematic diagram for the formula used and the formula are shown in FIG. 3.

The normalization factor (F) derived in this manner has the advantage that it can be calculated by a simpler and less system load method compared to the conventional method. The normalization factor (F) means a value obtained by dividing the mean height of the normalization control peak of all samples in the discovery cohort by the mean height of the normalization control peak of the sample to be analyzed. If this is multiplied by the area value of the peak region, the value converges to the mean of each group, allowing the amount to be corrected.

This is described in detail through the formula as follows. In order to follow a normal distribution, the discovery cohort C₁ (Table 1) with sample size n₁=32 and validation cohort C₂ (Table 2) with sample size n₂=52 were randomly selected. For each sample, the mean height (h) of the entire normalization control peak was calculated as follows.

$h=\frac{{\sum }_{i=1}^m{k}_i}{m}$

In the above formula, as the number m of normalization control peaks selected from the sample increases, the normalization value appears uniformly, allowing better normalization. In the present invention, the change in the normalization factor (F) was confirmed by changing the number of selected normalization control peaks from 1 to 232, which is further described with reference to FIG. 5. k is the average height of the i-th peak in the sample and is calculated as follows.

$k_i=\frac{\mathrm{the}\mathrm{area}{A}_i\mathrm{of}\mathrm{the}\mathrm{ith}\mathrm{peak}}{\mathrm{the}\mathrm{base}\mathrm{length}\mathrm{of}\mathrm{the}\mathrm{ith}\mathrm{peak}}$

The peak area was calculated as the sum of the tag enrichment within the peak. Since the widths of the selected peaks in each sample mean the length of the base sequence, they were all considered equivalent, so the mean height of the derived control peaks, was used to represent the mean amount of the control peaks selected for each sample. For each integer pair (a, b) such that 1≤a≤b≤2, the normalization factor (F) was defined as:

$F_{\mathrm{ab}}=\frac{s_a\left(=\frac{{\sum }_{i=1}^{n_a}{h}_{\mathrm{ai}}}{n_a}\right)}{h_{\mathrm{bi}}}$

where h_ai and h_bj are the mean heights of the i-th and j-th samples in cohorts C_a and C_b, respectively. The normalization factor F_ab compares the j-th sample in cohort C_b with those of cohort C_a. For example, f₁₁ compares the j-th sample in discovery cohort C₁ with those in discovery cohort C₁. That is F₁₁ means that the normalization factor of the discovery cohort is equally applied to the validation cohort. F₁₂ compares the j-th sample in discovery cohort C₁ with those in validation cohort C₂, meaning that the discovery cohort and the validation cohort are different, and a normalization factor (F) is derived, respectively.

The peak areas of each sample were normalized by multiplying their normalization factors (F) as follows:

Normalized area (A)=F_ab×A_q

where F_ab is the normalization factor calculated from the j-th sample in cohort C_b with those in cohort C_a and A_q is the area value of the q-th peak in the j-th sample in cohort C_b.

Hereinafter, the normalization factor (F) is used to normalize the differential peak, and in this case, the q-th peak means the differential peak of the sample.

FIG. 4 shows the normalization control peak selection and normalization process described above.

Example 3. Differential Peak Selection

As a second step, a differential peak was selected to identify the chromatin region among ATAC-seq data of CD8⁺ T cells that can reflect the clinical results of anti-PD-1 therapy. The discovery cohort shown in Table 1 was analyzed to select a differential peak that distinguishes response and non-response to anti-PD-1 therapy.

To select differential peaks from ATAC-seq data of CD8⁺ T cells in patients with gastric cancer who were receiving anti-PD-1 therapy, a responder group was chosen that included patients with complete response (CR) and partial response (PR); a non-responder group was chosen that included only patients with progressive disease (PD). That is, in the “selection step” to select the differential peak, the patient group with stable disease (SD) was excluded. Within the discovery cohort, the responder and non-responder groups were classified based on the clinical decision at a certain time point after finishing multiple cycles of anti-PD-1 therapy.

The responder and non-responder group data were pre-processed using the peak calling package, and as a result of peak calling detection using the peak callers Homer, MAC2, and CisGenome, 2,560 peaks commonly found in the three peak callers were selected to increase the reliability.

In order to quantitatively analyze the peaks distinguished between the responder and non-responder groups, the area values of 2,560 differential peaks in each sample were normalized using the normalization factor (F) value determined in the first step. In order to determine the number of normalization control peaks to be used at this time, Table 5 shows the normalization factors (F) calculated from three normalization control peak sets up to 5, 20, and 50 ranks according to rank.

TABLE 5
Normalization factor (F)
	Sample	Ranked	Ranked	Ranked
Response	ID	~5	~20	~50
Responders	SS01	1.11	1.14	1.16
(CR + PR)	SS11	1.05	1.07	1.09
	SS04	1.07	1.11	1.14
	SS06	0.95	0.96	0.98
	SS08	1.01	1.06	1.06
	SS10	0.75	0.75	0.75
	SS12	0.92	0.97	0.57
	SS13	0.90	0.92	0.91
	SS28	0.97	1.02	1.04
	SS29	0.90	0.91	0.93
Non-	SS03	0.78	0.76	0.77
responders	SS15	0.74	0.74	0.72
(SD)	SS16	1.11	1.15	1.16
	SS23	1.20	1.11	1.12
	SS24	1.24	1.18	1.17
	SS30	0.87	0.78	0.76
	SS32	0.82	0.74	0.72
Non-	SS02	0.90	0.94	0.95
responders	SS05	1.03	1.06	1.08
(PD)	SS07	1.07	1.04	1.02
	SS09	0.82	0.82	0.82
	SS14	1.39	1.38	1.37
	SS17	1.44	1.36	1.33
	SS18	1.29	1.36	1.41
	SS19	1.05	1.10	1.10
	SS20	1.13	1.12	1.11
	SS21	0.93	0.97	0.96
	SS22	0.82	0.84	0.83
	SS25	0.97	0.99	1.02
	SS26	1.15	1.04	1.02
	SS27	1.27	1.25	1.24
	SS31	1.44	1.42	1.43

Specifically, the F value and the corresponding normalized area of each sample converge to a constant value as the number of normalization control peaks (C₅, C₂₀, C₅₀) increased, respectively, or the rank of the normalized area was kept in a stable state. The stabilization process and the selection process of multiple normalization control peaks are shown in FIG. 5.

As shown in FIG. 5, the number of control peaks to be used (1≤m≤232, m is an integer) was selected among the 232 normalization control peaks selected in Example 2 and Table 4, which were ranked in descending order according to the mean area of the sample peaks. Individual normalization factors (F) were calculated by the method confirmed in Example 2.

As shown in FIG. 5A, pairwise variation was calculated for all series F_m and F_(m+1) in order to reflect the effect of adding the normalization control peak. The gray vertical line represents the number of the normalization control peaks for peak normalization between samples. As the number of normalization control peaks increased, the normalization factor (F) gradually converged to one value. After the number of normalization control peaks exceeds 50, the normalization factor (F) gradually converged to one value without any change according to the increase in the number m of control peaks.

In FIG. 5B, for 121 differential peaks, F_m was calculated according to the number of normalization control peaks, and they were sorted by normalized area values, respectively, and the rank change of each peak was visualized. In order to obtain normalized area values (A), when ATAC-seq data for 2,560 differential peaks identified in common in the three peak callers was normalized using F calculated with three normalization control sets (C₅, C₂₀, C₅₀), the differential peaks were maintained without reversal of the rank. Therefore, it can be confirmed that normalization can be performed well because the rank of the differential peaks does not change and does not show a large fluctuation even if the number of controls is changed in the normalization control set or more. Therefore, a normalization control set of C₅, C₂₀, and C₅₀ was used in the following.

A process of selecting a desirable predictive peak that can be used for prognosis prediction from the differential peak was additionally performed using the following two-step process.

First, as a first step, the mean of the normalized area values of the differential peaks among 2,560 differential peaks in the cohort was obtained. Distinct differential peaks with normalized area values exceeding the mean area values were selected. 121 differential peaks were selected by additionally selecting differential peaks in which the mean difference in normalized peak area values between the responder and non-responder groups was statistically significant (p<0.05). As a second step, normalized peak lists were compared in the normalization control sets of C₅, C₂₀, and C₅₀ using normalization controls with the number of controls 5, 20, and 50 (C₅, C₂₀, C₅₀) so that there was no reversal of the rank. In these sets, 67 differential peaks that appear in common without reordering the differential peaks were selected. The selected 67 differential peaks were ordered according to differences in the mean values shared by the three sets of controls. They were ranked by the relative distance between responder and non-responder groups, the area of each peak was divided by the largest area among all samples and then calculated means from each group.

The process of finally selecting 67 differential peaks among the 2,560 peaks described above is shown in FIG. 6.

As shown in FIG. 6A, three peak callers of Homer suite, MACS2, and CisGenome were used to select 2,560 differential peaks that distinguish anti-PD-1 therapy responder groups (complete response+partial response, CR+PR) and non-responder groups (progress disease, PD). 121 peaks were selected according to the criteria and calculation formula described above, and, finally, 67 peaks were selected. The 67 differential peaks were identically selected in the results processed with three sets of normalization control peaks (C₅, C₂₀, and C₅₀).

Chromatin accessibility heatmaps represent normalized peak area values (rows) and patients (columns). Minimum and maximum area values are indicated in navy and yellow, respectively.

FIG. 6B is a view of showing the visual result of nine representative differential peaks (M1, 2, 5, 17, 20, 29, 30, 35, 36) among the selected 67 differential peaks using the genome browser of the University of California (Santa Cruz).

Nine representative differentiation peaks were selected based on three criteria: i) a large difference in the relative numerical values between the mean area values of responder and non-responder groups, ii) higher mean area values in responder groups, or iii) low variance between area values of peaks in responder groups.

The genome browser track in FIG. 6B shows nine differential peaks that serve as targets (predictive markers) predicting anti-PD-1 therapy responder groups (CR+PR, green) and non-responder groups (PD, red), which are used as a chromatin openness. The number above each differential peak denotes the target ID. The y-axis represents the adjusted read count according to the normalized area value calculated using the normalization factor (F). Scale bars indicate base pairs of 400 bp in base sequence length.

Example 4. Predictability Verification Using Selected Differential Peaks

In order to use the selected differentiation marker determined according to the normalized area value of the peak as a responsiveness prediction marker for anti-PD-1 therapy, the responsiveness prediction effect was verified. For verification, the Cutoff Finder online tool (http://molpath.charite.de/cutoff) was used to find the best cut-off values for use as predictive markers and response guidelines for anti-PD-1 therapy in patients who were receiving anti-PD-1 therapy. The threshold was determined by comparing the responder and non-responder groups to minimize the Euclidean distance between the receiver operating characteristic (ROC) curves. The threshold of each differential peak was determined as follows: first, the predictive performance of the threshold for each differential peak was estimated according to the area under the receiver operating characteristic (AUROC) curve. Then, the discriminative ability of the threshold for both groups was assessed by calculating their sensitivity, specificity, and accuracy (ACC). Finally, the threshold of each differential peak was used in PFS analysis to evaluate the prognostic and diagnostic ability of anti-PD-1 therapy.

FIG. 7 shows the results of the receiver operating characteristics curve analysis, which verified the predictability of the differential peak selected as the predictive peak for prognostic diagnosis between the responder and the non-responder groups through the above process.

FIG. 7A shows the receiver operating characteristic (ROC) curves for nine representative differential peaks (M1, M2, M5, M17, M20, M29, M30, M35, and M36) selected as predictive peaks in the discovery cohort. FIG. 7B shows the sensitivity and specificity of the predictive peak and the threshold value determined through the Cutoff Finder online tool for the nine predictive peaks. Predictive peaks (target ID) were sorted in descending order with respect to the relative mean difference of normalized area values between responder groups (CR+PR) and non-responder groups (PD). Sensitivity and specificity were determined with samples larger or smaller than the threshold for normalized area values for patients who responded and did not respond to anti-PD-1 therapy, respectively.

The nine representative differential peaks showed statistically fair diagnostic ability with the area under the receiver operating characteristic (AUROC) values 0.7 and specificity and sensitivity of 88.0±3.6% and 70.0±3.9% (means±SEM), respectively. It was thus verified that it has a statistically significant ability to determine the responsiveness of anti-PD-1 therapy prognosis.

The additional results of predicting the anti-PD-1 therapy results with the predictive peaks discovered in the discovery cohort are shown in FIG. 8. The left plot of FIG. 8 shows the normalized area value results for four representative predictive peaks (M5, M17, M29, M35) in the anti-PD-1 therapy responder group (R, CR+PR) and non-responder group (NR, PD+SD) with normalized area values. The horizontal bar represents the mean value of peak area of each group. Middle plots are waterfall plots of normalized area values for an anti-PD-1 therapy responder group (CR+PR) and a progressive disease group (PD) minus threshold in descending order on the horizontal axis. Right plots show clinical outcomes of anti-PD-1 therapy determined using a threshold on a progression-free survival curve.

As confirmed on the left portion of FIG. 8, normalized area values (i.e., chromatin openness) by ATAC-seq analysis were clearly differentiated into anti-PD-1 therapy responder groups (R) and non-responder groups (NR) in the discovery cohort. Previous other studies have reported that microsatellite instability (MSI) and Epstein-Ban virus (EBV) positivity in tumor specimens can significantly predict responder group to anti-PD-1 therapy. However, according to the present invention, patients belonging to the non-responder group were distinguished despite the tumor MSI and EBV positive. That is, in the discovery cohort, all EBV-positive patients were classified as responder groups by ATAC-seq, and patients with MSI were also classified as responder groups by ATAC-seq. Despite the fact that the tumor was MSI, a patient (SS #20) who did not respond to anti-PD-1 therapy was correctly classified as a non-responder group by ATAC-seq. These results indicate that the predictive marker selected according to the method of the present invention is used to more accurately predict the responsiveness of anti-PD-1 therapy.

The right plot shows the progression-free survival curve, where the area value of the peak is greater than the threshold, for example, when the area value of the M17 predictive marker peak exceeds the threshold of 34,190, the progression-free survival period with no tumor growth was significantly longer compared to the groups below that value. In the M17, M29, and M36 predictive marker peaks, a progression-free survival period exceeding a 20-month measurement period was confirmed, and these were denoted as n.r. (not reached).

As a result, the differential peak selected according to the criteria of the present invention was classified by open chromatin (openness>threshold) and closed chromatin (openness threshold) with high sensitivity and specificity for the patient's responsiveness to anti-PD-1 therapy. Thus, it was confirmed that they could be used as predictive markers. Their sensitivity and specificity are additionally shown in FIG. 9 and Table 6.

	TABLE 6
			Sample
	Target			(n)	Sensi-	Speci-
	ID	AUROC		R*	NR**	tivity	ficity
	M1	0.745	>	7	8	70.0%	63.6%
			≤		14
	M2	0.777	>	8	8	90.0%	63. %
			≤	1	14
	M3	0.886	>	9	4	90.0%	81.8%
			≤	1	18
	M4	0.718	>	7	7	70.0%	68.2%
			≤	3	16
	M5	0.841	>	9	7	90.0%	68.2%
			≤	1	15
	M6	0.7	>	7	8	70.0%	77.3%
			≤	3	17
	M7	0.845	>	9	7	90.0%	68.2%
			≤	1	15
	M8	0.7 9	>	5	4	80.0%	81.8%
			≤	2	17
	M9	0.7 1	>	8	13	80.0%	40.9%
			≤	2	9
	M10	0.718	>	9	10	90.0%	54.5%
			≤	1	12
	M11	0.786	>	8	11	90.0%	5 %
			≤	1	11
	M12	0.70	>	9	12		45.5%
			≤	1	10
	M13	0.809	>	5	5	80.0%	77.3%
			≤	2	17
	M14	0.750	>	9	8	90.0%	83.8%
			≤	1	1
	M15	0.723	>	7		70.0%	9.1%
			≤	3	13
	M16	0.750	>	9	8	90.0%	63.6%
			≤	1	14
	M17	0.90	>	9	4	80.0%	61. %
			≤	1	18
	M18	0.814	>	9	8		88.2%
			≤	1	14
	M19	0. 34	>	10	7	100.0%	72.7%
			≤	0	15
	M20	0.7	>	9		90.0%	72.7%
			≤	1	16
	M21	0.818	>	9	6	90.0%	59. %
			≤	1	19
	M22	0.727	>	8	9	80.0%	59.1%
			≤	2	13
	M23	0.832	>	5	5		77.3%
			≤	2	17
	M24	0.736	>	7	7	70.0%	88.2%
			≤	3	15
	M25	0.741	>	8	8	80.0%	63.5%
			≤	2	14
	M26	0.785	>	9	8	90.0%	59.1%
			≤	1	13
	M27	0.818	>	7	9	70.0%	77.3%
			≤	3	17
	M28	0.823	>	9	6	90.0%	63.5%
			≤	1	14
	M29	0.777	>	10	9	100.0%
			≤	0	13
	M30	0.766	>	7	5	70.0%	77. %
			≤	3	17
	M31	0.73	>	8	8	80.0%	63.6%
			≤	2	14
	M32	0.791	>	9	9	90.0%	59.1%
			≤	1	13
	M33	0.81	>	9			59.1%
			≤	1	13
	M34	0.7 4	>	9	8	90.0%	63.8%
			≤	1	14
	M35	0.873	>	10	11	100.0%	50.0%
			≤	0	11
	M36	0.68	>		4	90.0%	81.6%
			≤	1	18
	M37	0.750	>	8		80.0%	77.3%
			≤	2	17
	M38	0.805	>	7	6	70.0%	72.7%
			≤	3	16
	M39	0.795	>			90.0%	852%
			≤	1	15
	M40	0.823	>	9	5		77.3%
			≤	1	17
	M41	0.745	>	6	8	80.0%	63.6%
			≤	2	14
	M42	0.754	>	7	5	70.0%	77.3%
			≤	3	17
	M43	0.736	>	7	6	70.0%	72.7%
			≤	9	18
	M44	0.727	>	7	4	70.0%	81.6%
			≤	3	15
	M45	0.70	>	7	6	70.0%	72.7%
			≤	3	16
	M46	0.682	>	7		70.0%	98.2%
			≤	3	13
	M47	0.806	>	9	7	80.0%	88.4%
			≤	1	15
	M48	0.814	>	7	3	70.0%	83.6%
			≤	3	19
	M49	0.68	>	6	8	80.0%	59.1%
			≤	2	14
	M50	0.791	>	9	9		6 %
			≤	1	13
	M51	0.786	>	9	7	90.0%
			≤	1	15
	M52	0.788	>	8	9	90.0%
			≤	1	13
	M53	0.758	>	9	9		59.1%
			≤	1	13
	M54	0.700	>	7	6	70.0%	72.7%
			≤	3
	M55	0.759	>	8	7	80.0%	68.2%
			≤	2
	M56	0.514	>	9	7		68.2%
			≤	1	15
	M57	0.877	>	9	4	90.0%	81.8%
			≤	1	15
	M58	0.764	>	7	5	70.0%	77.3%
			≤	3	17
	M59	0.784	>	7	4	70.0%	81. %
			≤	3	18
	M60	0.822	>	8	4		81. %
			≤	2	18
	M61	0.7 8	>	8	7	80.0%	69.2%
			≤	2	15
	M62	0. 7	>		7	90.0%	68.2%
			≤	1	15
	M63	0.773	>	9			53.8%
			≤	1	14
	M64	0.600	>		6	90.0%	72.7%
			≤	1
	M65	0.859	>	8	3		68.4%
			≤	2	1
	M66	0.777	>	7	5	70.0%	77.3%
			≤	3	17
	M67	0.764	>	8	8	80.0%	72.7%
			≤	2	16
	*R, responder = CR + PR
	**NR, non-responder = PD + SD
	indicates data missing or illegible when filed

As shown in FIG. 9, the mean±SEM of sensitivity and specificity were 82.8±1.1% and 68.6±1.2%, respectively (n=67).

Confirmation of Responsiveness Predictions by Weighting and Combining Predictive Peaks

In order to predict the responsiveness of anti-PD-1 therapy by combining nine predictive peaks selected as representative predictive peaks among several differential peaks, they were sorted as shown in Table 7 below according to the descending order of the accuracy (ACC) values of each predictive peak. The results determined by each predictive peak (i.e., open and closed) were weighted according to the order of accuracy, and the degree of chromatin openness was converted into a “weighted score.”

TABLE 7
Target ID ACC**
M36       84.4%
M17       84.4%
M20       78.1%
M30       75.0%
M5        75.0%
M29       71.9%
M2        71.9%
M1        65.6%
M35       65.6%
**ACC: Accuracy = (TP + TH)/(TP + FP + TN + FN)
TP = True positive;
FP = False positive
TH = True negative;
FN = False negative

Accordingly, 1 was assigned to the predictive peak M35 having the lowest accuracy among the predictive peaks shown in Table 7, and weights up to 9 were assigned to the predictive peak M36 having the highest accuracy. That is, according to the accuracy, weights, which are integers from 1 to 9, were assigned to chromatin openness samples by each predictive peak. Each combination of predictive peaks was performed by adding the next highest predictive peak one by one from M36 with high accuracy. Responsiveness to anti-PD-1 therapy was predicted by the combination of predictive peaks to which a weighted score was assigned, and the results are shown in Table 8 and FIG. 10.

TABLE 8
		Thres-		Sample (n)	Sensi-	Speci-
Combination of Target (ID)	AUROC	hold		R*	NR**	tivity	ficity
M36 + M17	0.927	8.5	>	9	4	90.0%	81.8%
			≤	1	18
M36 + M17 + M20	0.932	15.5	>	9	3	90.0%	86.4%
			≤	1	19
M26 + M17 + M20 + M30	0.923	15.5	>	10	3	100.0%	86.4%
			≤	0	19
M36 + M17 + M20 + M30 + M5	0.941	20.5	>	10	3	100.0%	86.4%
			≤	0	19
M36 + M17 + M20 + M30 + M5 + M29	0.948	24.5	>	10	3	100.0%	86.4%
			≤	0	19
M36 + M17 + M20 + M30 + M5 + M29 + M2	0.964	27.5	>	10	2	100.0%	90.9%
			≤	0	20
M36 + M17 + M20 + M30 + M5 + M29 + M2 + M1	0.973	27.5	>	10	2	100.0%	90.9%
			≤	0	20
M36 + M17 + M20 + M30 + M5 + M29 + M2 + M1 +	0.973	28.5	>	10	2	100.0%	90.9%
M35			≤	0	20
*R, responder = CR + PR
**NR, non-responder = PD + SD

As shown in Table 8 and FIG. 10, it was confirmed that compared to the predictive effect on the responsiveness of anti-PD-1 therapy using a single predictive peak, the weighted combination by combining multiple predictive peaks showed that the area under the receiver operating characteristic (AUROC) value exceeded 0.923 and that the ability to predict the responsiveness of anti-PD-1 therapy can be further improved by dividing the open chromatin (>threshold) and closed chromatin (≤threshold) groups corresponding to the clinical results. In particular, the combination consisting of 4 predictive peaks (M36+M17+M20+M30) showed high sensitivity and specificity for the responsiveness of anti-PD-1 therapy determination of 100% and 86.4%, respectively, and the median progression-free survival period (mPFS, 2.7 months versus not reached, p<0.001) was also significantly improved. In addition, the combination of only four predictive peaks achieved the optimal saturation point for sensitivity and specificity. This suggests that the combination of the selected predictive peaks is very effective in predicting the responsiveness of anti-PD-1 therapy.

Example 5. Independent Validation in Patients with Metastatic Gastric Cancer (mGC)

The predictive effect of the selected predictive peak on responsiveness was verified using an independent validation cohort of 52 patients with stage IV mGC who received anti-PD-1 therapy. The patient characteristics of the validation cohort used are summarized in Table 2. All threshold determinations and statistical evaluations used in the previously identified discovery cohort were applied to this new cohort study. As a result of applying nine selected predictive peaks to the validation cohort, responding and non-responding patients were distinguished with high sensitivity and specificity even in a single predictive peak, the same as in the discovery cohort, and the predictive effect on the responsiveness of anti-PD-1 therapy was further improved in the combination of weighted predictive peaks. The predictive effect according to the predicted peak combination is shown in Table 9 and FIGS. 11 and 12.

TABLE 9
		Thres-		Sample (n)	Sensi-	Speci-
Combination of Target (ID)	AUROC	hold		R*	NR**	tivity	ficity
M36 + M17	0.626	8.5	>	14	17	77.8%	50.0%
			≤	4	17
M36 + M17 + M20	0.688	15.5	>	14	13	77.8%	61.8%
			≤	4	21
M26 + M17 + M20 + M30	0.730	15.5	>	18	18	100.0%	47.1%
			≤	0	16
M36 + M17 + M20 + M30 + M5	0.720	20.5	>	18	17	100.0%	50.0%
			≤	0	17
M36 + M17 + M20 + M30 + M5 + M29	0.700	24.5	>	17	16	94.4%	52.9%
			≤	1	18
M36 + M17 + M20 + M30 + M5 + M29 + M2	0.706	27.5	>	16	14	88.9%	58.8%
			≤	2	20
M36 + M17 + M20 + M30 + M5 + M29 + M2 + M1	0.718	27.5	>	16	14	88.9%	58.8%
			≤	2	20
M36 + M17 + M20 + M30 + M5 + M29 + M2 + M1 +	0.717	28.5	>	16	14	88.9%	58.8%
M35			≤	2	20
*R, responder = CR + PR
**NR, non-responder = PD + SD

As shown in FIG. 11, the single predictive peak showed a sensitivity of up to 94.4% and a specificity of up to 67.6%, so responsiveness could be predicted even with a single marker. In addition, as a result of combining them and applying the weighting method thereto, as shown in Table 9 and FIG. 12, nine predictive peaks selected from the discovery cohort and the corresponding normalization factor (F) were applied to the mGC patient samples in the validation cohort. Thus, it was confirmed that the responsiveness of anti-PD-1 therapy could be predicted equally in the validation cohort. In addition, it was confirmed that regardless of whether the tumors of the validation cohort patients were MSI or EBV-positive, the thresholds of all predictive markers were clearly divided into open chromatin (>threshold) and closed chromatin threshold) groups, which correspond to the responsiveness of anti-PD-1 therapy determinations. The median progression-free survival period was significantly improved in patients (i.e., chromatin openness) whose area values normalized by each predictive peak exceeded the threshold. The possibility that the nine predictive peaks selected through this process could be used as biomarkers (predictive markers) predicting the responsiveness of anti-PD-1 therapy in patients with metastatic gastric cancer was confirmed in both the discovery and validation cohorts.

Example 6. Confirmation of Normalization Control Peaks in Cells Other than CD8⁺ T Cells

In order to determine the usefulness of the normalization control peak derived from the present invention in cells other than CD8⁺ T cells, normalization control peaks for eight hematopoietic cells, three acute myeloid leukemia cells (genome spatial event database, GSE74912), normal bronchial epithelial cells, small cell lung cancer cells, normal prostate basal epithelial cells, small cell prostate cancer cells (GSE118204), and EGFR negative and positive glioblastoma (GSE117685) were confirmed in the same manner as an Example 2 above. The results are shown in FIGS. 13 to 15.

Twenty normalization control peaks were confirmed using the genome browser. The ATAC-seq data before analysis which searched for NCBI GEO depository site was converted to Bigwig files using Homer Suite. Normalization control peaks of eight hematopoietic cells are confirmed in FIG. 13. Normalization control peaks of three acute myeloid leukemia cells are confirmed in FIG. 14. Normalization control peaks of normal bronchial epithelial cells, small cell lung cancer cells, normal prostate basal epithelial cells, small cell prostate cancer cells, and EGFR negative and positive glioblastoma are confirmed in FIG. 15. These results suggest that the peaks to be compared are quantitatively analyzed using a normalization process that converts a differential peak into a normalized area value using the normalization control peak of the present invention in cells other than CD8⁺ T cells, thereby being used as various biomarkers and performing predictions and diagnostics.

Claims

1. A method for selecting normalization control peak for normalization of ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing), the method comprising steps of: a) aligning and peak calling cell-derived ATAC-seq data;b) selecting overlapped peaks between cell samples among the peaks called in step a);c) selecting a peak coincident with DNase I hypersensitivity consensus peak among the peaks selected in step b); andd) selecting a peak having a coefficient of variation (CV) of less than 0.3 and peak width of less than 500 bp among the peaks selected in step c).

2. The method of claim 1, wherein the peak selected in step c) is located at a transcription start site (TSS) or 5′ untranslational region (5′ UTR).

3. The method of claim 1, wherein the cell is one selected from the group consisting of cancer cells, stem cells, immune cells, inflammatory cells, epithelial cells, hematopoietic cells, and fibroblasts.

4. A normalization control peak selected by the method of claim 1.

5. The normalization control peak of claim 4 comprising at least one selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 232.

6. The normalization control peak of claim 5, wherein the normalization control peak includes one or more selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 20.

7. A method for selecting ATAC-seq normalization factor (F), the method comprising steps of: a) deriving an average height (k) of an individual normalization control peak selected from an individual sample by dividing the area of the individual normalization control peak selected by the method of claim 1 by a peak width in an individual sample in a cohort as shown in the following formula: Average Height (k) of an Individual Peak

8. The method of claim 7, wherein the mean height (h) represents a degree of chromatin openness of the individual sample.

9. The method of claim 8, wherein m, which is the number of the normalization control peaks selected in the sample, is 5 to 50.

10. A method of normalizing the area value of a peak, wherein the method comprising multiplying the ATAC-seq normalization factor (F) selected by the method of claim 7 as in the following formula: Normalized area(A)=Fab×Aq wherein Fab is defined as follows: Fab=sa(=∑i=1nahaina)hbj⁢for⁢1≤a≤b≤2hai=the mean height of the ith simple in the cohort Ca hbj=the mean height of the jth sample in the cohort Cb na=the number of samples in the cohort Ca and Aq represents an area value of the q-th peak of the j-th sample of the cohort Cb.

11. The method of claim 10, wherein m, which is the number of the normalization control peaks selected in the sample, is 5 to 50.

12. A method of selecting a differential peak for predicting the responsiveness of anti-PD-1 therapy, the method comprising steps of: a) aligning and peak calling ATAC-seq data from patients who respond to anti-PD-1 therapy and those who do not;b) selecting a peak which is distinct from the responding and non-responding patient samples from among the peak calling values of step a);c) normalizing the selected peak area by multiplying a peak area value (Ad) of the peak selected in step b) by the ATAC-seq normalization factor (Fab) selected by the method of claim 9 as in the following formula: Normalized area(A)=Fab×Ad;d) selecting a peak having a normalized area value exceeding a mean area value of the normalized peak area values obtained in step c) from among the peaks selected in step b); ande) selecting a peak in which a mean difference between normalized peak area values from among patient samples responding to and non-responding to anti-PD-1 therapy among the peaks selected in step d) satisfies significance p<0.05.

13. The method of claim 12, wherein the peak calling of step a) is performed using two or more types of peak callers selected from the group consisting of HOMER (Hypergeometric Optimization of Motif EnRichment) suite, Model-based Analysis of ChIP-Seq 2 (MACS2), and CisGenome, and wherein the selection of step b) is performed by selecting a commonly distinct peak from the two or more types of peak callers.

14. A differential peak for predicting the responsiveness of anti-PD-1 therapy, which is selected by the method of claim 13.

15. The differential peak of claim 14, wherein the differential peak selected by the method includes one or more selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299.

16. The differential peak of claim 15, wherein the differential peak includes one or more selected from the group consisting of SEQ ID NO: 233, SEQ ID NO: 234, SEQ ID NO: 237, SEQ ID NO: 249, SEQ ID NO: 252, SEQ ID NO: 261, SEQ ID NO: 262, SEQ ID NO: 267, and SEQ ID NO: 268.

17. A biomarker composition for predicting the responsiveness of anti-PD-1 therapy, the composition comprising one or more predictive peaks selected from the group consisting of SEQ ID NO: 233 to SEQ ID NO: 299.

18. The biomarker composition of claim 17, wherein the predictive peak includes one or more selected from the group consisting of SEQ ID NO: 233, SEQ ID NO: 234, SEQ ID NO: 237, SEQ ID NO: 249, SEQ ID NO: 252, SEQ ID NO: 261, SEQ ID NO: 262, SEQ ID NO: 267, and SEQ ID NO: 268.

19. The biomarker composition of claim 17, wherein the biomarker composition includes four or more of the predictive peaks.

20. The biomarker composition of claim 17, wherein the predictive peak recognizes differences in chromatin openness between patients responding to and non-responding to anti-PD-1 therapy to provide responsiveness information to anti-PD-1 therapy.