Method for diagnosing cancer using transcriptome-based immune repertoire proflining

Abstract

The present invention relates to a method for diagnosing cancer using transcriptome-based immune repertoire profiling and, more particularly, to a method for diagnosing cancer by using a method of obtaining transcriptome sequence information from biological samples, then extracting, from the transcriptome sequence information, the features of immune cell receptors and the features of transcriptomes, and then analyzing same by input into an artificial intelligence model trained to determine the presence or absence of cancer. The method for diagnosing cancer using transcriptome-based immune repertoire profiling, according to the present invention, diagnoses all cancers on the basis of artificial intelligence by using both the features of immune cell receptors and the features of gene expression levels, the features being extracted from transcriptome samples generated by next generation sequencing (NGS), and is useful with high commercial applicability due to its high accuracy and sensitivity compared to diagnostic methods that use only the sequence information of immune cell receptors.

Description

(19) *EP004700789A1* (11) EP 4 700 789 A1 (12) EUROPEAN PATENT APPLICATION published in accordance with Art. 153(4) EPC (43) Date of publication: 25.02.2026 Bulletin 2026 / 09 (21) Application number: 24793086.0 (22) Date of filing: 22.04.2024 (51) International Patent Classification (IPC): G16H 50 / 20 (2018.01) G16B 30 / 00 (2019.01) G16B 40 / 20 (2019.01) C12Q 1 / 6886 (2018.01) G06N 3 / 04 (2023.01) G06N 3 / 08 (2023.01) (86) International application number: PCT / KR2024 / 005399 (87) International publication number: WO 2024 / 219902 (24.10.2024 Gazette 2024 / 43) (84) Designated Contracting States: AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC ME MK MT NL NO PL PT RO RS SE SI SK SM TR Designated Extension States: BA Designated Validation States: GE KH MA MD TN (30) Priority: 20.04.2023 KR 20230051756 (71) Applicant: GC Genome Corporation Yongin-si, Gyeonggi-do 16924 (KR) (72) Inventors: • KI, Chang-Seok Yongin-si Gyeonggi-do 16924 (KR) • CHO, Eun-Hae Yongin-si Gyeonggi-do 16924 (KR) • CHOI, Jung Kyoon Daejeon 34141 (KR) • SEO, Kayoung Daejeon 34141 (KR) (74) Representative: Mewburn Ellis LLP Aurora Building Counterslip Bristol BS1 6BX (GB) (54) METHOD FOR DIAGNOSING CANCER USING TRANSCRIPTOME‑BASED IMMUNE REPERTOIRE PROFLINING (57) The present invention relates to a method for diagnosing cancer using transcriptome-based immune repertoire profiling and, more particularly, to a method for diagnosing cancer by using a method of obtaining tran- scriptome sequence information from biological sam- ples, then extracting, from the transcriptome sequence information, the features of immune cell receptors and the features of transcriptomes, and then analyzing same by input into an artificial intelligence model trained to determine the presence or absence of cancer. The meth- od for diagnosing cancer using transcriptome-based im- mune repertoire profiling, according to the present inven- tion, diagnoses all cancers on the basis of artificial in- telligence by using both the features of immune cell receptors and the features of gene expression levels, the features being extracted from transcriptome samples generated by next generation sequencing (NGS), and is useful with high commercial applicability due to its high accuracy and sensitivity compared to diagnostic meth- ods that use only the sequence information of immune cell receptors. EP 4 70 0 78 9 A 1 Processed by Luminess, 75001 PARIS (FR) Description [Technical Field]

[0001] The present invention relates to a method of diagnosing cancer using transcriptome-based immune repertoire profiling, and more specifically, to a method of diagnosing cancer including obtaining transcriptome sequence information from a biological sample, extracting features of immune cells and features of transcriptomes from the transcriptome sequence information, and inputting the features to an artificial intelligence model trained to determine the presence or absence of cancer and analyzing the characteristics. [Background Art]

[0002] A major goal of precision oncology is to improve the diagnosis and treatment of cancer. For this purpose, known predictive markers are identified and classification of subtypes of molecules capable of estimating prognosis is induced to select therapies using a variety of genomic and other molecular assays for a tumor material. Also, somatic changes associated with tumor progression are characterized, disrupted pathways are detected and molecular discriminators of metastatic diseases are determined. Although various next-generation sequencing (NGS)‑based approaches have been used to characterize the tumor genome in detail, more accurate tumor types may be classified through comprehensive multiparameter analysis. For example, The Cancer Genome Atlas (TCGA) research network has produced comprehen- sive molecular profiles at the DNA, RNA, protein and epigenetic levels for hundreds of tumors. These multiparametric analyses have advanced our understanding of tumor types, the functional roles of identified new tumor subtypes and molecular variations. Importantly, these efforts have caused identification of novel drug targets, a prerequisite for realizing the promise of precision medicine. However, an approach to tumor materials for molecular profiling is not generally possible, but relies on invasive methods that are not suitable for continuous monitoring of tumor genotypes.

[0003] Thus, precision oncology has increasingly focused on liquid biopsies, which are noninvasive and allow repeated experimentation and easy monitoring of disease. In fact, attempts are being made to use these liquid biopsies for early detection of cancer. The term "liquid biopsy" was first used to describe how the same diagnostic information can be obtained from a blood sample derived from a tissue biopsy sample. In oncology, this term has been used in a broad sense to refer to the assay and sampling of various easily accessible biological fluids such as urine, ascites or pleura as well as blood.

[0004] In this case, the analyte of the body fluid peripheral blood contains circulating tumor cells (CTC), circulating cell- free DNA (cfDNA) of cancer patients containing circulating tumor DNA (ctDNA), small RNA, circulating cell-free RNA containing mRNA (cfRNA), circulating extracellular vesicles (EVs) such as exosomes, tumor educated platelets (TEPs), proteins and metabolites. In addition, these analytes have the potential to provide information about the characteristics of primary tumors or metastases commonly obtained by pathologists. In addition to information on genomic mutations and copy number alterations commonly obtained from CTCs or ctDNA, liquid biopsies are used to generate general information on transcriptomes, protoplasts, proteomes, and metabolomes (Jacob J. Chabon et al., Nature, Vol. 580, pp. 245‑25, 2020).

[0005] In recent years, the results of research on designing high-performance prediction models using mutation information or epigenomic information of DNA in blood have been published, and the importance of liquid biopsy in the field of cancer diagnosis is becoming more prominent (Korean Patent Laid-open No. 2022‑0072680, US Patent Laid- open No. 18 / 472,529).

[0006] Meanwhile, research on cancer-specific immunological aspects is actively conducted due to the development of immunotherapy for cancer.

[0007] For example, a cancer-associated TCR score as an indicator of whether or not the T cell receptor (TCR) sequence of a cancer patient is specific to cancer antigen was developed by Beshnova D. et al., Sci Transl Med. Vol.12(557), 2020. When the cancer score of a new patient in need of diagnosis is calculated by averaging the cancer- associated TCR score, which is an indicator of how many cancer-specific TCRs are present in the TCR pool of the patient, after training the amino acid sequence of the cancer-specific TCRs through a convolutional neural network, it was found that there was a difference in the distribution of cancer score between a cancer patient and a normal subject. Based on this finding, a technology useful for cancer diagnosis was developed.

[0008] In addition, the role of B cells in anticancer immune responses has recently been highlighted. For example, it has been reported that germinal center B cells, which are the core of tertiary lymphoid structures (TLS) formed in the cancer microenvironment, affect the responsiveness of immunotherapy (Helmink, B.A. et al., Nature, Vol. 577, pp. 549‑555, 2020; Petitprez, F. et al., Nature, Vol. 577, pp. 556‑560, 2020).

[0009] In addition, a technology has been developed to analyze the immune repertoire of patients using not only the TCR but also the complementary-determining region 3 (CDR3) sequence of the B cell receptor (BCR) from whole transcriptome sequencing (WTS) data. For example, the CDR3 sequence can be extracted from WTS data using the TRUST4 algorithm 2 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (L. Song et al., Nature Methods 18:627, 2021).

[0010] Technologies have been developed to evaluate anticancer immune responses and diagnose cancer using repertoire analysis of Tcell receptors and B cell receptors. However, to date, prediction models have only been designed and evaluated with a limited number of cancer types and patients, and there are thus drawbacks such as limitations in sensitivity and accuracy.

[0011] Under this technical background, as a result of efforts to develop a cancer diagnosis method using transcriptome- based immunological profiling, the present inventors have found that cancer can be diagnosed with high sensitivity and accuracy by extracting the features of immune cells and the features of transcriptomes from the acquired transcriptome sequence information, and inputting the features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject. Based on this finding, the present invention has been completed. [Disclosure]

[0012] Therefore, it is one object of the present invention to provide a method of providing information for cancer diagnosis based on immunological profiling using transcriptomes.

[0013] It is another object of the present invention to provide a device and computer-readable storage medium for the method for providing information for diagnosis of cancer.

[0014] It is another object of the present invention to provide a method of diagnosing cancer based on immunological profiling using transcriptomes.

[0015] In accordance with one aspect of the present invention, the above and other objects can be accomplished by the provision of a method of providing information for cancer diagnosis based on immunological profiling using transcrip- tomes, the method including: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; and (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject and comparing the analyzed output value with a cut-off value to determine whether or not there is cancer.

[0016] In accordance with another aspect of the present invention, provided is a method of diagnosing cancer based on immunological profiling using transcriptomes, the method including: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; and (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject and comparing the analyzed output value with a cut-off value to determine whether or not there is cancer.

[0017] In accordance with another aspect of the present invention, provided is a device for diagnosing cancer based on immunological profiling, the device including: a decoder configured to decode whole transcriptome sequencing (WTS) information from a biological sample; an aligner configured to align the decoded WTS information to a reference genome database; a feature detector configured to extract immune cell receptor features and transcript features from the aligned WTS information; and a cancer diagnostic unit configured to compare the analyzed output obtained by inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, with a cut-off value, and determine whether or not cancer is present.

[0018] In accordance with another aspect of the present invention, provided is a computer-readable storage medium including an instruction configured to be executed by a processor for providing information for diagnosis of cancer, through the following steps including: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, and comparing the analyzed output value with a cut-off value, and primarily determining whether or not there is cancer; and (e) inputting the immune cell receptor features and transcriptome features of the sample primarily determined that there is cancer into a second artificial intelligence model trained to distinguish between an immunoactive state and cancer, comparing the analyzed output value with a cut-off value and finally determining whether or not there is cancer. [Description of Drawings]

[0019] FIG. 1 is an overall flowchart illustrating a method of diagnosing cancer based on immunological profiling using transcriptomes according to the present invention, and shows the composition of data used to train the artificial intelligence model and the types of input values. FIG. 2 is a conceptual diagram illustrating an input matrix input to an artificial intelligence model constructed according 3 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 to an embodiment of the present invention. FIG. 3 shows the performance of a DNN model trained for use in immunological profiling-based cancer diagnosis using transcriptomes constructed according to an embodiment of the present invention, where (A) shows the result of various evaluation indices of the model and (B) shows the distribution of output values derived from the trained model. FIG. 4 shows the performance of a linear regression model and a random forest model trained for use in cancer diagnosis based on immunological profiling using transcriptomes constructed according to an embodiment of the present invention. FIG. 5 shows the importance of each feature in a DNN model constructed according to an embodiment of the present invention. FIG. 6 shows the confusion matrix to identify false positives and false negatives of the DNN model constructed according to an embodiment of the present invention. FIG. 7 is a flowchart illustrating construction of a diagnostic model for performing false positive filtering to optimize the DNN model constructed according to an embodiment of the present invention. FIG. 8 shows the result of comparison in the performance of the optimized DNN model constructed according to an embodiment of the present invention with a DNN model before optimization, wherein PR-AUC represents the overall performance of precision and reproduction, ROC-AUC represents the ability to distinguish positive / negative, and accuracy is an indicator of whether or not the prediction was correct. PR-AUC is useful for evaluation of the performance of a model in unbalanced data. [Best Mode]

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meanings as appreciated by those skilled in the field to which the present invention pertains. In general, the nomenclature used herein is well-known in the art and is ordinarily used.

[0021] Terms such as first, second, A, and B may be used to describe various elements, but these elements are not limited by these terms and are merely used to distinguish one element from another. For example, without departing from the scope of the technology described below, a first element may be referred to as a second element and in a similar way, the second element may be referred to as a first element. "And / or" includes any combination of a plurality of related recited items or any one of a plurality of related recited items.

[0022] Singular forms are intended to include plural forms as well, unless the context clearly indicates otherwise. Itwill be further understood that the terms "comprises" and / or "comprising", when used in this specification, specify the presence of features, numbers, steps, actions, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0023] Prior to the detailed description of the drawings, it is to be clarified that the classification of components in the present specification is merely made depending on the main function of each component. That is, two or more components described below may be combined into one component or one component may be divided into two or more depending on each more detailed function. In addition, each component to be described below may further perform some or all of the functions of other components in addition to its main function, and some of the main functions of each component may be performed exclusively by other components.

[0024] In addition, in implementing a method or operation method, respective steps constituting the method may occur in a different order from a specific order unless the specific order is clearly described in context. That is, the steps may be performed in the specific order, substantially simultaneously, or in reverse order to that specified.

[0025] The present invention is intended to determine whether or not cancer can be detected with high sensitivity and accuracy by aligning whole transcriptome sequencing (WTS) data obtained from a sample with a reference genome database, extracting immunological features and transcriptomic features from the aligned WTS information, and inputting the features to an artificial intelligence model trained to distinguish a cancer patient from a normal subject.

[0026] That is, in one embodiment of the present invention, a training model capable of diagnosing cancer was constructed using 124 features including immunological features and transcriptome features from the WTS data (FIG. 1). The performance was tested using WTS data of 183 cancer patients. The result showed that the training model had excellent performance (FIGS. 3 and 8).

[0027] As used herein, the term "read" refers to a single nucleic acid fragment, sequence information of which is analyzed using various methods known in the art. Therefore, the terms "sequence information", "transcriptome sequen- cing information", and "read" have the same meaning in that all are sequence information obtained through a sequencing process.

[0028] In one aspect, the present invention is directed to a method of providing information for cancer diagnosis based on immunological profiling using transcriptomes, the method including: 4 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; and (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject and comparing the analyzed output value with a cut-off value to determine whether or not there is cancer.

[0029] In another aspect, the present invention is directed to a method of diagnosing cancer based on immunological profiling using transcriptomes, the method including: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; and (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject and comparing the analyzed output value with a cut-off value to determine whether or not there is cancer.

[0030] In the present invention, the immune cell receptor feature may be used without limitation as long as it is a feature of an immune-related cell receptor that can be obtained from WTS information, and preferably, the immune cell receptor feature may be a feature of T cell receptors (TCR), or a feature of B cell receptors (BCR), based on the CDR3 (complementary-determining region 3) amino acid sequence derived from WTS information, or a feature of the CDR3 amino acid sequence, but is not limited thereto.

[0031] In the present invention, any transcriptome feature may be used without limitation as long as it is a transcriptome- related feature that may be obtained from WTS information, and preferably, the transcriptome feature is an enrichment score or an immune cell composition of a cancer‑ or immune response-related gene, but is not limited thereto.

[0032] In the present invention, the obtaining whole transcriptome sequencing (WTS) information from the biological sample in step (a) may include the following steps: (a-i) randomly fragmenting RNA obtained from a biological sample using an enzymatic cleavage method; (a-ii) replacing the cleavage RNA with cDNA and then producing a library; and (a-iii) performing single-end sequencing or pair-end sequencing on the produced library using a next-generation sequencer to obtain WTS information.

[0033] In the present invention, the obtaining WTS information in step (a) may include obtaining WTS information from the obtained cDNA through whole transcriptome sequencing at a read depth of 1 million to 150 million, preferably at a read depth of 5 million to 100 million, and most preferably at an average read depth of 90 million, but is not limited thereto.

[0034] In the present invention, the biological sample refers to any substance, biological fluid, tissue or cell obtained from or derived from a subject, and examples thereof include, but are not limited to, whole blood, leukocytes, peripheral blood mononuclear cells, leukocyte buffy coat, blood including plasma and serum, sputum, tears, mucus, nasal washes, nasal aspirates, breath, urine, semen, saliva, peritoneal washings, pelvic fluids, cystic fluids, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extracts, semen, hair, saliva, urine, oral cells, placenta cells, cerebrospinal fluid, and mixtures thereof.

[0035] As used herein, the term "reference population" refers to a reference group that is used for comparison like a reference genome database and refers to a population of subjects who do not currently have a specific disease or condition. In the present invention, the reference nucleotide sequence in the reference genome database of the reference population may be a reference chromosome registered with public health institutions such as the NCBI.

[0036] In the present invention, the RNA in step (a) may be mRNA, but is not limited thereto.

[0037] In the present invention, the next-generation sequencer may be used for any sequencing method known in the art. Sequencing of nucleic acids isolated using the selection method is typically performed using next-generation sequencing (NGS). Next-generation sequencing includes any sequencing method that determines the nucleotide sequence either of each nucleic acid molecule or of a proxy cloned from each nucleic acid molecule so as to be highly similar thereto (e.g., 105 or more molecules are sequenced simultaneously). In one embodiment, the relative abundance of nucleic acid species in the library can be estimated by counting the relative number of occurrences of the sequence homologous thereto in data produced by sequencing experimentation. Next-generation sequencing is known in the art, and is described, for example, in Metzker, M. (2010), Nature Biotechnology Reviews 11:31‑46, which is incorporated herein by reference. 5 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55

[0038] In one embodiment, next-generation sequencing is performed to determine the nucleotide sequence of each nucleic acid molecule (using, for example, a HelioScope Gene-Sequencing system from Helicos Biosciences or a PacBio RS system from Pacific Biosciences). In other embodiments, massive parallel short-read sequencing, which produces more bases of the sequence per sequencing unit than other sequencing methods, for example, other sequencing methods that produce fewer but longer reads, determines the nucleotide sequence of a proxy cloned from each nucleic acid molecule (using, for example, a Solexa sequencer from Illumina Inc., located in San Diego, CA; 454 Life Sciences (Branford, Connecticut) and Ion Torrent). Other methods or devices for next-generation sequencing may be provided by 454 Life Sciences (Branford, Connecticut), Applied Biosystems (Foster City, CA; SOLiD Sequencer), Helicos Biosciences Corporation (Cambridge, MA) and emulsion and microfluidic sequencing nanodrops (e.g., GnuBIO Drops), but are not limited thereto.

[0039] Platforms for next-generation sequencing include, but are not limited to, the FLX System genome sequencer (GS) from Roche / 454, the Illumina / Solexa genome analyzer (GA), the Support Oligonucleotide Ligation Detection (SOLiD) system from Life / APG, the G.007 system from Polonator, the HelioScope gene-sequencing system from Helicos Biosciences, and the PacBio RS system from Pacific Biosciences.

[0040] In the present invention, the alignment of step (b) may be performed using the STAR algorithm and the Hgl9 sequence, but is not limited thereto.

[0041] The parameters of the STAR algorithm used herein have the following values, but are not limited thereto: --outFilterMismatchNoverLmax 0.05: output only when the mismatch ratio of the mapped length is lower than this value. --outFilterMatchNmin 20: output only when the number of matching bases is greater than this value. --alignIntronMax 0: the maximum intron size is set to 0.

[0042] In the present invention, the WTS information obtained after the alignment of (b) may be in a bam file format, but is not limited thereto.

[0043] In the present invention, the step (c) of extracting the immune cell receptor features may include the following steps: (c-i) extracting a CDR3 (complementary-determining region 3) amino acid sequence of the immune cell receptor from the aligned WTS information; and (c-ii) calculating at least one feature selected from the group consisting of immune repertoire properties, Vand J gene usage, and chemical features of the CDR3 amino acid sequence based on the extracted sequence information.

[0044] In the present invention, the immune repertoire properties include at least one selected from the group consisting of the total number of TCR sequences, the unique number of TCR sequences, the number of TCR clonotypes per kilo- reads, the entropy of TCR, the clonality of TCR, the total number of BCR sequences, the unique number of BCR sequences, the number of BCR clonotypes per kilo-reads, the entropy of BCR, the clonality of BCR, the somatic hypermutation rate of BCR sequences, and the number of BCR clusters.

[0045] In the present invention, the V&J gene usage may be a genotype-specific proportion of the CDR3 sequence of each of V and J regions for each sample, and may be a proportion of two or more genotypes selected from the group consisting of IGH, IGK, IGL, TRA, TRB, TRD, TRG, IGHA1, IGHA2, IGHD, IGHE, IGHG1, IGHG2, IGHG3, IGHG4, IGHGP, and IGHM, and may be most preferably a proportion of each of the 17 genotypes using the following Equation 2:

[0046] In the present invention, the chemical properties of the CDR3 amino acid sequence include at least one selected from the group consisting of the mean of length in TCR, the mean of gravy index in TCR, the mean of bulkiness of amino acids in TCR, the mean of polarity of amino acids in TCR, the mean of net charge in TCR, the mean of fraction of basic amino acids in TCR, the mean of fraction of acidic amino acids in TCR, the mean of fraction of aromatic amino acids in TCR, the mean of aliphatic index in TCR, the mean of length in BCR, the mean of gravy index in BCR, the mean of bulkiness of amino acids in BCR, the mean of BCR amino acids, the mean of polarity of amino acids in BCR, the mean of net charge in 6 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 BCR, the mean of fraction of basic amino acids in BCR, the mean of fraction of acidic amino acids in BCR, the mean of fraction of aromatic amino acids in BCR, and the mean of aliphatic index in BCR.

[0047] In the present invention, the step (c) of extracting transcriptome features may include calculating a cancer‑ or immune-related gene enrichment score (single sample gene set enrichment analysis, ssGESA score) or an immune cell composition.

[0048] In the present invention, the cancer‑ or immune-related gene enrichment score (single sample gene set enrichment analysis, ssGESA score) is obtained by calculating an enrichment score of a gene associated with at least one selected from the group consisting of Tcell activation involved in immune response, innate immune response, innate immune response activating cell surface receptor signaling pathway, lymphocyte activation involved in immune response, mucosal immune response, natural killer cell activation involved in immune response, negative regulation of immune response, negative regulation of innate immune response, neutrophil activation involved in immune response, positive regulation of cytokine, production involved in immune response, positive regulation of immune effector process, positive regulation of immune response, positive regulation of production of molecular mediator of immune response, regulation of adaptive immune response, regulation of cytokine production involved in immune response, regulation of humoral immune response, regulation of immune effector process, regulation of immune response, regulation of innate immune response, antigen processing and presentation, B cell receptor signaling pathway, chemokine signaling pathway, complement and coagulation cascades, cytosolic DNA-sensing pathway, Fc epsilon RI signaling pathway, Fc gamma R-mediated phagocytosis, hematopoietic cell lineage, IL‑17 signaling pathway, intestinal immune network for IgA production, leukocyte transendothelial migration, NOD-like receptor signaling pathway, natural killer cell mediated cytotoxicity, neutrophil extracellular trap formation, platelet activation, RIG-I-like receptor signaling pathway, T cell receptor signaling pathway, Th1 and Th2 cell differentiation, toll-like receptor signaling pathway, cellular response to tumor necrosis factor, negative regulation of tumor necrosis factor production, negative regulation of tumor necrosis factor superfamily cytokine production, negative regulation of tumor necrosis factor-mediated signaling pathway, positive regulation of tumor necrosis factor production, positive regulation of tumor necrosis factor superfamily cytokine produc- tion, regulation of tumor necrosis factor production, regulation of tumor necrosis factor-mediated signaling pathway, response to tumor necrosis factor, tumor necrosis factor-mediated signaling pathway, central carbon metabolism in cancer, endometrial cancer, PD-L1 expression and PD‑1 checkpoint pathway in cancer, and transcriptional misregulation in cancer.

[0049] In the present invention, any gene may be used without limitation as long as it is a gene involved in the pathway, and preferably, the gene may include at least one selected from the group of genes for each pathway set in Table 5 below, and most preferably, the gene may be calculated using all of the genes described in Table 5 below, but is not limited thereto.

[0050] In the present invention, the expression "at least one selected from the group of genes for each pathway set in Table 5" means, for example, when the gene is involved in the negative regulation of tumor necrosis factor-mediated signaling pathway, the gene includes at least one selected from the group consisting of ADIPOQ, NLRP2B, GSTP1, CARDS, APOA1, F2RL1, PYDC2, CCDC3, PTPN2, GPS2, PELI3, TRAIP, H2BC11, PYDC1, CARD16, GAS6, ZNF675 and RFFL. [Table 5] Cellular response to tumor necrosis factor (GO:0071356) ACTN4 AKT1 ZC3H12A TNFRSF11B PSMB7 ZFAND6 TNFRSF4 TNFRSF18 ACOD1 PSMD1 OCSTAMP CX3CL1 SMPD4 ZFP36L1 EIF5A TNFRSF14 PSMB9 PSMD2 INPPSK ANKRD1 IKBKB TNF XCL1 CHUK PSMC6 GBP1 NKX3‑1 RIPK1 TMSB4X CIB1 TNFRSF 1A AIM2 PSMC4 GBP3 TNFSF11 STAT1 PSMB8 FOXO3 TNFRSF 1B LTBR PSMC5 GBP2 TNFSF1 2 ERBIN PSMBS TANK NFKBIA LTB PSMC2 ST18 PSMF1 DAB2IP PSMB6 ZFP36L2 TNFRSF 21 CDIP1 TNFSF4 EDARADD CCL26 PSMA6 HYAL1 PYCARD PYDC1 LTA PSMC3 TNFSF1 8 GSDME PSMA7 PSMB3 ZFP36 XCL2 YBX3 PSMC1 TNFSF1 5 CCL25 PSMA4 HYAL2 HAS2 TNFRSF 25 TNFSF1 3B TNFSF9 TNFSF1 3 CCL24 PSMA5 PSMB4 RPS3 CHI3L1 CCL8 TNFSF8 TNFSF1 4 CCL23 ADAM17 PSMB1 TRAF1 NFE2L2 CCL7 TP53 NR1D1 CCL22 PSMA2 HYAL3 TRAF3 EDA CCL5 7 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Cellular response to tumor necrosis factor (GO:0071356) SFRP1 COMMD7 TNFRSF 12A PSMA3 PSMB2 TRAF2 PSMD11 CCL4 ASAH1 PSME4 CCL21 PSMA1 CCL19 TRAF5 CXCL8 PTK2B PSMB11 PSME2 CCL20 CACTIN CD58 TRAF6 PSMD10 CCL3 PSMB10 PSME3 SIRT1 CD40LG CCL18 MAP3K1 4 PSMD13 CCL2 PSMD9 PSME1 DCSTAM P CCL15 CCL17 CARD14 PSMD12 CCL1 CRHBP TDGF1 ARHGEF 2 CCL14 JAK2 BIRC2 CCL3L1 ADAMTS 7 PSMD7 PID1 CD27 CCL13 CCL16 BIRC3 PSMD14 PSMD8 TNFRSF 6B LIMS1 GBA NPNT CCL4L1 ILK PSMD5 GATA3 CD40 CCL11 CD70 HMHB1 TRADD PSMD6 EDA2R TNFRSF 11A TNFRSF 13C EDAR BAG4 ADAMTS 12 PSMD3 TXNDC1 7 DHX9 TNFRSF 13B RELA TNFRSF 8 GPER1 PSMD4 TCL1A BRCA1 PSMA8 PLVAP TNFRSF 9 TNFRSF 17 Negative regulation of tumor necrosis factor production (GO:0032720) ADIPOQ LILRB1 POMC BPI SLAMF1 PTPN22 ZC3H12 A LGALS9 IL10 NR1H4 MC1R GHRL ORM1 LILRA4 IRAK3 CD34 IL37 DEFB11 4 SELENO S CACTIN CHRNA7 CX3CL1 SIRPA GHSR ARG2 IGF1 SYT11 PTPN6 GSTP1 NLRC3 LBP CD33 NFKBIL 1 CIDEA CLEC4A GAS6 C5AR2 VSIR TRIM27 DICER1 IL4 RARA TLR4 TNFAIP 3 ILRUN HAVCR2 negative regulation of tumor necrosis factor superfamily cytokine production (GO:1903556) ADIPOQ LILRB1 POMC BPI TLR4 C5AR2 VSIR TRIM27 IL10 NR1H4 MC1R GHRL SLAMF1 TNFAIP 3 ILRUN HAVCR2 IL37 DEFB11 4 SELENO S CACTIN CD274 PTPN22 ZC3H12 A LGALS9 ARG2 IGF1 SYT11 LTF ORM1 LILRA4 IRAK3 CD34 NFKBIL 1 CIDEA CLEC4A PTPN6 CHRNA7 CX3CL1 SIRPA GHSR DICER1 IL4 RARA GAS6 GSTP1 NLRC3 LBP CD33 Negative regulation of tumor necrosis factor-mediated signaling pathway (GO:0010804) ADIPOQ NLRP2B GSTP1 CARD8 APOA1 F2RL1 PYDC2 CCDC3 PTPN2 GPS2 PELI3 TRAIP H2BC11 PYDC1 CARD16 GAS6 ZNF675 RFFL positive regulation of tumor necrosis factor production (GO:0032760) ARFGEF 2 IL1A TLR9 NOD2 HAVCR2 HLA-E TWIST1 IL12B CCR2 TLR2 ARHGEF 2 CLU IL33 IL17A LILRA2 PLCG2 MMP8 CD2 PF4 PTPRJ DDX58 MAVS AGER CD14 SPHK2 TLR1 TLR4 HSPB1 IFNGR1 FRMD8 LILRA5 CCL19 WNT5A PTPRC TLR3 PYCARD FZD5 ORM2 IFIH1 LGALS9 PTPN11 IL6 TMEM10 6A SPN SYK ORM1 C1QTNF 4 EPHB2 8 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) positive regulation of tumor necrosis factor production (GO:0032760) AZU1 OAS2 APP CLEC7A STAT3 SETD4 DDT JAK2 OAS1 IL23A DHX9 LBP MIF LPL RASGRP 1 TYROBP OAS3 LY96 FADD TIRAP THBS1 TNFRSF 8 ISL1 IFNG HMGB1 RIPK1 CYBA PSEN1 CCL3 Positive regulation of tumor necrosis factor superfamily cytokine production (GO:1903557) ARFGEF 2 IL1A TLR9 LY96 FADD TIRAP LPL DDT CCR2 TLR2 ARHGEF 2 HMGB1 RIPK1 CYBA BCL10 RASGRP 1 MMP8 CD2 ADAM8 NOD2 HAVCR2 HLA-E THBS1 TNFRSF 8 SPHK2 TLR1 PF4 CLU IL33 IL17A PSEN1 CCL3 WNT5A PTPRC TLR4 PTPRJ DDX58 MAVS TWIST1 IL12B PTPN11 IL6 TLR3 HSPB1 IFNGR1 FRMD8 LILRA2 PLCG2 AZU1 OAS2 TMEM10 6A PYCARD FZD5 IL17F AGER CD14 OAS1 IL23A APP SPN SYK ORM2 LILRA5 CCL19 TYROBP OAS3 CD86 CLEC7A STAT3 ORM1 IFIH1 LGALS9 ISL1 IFNG DHX9 LBP MIF SETD4 C1QTNF 4 EPHB2 JAK2 Regulation of tumor necrosis factor production (GO:0032680) WNT5A NOD2 C5AR2 MMP8 ARHGEF 2 SPN NFKBIL 1 THBS1 DICER1 CLU PSEN1 SPHK2 RARA CLEC7A NR1H4 LILRA2 CIDEA PYCARD TWIST1 LILRB1 GAS6 IRAK3 DEFB11 4 LILRA4 TYROBP ZFP36 AGER PTPN11 TLR4 LBP IL17A VSIR ISL1 GHSR C1QTNF 4 AZU1 TLR3 FADD POMC LILRA5 FOXP1 DDX58 RASGRP 1 OAS1 TMEM10 6A RIPK1 SYT11 IFIH1 CD2 FZD5 ZC3H12 A IL1A DHX9 HAVCR2 BPI DDT IL23A MIF SIRPA TLR2 TNFAIP 3 CD34 GHRL CCL3 SELENO S TIRAP TNFRSF 8 TLR1 PTPN22 CD33 CACTIN TRIM27 MAPKAP K2 IGF1 IL12B IL4 LY96 ADIPOQ CD47 CCL19 CLEC4A CYBA PLCG2 PTPRC HMGB1 IL33 LTF EPHB2 PF4 HLA-E CD14 IL6 PTPRJ IFNGR1 ORM2 JAK2 PTPN6 MC1R LGALS9 OAS2 CX3CL1 IL37 ORM1 SLAMF1 MAVS ARFGEF 2 OAS3 NLRC3 SYK SETD4 APP FRMD8 CCR2 IFNG HSPB1 ARG2 CX3CR1 CHRNA7 LPL IL10 TLR9 ILRUN STAT3 GSTP1 Regulation of tumor necrosis factor-mediated signaling pathway (GO:0010803) PTPN2 MADD TAX1BP 1 HSPA1B RNF31 CASP1 LAPTM5 BIRC2 NLRP2B CYLD PYDC2 PRKN PYCARD RIPK1 TRAF2 BIRC3 SPHK1 APOA1 RBCK1 GPS2 CASP8 RFFL TRAIP SPPL2B CFLAR OTULIN CCDC3 TRADD SHARPI N ADIPOQ CLIP3 GSTP1 9 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Regulation of tumor necrosis factor-mediated signaling pathway (GO:0010803) CARD8 H2BC11 UBE2K SPATA2 CASP4 SYK ADAM17 PELI3 HIPK1 HSPA1A GAS6 TNFAIP 3 CPNE1 CHUK F2RL1 SPPL2A TNFRSF 1A PYDC1 ZNF675 TNF FADD TRAF1 CARD16 IKBKB RACK1 IKBKG Response to tumor necrosis factor (GO:0034612) NPNT HAS2 NUB1 CCL23 CCL3L1 IGBP1 SMPD4 CCL18 ACOD1 AKT1 TCL1A CCL22 TNFRSF 11A MAP4K3 IKBKB CCL17 ADAM9 GBP1 ZC3H12 A CCL21 DHX9 GCH1 HYAL1 ADAMTS 7 RELA RPS3 BAG4 CCL20 YTHDC2 CHUK CCL8 JAK2 ZFAND6 GBP3 UBD XCL1 TRADD ADAM10 HYAL2 CCL16 ZFP36L 1 GBP2 OCSTAM P SPHK1 BRCA1 ERBIN CCL7 CIB1 GGT5 INPP5K SIRT1 CX3CL1 DAB2IP HYAL3 SFRP1 GGT7 NKX3‑1 TNFRSF 21 ADAMTS 12 CACTIN SMPD1 ASAH1 NR1D1 GGT2 DCSTAM P AFF3 TRIM32 CCL5 TANK SELE GGT1 XCL2 CASP8 CCL15 CCL4 CXCL16 TDGF1 MAP2K7 ARHGEF 2 GPER1 CCL14 CCL3 ZFP36L 2 PID1 CCL26 CHI3L1 CASP3 CCL13 CCL2 CRHBP CCL4L1 GSDME NFE2L2 ANKRD1 GBA CCL1 PYCARD HMHB1 CCL25 CXCL8 RIPK1 CCL11 CCL19 ZFP36 GATA3 CCL24 CD40 ADIPOQ YBX3 CD58 Tumor necrosis factor-mediated signaling pathway (GO:0033209) ACTN4 RELA PSMD1 TRAF6 TNFRSF 9 PSMD11 STAT1 LTA CD70 PLVAP PSMD2 PSME2 TNFSF1 2 PSMD10 CHUK CD40LG PSMB9 TNF ST18 PSME3 PSMF1 PSMD13 PSMA6 TNFRSF 13C EDAR FOXO3 EDARAD D PSME1 TNFRSF 4 CD40 PSMA7 TNFRSF 13B PSMC6 PSMB11 TNFSF1 8 MAP3K1 4 EIF5A PSMD12 AIM2 TNFSF1 3B PSMC4 PSMB10 TNFSF1 5 CARD14 TNFRSF 12A TNFRSF 11A PSMA4 PSMA8 PSMC5 PSMD9 TNFSF1 3 BIRC2 TNFRSF 1A PSMD14 PSMA5 PSMB7 PSMC2 PYCARD TNFSF1 4 BIRC3 TNFRSF 1B ILK ADAM17 IKBKB TNFSF4 PSMD7 TRAF1 TNFRSF 6B NFKBIA TRADD LTBR TMSB4X PSMC3 PSMD8 TRAF3 EDA2R PYDC1 TNFRSF 11B PSMA2 PSMB8 PSMC1 PSMD5 TRAF2 TXNDC1 7 CD27 TNFRSF 17 PSMA3 PSMB5 TNFSF9 PSMD6 COMMD7 BAG4 LIMS1 TNFRSF 18 PSMA1 PSMB6 TNFSF8 PSMD3 TRAF5 TNFRSF 8 TNFRSF 25 RIPK1 LTB PSMB3 TP53 PSMD4 PSME4 TNFSF1 1 EDA TNFRSF 14 CDIP1 PSMB4 10 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Tumor necrosis factor-mediated signaling pathway (GO:0033209) PSMB1 PSMB2 PTK2B JAK2 T cell activation involved in immune response (GO:0002286) IFNA14 SLC11A 1 IFNW1 IFNK IFNA8 IFNA4 MDK CD74 IFNA17 IFNE IFNA10 IFNA7 ITGAL IFNB1 TNFSF1 8 EIF2AK 4 LCP1 IFNA6 IFNA2 CD1C FCER1G IFNA16 LILRB1 F2RL1 IFNA5 IFNA1 ICAM1 IFNA21 Innate immune response (GO:0045087) KRT1 APOBEC 3G LYN ZDHHC1 1 DEFA1 CRISP3 TRIM51 SHFL CHGA KIR2DS 1 DEFB10 4A STING1 TYROBP FCN3 DDX3X TRIM34 MX1 KIR2DS 2 SIGLEC 16 RNASE3 MID2 CALCOC O2 TRIM64 C TRIM35 PML SLC11A 1 SIGLEC 15 DEFB10 6A ANKHD1 ANKRD1 7 SHMT2 NRROS POLR3A KIR2DS 4 SIGLEC 14 RNASE2 CXCL10 NCF2 HMGB1 CRP IFI27 KIR2DS 5 CARDS HCK CLEC4D PHB NLRC5 COLEC1 2 OTULIN PTK6 SFTPD IGLL5 CLEC4E CORO1A TREM1 SRC SLPI SRPK1 TLR2 IGLL1 CAPZA2 AGER ADAR COLEC1 1 TRIM14 HLA-E NCR2 ADAM15 CD300E CLEC5A TREM2 KIR3DS 1 TRIM15 SRPK2 BST2 TTC4 S100A7 UBD SIRPB1 COLEC1 0 TRIM17 PTK2 CYLD PPARG CAPZA1 TNK1 INAVA TRIM31 TRIM10 BPIFB1 KRT16 TRIML1 KLRD1 PI3 TKFC PLA2G1 B ALPK1 TREML1 CFHR5 SAA1 NCF1 KLRC2 CD300L B CX3CR1 TRIM11 VNN1 TLR9 TRIML2 RNF187 TRIM68 IRAK1 PIK3CD TRIM13 BPIFB3 REL MSRB1 FRK TRIM61 LBP PIK3CG WFDC13 BTK TLR8 APCS FCN1 TRIM62 TRIM48 MALT1 AKAP1 WFDC10 B TLR4 PGLYRP 4 RELA TRIM64 TRIM49 H2BC21 APP WFDC10 A TLR3 PGLYRP 3 MASP1 TRIM60 TRIM43 DHX58 CYBC1 APOBEC 3A CD40 ZDHHC1 SLA IFNLR1 CIITA KYNU WFDC12 CFB CITED1 DEFB12 7 RNF39 IRAK4 CR1 TRIM25 WFDC11 WFDC9 MEFV IFIH1 IFI6 APOA4 PPP1R1 4B PTK2B IFITM2 CFD JCHAIN IKBKB DEFB1 OAS1 TRIM43 B TRIM26 IFITM3 PHB2 TRIM8 RNF135 RELB PJA2 TRIM40 TRIM27 SP100 IL23R CLEC7A SMPD1 CXCL16 IL4 CR2 TRIM28 CLPB BCL10 TRIM5 DMBT1 NLRP9 FES STYK1 FYN IFITM1 WFDC2 TRIM6 RNASE8 NLRP1 FER CHUK NOS2 TIFA WFDC5 TRIM4 H2BS1 DDX58 IRF5 S100B TRIM21 NOD2 CFP RIPK2 IKBKG TNK2 H2BC10 IRGM TRIM22 IFNL1 IFIT1 TRIM49 C PGLYRP 2 CYBB MFAP4 FGR MBL2 IFNL3 WFDC3 TRIM49 B PGLYRP 1 CYBA H2BC11 ZAP70 11 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Innate immune response (GO:0045087) IFNL2 C3 FCER1G SETD2 ISG15 H2BC12 LCK B2M NUB1 CAMP CD74 MFHAS1 TRIM58 ANG APOL1 CYP27B 1 DEFB11 9 DEFA5 MYD88 PYDC1 TRIM38 IFNL4 BPIFA1 SYK RARRES 2 MAVS TRIM59 BPI SNCA TBK1 GCH1 DEFA6 CLEC6A IRF7 PTX3 S100A1 2 RPL39 DEFB11 8 F12 LCN2 SERINC 3 MASP2 YES1 TRIM49 D1 RNASE7 DEFA3 TRIM77 TRIM56 TRIM39 SRMS PLCG2 RNASE6 DEFA4 FCN2 SERINC 5 TRIM32 APOBEC 3F SKP2 DEFB11 4 ATAD3A BLK TRIM64 B LTF Innate immune response activating cell surface receptor signaling pathway (GO:0002220) FBXW11 KLRD1 PSMD8 CREBBP MUC20 PRKACB SYK RPS6KA 5 TYROBP PSMC1 PAK1 CARD11 TAB1 SKP1 CHUK PSMB7 MUC3A RAF1 PSMD5 PRKCD MUC21 PRKACA PDPK1 IKBKB MUC3B FCN1 PSMD6 MUC17 BCL10 MUC12 PSMA6 PSMB8 PSMB9 RELA PSMD3 KIR2DS 2 ICAM3 LYN PSMA7 PSMB5 CLEC4C CLEC10 A PSMD4 MUC19 CUL1 CARD9 PSMA4 PSMB6 PSMC6 RELB PSMD1 NFKB1 ICAM2 MUC5AC PSMA5 PSMB3 CLEC4D PSMB11 PSMD2 CLEC6A PRKACG PSMD11 PSMA2 PSMB4 CLEC4E PSMB10 MUCL1 PSME4 MUC13 PSMD10 PSMA3 PSMB1 PSMC4 PSMD9 MAP3K7 TRAF6 MUC15 PSMD13 PSMA1 EP300 PSMC5 MUC1 MUC4 PSME2 PSMF1 PSMD12 KRAS PSMB2 PSMC2 NRAS PAK3 PSME3 KLRC2 PSMD14 SRC HRAS CD209 MUC2 BTRC TAB3 FFAR2 CLEC7A LILRA2 FYN PSMC3 PSMD7 MUC6 TAB2 PLCG2 FCER1G PSMA8 IKBKG CLEC4A PAK2 MUC7 PSME1 MUC16 MUC5B MALT1 Lymphocyte activation involved in immune response (GO:0002285) IFNA6 CD74 IFNA2 IFNA17 CD1C MDK F2RL1 IFNA21 IFNA5 IFNA8 IFNA16 IFNA4 IFNW1 LCP1 IFNB1 IFNA10 IFNA14 IFNA7 IFNA1 EIF2AK 4 IFNE GPR183 IFNK CD244 Mucosal immune response (GO:0002385) DEFA5 IFNLR1 RNASE2 H2BC11 PLA2G1 B IFNL2 FFAR2 DEFA6 DEFA1 BPIFB1 H2BC12 PIGR H2BC21 NOS2 DEFA3 APOA4 IL4 RAB17 OTUD7B FFAR3 H2BS1 DEFA4 RNASE3 H2BC10 LTF DEFB1 RPL39 CAMP natural killer cell activation involved in immune response (GO:0002323) IFNA6 IFNA14 IFNA7 IFNA16 IFNA17 CORO1A IFNE KLRF2 IFNA5 IFNA8 IFNA2 IFNA1 IFNA4 IFNW1 VAMP7 VAMP2 IFNB1 IFNK IFNA21 IFNA10 CD244 12 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Negative regulation of immune response (GO:0050777) SPINK5 ADCY5 PRKAR1 A CALCR SIRT2 ADRB3 GLP2R PTH CD163 ADCY4 RLN3 LGALS3 MC3R CD3G GPR32 GPHA2 CD300A ADCY3 RLN2 HTR7 FCGR2A NLRC3 SCTR FURIN RC3H1 ADCY2 HLA-DRB1 GPR176 COL3A1 TREM2 GIP PTGER2 MUL1 ADCY9 CRHR1 PRKAR2 B FCGR2B ILRUN PTH2R TRAF3I P1 ACOD1 ADCY8 CALCRL PCBP2 IL6 MC2R ITCH GPR45 GCG ADCY7 CALCB PRKAR2 A FER FCGR3A HCK PTGER4 TAAR1 ADCY6 SPI1 PLCG1 ADORA2 B HRH2 TSHR NPS FOXP3 GNG2 LHB PLCG2 ADORA2 A IRAK3 ADCYAP 1 FCGR1A MAPK14 IFNL1 CALCA PRKACB FSHR C1QBP FGR DHX58 IAPP CYSLTR 2 MAPKBP 1 CGA MYH9 DRD1 PTHLH MC4R TRAFD1 CYSLTR 1 GPR84 GGT1 CD247 HAVCR2 POMC ENPP3 ADM2 IFI16 RXFP1 PRKACA GIPR LYAR PPM1B TRIM27 DPEP1 GPR150 SAMSN1 CCR2 GPR27 DRD5 ADAM17 FYN PDCD1 PTGDR HTR6 LYN GPR25 NPSR1 MC5R TRIM21 GPHB5 YES1 RXFP2 GPR15 GLP1R RHBDF2 GHRH INSL3 VIP GGT5 ADCYAP 1R1 IL10 ALOX15 GPR39 TYRO3 TSHB SCT PLK2 GPR83 AVPR2 TNFAIP 3 CR1 CRH RAMP3 HLA-G CRHR2 FSHB GPR20 LHCGR AVP VIPR2 CREB1 HTR4 AMBP ADM P2RY11 CACTIN ADCY1 MC1R FCRLB GPBAR1 ADRB1 PTGIR RAMP1 VIPR1 GNB1 GHRHR NMI PTH1R SYK RAMP2 PTH2 PRKAR1 B PRKACG LILRB1 ADRB2 RNF26 SRC Negative regulation of innate immune response (GO:0045824) CR1 FAM3A ISG15 SERPIN G1 GRN DCST1 IFI16 A2M PTPN2 METTL3 OAS1 TYRO3 TNFAIP 3 NLRC3 DHX58 TRIM21 YTHDF2 NMI CNOT7 TTLL12 NLRC5 SAMHD1 SUSD4 LYAR YTHDF3 MUL1 OAS3 CACTIN USP18 RPS19 SLAMF8 STAT2 ACOD1 TRAFD1 USP15 INS IRAK3 VSIG4 neutrophil activation involved in immune response (GO:0002283) UNC13D FUCA1 OLR1 RAB44 ITGAX DERA ORM1 C5AR1 ARMC8 GSDMD GSN GLIPR1 ITGAV BST2 GDI2 COPB1 MCEMP1 ANXA2 IST1 MMP25 PGM2 CEACAM 3 FAF2 SLC2A3 SCAMP1 ANXA3 AGL CLEC5A PGM1 PTPRC CAB39 PSEN1 FLG2 JUP AOC1 TXNDC5 PTPRN2 RAP2B AP2A2 PLAU DYNC1L I1 FUCA2 DEFA4 TOM1 KRT1 BST1 LILRA2 SRP14 GALNS NFAM1 DEFA1 TMEM17 9B NBEAL2 CEACAM 1 LILRA3 SLC2A5 TOLLIP AZU1 ATP11B GYG1 MAPK14 RAP2C CST3 CNN2 13 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) neutrophil activation involved in immune response (GO:0002283) PSMC2 TMC6 ILF2 SLC15A 4 CRISPL D2 CEACAM 8 CXCR2 ADGRG3 PSMC3 LYZ ATP11A ACTR2 SLPI PTPRB CXCR1 ALDH3B 1 HSPA1A SIGLEC 5 TICAM2 HSP90A A1 TMEM63 A CEACAM 6 STBD1 SIRPA GLA SIGLEC 9 CLEC4C APAF1 STXBP2 ALDOC RAC1 ACAA1 NHLRC3 GPI CLEC4D PA2G4 FGL2 ACTR10 ENPP4 HGSNAT CDK13 PAFAH1 B2 RAB31 NDUFC2 STXBP3 ALDOA CD59 MMP8 CDA SERPIN A3 OLFM4 TNFRSF 1B PRCP RAB7A CCT8 HSPA6 HSPA1B SERPIN A1 RAB37 TMBIM1 CALML5 RAB5C SPTAN1 GGH PRSS2 FPR2 S100A7 DNAJC3 PNP SLC44A 2 CD58 HSPA8 CYFIP1 FPR1 ARL8A FCGR2A CPNE1 HBB CD55 CMTM6 PDXK LRRC7 ADAM8 NPC2 CPNE3 CTSZ TBC1D1 0C MMP9 C3AR1 IQGAP1 DSC1 DNAJC5 B2M PTPRJ LAIR1 ADA2 FRMPD3 IQGAP2 PTPN6 DSG1 ARSA CTSS CHRNB4 DNASE1 RETN TIMP2 PKP1 PECAM1 ARSB DSN1 CD300A MAN2B1 ASAH1 AP1M1 S100A9 VPS35L XRCC5 ACTR1B GCA MS4A3 CRACR2 A DYNLL1 GHDC SNAP25 STK11I P RAP1A IRAG2 DOCK2 PSMD7 SERPIN B3 S100A8 SNAP23 SURF4 RAP1B PDAP1 CHI3L1 PSMD6 ARG1 FRK UBR4 SLC11A 1 MAGT1 HUWE1 PSMD11 PSMD3 AHSG CD177 FCGR3B XRCC6 ORMDL3 TYROBP B4GALT 1 ALOX5 DGAT1 SLC27A 2 ALAD COMMD3 NEU1 ATAD3B PSMD13 PSMD1 PGAM1 ROCK1 PSAP PRG3 MLEC OSCAR DDX3X STOM SERPIN B1 HP ACP3 PPBP QSOX1 EEF1A1 PSMD12 PSMD2 ADAM10 EPX CEP290 RAB27A CTSH PPIA PSMD14 NCSTN STING1 APEH SNAP29 MIF CTSG DOK3 TNFAIP 6 PRSS3 LRG1 NRAS CR1 HLA-C FCER1G DEGS1 MOSPD2 S100A1 2 RNASE3 PYCARD DSP PRG2 CD36 CD68 HMGB1 S100A1 1 RNASE2 LAMP2 RNASET 2 HLA-B CTSD YPEL5 CXCL1 ELANE TUBB4B LAMP1 CPPED1 VAT1 CAMP PPIE AGPAT2 PRKCD SERPIN B6 RAB24 PKM VNN1 CD33 FCN1 PLD1 ATP6AP 2 ERP44 KCMF1 CHIT1 CSNK2B CTSC MPO HK3 NCKAP1 L FABP5 RAB4B KPNB1 CYSTM1 RAB6A ARHGAP 45 SIRPB1 HRNR PSMA5 TRAPPC 1 NFASC MGST1 CD53 COTL1 HEBP2 VAMP7 PSMA2 ATP8B4 PFKL TRPM2 CAP1 CAPN1 SYNGR1 VAMP8 SLCO4C 1 ARPC5 RAB10 COMMD9 CCT2 OSTF1 ADGRE3 BIN2 LAMTOR 1 KCNAB2 TSPAN1 4 LGALS3 DBNL IDH1 DPP7 TCN1 PLEKHO 2 EEF2 VAPA ANPEP MME MGAM ADGRE5 VAMP2 LAMTOR 3 C60RF1 20 IMPDH1 FTH1 SYK IGF2R TMEM30 A PADI2 LAMTOR 2 NFKB1 IMPDH2 CD14 GAA ATP6V0 C PLAUR CFD SERPIN B12 ACLY P2RX1 GOLGA7 CREG1 MANBA FGR 14 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) neutrophil activation involved in immune response (GO:0002283) GMFG GRN DDOST RAB14 ATP6V0 A1 QPCT CD93 GLB1 GNS PIGR HPSE MNDA CTSB PGRMC1 DNAJC1 3 CAT A1BG PYGB LCN2 BPI CTSA ANO 6 CYBB S100P ABCA13 PTAFR NIT2 RAB18 RHOA SVIP CYBA PTX3 CFP SERPIN B10 FTL FOLR3 PTGES2 APRT SDCBP DYNLT1 C3 HEXB RAB5B ITGB2 SIGLEC 14 RAB9B CKAP4 LTF PRDX4 GSTP1 RAB3D CANT1 TUBB CD47 DIAPH1 CLEC12 A CYB5R3 PYGL RAB3A HSP90A B1 RHOF DNASE1 L3 TARM1 MVP PRDX6 LPCAT1 ARHGAP 9 TCIRG1 LILRB2 DNASE1 L1 SELL CAND1 PLAC8 PSMB7 ATP8A1 ITGAM AMPD3 DYNC1H 1 PRTN3 MAPK1 NAPRT PSMB1 BRI3 ITGAL LILRB3 CD44 VCL GUSB HMOX2 METTL7 A HVCN1 FCAR RHOG ORM2 CRISP3 LTA4H AGA GPR84 TTR NME2 CSTB ATP6V1 D ATG7 PGLYRP 1 STK10 GM2A TLR2 CD63 VCP Positive regulation of cytokine production involved in immune response (GO:0002720) FZD5 WNT5A TRAF2 SLC7A5 TNFSF4 NOD2 CLEC7A PLCG2 XCL1 LAPTM5 HLA-G RAET1G TRAF6 KIR2DL 4 TRIM6 B2M CD160 HLA-A HLA-E KIT F2RL1 MALT1 FFAR3 MAP3K7 SPHK2 SCIMP HLA-F LACC1 CD226 INAVA FFAR2 TNFRSF 14 CD55 Positive regulation of immune effector process (GO:0002699) CCR2 IL2 IL23A CD46 CD81 DDX60 DHX58 LGALS9 IL10 USP17L 2 TNFSF4 HLA-DRB1 PHB HLA-DMB LBP PCK1 XBP1 IL1B IFNG ANKRD1 7 PLA2G5 MYB MYO18A IL12RB 1 LILRB1 PTPRC HLA-DRA IL23R PTPRJ RPS19 IL12B APPL2 positive regulation of immune response (GO:0050778) AKIRIN 2 EIF2AK 4 POLR3C IFNK FPR2 STX4 NLRP10 PLA2G1 B CD74 NMI IL23A ADAM8 NOD2 PCK1 F2RL1 IL23R SIGLEC 16 LILRB1 POLR3D IL6ST IFNL1 IL12RB 1 CD226 IFI35 TASL SIRT1 TNFSF4 SKAP1 PYCARD GBP5 ZP4 ITGAM TGFB2 EREG POLR3F HRG MYB SASH3 CD46 CD1D IL10 IL2 IFNG CD177 GPR151 XBP1 ZP3 HLA-DMB IL15 PTPRC POLR3G DHX9 NLRP3 SH2D1B HLA-DRB1 CCL5 SPHK2 FCGR2B HLA-DRA CD81 FADD CYBA ITGB2 IFNB1 RSAD2 POLR3B TLR8 KARS1 CCR7 PLSCR1 GRN IL12B RIOK3 SLC15A 4 LGALS9 Positive regulation of production of molecular mediator of immune response (GO:0002702) IL10 LAPTM5 PTPRC CLCF1 CD244 PHB FFAR3 EPHB2 15 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Positive regulation of production of molecular mediator of immune response (GO:0002702) XBP1 SCIMP IL6 F2RL1 MZB1 PTPN22 DNAJB9 TNFRSF 14 XCL1 HLA-E RBP4 TLR9 CD86 NOD2 FFAR2 SASH3 SPHK2 IL2 LACC1 VAMP3 PHB2 INAVA STX4 WNT5A SLC7A5 TNFSF4 GPI IL21 TRIM6 TNFRSF 4 regulation of adaptive immune response (GO:0002819) RIPK3 AHR ADCY7 IRF1 FADD IL6ST ALOX15 SIRT1 IL4I1 FCGR2B TRIM27 SKAP1 EIF2AK 4 SAMSN1 PYCARD TNFSF4 IRF7 SASH3 Regulation of cytokine production involved in immune response (GO:0002718) CCR2 WNT5A LAPTM5 TNF INAVA APOA1 CLC FFAR2 IL10 TRIL APOA2 SCIMP SLC7A5 LACC1 BTK TNFRSF 14 SPHK2 LILRB1 NOD2 TNFRSF 1B TRIM6 F2RL1 FFAR3 TRPM4 regulation of humoral immune response (GO:0002920) CFHR1 CD81 C8B CR1 CFB CFI VTN C7 FCGR2B C3AR1 C4A CR2 C1QC C5AR1 C2 C9 CXCL13 PROS1 C8G F2 C1QB CFH CPN2 CD19 CFHR2 C4BPA C4B SERPIN G1 C1QA C1S C3 CD59 CR1L C8A C1QBP ZP4 PHB2 C5AR2 CPN1 CD55 CFHR5 CLU SUSD4 CD46 C1R GATA6 C5 CFHR4 C4BPB CCR7 ZP3 CPB2 CFP C6 Regulation of immune effector process (GO:0002697) ZNF683 CFHR4 CLU SUSD4 HLA-DRB1 CFI C2 C9 CFHR1 HLA-DRA C4BPB CR1 C1QC C5AR1 CPN2 CD19 FCGR2B CD81 C8B CR2 C1QB CFH C3 CD59 IRF4 C3AR1 C4A F2 C1QA C1S CPN1 CD55 CFHR2 PROS1 C8G SERPIN G1 PHB2 C5AR2 C5 CR1L C4BPA C4B CD46 C1R CFP C6 CFHR5 C8A C1QBP CFB CPB2 VTN C7 Regulation of immune response (GO:0050776) SPINK5 CLEC4G HLA-C COL17A 1 ITGA4 SIGLEC 9 CD300L D KLRF1 TGFB2 CD200 TIRAP C17ORF 99 RIPK3 FCGR2C MADCAM 1 NECTIN 2 LAG3 IFITM1 TRAF3 SPPL2B SFTPD SIGLEC 7 FCER1A ITGB2 CD300A FGL1 HLA-A SPPL2A AMBP CD22 CD300L F ITGB1 CD160 CD81 HLA-B ICAM3 PILRA CD40 CD300L G COLEC1 2 CRTAM PVR HLA-G ICAM4 LILRB1 DENND1 B CD34 PLA2G1 B RSAD2 AHR HLA-E ICAM5 LILRB2 PTPN22 CD33 LILRA1 TYROBP ADCY7 HLA-F CD1D PILRB KIR2DL 1 CXADR ITGAL VCAM1 LRP8 TREML2 CD1C CD200R 1 CD3G PIANP SAMHD1 FOXP3 IFNL1 RAET1E ICAM1 NCR2 KIR2DL 2 SYK RNF135 16 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Regulation of immune response (GO:0050776) COL1A2 HERC5 COL2A1 CD1B IL4 TREM1 ERAP1 FCGR1A FOXP1 GPR151 TREML1 SLA2 IRF1 KIR2DL 3 ELMOD2 DHX58 OSCAR SLAMF7 PLSCR1 ICAM2 NCR3 CD3E KIR3DL 1 CSK COL1A1 SLAMF 6 CD8B C3 FCGR2A TREM2 NCR3LG 1 ITGB7 CXCL13 B2M CD8A CD1A COL3A1 KIR2DL 4 KIR3DL 2 APPL1 TNFSF4 MICA TREML4 FCRLB FCGR2B ZMPSTE 24 FGR LAIR2 JAML MICB SELL IL4I1 IRF4 CD3D FKBP1A LAIR1 CD300E N4BP1 GPR108 KLRK1 BCL6 SPN CLEC2B APPL2 KLRD1 CD96 KLRB1 CD19 NCR1 FCGR3A XIAP CD300C KIR2DS 1 BIRC2 KLRC1 HLA-DRA CASP8 CD226 ULBP1 KIR2DS 2 BIRC3 CD99 HCST FCGR3B CLEC2D PDCD1 SH2D1A HLA-DRB1 LYN IRF7 TKFC CD40LG ULBP3 SH2D1B CD276 IL15 CD247 CD300L B NPDC1 Regulation of innate immune response (GO:0045088) PTPN1 IRF1 TLR8 NLRC3 LYAR PLSCR1 ABCE1 DHX58 AKIRIN 2 FCGR2B IFNK TREM2 N4BP1 CDC37 RNASEL CCL5 TASL POLR3B ADAM8 LRP8 ERAP1 XIAP HSP90A B1 IFNB1 NMI POLR3C PTPN6 CASP8 SH2D1B TYRO3 IFI35 RIOK3 MUL1 POLR3D DHX9 TKFC TIRAP CACTIN USP18 SLC15A 4 PTPN11 TRAFD1 TNFAIP 3 IRAK3 HLA-E BIRC2 CD1D TRIM21 ACOD1 POLR3F FPR2 IFI16 PARP9 BIRC3 SAMHD1 APPL1 EREG POLR3G PTPN22 GBP5 FGR IFNAR2 RNF135 APPL2 Antigen processing and presentation CTSB TAP2 KLRD1 B2M NFYB KIR3DL 2 CD8A HSP90A B1 CD74 HSPA4 CALR HLA-DOB KIR2DS 2 HLA-A CANX IFI30 HSP90A A1 TAP1 HSPA1B KIR2DL 5A NFYC HLA-B LGMN HLA-DMB KLRC3 TAPBP TNF CIITA KIR2DS 3 KIR3DL 3 PSME2 HLA-DPA1 HSPA6 CD4 KIR2DL 1 HLA-DQA2 KIR2DS 4 HLA-G PSME3 KLRC1 HSPA5 RFX5 KIR2DL 2 HLA-DOA KIR2DS 5 HLA-E HLA-DPB1 KLRC2 HSPA1L IFNG KIR2DL 3 HLA-DQA1 HLA-C HLA-F PSME1 NFYA KLRC4 HSPA1A KIR2DL 4 PDIA3 RFXANK CREB1 HLA-DRB4 HLA-DMA HSPA8 HLA-DRA CTSS HLA-DRB5 KIR3DL 1 CD8B HLA-DRB3 HSPA2 HLA-DQB1 CTSL KIR2DS 1 CD8B2 RFXAP HLA-DRB1 B cell receptor signaling pathway LYN LILRB4 SOS2 PIK3R3 BLNK PIK3R1 LILRA3 PPP3R2 JUN VAV1 PTPN6 INPPL1 CARD11 BTK LILRA4 CD79A CD72 LILRB5 NFKBIE PIK3R2 MAP2K2 KRAS DAPP1 CD19 IGH VAV2 RAF1 PPP3CA CR2 GRB2 LILRA5 MAPK3 17 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) B cell receptor signaling pathway NFATC3 VAV3 CD22 NRAS SYK PIK3AP 1 LILRA6 RAC3 LILRB1 NFKBIB SOS1 PPP3CB MAP2K1 BCL10 MALT1 RAC1 LILRB2 NFKBIA RELA PPP3CC CHUK PIK3CD INPP5D MAPK1 NFATC2 FCGR2B GSK3B AKT3 FOS LILRA1 IKBKB HRAS NFATC1 PIK3CB IFITM1 AKT1 PRKCB LILRA2 PPP3R1 RAC2 LILRB3 PIK3CA CD81 AKT2 NFKB1 RASGRP 3 CD79B PLCG2 IKBKG Chemokine signaling pathway BAD GNG2 PIK3R1 CCR1 CCL27 GRK1 PLCB4 CXCR1 CXCL10 CRKL GNB3 CCL24 CXCL8 RAP1A CRK ARRB1 CXCL11 NRAS GNB2 CCL23 SHC3 GRK4 KRAS CCL7 CXCL12 GNG4 GNAQ CCL22 GSK3B RAP1B PLCB2 CXCR4 CXCL13 PAK1 GNB5 CCL21 SHC4 GRK3 PLCB3 CXCR3 ELMO1 GNG3 GNB4 CCL20 CXCL9 GRK6 PLCB1 CCL5 NCF1 GNG5 GRB2 XCL1 GSK3A GRK5 CCL15 MAPK3 PF4 AKT3 PXN BRAF CCL3L1 GRK7 CCL14 CCL4 RAF1 PREX1 CCL4L1 GNG13 SHC1 CCR9 SRC RAC3 RELA GNG8 CCL4L2 GNG12 SHC2 CCR8 CCL13 PTK2B ROCK1 GNG7 RASGRP 2 VAV1 CCL3L3 CCR7 CCL11 CCL3 ADCY1 AKT1 PRKACG VAV2 PIK3R5 CCR6 CX3CR1 RAC1 ROCK2 AKT2 GNG11 VAV3 PIK3R3 CCR5 CXCR6 CCL2 ADCY5 XCR1 GNG10 NFKBIB CXCL2 CCR4 CXCR5 MAPK1 ADCY4 STAT5B PLCG1 NFKBIA CXCL1 CCR3 WAS HRAS ADCY3 PRKCD PLCG2 PIK3CB PIK3R2 PF4V1 PIK3CD RAC2 ADCY2 MAP2K1 PRKACB PIK3CA CXCL3 STAT1 ARRB2 CCL1 CXCL14 PPBP PRKACA XCL2 CX3CL1 STAT2 GNAI1 CCL19 ADCY9 PARD3 CCR10 SOS2 CXCL6 CHUK GNAI2 JAK3 FOXO3 PRKCB CCL26 DOCK2 CXCL5 STAT3 GNAI3 IKBKG ADCY8 NFKB1 CCR2 ITK PIK3R6 HCK PIK3CG CCL18 ADCY7 PTK2 LYN CCL28 GNGT2 TIAM1 IKBKB CCL17 CXCL16 PRKCZ CCL25 BCAR1 GRK2 CDC42 CXCR2 JAK2 ADCY6 GNB1 RHOA SOS1 GNGT1 FGR CCL8 CCL16 Complement and coagulation cascades F11 SERPIN A1 SERPIN A5 CR2 F8 ITGB2 F13A1 C7 F10 MASP1 C4A FGG F9 C1QB PLAU C9 F12 SERPIN E1 KNG1 F2R PROCR CFD PLAT ITGAX SERPIN D1 C3AR1 C8G VWF SERPIN G1 C1QA PLG VSIG4 CFHR1 PROS1 C4B SERPIN F2 F13B C1R ITGAM A2M 18 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Complement and coagulation cascades CFHR3 C4BPA BDKRB1 F2 F2RL2 CPB2 VTN CD59 CFHR2 C8A BDKRB2 PLAUR MASP2 SERPIN C1 C2 MBL2 CR1L CLU FGA F3 CD46 CFI C3 CD55 CFHR5 C4BPB CR1 F5 F2RL3 C5AR1 C5 CFHR4 TFPI FGB F7 CFB CFH THBD KLKB1 C8B SERPIN B2 PROC C1QC C1S C6 Cytosolic DNA-sensing pathway ZBP1 POLR3A POLR3G CASP1 IFNA17 IFNA6 IFNA4 IFNB1 POLR3G L IL6 POLR3H RIPK1 STING1 IFNA5 IKBKB POLR2H RIPK3 POLR3B IRF7 IL33 NFKB1 IFNA8 TBK1 IKBKG IL18 POLR3C POLR3K IFNA13 AIM2 IFNA7 POLR2E POLR2K CXCL10 POLR3D RELA IFNA14 MAVS IFNA2 CCL5 IKBKE NFKBIB POLR3E CGAS DDX58 POLR1C CCL4L1 CCL4 POLR2L IL1B POLR3F ADAR IFNA16 POLR1D IFNA1 POLR2F IFNA21 NFKBIA IRF3 PYCARD CHUK IFNA10 CCL4L2 TREX1 Fc epsilon RI signaling pathway LYN MAPK14 SOS2 NRAS MAP2K3 ALOX5A P INPP5D RAC2 IL13 MAPK11 MS4A2 AKT3 PLA2G4 E PLA2G4 B MAPK9 PLCG2 GAB2 IL5 RAF1 ALOX5 SYK PRKCA MAPK8 FYN IGH IL4 LAT AKT1 PLA2G4 F PIK3R1 MAPK3 MAP2K6 VAV1 MAPK12 SOS1 AKT2 MAP2K1 JMJD7- PLA2G4 B RAC3 MAP2K7 VAV2 MAPK10 CSF2 FCER1A PDPK1 BTK PLCG1 IL3 PIK3CB PIK3R3 FCER1G PLA2G4 C KRAS RAC1 MAPK13 PIK3CA TNF MAP2K4 PLA2G4 D GRB2 MAPK1 VAV3 LCP2 PIK3R2 MAP2K2 PLA2G4 A PIK3CD HRAS Fc gamma R-mediated phagocytosis GSN CFL2 PLA2G4 B PLPP1 LIMK1 PIK3CA WASF2 PIK3CD IGH AKT1 PRKCB PLA2G6 SPHK2 DOCK1 WASF3 FCGR1A MARCKS CFL1 ARPC5 ACTR3B LIMK2 LAT MARCKS L1 MAPK3 NCF1 AKT2 PRKCA ACTR3C SPHK1 ARPC1A SYK RAC1 RAF1 PRKCG VASP INPP5D GAB2 ARPC1B MYO10 MAPK1 ARPC5L PRKCD ARPC3 PIP5K1 A VAV1 PIK3R3 HCK RAC2 ASAP2 PLA2G4 E PIK3R1 PIP5K1 B VAV2 PIK3R2 CDC42 INPPL1 PLA2G4 F ARPC4 PLCG1 DNM2 PLD2 RPS6KB 2 ASAP3 MAP2K1 BIN1 PIP5K1 C VAV3 PLD1 RPS6KB 1 CRKL PLA2G4 C ARPC2 PLCG2 PTPRC FCGR3A JMJD7- PLA2G4 B PAK1 PLA2G4 D AMPH ACTR3 FCGR2A SCIN CRK ASAP1 PRKCE PLPP3 LYN FCGR2B FCGR3B ARF6 19 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Fc gamma R-mediated phagocytosis AKT3 PLA2G4 A PLPP2 ACTR2 PIK3CB WASF1 WAS Hematopoietic cell lineage IL4R CSF2 IL7R CD14 IL5 FCER2 HLA-DQA1 ITGB3 IL1R1 CSF1 HLA-DRB1 IL6R IL1B CD22 CR1 ITGAM IL1R2 ITGA2B CSF1R IL11 IL4 GYPA CR2 HLA-DMB IGH TNF TFRC ITGA1 IL7 CD3G MME FCGR1A CD2 FLT3LG IL5RA ITGA5 IL6 CD3E IL11RA HLA-DPA1 CD5 HLA-DOB CD1E ITGA4 KITLG CD3D GP1BB CD59 CD4 HLA-DOA CSF2RA ITGA3 IL3RA THPO GP1BA HLA-DMA CD7 HLA-DRB5 CD1D ITGA2 HLA-DRA CD38 EPOR CD55 CR1L CD8B2 CD1C DNTT HLA-DQB1 CD37 KIT CD9 CD8B CD1B GP5 IL9R CD36 IL2RA CSF3R CD8A CD1A GP9 ITGA6 CD34 HLA-DPB1 EPO HLA-DRB4 ANPEP IL3 CD24 HLA-DQA2 CD44 CSF3 HLA-DRB3 CD19 IL1A MS4A1 CD33 FLT3 IL‑17 signaling pathway USP25 MAPK11 DEFB4B CXCL2 IL17RA FOSL1 IL17B CCL7 JUN CXCL10 S100A9 TNF JUND TRAF5 CEBPB MAPK3 HSP90A A1 MMP13 S100A8 CXCL1 MUC5B TRAF4 CCL11 CCL2 MMP1 IL5 DEFB4A ELAVL1 CHUK FOSB HSP90A B1 MAPK1 CCL20 IL1B CXCL8 CXCL3 FOS TRAF6 TRAF3I P2 IKBKG MMP3 IL4 RELA HSP90B 1 TRAF3 LCN2 IL25 MAPK7 IL13 NFKBIA GSK3B CXCL6 TRAF2 ANAPC5 PTGS2 MAPK6 MUC5AC MAPK12 CSF3 CXCL5 IL17RE TAB3 S100A7 A IKBKE MMP9 MAPK10 CSF2 CASP8 NFKB1 IL17F IKBKB CCL17 MAPK15 IL6 SRSF1 CASP3 IL17RC TAB2 MAPK9 MAPK4 MAPK13 IFNG TRADD FADD IL17RB IL17D MAPK8 MAPK14 S100A7 TNFAIP 3 MAP3K7 IL17A IL17C TBK1 Intestinal immune network for IgA production TGFB1 IL15 IL4 CD28 CCL28 PIGR HLA-DPA1 HLA-DQA2 AICDA ITGA4 CXCL12 HLA-DPB1 HLA-DRB1 CD80 CCR9 ICOS CCL25 TNFSF1 3 IL6 CD40LG CD86 TNFSF1 3B MADCAM 1 CCR10 IL15RA IGH LTBR MAP3K1 4 CD40 HLA-DMB ITGB7 HLA-DOA IL10 IL2 HLA-DRA HLA-DRB4 TNFRSF 13C CXCR4 HLA-DOB ICOSLG HLA-DRB5 IL5 HLA-DQB1 HLA-DRB3 TNFRSF 13B TNFRSF 17 HLA-DMA HLA-DQA1 Leukocyte transendothelial migration ACTN4 ROCK2 PRKCA F11R VAV2 RAPGEF 4 CLDN9 CXCR4 20 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Leukocyte transendothelial migration ACTN1 CLDN3 VASP ICAM1 VAV3 ITK CLDN8 CLDN25 VCAM1 CLDN2 PTK2 ARHGAP 5 AFDN BCAR1 SIPA1 CLDN24 MAPK13 CLDN1 OCLN RASSF5 CLDN11 PIK3R3 ITGB2 CTNNA2 MAPK14 MYL12B MYL12A PLCG1 CLDN10 PIK3R2 ITGB1 PTK2B MAPK11 ARHGAP 35 MYLPF PLCG2 PIK3CB CD99L2 CTNNB1 CTNNAL MYL5 ACTB PIK3R1 CD99 CLDN16 THY1 PIK3CD RAC1 MAPK12 CDH5 ESAM RHOA PIK3CA RAP1A ITGAM RAC2 CXCL12 MYL10 EZR ITGA4 CLDN15 RAP1B GNAI1 CTNNA3 MYL7 JAM2 VCL MMP2 CLDN14 CDC42 ITGAL MYL2 JAM3 NCF2 MSN CLDN19 CLDN7 GNAI2 NCF1 PRKCG PXN RHOH PECAM1 CLDN34 GNAI3 MYL9 PRKCB NCF4 PTPN11 RAPGEF 3 CLDN6 CLDN23 ROCK1 CYBB TXK MMP9 CLDN18 CLDN5 CLDN22 CTNND1 CYBA ACTG1 VAV1 CLDN17 CLDN4 CLDN20 NOD-like receptor signaling pathway GBP7 ANTXR2 CASR BIRC3 CARD8 CXCL8 CAMP IFNA8 DEFA5 TANK PRKCD IFNA10 IRAK4 YWHAE STAT1 RNASEL DEFA6 RNF31 IFNA16 IFNAR2 IL18 MEFV STAT2 IFNA7 DEFA3 PYCARD IFNA17 TXN2 CARD6 ITPR2 CHUK IFNA2 DEFA4 ANTXR1 NEK7 DEFB10 3B OAS1 TNFAIP 3 ERBIN HSP90A B1 DEFA1 DHX33 CYBB GABARA P TYK2 ITPR3 STING1 IFNA1 TRPV2 NLRP7 TRAF3 DEFB10 3A NFKBIB CXCL2 P2RX7 IFNA4 TICAM1 IFI16 CYBA TXN IL1B NLRC4 AIM2 IKBKB MAPK13 NLRP6 TRAF2 BRCC3 NFKBIA CXCL1 PLCB4 GPRC6A MAPK14 MFN2 NFKB1 TRPM2 IL6 CXCL3 NLRP12 MAPK9 ATG12 NLRP3 MYD88 TBK1 OAS2 ITPR1 XIAP MAPK8 MAPK11 GBP1 MAVS TP53BP 1 OAS3 CASP8 TXNIP NAMPT MAPK12 NLRP1 TRAF5 TRPM7 IRF3 SHARPI N PKN1 CCL5 MAPK10 GBP3 TRAF6 ATG5 PYDC1 PSTPIP 1 SUGT1 MAPK3 ATG16L 1 MAP3K7 BCL2L1 IFNA21 BCL2 PANX1 VDAC3 IFNB1 TRIP6 GBP2 CARD18 DNM1L IRF9 CASP12 VDAC2 CCL2 RBCK1 GBP5 TAB3 CTSB PYDC2 CASP4 PKN2 MAPK1 NAIP MFN1 CARD16 RHOA IRF7 CASP5 VDAC1 IKBKG RELA GBP4 TAB2 GSDMD DEFB4B FADD PLCB2 IKBKE NLRX1 IFNA13 CARD17 JUN PYDC5 RIPK2 PLCB3 JAK1 NOD1 IFNA14 TAB1 HSP90A A1 TLR4 DEFA1B PLCB1 NOD2 GABARA PL2 BIRC2 RIPK3 DEFB4A CASP1 IFNA6 21 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) NOD-like receptor signaling pathway TNF GABARA PL1 IFNAR1 CARD9 MCU RIPK1 IFNA5 Natural killer cell mediated cytotoxicity KLRC3 MICA PRKCB FASLG VAV2 SHC1 IFNGR2 PIK3CD IGH MICB TNFRSF 10B ICAM1 VAV3 SHC2 GZMB ITGAL TYROBP IFNA13 HLA-A ICAM2 NCR2 PIK3R3 KIR3DL 1 MAPK3 FAS PRKCG TNFRSF 10A PPP3R1 NCR3 KIR2DL 1 KIR3DL 2 RAC3 KLRD1 IFNA14 PRKCA PPP3R2 PIK3CB PIK3R2 KIR3DL 3 PTK2B ULBP1 MAP2K2 HLA-B KLRK1 PIK3CA KIR2DL 2 ZAP70 IFNB1 ULBP3 KIR2DS 1 HLA-G PLCG1 NCR1 KIR2DL 3 LCK RAC1 PTPN6 IFNA16 HLA-E KLRC1 IFNG PPP3CA CD48 MAPK1 ULBP2 KIR2DS 2 RAET1E TNFSF1 0 HCST KLRC4- KLRK1 KRAS HRAS RAF1 MAP2K1 RAET1G KLRC2 LCP2 PPP3CB IFNA6 RAC2 CSF2 KIR2DS 3 PIK3R1 PLCG2 CD247 FCGR3A ITGB2 FYN TNF IFNA17 RAET1L IFNA21 SOS2 PPP3CC IFNA5 BID ARAF SH2D1A GRB2 PTPN11 CD244 FCGR3B IFNA8 NRAS KIR2DS 4 IFNAR1 NFATC2 LAT CASP3 IFNA7 PAK1 SH2D1B IFNA10 NFATC1 SOS1 FCER1G IFNA2 SH3BP2 KIR2DS 5 IFNAR2 BRAF SHC3 IFNGR1 IFNA1 KIR2DL 5A HLA-C PRF1 VAV1 SHC4 SYK IFNA4 Neutrophil extracellular trap formation H2AZ2 H2AB2 H2AC1 H2AC18 H2BC18 PIK3R2 H4‑16 PIK3CD H2BC8 H2AB3 CYBA C3 TLR2 FPR3 MTOR ITGAM H2AZ1 MPO PRKCA H2AC19 H2BC13 FCGR3A H3‑4 HDAC9 H2BC9 H2AB1 NFKB1 H2AC12 H2BC14 FCGR3B PLCB4 CLCN4 H2BC6 MACROH 2A2 H2BE1 C5 FCGR2A CLEC7A H3‑5 ITGAL H2BC7 MACROH 2A1 SELP H2AC13 H2BC15 CASP4 VDAC3 CLCN3 H2BC4 ACTB PIK3R1 H2AC14 CLCN5 H3C2 VDAC2 HDAC7 H2BC5 AKT3 H2AC20 H2AC15 H2AJ H3C1 VDAC1 HDAC8 IGH AKT1 H4C15 H2AC11 PIK3CB H3C4 PLCB2 FCGR1A H2BC3 AKT2 H4C14 H4C1 PIK3CA CASP1 SLC25A 6 H3C14 H2BC1 MAP3K7 H2AC21 PLCG1 H2BC10 CTSG SLC25A 5 H2BC21 MAPK13 H2BU1 H4C13 H4C3 H2BC11 H3C3 PLCB3 H3C15 MAPK14 ELANE PADI4 PLCG2 H2BC12 H3C6 PLCB1 H3C12 MAPK11 H4C9 H4C12 H4C2 TLR8 FGA ITGB2 H3C13 MAPK12 PRKCG H4C11 ATG7 TLR7 H3‑3B HDAC5 MAPK3 22 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Neutrophil extracellular trap formation HAT1 H4C8 NCF2 H4C5 SIGLEC 9 H3C8 HDAC6 H3C10 CR1L MAP2K2 HDAC11 H2BW2 TLR4 CAMP H2AX H3C11 SLC25A 31 H2AC7 C5AR1 H4C4 H2AW H3C7 SELPLG RAC1 PPIF MAP2K1 HDAC10 H4C7 H3‑3A CR1 SRC MAPK1 NCF1 H2AC8 NCF4 H4C6 SLC25A 4 FGB HDAC3 RAC2 RAF1 H2AC6 ACTG1 H2BW1 FPR2 FGG HDAC4 H2BS1 RELA H2AC4 AGER GSDMD FPR1 SYK HDAC1 AQP9 PRKCB H2AC16 AZU1 PIK3R3 VWF HDAC2 ITGA2B CYBB H2AC17 H2BC17 HMGB1 GP1BA ITGB3 Platelet activation GUCY1A 2 ROCK2 AKT2 GNAQ ITGA2 PIK3R2 SYK ITGB3 GUCY1A 1 ITGA2B ARHGEF 12 GNAS GP5 ITPR1 VWF PIK3CD MAPK13 ADCY5 PRKCI BTK GP6 PIK3R6 GP1BB GNAI1 PPP1CA ADCY4 PLA2G4 E FERMT3 GP9 RAP1A F2 GNAI2 PPP1CB ADCY3 F2R GUCY1B 1 APBB1I P RAP1B GP1BA PTGS1 MAPK14 ADCY2 PLA2G4 F ACTG1 FCGR2A TBXA2R PLCB4 MYLK4 MAPK11 ADCY9 PLA2G4 C RASGRP 2 COL3A1 P2RY1 P2RX1 GNAI3 COL1A2 ADCY8 PLA2G4 D MYLK PIK3CB FCER1G JMJD7- PLA2G4 B PIK3CG COL1A1 ADCY7 PLA2G4 A PRKACG PIK3CA FGA PLCB2 GNA13 MAPK12 MYL12B PLA2G4 B RASGRP 1 LCP2 PRKG2 PLCB3 MAPK3 STIM1 ADCY6 VASP PLCG2 ARHGEF 1 PRKG1 F2RL3 MAPK1 ORAI1 ARHGAP 35 MYL12A PRKACB SNAP23 FGB PLCB1 FYN TLN2 PPP1CC PRKCZ PRKACA PIK3R5 P2RY12 MYLK2 TLN1 ACTB VAMP8 LYN ITPR2 FGG MYLK3 ROCK1 AKT3 PIK3R1 RHOA ITPR3 NOS3 ITGB1 ADCY1 AKT1 TBXAS1 PPP1R1 2A PIK3R3 PTGIR SRC RIG-I-like receptor signaling pathway MAP3K1 MAPK12 CXCL8 CASP10 CHUK MAVS IFNA1 IFNB1 MAPK13 CYLD RELA FADD IFNA17 TRAF6 IFNA4 IL12B MAPK14 SIKE1 NLRX1 RIPK1 TRAF3 PIN1 IFIH1 IL12A ATG12 MAPK10 DDX3X MAP3K7 TRAF2 IFNA10 IKBKB IKBKG MAPK11 IRF3 TRADD AZI2 ISG15 IFNA6 MAPK9 IKBKE CXCL10 TBKBP1 TNF IFNA13 STING1 IFNA5 MAPK8 ATG5 NFKBIB IFNK TANK IFNA14 NFKB1 IFNA8 TBK1 IFNA21 IFNE IRF7 CASP8 DDX58 IFNW1 IFNA7 DHX58 NFKBIA OTUD5 TKFC IFNA16 RNF125 IFNA2 TRIM25 T cell receptor signaling pathway MAPK13 TNF PAK6 MAP3K1 4 VAV1 CD28 CD3D CD40LG 23 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) T cell receptor signaling pathway BUB1B- PAK6 CBLB PAK5 BCL10 VAV2 LCP2 PPP3CA PIK3CD MAPK14 NRAS CARD11 PPP3R1 VAV3 CD247 PPP3CB MALT1 MAPK11 PAK2 MAP2K2 RASGRP 1 IL2 SOS2 PPP3CC IKBKB TEC PAK1 MAP2K1 PPP3R2 NFKBIB NFKBIE CTLA4 MAPK9 MAPK12 AKT3 CD8B2 PLCG1 IL5 ITK ICOS MAPK8 MAPK10 AKT1 NFKB1 MAP2K7 IL4 LAT CHUK MAPK3 CD4 AKT2 DLG1 RHOA NFKBIA SOS1 PDPK1 MAPK1 PDCD1 GRAP2 PIK3R1 IL10 PTPRC GSK3B FOS HRAS PTPN6 MAP3K7 CD8B JUN PIK3CB PIK3R3 CDC42 FYN RAF1 PAK4 CD8A NFATC3 PIK3CA PIK3R2 ZAP70 IKBKG RELA PAK3 PRKCQ NFATC2 CDK4 CD3G LCK NCK1 CSF2 MAP3K8 GRB2 NFATC1 IFNG CD3E KRAS NCK2 Th1 and Th2 cell differentiation IL4R NFKBIB LAT DLL4 STAT5B ZAP70 GATA3 MAPK1 JUN IL5 RELA PPP3CA JAG1 LCK RBPJ IL12A IL13 IL4 RBPJL PPP3CB JAG2 IL2RA IKBKB JAK3 NFATC3 NFKBIA MAML3 PPP3CC HLA-DRB5 IL2RB PPP3R1 IKBKG NFATC2 MAPK12 MAML2 TBX21 STAT1 HLA-DPB1 PPP3R2 HLA-DMA NFATC1 MAPK10 MAML1 STAT6 IFNGR1 PRKCQ MAPK9 JAK1 TYK2 CD4 CD3G IL12RB 2 IFNGR2 HLA-DRB4 HLA-DMB STAT5A RUNX3 IFNG IL2RG HLA-DOB CHUK HLA-DRB3 MAPK8 JAK2 MAPK13 HLA-DRA DLL1 IL12RB 1 STAT4 HLA-DRB1 MAPK3 IL2 HLA-DQB1 CD3E HLA-DQA2 FOS NOTCH3 HLA-DPA1 MAPK14 CD247 DLL3 HLA-DOA NFKB1 NOTCH1 PLCG1 MAPK11 NFKBIE CD3D HLA-DQA1 MAF NOTCH2 IL12B Toll-like receptor signaling pathway TICAM2 TNF MAP2K1 CCL4L1 TLR1 TLR4 LBP PIK3CD TICAM1 AKT3 IFNA17 CCL4L2 NFKBIA TLR3 FADD IKBKB MAPK13 AKT1 TIRAP TBK1 IL6 CXCL8 RIPK1 MAPK9 MAPK14 AKT2 TRAF3 IL12B PIK3CB CD40 STAT1 MAPK8 MAPK11 SPP1 NFKB1 IL12A IRF5 CXCL9 CHUK CCL5 CXCL10 MAP3K7 MYD88 CD14 PIK3CA CCL3L1 FOS MAPK3 CXCL11 MAP3K8 PIK3R1 MAP2K6 IRF3 CCL3L3 IFNA6 CCL4 MAPK12 MAP2K4 TRAF6 IFNA21 TLR9 LY96 IFNA5 CCL3 MAPK10 IFNA13 TAB2 MAP2K7 TLR8 PIK3R3 IFNA8 IFNB1 TOLLIP IFNA14 TAB1 JUN IRF7 PIK3R2 IFNA7 RAC1 CD86 MAP2K2 IFNAR1 IRAK4 TLR7 CASP8 IFNA2 MAPK1 RELA MAP2K3 IFNA10 TLR2 TLR6 CTSK IFNA1 IKBKG 24 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Toll-like receptor signaling pathway CD80 IFNA16 IFNAR2 IL1B TLR5 IRAK1 IFNA4 IKBKE Central carbon metabolism in cancer PDGFRB SLC7A5 PIK3R3 LDHC IDH1 PKM KRAS PTEN PDGFRA PIK3CB SLC1A5 LDHB NTRK3 PFKM LDHAL6 A PIK3CD PDHA2 SCO2 PIK3R2 LDHA MAP2K2 PFKL FGFR3 HIF1A G6PD PIK3CA GLS AKT3 NTRK1 KIT LDHAL6 B MAPK3 TIGAR TP53 HK1 ERBB2 MAP2K1 PIK3R1 FGFR2 MAPK1 PDHA1 RAF1 EGFR MYC PGAM1 HKDC1 PFKP HRAS SIRT6 RET HK3 AKT1 IDH2 FGFR1 FLT3 PDK1 SIRT3 GLS2 HK2 AKT2 PGAM2 PGAM4 SLC2A1 GCK PDHB NRAS SLC16A 3 MTOR MET SLC2A2 Endometrial cancer EGF BAX PIK3R3 AKT3 TCF7L1 DDB2 PIK3CD HRAS BAD SOS2 PIK3R2 CDH1 MLH1 PIK3R1 ELK1 CTNNA3 AXIN2 TP53 ARAF ERBB2 APC2 TCF7 CTNNA2 AXIN1 RAF1 FOXO3 MYC GADD45 B KRAS MAPK3 BRAF SOS1 EGFR AKT1 MAP2K1 GRB2 POLK APC LEF1 NRAS AKT2 PDPK1 CDKN1A CTNNA1 PIK3CB GSK3B CASP9 MAP2K2 GADD45 A CTNNB1 MAPK1 PIK3CA ILK CCND1 TCF7L2 GADD45 G PTEN BAK1 PD-L1 expression and PD‑1 checkpoint pathway in cancer MAP3K3 EML4 CD247 CD3E MAP2K2 TIRAP KRAS HRAS EGF MAPK11 PDCD1 EGFR MAP2K3 NFKB1 CD274 IKBKG JUN TLR2 PTPN6 CD3D STAT1 MTOR PTEN JAK1 NFATC3 NFKBIB NFKBIE PPP3CA CSNK2A 2 MYD88 PIK3CD MAP2K6 PTPN11 NFKBIA TLR4 NRAS IFNGR1 ZAP70 HIF1A JAK2 NFATC2 MAPK12 RAF1 PPP3CB CSNK2A 1 LCK IKBKB NFATC1 CD4 LAT PPP3CC MAP2K1 PIK3R1 PPP3R1 TICAM2 PIK3CB ALK AKT3 IFNGR2 RPS6KB 2 RASGRP 1 TICAM1 PIK3CA RELA AKT1 CHUK RPS6KB 1 PPP3R2 BATF IFNG PIK3R3 AKT2 STAT3 CSNK2B MAPK3 MAPK13 TLR9 PIK3R2 BATF2 CSNK2A 3 TRAF6 PLCG1 MAPK14 CD28 CD3G BATF3 FOS PRKCQ MAPK1 Transcriptional misregulation in cancer LMO2 CCNT2 IGFBP3 CSF1R PAX5 BCL6 KDM6A FLT1 IL1R2 PRCC TRAF1 GRIA3 HMGA2 FEV H3‑3B CDKN1B ZBTB17 CSF2 GADD45 G SLC45A 3 NFKBIZ ID2 H3C8 CEBPE DEFA5 HPGD PBX3 MAX ETV1 ATM H3C7 HDAC1 DEFA6 CCNT1 ASPSCR 1 SPI1 PAX7 REL ARNT2 FLT3 25 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Transcriptional misregulation in cancer ZBTB16 EWSR1 IGF1 DDX5 MMP9 RARA CDKN2C CDKN1A DEFA3 MPO NFKB1 KMT2A ETV4 DOT1L TAF15 HDAC2 RUNX1 FOXO1 FLI1 JMJD1C BAIAP3 CXCL8 PTCRA LDB1 IGH HOXA11 PBX1 PDGFA ETV5 H3‑3A EYA1 SIX1 MITF HOXA10 PTK2 PLAU MLF1 CD40 NTRK1 ITGAM DEFA4 RXRA NR4A3 PLAT PAX3 BCL2A1 MEF2C LYL1 DEFA1 HOXA9 MAF IGF1R ETV6 TMPRSS 2 GZMB HHEX PML RXRB CCNA2 ELK4 TGFBR2 MLLT1 KLF3 FCGR1A RUNX2 CCND2 CCNA1 TSPAN7 IL3 MLLT3 DDB2 H3C14 WT1 NSD2 TLX3 CD14 WNT16 BMI1 H3‑4 H3C15 MYCN MYC TLX1 PROM1 SUPT3H AFF1 PER2 SIN3A NUPR1 RXRG BCL2L1 MEN1 ETV7 FUT8 H3‑5 H3C12 SPINT1 RUNX1T 1 ERG SSX1 ZEB1 TFE3 IL2RB SIX4 BAX ELANE COMMD3 ‑BMI1 SSX2 CDK9 H3C2 PPARG H3C13 CDK14 NGFR BMP2K SS18 NCOR1 H3C1 DDIT3 POLK TP53 GADD45 B TCF3 DUSP6 SP1 DEFA1B MET H3C10 CD86 BCL11B BIRC2 FUS IL6 H3C4 SSX2B H3C11 RELA GADD45 A BIRC3 JUP MEIS1 H3C3 CEBPA BAK1 ATF1 SMAD1 MDM2 MMP3 PAX8 H3C6 CEBPB ITGB7

[0051] In the present invention, the immune cell composition is obtained by calculating a proportion of two or more immune cells selected from the group consisting of: naive CD4+ Tcells, CD4+ Tcells, naive CD8+ Tcells, CD8+ Tcells, central memory Tcells, cytotoxic Tcells, dendritic cells, effector memory Tcells, exhausted Tcells, γδ Tcells, natural Treg cells, induced Treg cells, mucosal-associated invariant Tcells, NK cells, NK Tcells, follicular helper Tcells, type 1 helper T cells, type 17 helper Tcells, type 2 helper Tcells, type 1 treg Tcells, monocytes, macrophages, neutrophils, B cells, and infiltration scores.

[0052] In the present invention, the immune cell receptor features and transcriptome features in step (c) may include the features shown in Table 1 below. [Table 1] Feature List Level Feature type Feature description Total number of TCR sequences Unique number of TCR sequences Number of TCR clonotypes per kilo-reads Entropy of TCR Clonality of TCR Immune repertoi re properti es Total number of BCR sequences Unique number of BCR sequences Number of BCR clonotypes per kilo-reads Entropy of BCR 26 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Feature List Level Feature type Feature description Clonality of BCR Somatic hypermutation rate of BCR sequences Number of BCR clusters CDR3 sequenc e IGH IGK IGL TRA TRB TRD TRG V&J gene usage, proporti on IGHA1 IGHA2 IGHD IGHE IGHG1 IGHG2 IGHG3 IGHG4 IGHGP IGHM Mean of length in TCR Mean of gravy index in TCR Mean of bulkiness of amino acids in TCR Mean of polarity of amino acids in TCR Mean of net charge in TCR Mean of fraction of basic amino acids in TCR Mean of fraction of acidic amino acids in TCR Mean of fraction of aromatic amino acids in TCR Chemical properti es of amino acids Mean of aliphatic index in TCR Mean of length in BCR Mean of gravy index in BCR Mean of bulkiness of amino acids in BCR Mean of polarity of amino acids in BCR Mean of net charge in BCR Mean of fraction of basic amino acids in BCR Mean of fraction of acidic amino acids in BCR Mean of fraction of aromatic amino acids in BCR Mean of aliphatic index in BCR 27 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Feature List Level Feature type Feature description T cell activation involved in immune response Innate immune response Innate immune response activating cell surface recep- tor signaling pathway Lymphocyte activation involved in immune response Mucosal immune response Transcr ipt Single sample gene set enrichme nt analy- sis , ssGESA score Natural killer cell activation involved in immune re- sponse Negative regulation of immune response Negative regulation of innate immune response Neutrophil activation involved in immune response Positive regulation of cytokine Positive regulation of immune effector process Positive regulation of immune response Positive regulation of production of molecular mediator of immune response Regulation of adaptive immune response Regulation of cytokine production involved in immune response Regulation of humoral immune response Regulation of immune effector process Regulation of immune response Regulation of innate immune response Antigen processing and presentation B cell receptor signaling pathway Chemokine signaling pathway Complement and coagulation cascades Cytosolic DNA-sensing pathway Fc epsilon RI signaling pathway Fc gamma R-mediated phagocytosis Hematopoietic cell lineage IL‑17 signaling pathway Intestinal immune network for IgA production Leukocyte transendothelial migration NOD-like receptor signaling pathway Natural killer cell mediated cytotoxicity Neutrophil extracellular trap formation Platelet activation RIG-I-like receptor signaling pathway 28 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Feature List Level Feature type Feature description T cell receptor signaling pathway Th1 and Th2 cell differentiation Toll-like receptor signaling pathway Cellular response to tumor necrosis factor Negative regulation of tumor necrosis factor production Negative regulation of tumor necrosis factor superfam- ily cytokine production Negative regulation of tumor necrosis factor-mediated signaling pathway Positive regulation of tumor necrosis factor production Positive regulation of tumor necrosis factor superfamily cytokine production Regulation of tumor necrosis factor production Regulation of tumor necrosis factor-mediated signaling pathway Response to tumor necrosis factor Tumor necrosis factor-mediated signaling pathway Central carbon metabolism in cancer Endometrial cancer PD-L1 expression and PD‑1 checkpoint pathway in cancer Transcriptional misregulation in cancer naive CD4+ T cell CD4+ T cell naive CD8+ T cell CD8+ T cell Central memory T cell Cytotoxic T cell Dendritic cell Effector memory T cell Exhausted T cell γδ T cell Natural Treg cell Induced Treg cell Immune cell composit ion Mucosal-associated invariant T cell NK cell NK T cell Follicular helper T cell Type 1 helper T cell Type 17 helper T cell 29 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Feature List Level Feature type Feature description Type 2 helper T cell Type 1 Treg cell Monocyte Macrophage Neutrophil B cell Infiltration score

[0053] In the present invention, the cut-off value in step (d) may be used without limitation as long as it is a value used to distinguish between a cancer patient and a normal subject, and may be preferably 0.5, but is not limited thereto. When the cut-off value is 0.5, a case with an output value of 0.5 or more is determined that there is cancer.

[0054] In the present invention, when the artificial intelligence model was trained to label a cancer patient as 1 and a normal sample as 0, so that the output value can be interpreted as the probability of having cancer. Based on this result, when the probability of having cancer is 0.5 or higher, the patient may be diagnosed as having cancer, and when the probability of having cancer is 0.5 or lower, the patient may be diagnosed as normal.

[0055] Here, it will be apparent to those skilled in the art that the cut-off value of 0.5 may be arbitrarily changed. For example, in an attempt to reduce false positives, the cut-off value may be set to be higher than 0.5 as a stricter criterion for determining whether or not there is cancer, and in an attempt to reduce false negatives, the cut-off value may be set to be lower than 0.5 as a weaker criterion for determining that there is cancer.

[0056] According to the present invention, the method may further include: (e) inputting the immune cell receptor features and transcriptome features of the sample primarily determined that there is cancer into a second artificial intelligence model trained to distinguish between an immunoactive state and cancer, comparing the analyzed output value with a cut-off value and finally determining whether or not there is cancer.

[0057] In the present invention, the immunoactive state means a state in which the overall immune system is activated due to reasons such as an immune-related disease other than cancer or inflammation, and the calculated immune cell receptor features and transcriptome features are similar to those of cancer patients, and the step (e) of the present invention means training to distinguish between a normal sample of the immunoactive state and a cancer sample, to distinguish the immunoactive state as normal and the cancer sample as cancer.

[0058] It is well known that the immunological profiling of normal subjects in immune-related diseases or inflammation states is similar to that of cancer patients (Zeev Elkoshi, Front. Immunol., Vol. 13:821598, 2022; Giat E. et al., Autoimmunity Reviews, Vol. 16(10), pp. 1049‑1057, 2017).

[0059] In the present invention, the cut-off value in step (d) may be used without limitation as long as it is a value used to distinguish between a cancer patient and a normal subject, and may be preferably 0.5, but is not limited thereto. When the cut-off value is 0.5, a case having an output value of 0.5 or more is determined that there is cancer.

[0060] In the present invention, the artificial intelligence model may be used without limitation as long as it is a model trained to distinguish between cancer and normality, and preferably includes at least one selected from the group consisting of a linear regression, a random forest, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), and an autoencoder, but is not limited thereto.

[0061] In the present invention, when the artificial intelligence model is a deep neural network, the loss function may be represented by the following Equation 1: 30 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 wherein N is the total number of samples, yi is the actual label of the ith input value, and is the label of the ith input value predicted by the model.

[0062] In the present invention, when the artificial intelligence model is a DNN, the training includes the following steps: i) classifying the produced transcriptome data into training, validation, and test data, wherein the training data is used to train the model and validate hyper-parameter tuning, and the test data is used for the test after optimal model production; and ii) evaluating the performance of the optimal model built using test data using various indicators and analyze the feature importance of the model.

[0063] In the present invention, the hyper-parameter tuning is a process of optimizing the values of various parameters (such as the number of convolution layers, the number of dense layers, and the number of convolution filters) constituting the artificial intelligence model. Hyper-parameter tuning is performed using Bayesian optimization, manual search or grid search.

[0064] In the present invention, the internal parameters (weights) of the artificial intelligence model are optimized using predetermined hyper-parameters, and model training is stopped before the training loss stops in order to prevent overfitting.

[0065] In the present invention, the output value obtained by analyzing immune cell receptor features and transcriptome features input by the artificial intelligence model in step (d) or (e) may be used without limitation, as long as it is a specific score or real number, and the value is preferably a real number, but is not limited thereto.

[0066] In the present invention, when the artificial intelligence model is a DNN, the real number means a value expressed as a probability value by adjusting the output of the artificial intelligence model to a scale of 0 to 1 using applying the sigmoid function or SoftMax function for the last layer.

[0067] In another aspect, the present invention is directed to a device for diagnosing cancer based on immunological profiling, the device including: a decoder configured to decode whole transcriptome sequencing (WTS) information from a biological sample; an aligner configured to align the decoded WTS information to a reference genome database; a feature detector configured to extract immune cell receptor features and transcript features from the aligned WTS information; and a cancer diagnostic unit configured to compare the analyzed output obtained by inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, with a cut-off value, and determine whether or not cancer is present.

[0068] In the present invention, the decoder may include a nucleic acid injector configured to inject the nucleic acid extracted from an independent device, and a sequence information analyzer configured to analyze the sequence information of the injected nucleic acid, preferably an NGS analyzer, but is not limited thereto.

[0069] In the present invention, the decoder may receive and decode sequence information data generated in the independent device.

[0070] In another aspect, the present invention is directed to a computer-readable storage medium including an instruction configured to be executed by a processor for providing information for diagnosis of cancer, through the following steps including: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; 31 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, comparing the analyzed output value with a cut-off value, and primarily determining whether or not there is cancer; and (e) inputting the immune cell receptor features and transcriptome features of the sample primarily determined that there is cancer into a second artificial intelligence model trained to distinguish between an immunoactive state and cancer, comparing the analyzed output value with a cut-off value, and finally determining whether or not there is cancer.

[0071] In another aspect, the method according to the present disclosure may be implemented using a computer. In one embodiment, the computer includes one or more processors coupled to a chipset. In addition, a memory, a storage device, a keyboard, a graphics adapter, a pointing device, a network adapter and the like are connected to the chipset. In one embodiment, the performance of the chipset is acquired by a memory controller hub and an I / O controller hub. In another embodiment, the memory may be directly coupled to a processor instead of the chipset. The storage device is any device capable of maintaining data, including a hard drive, compact disc read-only memory (CD-ROM), DVD, or other memory devices. The memory relates to data and instructions used by the processor. The pointing device may be a mouse, track ball or other typeof pointing device, and is used in combination with a keyboard to transmit input data to a computer system. The graphics adapter presents images and other information on a display. The network adapter is connected to the computer system through a local area network or a long distance communication network. However, the computer used herein is not limited to the above configuration, may not have some configurations, may further include additional configurations, and may also be part of a storage area network (SAN), and the computer of the present invention may be configured to be suitable for the execution of modules in the program for the implementation of the method according to the present invention.

[0072] The module used herein may mean a functional and structural combination of hardware to implement the technical idea according to the present invention and software to drive the hardware. For example, it is apparent to those skilled in the art that the module may mean a logical unit of predetermined code and a hardware resource to execute the predetermined code, and does not necessarily mean physically connected code or one type of hardware. Example

[0073] Hereinafter, the present invention will be described in more detail with reference to examples. However, it will be obvious to those skilled in the art that these examples are provided only for illustration of the present invention, and should not be construed as limiting the scope of the present invention. Example 1. Extracting RNA from blood and performing next-generation sequencing

[0074] 10 mL of blood was collected from 72 normal people and 131 cancer patients (15 colon cancer patients, 18 esophageal cancer patients, 21 liver cancer patients, 42 lung cancer patients, 14 ovarian cancer patients, and 21 pancreatic cancer patients) and stored in blood storage containers (Streck tubes). After collection, the blood was centrifuged at 3,000 rpm for 10 minutes within 24 hours, and the buffy coat was collected and used for RNA extraction. RNA was extracted using RiboPure™ RNA Purification Kit (ThermoFisher scientific, USA), mRNA was isolated using Dynabeads mRNA purification kit (ThermoFisher scientific, USA), and then subjected to RNA fragmentation, a library was produced using xGen broad-range RNA library prep kit (IDT, USA), and sequenced using Novaseq 6000 system (illumina, USA) equipment at 200 cycles.

[0075] The result showed that an average of 90 million reads were produced for each sample.

[0076] The patient composition is shown in Table 2 below. [Table 2] Type of cancer Number of patients Type of cancer Number of patients Type of cancer Number of patients Colon cancer 15 Esophageal cancer 18 Liver cancer 21 Lung cancer 42 Ovarian cancer 14 Pancreatic cancer 21 32 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Type of cancer Number of patients Type of cancer Number of patients Type of cancer Number of patients Normal 72 1‑2. Acquisition of mass data for model construction and validation

[0077] To produce a cancer diagnosis model, 10,929 cancer and 10,845 normal tissue sample WTS data were collected from the cancer genome atlas (TCGA) and the genotype-tissue expression (GTEx) consortium, respectively.

[0078] In addition, for model optimization, whole blood WTS data from 356 immunological disease (inflammatory bowel disease (IBD), juvenile idiopathic arthritis (JIA), and COVID19 patients were obtained from GSE190680, GSE179627, and GSE112057 in gene expression omnibus. Source: TCGA (https: / / portal.gdc.cancer.gov / ) GTEx (https: / / gtexportal.org / ) GEO (https: / / www.ncbi.nlm.nih.gov / geo / ) 1‑3. Pretreatment of acquired sequence information

[0079] TCR / BCR sequences wereextracted from the bam files of the aligned samplesobtained in Example 1‑1using the TRUST4 tool.The TRUST4 tool is an algorithm that obtains and analyzes TCR / BCR sequences including the CDR3 region from RNA sequencing data through de novo assembly (https: / / github.com / liulab-dfci / TRUST4). At this time, samples having three or less sequences were excluded and sequences containing N or having a length greater than 20 and less than 12 were excluded. Example 2. Extraction of immune cell receptor features and transcriptome features for cancer diagnosis 2‑1. Extraction of immune cell receptor features

[0080] From the extracted CDR3 sequence data, the TCR / BCR repertoire features of the CDR3 sequences extracted by TRUST4 into the R package called "RIMA" (Aashna Jhaveri et al., RIMA_pipeline, (2021), GitHub repository, https: / github.com / liulabdfci / RIMA_pipeline) were calculated for each of TCR and BCR. The number of unique CDR3 sequences was divided by the total number and multiplied by 1000 to calculate CPK, and the remaining features were calculated using the basic function provided by RIMA, and used as features of the AI model. The entire features related to the immune repertoire are described in the immune repertoire features in Table 1.

[0081] In addition, the V and J gene usages were calculated from the extracted CDR3 sequence data and used as features of the artificial intelligence model. The genotype ratios of IGH, IGK, IGL, TRA, TRB, TRD, TRG, IGHA1, IGHA2, IGHD, IGHE, IGHG1, IGHG2, IGHG3, IGHG4, IGHGP, and IGHM were calculated for each sample using the following Equation 2.

[0082] In addition, the chemical properties of the CDR3 amino acid sequence were calculated using the R package "alakazam" (Namita T.et al., Bioinformatics, Vol.31,No. 20, pp. 3356‑3358,2015) and wereused as features of the artificial intelligence model. The overall features of the chemical properties of the CDR3 amino acid sequence are described in the chemical properties of the amino acid sequence in Table 1. 2‑2. Extraction of transcriptome features

[0083] The bam file of the WTS data obtained in Example 1 was converted into a gene expression matrix using the featureCounts command of the algorithm in order to use the bam file in the ImmuCellAI (YR Miao et al., Adv. Sci. 7:1902880, 2020) algorithm. Then, TPM normalization was performed. That is, the read count of each gene was divided by 33 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 the length of the transcript and then the result was divided again by the sum of the corrected values, so that the total TPM of each sample was adjusted to 1.

[0084] In the normalized gene expression matrix, the ensemble gene ID was converted to HGNC gene name, duplicated genes were removed, and then 24 immune cell compositions (immune cell composition) and 1 immune filtration score were calculated using the ImmuCellAI basic function and used as features of the AI model. The overall features related to the composition of immune cells are described in the immune cell composition in Table 1.

[0085] In addition, the enrichment scores of a total of 52 gene sets related to immune response (38) or cancer (14) were calculated using single sample gene set enrichment analysis (ssGSEA, https: / / gseapy.readthedocs.io / en / latest / introduc tion.html) and used as features of the AI model. The overall features of the scores are described in the cancer‑ or immune- related gene enrichment scores in Table 1.

[0086] Specifically, among the gene sets related to immune response or cancer, GO terms were selected from https: / / www.ebi.ac.uk / QuickGO / annotations, KEGG pathway was selected from https: / / www.kegg.jp / kegg / pathway.html, and the gene list for each gene set is shown in Table 5 above. The TPM normalization matrix and the selected gene set list were input and the features were calculated using the GSEApy basic function. Example 3. Construction of cancer diagnosis model and training process

[0087] The features extracted in Example 2 were created as input matrixes as shown in FIG. 2 and used to construct a cancer diagnosis model.

[0088] The data sets used for training were 10,929 cancer and 10,845 normal tissue sample WTS data from the cancer genome atlas (TCGA) and the genotype-tissue expression (GTEx) consortium obtained in Example 1‑2, respectively, and were divided into training set, validation set, and test set as shown in Table 3 below and used as learning and internal test set. [Table 3] Train Validation Test TCGA 9345 500 1000 GTEx 9429 500 1000

[0089] A binary classification model to distinguish between cancer patients and normal subjects was constructed using the AI algorithm. Three AI models were used for model training: linear regression, random forest, and deep neural network (DNN).

[0090] In order to optimize the linear regression and random forest models, the training data was repeatedly classified into training data and validation data using 5-fold cross validation, and the hyper-parameter tuning process was performed.

[0091] The DNN included an input layer that recognizes each feature of the input matrix as an independent node, three hidden layers, and an output layer that calculates the cancer score. Only the output layer used the sigmoid function as an activation function to calculate the final cancer score, and the remaining layers used the Relu function as an activation function.

[0092] The loss function of DNN used herein was binary crossentropy represented by Equation 1 and the loss function optimization was performed using the ADAM (Adaptive Moment Estimation) algorithm (Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press).

[0093] The validation loss was monitored during model training using the basic function called "EarlyStopping" built in Keras (https: / / keras.io). When the training was not improved, it was stopped and the final performance evaluation was performed. Example 4. Performance verification of constructed cancer diagnosis model

[0094] An input matrix consisting of 124 features was created for the three models trained using the validation set described in Table 3 of Example 3, and the model performance was evaluated by receiver operating characteristic (ROC) curve analysis, and prediction accuracy, sensitivity, and specificity.

[0095] The result showed, as shown in B of FIG. 3, that the DNN model properly distinguished cancer samples from normal samples in all data sets, and as can be seen from A of FIG. 3, the DNN model exhibited superior AUC, accuracy, sensitivity, and specificity in all data sets. 34 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 [Table 4] Validation ROC-AUC 0.99 PR-AUC 0.98

[0096] On the other hand, as can be seen from FIG. 4, the linear regression and random forest models exhibited high performance in tissue sample data, but significantly lower prediction performance in whole blood samples. Example 5. Analysis of important features of cancer diagnosis model

[0097] Which feature among the 124 features used in training the algorithm had an important role in performing cancer diagnosis was determined.

[0098] The SHAP algorithm of Python (https: / / shap.readthedocs.io / en / latest / ) can be used to calculate which features are given greater weight when the model is trained. Based thereon, which features among the features calculated from the WTS data have an effect on cancer diagnosis was determined.

[0099] Specifically, the Shapley value indicates how much each feature contributed to the model predicting the result, and is the sum of the differences between the average of the predicted values from all possible combinations when a specific feature is excluded and the predicted values when the corresponding feature is added to each combination. The model trained in Example 3, the input values used for training were input into the built-in function called "DeepExplainer" of the SHAP package to calculate the Shapley value, and then the Shapley value calculated for each sample was converted into a matrix and visualized as a heatmap using R Studio.

[0100] The result showed that, as shown in FIG. 5, among the features related to the immune repertoire, features related to BCR were at the top. Example 6. Additional model construction and performance verification for false positive removal 6‑1. Model construction

[0101] The input matrix consisting of 124 features from the WTS data of 183 cancer patients obtained in Example 1‑1 was input into the DNN model of Example 3 and then output. The result showed, among the samples determined as cancer patients, many immunoactive normal samples were found as false positives (FIG. 6).

[0102] Therefore, an additional model capable of distinguishing between an immunoactive state and cancer was constructed (FIG. 7). The input matrix was the same as Example 3, but the training data used herein was the WTS data of 356 immunological disease patients’ whole blood obtained in Example 1‑2 and the normal whole blood data used for model verification, and the same training process as in Example 3 was performed. 6‑2. Verification of performance of constructed false positive removal model

[0103] The performance of the DNN model of Example 3, to which the model constructed in Example 6‑1 was added, was verified by creating an input matrix consisting of 124 features from the WTS data of 183 cancer patients obtained in Example 1‑1. The result showed that the performance of the DNN model with the false positive removal model added was improved, as can be seen from FIG. 8. [Industrial applicability]

[0104] The method of diagnosing cancer based on immunological profiling using transcriptomes according to the present invention provides diagnosis of all cancer types based on artificial intelligence using both the features of immune cell receptors and the features of gene expression levels extracted from transcriptome samples produced by next- generation sequencing (NGS) and is highly commercially applicable due to the high accuracy and sensitivity thereof compared to diagnostic methods using only immune cell receptor sequence information.

[0105] Although specific configurations of the present invention have been described in detail, those skilled in the art will appreciate that this description is provided to set forth preferred embodiments for illustrative purposes, and should not be construed as limiting the scope of the present invention. Therefore, the substantial scope of the present invention is defined by the accompanying claims and equivalents thereto. 35 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 Claims 1. A method of providing information for cancer diagnosis based on immunological profiling using transcriptomes, the method comprising: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; and (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, and comparing the analyzed output value with a cut-off value to determine whether or not there is cancer. 2. A method of diagnosing cancer diagnosis based on immunological profiling using transcriptomes, the method comprising: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; and (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject and comparing the analyzed output value with a cut-off value to determine whether or not there is cancer. 3. The method according to claim 1 or 2, wherein the obtaining whole transcriptome sequencing (WTS) information from a biological sample in step (a) comprises: (a-i) randomly fragmenting RNA obtained from a biological sample using an enzymatic cleavage method; (a-ii) replacing the cleavage RNA with cDNA and then producing a library; and (a-iii) performing single-end sequencing or pair-end sequencing on the produced library using a next-generation sequencer to obtain WTS information. 4. The method according to claim 1 or 2, wherein the step (c) of extracting the immune cell receptor features comprises: (c-i) extracting a CDR3 (complementary-determining region 3) amino acid sequence of the immune cell receptor from the aligned WTS information; and (c-ii) calculating at least one feature selected from the group consisting of immune repertoire properties, V&J gene usage, and chemical features of CDR3 amino acid sequences based on the extracted sequence informa- tion. 5. The method according to claim 1 or 2, wherein the step (c) of extracting transcriptome features comprises calculating a cancer‑or immune-related gene enrichment score (single sample gene set enrichment analysis, ssGESA score) or an immune cell composition. 6. The method according to claim 4, wherein the immune repertoire properties comprise at least one selected from the group consisting of a total number of TCR sequences, a unique number of TCR sequences, a number of TCR clonotypes per kilo-reads, an entropy of TCR, clonality of TCR, a total number of BCR sequences, a unique number of BCR sequences, a number of BCR clonotypes per kilo-reads, an entropy of BCR, clonality of BCR, a somatic hypermutation rate of BCR sequences, and a number of BCR clusters. 7. The method according to claim 4, wherein the V&J gene usage is a proportion of two or more genotypes selected from the group consisting of IGH, IGK, IGL, TRA, TRB, TRD, TRG, IGHA1, IGHA2, IGHD, IGHE, IGHG1, IGHG2, IGHG3, IGHG4, IGHGP, and IGHM. 8. The method according to claim 4, wherein the chemical properties of the CDR3 amino acidsequence comprise at least one selected from the group consisting of a mean of length in TCR, a mean of gravy index in TCR, a mean of bulkiness of amino acids in TCR, a mean of polarity of amino acids in TCR, a mean of net charge in TCR, a mean of fraction of basic amino acids in TCR, a mean of fraction of acidic amino acids in TCR, a mean of fraction of aromatic amino acids in TCR, a mean of aliphatic index in TCR, a mean of length in BCR, a mean of gravy index in BCR, a mean of bulkiness of amino acids in BCR, a mean of BCR amino acids, a mean of polarity of amino acids in BCR, a mean of net charge in 36 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 BCR, a mean of fraction of basic amino acids in BCR, a mean of fraction of acidic amino acids in BCR, a mean of fraction of aromatic amino acids in BCR, and a mean of aliphatic index in BCR. 9. The method according to claim 5, wherein the cancer‑ or immune-related gene enrichment score (single sample gene set enrichment analysis, ssGESA score) is obtained by calculating an enrichment score of a gene associated with at least one selected from the group consisting of T cell activation involved in immune response, innate immune response, innate immune response activating cell surface receptor signaling pathway, lymphocyte activation involved in immune response, mucosal immune response, natural killer cell activation involved in immune response, negative regulation of immune response, negative regulation of innate immune response, neutrophil activation involved in immune response, positive regulation of cytokine, production involved in immune response, positive regulation of immune effector process, positive regulation of immune response, positive regulation of production of molecular mediator of immune response, regulation of adaptive immune response, regulation of cytokine production involved in immune response, regulation of humoral immune response, regulation of immune effector process, regulation of immune response, regulation of innate immune response, antigen processing and presentation, B cell receptor signaling pathway, chemokine signaling pathway, complement and coagulation cascades, cytosolic DNA-sensing pathway, Fc epsilon RI signaling pathway, Fc gamma R-mediated phagocytosis, hematopoietic cell lineage, IL‑17 signaling pathway, intestinal immune network for IgA production, leukocyte transendothelial migration, NOD-like receptor signaling pathway, natural killer cell mediated cytotoxicity, neutrophil extracellular trap formation, platelet activation, RIG-I-like receptor signaling pathway, Tcell receptor signaling pathway, Th1 and Th2 cell differentiation, toll-like receptor signaling pathway, cellular response to tumor necrosis factor, negative regulation of tumor necrosis factor production, negative regulation of tumor necrosis factor superfamily cytokine production, negative regulation of tumor necrosis factor-mediated signaling pathway, positive regulation of tumor necrosis factor production, positive regulation of tumor necrosis factor superfamily cytokine production, regulation of tumor necrosis factor production, regulation of tumor necrosis factor-mediated signaling pathway, response to tumor necrosis factor, tumor necrosis factor-mediated signaling pathway, central carbon metabolism in cancer, endometrial cancer, PD-L1 expression and PD‑1 checkpoint pathway in cancer, and transcriptional misregulation in cancer. 10. The method according to claim 5, wherein the immune cell composition is obtained by calculating a proportion of two or more immune cells selected from the group consisting of: naive CD4+ Tcells, CD4+ Tcells, naive CD8+ Tcells, CD8+ Tcells, central memory Tcells, cytotoxic Tcells, dendritic cells, effector memory Tcells, exhausted Tcells, γδ Tcells, natural Treg cells, induced Treg cells, mucosal-associated invariant T cells, NK cells, NK T cells, follicular helper T cells, type 1 helper Tcells, type 17 helper Tcells, type 2 helper Tcells, type 1 Treg Tcells, monocytes, macrophages, neutrophils, B cells, and infiltration scores. 11. The method according to claim 1 or 2, wherein the immune cell receptor features and transcriptome features in step (c) comprise features shown in Table 1 below. [Table 1] Feature List Level Feature type Feature description Total number of TCR sequences Unique number of TCR sequences Number of TCR clonotypes per kilo-reads Entropy of TCR Immune repertoi re properti es Clonality of TCR Total number of BCR sequences Unique number of BCR sequences Number of BCR clonotypes per kilo-reads CDR3 sequenc e Entropy of BCR Clonality of BCR Somatic hypermutation rate of BCR sequences 37 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Feature List Level Feature type Feature description Number of BCR clusters IGH IGK V&J gene usage, pro- porti on IGL TRA TRB TRD TRG IGHA1 IGHA2 IGHD IGHE IGHG1 IGHG2 IGHG3 IGHG4 IGHGP IGHM Mean of length in TCR Mean of gravy index in TCR Mean of bulkiness of amino acids in TCR Mean of polarity of amino acids in TCR Mean of net charge in TCR Mean of fraction of basic amino acids in TCR Mean of fraction of acidic amino acids in TCR Chemical properti es of amino acids Mean of fraction of aromatic amino acids in TCR Mean of aliphatic index in TCR Mean of length in BCR Mean of gravy index in BCR Mean of bulkiness of amino acids in BCR Mean of polarity of amino acids in BCR Mean of net charge in BCR Mean of fraction of basic amino acids in BCR Mean of fraction of acidic amino acids in BCR Mean of fraction of aromatic amino acids in BCR Mean of aliphatic index in BCR 38 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Feature List Level Feature type Feature description Transcr ipt Single sample gene set enrichme nt analysis T cell activation involved in immune response Innate immune response Innate immune response activating cell surface receptor signaling pathway Lymphocyte activation involved in immune response Mucosal immune response , ssGESA score Natural killer cell activation involved in immune response Negative regulation of immune response Negative regulation of innate immune response Neutrophil activation involved in immune response Positive regulation of cytokine Positive regulation of immune effector process Positive regulation of immune response Positive regulation of production of molecular mediator of immune response Regulation of adaptive immune response Regulation of cytokine production involved in immune response Regulation of humoral immune response Regulation of immune effector process Regulation of immune response Regulation of innate immune response Antigen processing and presentation B cell receptor signaling pathway Chemokine signaling pathway Complement and coagulation cascades Cytosolic DNA-sensing pathway Fc epsilon RI signaling pathway Fc gamma R-mediated phagocytosis Hematopoietic cell lineage IL‑17 signaling pathway Intestinal immune network for IgA production Leukocyte transendothelial migration NOD-like receptor signaling pathway Natural killer cell mediated cytotoxicity Neutrophil extracellular trap formation Platelet activation RIG-I-like receptor signaling pathway T cell receptor signaling pathway Th1 and Th2 cell differentiation 39 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Feature List Level Feature type Feature description Toll-like receptor signaling pathway Cellular response to tumor necrosis factor Negative regulation of tumor necrosis factor production Negative regulation of tumor necrosis factor superfamily cytokine production Negative regulation of tumor necrosis factor-mediated signaling pathway Positive regulation of tumor necrosis factor production Positive regulation of tumor necrosis factor superfamily cytokine production Regulation of tumor necrosis factor production Regulation of tumor necrosis factor-mediated signaling pathway Response to tumor necrosis factor Tumor necrosis factor-mediated signaling pathway Central carbon metabolism in cancer Endometrial cancer PD-L1 expression and PD‑1 checkpoint pathway in cancer Transcriptional misregulation in cancer Naive CD4+ T cell CD4+ T cell Naive CD8+ T cell CD8+ T cell Central memory T cell Cytotoxic T cell Dendritic cell Immune cell com- posit ion Effector memory T cell Exhausted T cell γδ T cell Natural Treg cell Induced Treg cell Mucosal-associated invariant T cell NK cell NK T cell Follicular helper T cell Type 1 helper T cell Type 17 helper T cell Type 2 helper T cell Type 1 Treg cell Monocyte Macrophage Neutrophil 40 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (continued) Feature List Level Feature type Feature description B cell Infiltration score 12. The method according to claim 1 or 2, wherein the cut-off value of step (d) is 0.5 and a case with an output value of 0.5 or higher is determined that there is cancer. 13. The method according to claim 1 or 2, further comprising: (e) inputting the immune cell receptor features and transcriptome features of the sample primarily determined that there is cancer into a second artificial intelligence model trained to distinguish between an immunoactive state and cancer, comparing the analyzed output value with a cut-off value, and finally determining whether or not there is cancer. 14. The method according to claim 13, wherein the cut-off value of step (d) is 0.5 and a case with an output value of 0.5 or higher is determined that there is cancer. 15. The method according to claim 1, 2 or 13, wherein the artificial intelligence model comprises at least one selected from the group consisting of a linear regression, a random forest, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), and an autoencoder. 16. The method according to claim 15, wherein when the artificial intelligence model is a deep neural network, the loss function is represented by the following Equation 1: wherein N is a total number of samples, yi is an actual label of an ith input value, and is a label of an ith input value predicted by the model. 17. A device for diagnosing cancer based on immunological profiling, the device comprising: a decoder configured to decode whole transcriptome sequencing (WTS) information from a biological sample; an aligner configured to align the decoded WTS information to a reference genome database; a feature detector configured to extract immune cell receptor features and transcript features from the aligned WTS information; and a cancer diagnostic unit configured to compare the analyzed output obtained by inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, with a cut-off value, and determine whether or not cancer is present. 18. A computer-readable storage medium including an instruction configured to be executed by a processor for providing information for diagnosis of cancer, through the following steps comprising: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, comparing the analyzed output value with a cut-off value, and primarily determining whether or not there is cancer; and 41 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 (e) inputting the immune cell receptor features and transcriptome features of a sample primarily determined that there is cancer into a second artificial intelligence model trained to distinguish between an immunoactive state and cancer, comparing the analyzed output value with a cut-off value, and finally determining whether or not there is cancer. 42 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 43 EP 4 700 789 A1 44 EP 4 700 789 A1 45 EP 4 700 789 A1 46 EP 4 700 789 A1 47 EP 4 700 789 A1 48 EP 4 700 789 A1 49 EP 4 700 789 A1 50 EP 4 700 789 A1 51 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 52 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 53 EP 4 700 789 A1 5 10 15 20 25 30 35 40 45 50 55 54 EP 4 700 789 A1 REFERENCES CITED IN THE DESCRIPTION This list of references cited by the applicant is for the reader’s convenience only. It does not form part of the European patent document. Even though great care has been taken in compiling the references, errors or omissions cannot be excluded and the EPO disclaims all liability in this regard. Patent documents cited in the description • KR 20220072680

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[0092] 摘要 本发明涉及一种用于使用基于转录组的免疫组库谱分析来诊断癌症的方法，并且更特 别地涉及一种通过使用以下方法来诊断癌症的方法：从生物样品获得转录组序列信息， 然后从该转录组序列信息中提取免疫细胞受体的特征和转录组的特征，并且然后通过 输入到被训练以确定是否存在癌症的人工智能模型中来对其进行分析。根据本发明，该 用于使用基于转录组的免疫组库谱分析来诊断癌症的方法通过使用这些免疫细胞受体 的特征和基因表达水平的特征两者在人工智能的基础上诊断所有癌症，这些特征是从 通过下一代测序（NGS）产生的转录组样品中提取的，并且该方法与仅使用免疫细胞受 体的序列信息的诊断方法相比准确性和灵敏度更高，因此具有高商业适用性。

Claims

1. A method of providing information for cancer diagnosis based on immunological profiling using transcriptomes, the method comprising: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; and (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, and comparing the analyzed output value with a cut-off value to determine whether or not there is cancer.

2. A method of diagnosing cancer diagnosis based on immunological profiling using transcriptomes, the method comprising: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; and (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject and comparing the analyzed output value with a cut-off value to determine whether or not there is cancer.

3. The method according to claim 1 or 2, wherein the obtaining whole transcriptome sequencing (WTS) information from a biological sample in step (a) comprises: (a-i) randomly fragmenting RNA obtained from a biological sample using an enzymatic cleavage method; (a-ii) replacing the cleavage RNA with cDNA and then producing a library; and (a-iii) performing single-end sequencing or pair-end sequencing on the produced library using a next-generation sequencer to obtain WTS information.

4. The method according to claim 1 or 2, wherein the step (c) of extracting the immune cell receptor features comprises: (c-i) extracting a CDR3 (complementary-determining region 3) amino acid sequence of the immune cell receptor from the aligned WTS information; and (c-ii) calculating at least one feature selected from the group consisting of immune repertoire properties, V&J gene usage, and chemical features of CDR3 amino acid sequences based on the extracted sequence information.

5. The method according to claim 1 or 2, wherein the step (c) of extracting transcriptome features comprises calculating a cancer- or immune-related gene enrichment score (single sample gene set enrichment analysis, ssGESA score) or an immune cell composition.

6. The method according to claim 4, wherein the immune repertoire properties comprise at least one selected from the group consisting of a total number of TCR sequences, a unique number of TCR sequences, a number of TCR clonotypes per kilo-reads, an entropy of TCR, clonality of TCR, a total number of BCR sequences, a unique number of BCR sequences, a number of BCR clonotypes per kilo-reads, an entropy of BCR, clonality of BCR, a somatic hypermutation rate of BCR sequences, and a number of BCR clusters.

7. The method according to claim 4, wherein the V&J gene usage is a proportion of two or more genotypes selected from the group consisting of IGH, IGK, IGL, TRA, TRB, TRD, TRG, IGHA1, IGHA2, IGHD, IGHE, IGHG1, IGHG2, IGHG3, IGHG4, IGHGP, and IGHM.

8. The method according to claim 4, wherein the chemical properties of the CDR3 amino acid sequence comprise at least one selected from the group consisting of a mean of length in TCR, a mean of gravy index in TCR, a mean of bulkiness of amino acids in TCR, a mean of polarity of amino acids in TCR, a mean of net charge in TCR, a mean of fraction of basic amino acids in TCR, a mean of fraction of acidic amino acids in TCR, a mean of fraction of aromatic amino acids in TCR, a mean of aliphatic index in TCR, a mean of length in BCR, a mean of gravy index in BCR, a mean of bulkiness of amino acids in BCR, a mean of BCR amino acids, a mean of polarity of amino acids in BCR, a mean of net charge in BCR, a mean of fraction of basic amino acids in BCR, a mean of fraction of acidic amino acids in BCR, a mean of fraction of aromatic amino acids in BCR, and a mean of aliphatic index in BCR.

9. The method according to claim 5, wherein the cancer- or immune-related gene enrichment score (single sample gene set enrichment analysis, ssGESA score) is obtained by calculating an enrichment score of a gene associated with at least one selected from the group consisting of T cell activation involved in immune response, innate immune response, innate immune response activating cell surface receptor signaling pathway, lymphocyte activation involved in immune response, mucosal immune response, natural killer cell activation involved in immune response, negative regulation of immune response, negative regulation of innate immune response, neutrophil activation involved in immune response, positive regulation of cytokine, production involved in immune response, positive regulation of immune effector process, positive regulation of immune response, positive regulation of production of molecular mediator of immune response, regulation of adaptive immune response, regulation of cytokine production involved in immune response, regulation of humoral immune response, regulation of immune effector process, regulation of immune response, regulation of innate immune response, antigen processing and presentation, B cell receptor signaling pathway, chemokine signaling pathway, complement and coagulation cascades, cytosolic DNA-sensing pathway, Fc epsilon RI signaling pathway, Fc gamma R-mediated phagocytosis, hematopoietic cell lineage, IL-17 signaling pathway, intestinal immune network for IgA production, leukocyte transendothelial migration, NOD-like receptor signaling pathway, natural killer cell mediated cytotoxicity, neutrophil extracellular trap formation, platelet activation, RIG-I-like receptor signaling pathway, T cell receptor signaling pathway, Th1 and Th2 cell differentiation, toll-like receptor signaling pathway, cellular response to tumor necrosis factor, negative regulation of tumor necrosis factor production, negative regulation of tumor necrosis factor superfamily cytokine production, negative regulation of tumor necrosis factor-mediated signaling pathway, positive regulation of tumor necrosis factor production, positive regulation of tumor necrosis factor superfamily cytokine production, regulation of tumor necrosis factor production, regulation of tumor necrosis factor-mediated signaling pathway, response to tumor necrosis factor, tumor necrosis factor-mediated signaling pathway, central carbon metabolism in cancer, endometrial cancer, PD-L1 expression and PD-1 checkpoint pathway in cancer, and transcriptional misregulation in cancer.

10. The method according to claim 5, wherein the immune cell composition is obtained by calculating a proportion of two or more immune cells selected from the group consisting of: naive CD4+ T cells, CD4+ T cells, naive CD8+ T cells, CD8+ T cells, central memory T cells, cytotoxic T cells, dendritic cells, effector memory T cells, exhausted T cells, γδ T cells, natural Treg cells, induced Treg cells, mucosal-associated invariant T cells, NK cells, NK T cells, follicular helper T cells, type 1 helper T cells, type 17 helper T cells, type 2 helper T cells, type 1 Treg T cells, monocytes, macrophages, neutrophils, B cells, and infiltration scores.

11. The method according to claim 1 or 2, wherein the immune cell receptor features and transcriptome features in step (c) comprise features shown in Table 1 below. Feature ListLevelFeature typeFeature descriptionTotal number of TCR sequencesUnique number of TCR sequencesNumber of TCR clonotypes per kilo-readsEntropy of TCRImmune repertoi re properti esClonality of TCRTotal number of BCR sequencesUnique number of BCR sequencesNumber of BCR clonotypes per kilo-readsCDR3 sequenc eEntropy of BCRClonality of BCRSomatic hypermutation rate of BCR sequencesNumber of BCR clustersIGHIGKV&J gene usage, proporti onIGLTRATRBTRDTRGIGHA1IGHA2IGHDIGHEIGHG1IGHG2IGHG3IGHG4IGHGPIGHMMean of length in TCRMean of gravy index in TCRMean of bulkiness of amino acids in TCRMean of polarity of amino acids in TCRMean of net charge in TCRMean of fraction of basic amino acids in TCRMean of fraction of acidic amino acids in TCRChemical properti es of amino acidsMean of fraction of aromatic amino acids in TCRMean of aliphatic index in TCRMean of length in BCRMean of gravy index in BCRMean of bulkiness of amino acids in BCRMean of polarity of amino acids in BCRMean of net charge in BCRMean of fraction of basic amino acids in BCRMean of fraction of acidic amino acids in BCRMean of fraction of aromatic amino acids in BCRMean of aliphatic index in BCRTranscr iptSingle sample gene set enrichme nt analysisT cell activation involved in immune responseInnate immune responseInnate immune response activating cell surface receptor signaling pathwayLymphocyte activation involved in immune responseMucosal immune response, ssGESA scoreNatural killer cell activation involved in immune responseNegative regulation of immune responseNegative regulation of innate immune responseNeutrophil activation involved in immune responsePositive regulation of cytokinePositive regulation of immune effector processPositive regulation of immune responsePositive regulation of production of molecular mediator of immune responseRegulation of adaptive immune responseRegulation of cytokine production involved in immune responseRegulation of humoral immune responseRegulation of immune effector processRegulation of immune responseRegulation of innate immune responseAntigen processing and presentationB cell receptor signaling pathwayChemokine signaling pathwayComplement and coagulation cascadesCytosolic DNA-sensing pathwayFc epsilon RI signaling pathwayFc gamma R-mediated phagocytosisHematopoietic cell lineageIL-17 signaling pathwayIntestinal immune network for IgA productionLeukocyte transendothelial migrationNOD-like receptor signaling pathwayNatural killer cell mediated cytotoxicityNeutrophil extracellular trap formationPlatelet activationRIG-I-like receptor signaling pathwayT cell receptor signaling pathwayTh1 and Th2 cell differentiationToll-like receptor signaling pathwayCellular response to tumor necrosis factorNegative regulation of tumor necrosis factor productionNegative regulation of tumor necrosis factor superfamily cytokine productionNegative regulation of tumor necrosis factor-mediated signaling pathwayPositive regulation of tumor necrosis factor productionPositive regulation of tumor necrosis factor superfamily cytokine productionRegulation of tumor necrosis factor productionRegulation of tumor necrosis factor-mediated signaling pathwayResponse to tumor necrosis factorTumor necrosis factor-mediated signaling pathwayCentral carbon metabolism in cancerEndometrial cancerPD-L1 expression and PD-1 checkpoint pathway in cancerTranscriptional misregulation in cancerNaive CD4+ T cellCD4+ T cellNaive CD8+ T cellCD8+ T cellCentral memory T cellCytotoxic T cellDendritic cellImmune cell composit ionEffector memory T cellExhausted T cellγδ T cellNatural Treg cellInduced Treg cellMucosal-associated invariant T cellNK cellNK T cellFollicular helper T cellType 1 helper T cellType 17 helper T cellType 2 helper T cellType 1 Treg cellMonocyteMacrophageNeutrophilB cellInfiltration score12. The method according to claim 1 or 2, wherein the cut-off value of step (d) is 0.5 and a case with an output value of 0.5 or higher is determined that there is cancer.

13. The method according to claim 1 or 2, further comprising: (e) inputting the immune cell receptor features and transcriptome features of the sample primarily determined that there is cancer into a second artificial intelligence model trained to distinguish between an immunoactive state and cancer, comparing the analyzed output value with a cut-off value, and finally determining whether or not there is cancer.

14. The method according to claim 13, wherein the cut-off value of step (d) is 0.5 and a case with an output value of 0.5 or higher is determined that there is cancer.

15. The method according to claim 1, 2 or 13, wherein the artificial intelligence model comprises at least one selected from the group consisting of a linear regression, a random forest, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), and an autoencoder.

16. The method according to claim 15, wherein when the artificial intelligence model is a deep neural network, the loss function is represented by the following Equation 1: BCE = − ∑ i = 0 N y i log y i ^ + 1 − y i log 1 − y i ^ wherein N is a total number of samples, yi is an actual label of an ith input value, and y i ^ is a label of an ith input value predicted by the model.

17. A device for diagnosing cancer based on immunological profiling, the device comprising: a decoder configured to decode whole transcriptome sequencing (WTS) information from a biological sample; an aligner configured to align the decoded WTS information to a reference genome database; a feature detector configured to extract immune cell receptor features and transcript features from the aligned WTS information; and a cancer diagnostic unit configured to compare the analyzed output obtained by inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, with a cut-off value, and determine whether or not cancer is present.

18. A computer-readable storage medium including an instruction configured to be executed by a processor for providing information for diagnosis of cancer, through the following steps comprising: (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample; (b) aligning the obtained WTS information to a reference genome database; (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information; (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, comparing the analyzed output value with a cut-off value, and primarily determining whether or not there is cancer; and (e) inputting the immune cell receptor features and transcriptome features of a sample primarily determined that there is cancer into a second artificial intelligence model trained to distinguish between an immunoactive state and cancer, comparing the analyzed output value with a cut-off value, and finally determining whether or not there is cancer.

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