Composition for preventing or treating cancer comprising TPST2 inhibitor or ifngr1 sulfation inhibitor

Abstract

The present invention relates to a composition for preventing or treating cancer, comprising a TPST2 inhibitor or a sulfation inhibitor of IFNGR1, and can effectively prevent or treat tumors by using combined treatment of TPST2 inhibition with anti-PD1.

Description

Composition for preventing or treating cancer comprising a TPST2 inhibitor or an IFNGR1 oxidation inhibitor

[0001] The present invention relates to a composition for preventing or treating cancer comprising a TPST2 inhibitor or an IFNGR1 oxidation inhibitor.

[0002] Immune checkpoint therapy (ICT), including anti-PD1 antibodies, has demonstrated durable tumor responses and cure in some cancer patients. One obstacle to this therapy is the development of resistance due to both intrinsic and extrinsic factors in tumor cells, which is observed in a significant proportion of patients. To overcome this resistance, various combination approaches based on immune checkpoint therapy have been proposed, many of which have been validated in clinical trials. However, the precise underlying mechanisms that determine resistance to immune checkpoint therapy remain largely unknown.

[0003] Meanwhile, to overcome the limitations of immune checkpoint therapy in cancer patients, various combination therapies based on immune checkpoint therapy are being attempted, and one of the molecular mechanisms of these combination therapies is the induction of IFNγ signaling. The combination of anti-CTLA4 and anti-PD1 antibodies is effective against melanoma, renal cell carcinoma, and microsatellite instability-high (MSI-H) colorectal cancers, and simultaneous blockade of CTLA4 and PD1 increased IFNγ production in CD8+ cells. Targeting other immunosuppressive molecules, such as TIM3 and LAG3, demonstrated synergistic effects with anti-PD1 antibodies, increasing the proportion of IFNγ-producing CD8+ cells. Several targeted agents, including the receptor tyrosine kinase inhibitor lenvatinib and the PARP inhibitor niraparib, have enhanced the efficacy of immune checkpoint therapy through the activation of IFNγ signaling. However, tumor-specific T cells that highly express the IFNγ receptor are more susceptible to apoptosis, and clonal deletion of these cells confers resistance to immune checkpoint therapy.

[0004] Therefore, optimal activation of IFNγ signaling is one of the key determinants of successful combination immunotherapy, and the development of rational combination strategies based on the molecular mechanisms of resistance is necessary to overcome the resistance of immune checkpoint therapy.

[0005] Meanwhile, it is disclosed that the present invention was made possible with the support of the following national research and development project.

[0006] [National Research and Development Project 1]

[0007] [Project ID] 9991008004

[0008] [Assignment Number] HN21C0196000021

[0009] [Buddha Name] Many Buddhas

[0010] [Name of Project Management (Specialist) Institution] (Foundation) National Drug Development Foundation

[0011] [Research Project Name] National New Drug Development Project (R&D) (Ministry of Science and ICT, Ministry of Health and Welfare, Ministry of Trade, Industry and Energy)

[0012] [Research Project Name] A Study on the Development of Small-Molecule Lead Immune Anticancer Agents for Immune Checkpoint Inhibitor-Resistant Cancers Through the TPST2-Targeted IFN-γ Activation Mechanism

[0013] [Name of the project performing organization] Gwangju Institute of Science and Technology

[0014] Research Period: July 1, 2021 - June 30, 2024

[0015] [National Research and Development Project 2]

[0016] [Project ID] 1711182566

[0017] [Assignment Number] 2021R1A2C3008021

[0018] [Ministry Name] Ministry of Science and ICT

[0019] [Name of Project Management (Specialist) Institution] National Research Foundation of Korea

[0020] [Research Project Name] Individual Basic Research (Ministry of Science and ICT)

[0021] [Research Project Title] Synthetic Lethality Study of Colorectal Cancer Mutations Based on Genomic CRISPR Screening

[0022] [Name of Project Performing Organization] Seoul National University

[0023] Research Period: March 1, 2023 - February 28, 2025

[0024] [National Research and Development Project 3]

[0025] [Project ID] 1711182362

[0026] [Assignment Number] 2021R1C1C2009923

[0027] [Ministry Name] Ministry of Science and ICT

[0028] [Name of Project Management (Specialist) Institution] National Research Foundation of Korea

[0029] [Research Project Name] Individual Basic Research (Ministry of Science and ICT)

[0030] [Research Project Title] Overcoming Immune Checkpoint Inhibitor Resistance by Modulating Protein Tyrosine Sulfation

[0031] [Name of Project Performing Organization] Seoul National University

[0032] Research Period: March 1, 2023 - February 28, 2025

[0033] One aspect is to provide a pharmaceutical composition for preventing or treating cancer comprising a TPST2 (Tyrosylprotein sulfotransferase-2) inhibitor or an IFNGR1 sulfation inhibitor.

[0034] Another aspect provides a method for screening a substance for the prevention or treatment of cancer, comprising the step of determining the degree of TPST2 expression, the degree of sulfation of IFNGR1 protein, or the degree of sulfation of tyrosine 397 of the amino acid sequence of IFNGR1 protein.

[0035] Another aspect is to provide a pharmaceutical composition for preventing or treating cancer, wherein PD-1 or PD-L1 is used in combination with a TPST2 inhibitor or an IFNGR1 oxidation inhibitor.

[0036] One aspect provides a pharmaceutical composition for preventing or treating cancer, comprising a TPST2 (Tyrosylprotein sulfotransferase-2) inhibitor or an IFNGR1 sulfation inhibitor.

[0037] In one example, we confirmed that TPST2 regulates the responsiveness of cancer cells to immune checkpoint therapy. Specifically, to identify tumor-intrinsic factors associated with responsiveness to cancer immunotherapy, we performed a genome-wide CRISPR-Cas9 loss-of-function screen of cancer cells using mice possessing a human immune system, and identified TPST2 as a therapeutic target that enhances the efficacy of anti-PD1.

[0038] Furthermore, in another example, we confirmed that combined treatment with knockdown TPST2 and anti-PD1 increased the number of total T cells, including CD4+ T cells and CD8+ T cells, as well as effector CD4+ and effector CD8+ T cells. Furthermore, we confirmed that TPST2 mediates tyrosine O-sulfation of membrane and secretory proteins in the trans-Golgi network and regulates extracellular protein-protein interactions.

[0039] That is, we demonstrated a molecular mechanism by which TPST2-mediated tyrosine sulfation of IFNGR1 inhibits IFNγ signaling by altering the interaction between IFNγ and the IFNγ receptor.

[0040] Accordingly, another aspect provides a pharmaceutical composition for preventing or treating cancer comprising a TPST2 inhibitor or an IFNGR1 oxidation inhibitor, wherein the TPST2 inhibitor or the IFNGR1 oxidation inhibitor is used in combination with a PD-1 or PD-L1 inhibitor.

[0041] As used herein, the term “Tyrosylprotein sulfotransferase-2 (TPST2)” is a Golgi enzyme that catalyzes the post-translational modification of proteins by transferring the sulfate of 3'-phosphoadenosine-5'-phosphosulfate to the hydroxyl group of protein-bound tyrosine residues, generating protein tyrosine O-sulfation. Targeted knockout of TPST2 caused male infertility due to reduced sperm motility and moderate primary hypothyroidism due to a deficiency of exocrine secretory granules. Since TPST2 is located within the cellular network, it is suggested that TPST2 exerts enzymatic activity on membrane and secreted proteins and regulates extracellular protein-protein interactions. Therefore, TPST2 plays a role in regulating immune responses by regulating extracellular protein-protein interactions.

[0042] Furthermore, “IFNGR1 (Interferon gamma receptor 1)” is a single-pass membrane receptor with 489 amino acids and is expressed in both cancer and immune cells. The activity and stability of the IFNGR1 protein are significantly regulated through various post-translational modifications. Complex glycosylation of IFNGR1 occurs in the Golgi apparatus during protein targeting, and mature glycosylated IFNGR1 participates in IFNγ signaling at the plasma membrane. Phosphorylation of IFNGR1 at Y457 by JAK1 and JAK2 provides a docking site for STAT1, which is required for proper IFNγ signaling. Furthermore, phosphorylation of IFNGR1 by glycogen synthase kinase 3 beta (GSK3β) inhibits IFNGR1 ubiquitination and increases protein stability.

[0043] In one specific example, the PD-1 inhibitor may be selected from the group consisting of BGB-A317, Nivolumab, Pembrolizumab, PDR001, Pidilizumab, REGN-2810, PF-06801591, BGB-108, INCSHR1210, TSR-042, and AMP 514. In addition, the PD-L1 inhibitor may be selected from the group consisting of CX-072, WBP-3155, KN035, A167, Cosibelimab, Atezolizumab, Avelumab, Durvalumab, Adebrelimab, and BMS-936559.

[0044] In another specific embodiment, the TPST2 inhibitor or the IFNGR1 sulfation inhibitor may inhibit sulfation of tyrosine (Y) at position 397 of the IFNGR1 protein amino acid sequence. For example, since TPST2 sulfates tyrosine (Y) at position 397 of the IFNGR1 protein amino acid sequence, the TPST2 inhibitor may be an IFNGR1 sulfation inhibitor or a sulfation inhibitor of tyrosine (Y) at position 397 of the IFNGR1 protein amino acid sequence.

[0045] For example, a TPST2 inhibitor is a general term for all agents that reduce the expression or activity of TPST2 mRNA or protein, and may include all agents that reduce the expression of TPST2 by interfering with the expression of TPST2 at the transcriptional level, reduce the activity by binding to TPST2, or reduce the activity by degrading TPST2. For example, the agent may include, but is not limited to, natural products and chemical substances, small molecules, such as siRNA, peptides, peptide mimetics, aptamers, specifically binding antibodies, and substrate analogs.

[0046] As another example, the IFNGR1 oxidation inhibitor of the present invention includes a substance capable of directly or indirectly inhibiting the oxidation of tyrosine (Y) at position 397 of the IFNGR1 protein amino acid sequence, and may include natural products and chemical substances, small molecules, such as siRNA, peptides, peptide mimetics, aptamers, specifically binding antibodies, and substrate analogs, but is not limited thereto.

[0047] The cancer can be solid or non-solid. Solid cancer refers to cancerous tumors that develop in organs such as the liver, lungs, breasts, or skin. Non-solid cancer refers to cancers that develop in the blood and are also called hematologic cancers. The cancer can be carcinoma, sarcoma, cancer of hematopoietic cells, germ cell tumor, or blastoma. The cancer may be selected from the group consisting of bladder cancer, urothelial cancer, colon cancer, large intestine cancer, esophageal cancer, head and neck cancer, ovarian cancer, stomach cancer, testicular cancer, breast cancer, skin cancer, pancreatic cancer, lung cancer, colorectal cancer, prostate cancer, bladder cancer, urethral cancer, liver cancer, kidney cancer, clear cell sarcoma, melanoma, cerebrospinal tumor, brain cancer, thymoma, mesothelioma, esophageal cancer, biliary tract cancer, testicular cancer, germ cell tumor, thyroid cancer, parathyroid cancer, cervical cancer, endometrial cancer, lymphoma, myelodysplastic syndromes (MDS), myelofibrosis, acute leukemia, chronic leukemia, multiple myeloma, Hodgkin's Disease, endocrine cancer, and sarcoma.

[0048] The term "prevention" refers to any action that suppresses or delays the onset of a disease by administering the pharmaceutical composition. The term "treatment" refers to any action that improves or beneficially alters the symptoms of a disease by administering the pharmaceutical composition.

[0049] The pharmaceutical composition may include a pharmaceutically acceptable carrier. The carrier is used to mean an excipient, diluent, or auxiliary. The carrier may be selected from the group consisting of, for example, lactose, dextrose, sucrose, sorbitol, mannitol, xylitol, erythritol, maltitol, starch, acacia gum, alginate, gelatin, calcium phosphate, calcium silicate, cellulose, methyl cellulose, polyvinyl pyrrolidone, water, saline, a buffer such as PBS, methylhydroxybenzoate, propylhydroxybenzoate, talc, magnesium stearate, and mineral oil. The composition may include fillers, anticoagulants, lubricants, wetting agents, flavoring agents, emulsifiers, preservatives, or combinations thereof.

[0050] The pharmaceutical composition described above may be prepared in any dosage form according to conventional methods. For example, the composition may be formulated as an oral dosage form (e.g., powder, tablet, capsule, syrup, pill, or granule) or a parenteral dosage form (e.g., injection). Furthermore, the composition may be prepared as a systemic dosage form or a topical dosage form.

[0051] The pharmaceutical composition may be a single composition or individual compositions. For example, the composition may be a composition for parenteral administration, and the anticancer agent may be a composition for oral administration.

[0052] The pharmaceutical composition may contain an effective amount of the protein complex. The term "effective amount" refers to an amount sufficient to exhibit a preventive or therapeutic effect when administered to a subject in need of prevention or treatment of a disease. The effective amount can be appropriately selected by those skilled in the art depending on the cell or subject being selected. The effective amount may be determined based on factors including the severity of the disease, the patient's age, weight, health, and sex, the patient's sensitivity to the drug, the time of administration, the route of administration, and the excretion rate, the duration of treatment, drugs combined with or co-administered with the composition used, and other factors well known in the medical field.

[0053] Another aspect provides a method for preventing or treating cancer comprising administering to a cell or organism a TPST2 inhibitor or an IFNGR1 anti-sulfation inhibitor according to one aspect.

[0054] Specific details regarding the protein complex, cell, cancer, prevention, or treatment are as described above.

[0055] The subject may be a mammal, such as a human, cow, horse, pig, dog, sheep, goat, or cat. The subject may be an individual suffering from or likely to suffer from cancer.

[0056] The method may further comprise administering a second active ingredient to the subject. The second active ingredient may be an active ingredient for the prevention or treatment of cancer. The active ingredient may be administered simultaneously, separately, or sequentially with the TPST2 inhibitor or the IFNGR1 anti-sulfation inhibitor. The second active ingredient may be, for example, a PD-1 inhibitor or a PD-L1 inhibitor.

[0057] The TPST2 inhibitor or IFNGR1 sulfation inhibitor may be administered directly to a subject by any means, such as, for example, oral, intravenous, intramuscular, oral, transdermal, mucosal, intranasal, intratracheal, or subcutaneous administration. The TPST2 inhibitor or IFNGR1 sulfation inhibitor may be administered systemically or locally, and may be administered alone or in combination with other pharmaceutically active compounds.

[0058] The preferred dosage of the TPST2 inhibitor or the IFNGR1 sulfation inhibitor varies depending on the patient's condition and weight, the extent of the disease, the drug form, the route and duration of administration, and can be appropriately selected by those skilled in the art. The dosage may be, for example, within the range of about 0.001 mg / kg to about 100 mg / kg, about 0.01 mg / kg to about 10 mg / kg, or about 0.1 mg / kg to about 1 mg / kg for adults. The administration may be administered once a day, multiple times a day, or once a week, once every two weeks, once every three weeks, or once every four weeks, or once a year.

[0059] Another aspect provides a method for screening a substance for the prevention or treatment of cancer, comprising: treating a candidate substance to cancer cells or a cancer animal model; determining the level of TPST2 expression, the level of sulfation of IFNGR1 protein, or the level of sulfation of tyrosine 397 of the amino acid sequence of IFNGR1 protein in the cancer cells or the cancer animal model after treating the candidate substance; and selecting a candidate substance that reduces TPST2 expression, reduces sulfation of IFNGR1 protein, or reduces sulfation of tyrosine 397 of the amino acid sequence of IFNGR1 protein compared to a control group that has not been treated with the candidate substance.

[0060] Specifically, the cancer cells or cancer animal models are intended for use in diagnosing cancer or predicting prognosis after cancer treatment, i.e., whether or not a cancer will recur. The specific details of the cancer are as described above.

[0061] In addition, the level of TPST2 expression can be confirmed by measuring the mRNA level of the gene or measuring the protein level. Specifically, the mRNA level measurement is a process of confirming the presence and expression level of the mRNA of the gene in a sample of cancer cells or cancer animals, and is a process of measuring the amount of mRNA. Analysis methods for this purpose include reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chips, etc. In addition, the protein level measurement is a process of confirming the presence and expression level of a cancer diagnostic marker protein in a sample of an individual in order to diagnose cancer. The amount of protein can be confirmed using an antibody that specifically binds to the marker protein, and the protein expression level itself can be measured without using an antibody.The above protein level measurement or comparative analysis methods include protein chip analysis, immunoassay, ligand binding assay, MALDI-TOF (Matrix Desorption / Ionization Time of Flight Mass Spectrometry) analysis, SELDI-TOF (Surface Enhanced Laser Desorption / Ionization Time of Flight Mass Spectrometry) analysis, radioimmunoassay, radioimmunodiffusion, aukteroni immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, complement fixation assay, two-dimensional electrophoresis analysis, liquid chromatography-mass spectrometry (LC-MS), liquid chromatography-mass spectrometry / mass spectrometry (LC-MS), Western blot, and enzyme linked immunosorbent assay (ELISA).

[0062] The degree of sulfation of the IFNGR1 protein or the degree of sulfation of the 397th tyrosine of the IFNGR1 protein amino acid sequence can be determined by measuring the sulfation level of the protein. Specifically, measuring the sulfation level is to determine the position and level of sulfation in the amino acid sequence of the protein. For this purpose, analytical methods such as the sulfation prediction model Sulfinator (https: / web.expasy.org / sulfinator / ) and SulfoSite (http: / sulfosite.mbc.nctu.edu.tw / ) can be used.

[0063] The above candidate substances collectively refer to agents tested for activity in preventing or treating cancer, and include any molecule, including proteins, oligopeptides, organic molecules, polysaccharides, polynucleotides, and a wide range of compounds and extracts, without particular limitation. These candidate substances may include both natural and synthetic substances.

[0064] In one specific example, the method may further include a step of determining the level of IFNγ signaling in the cancer cells or cancer animal model after treating the candidate substance; and a step of selecting a candidate substance that increases IFNγ signaling compared to a control group that has not been treated with the candidate substance. For example, if the level of IFNγ signaling is significantly increased compared to the control group, the candidate substance may be selected as a candidate substance for preventing or treating cancer.

[0065] The composition according to the aspect comprises a TPST2 inhibitor as an active ingredient and can enhance T cell immunity by combined treatment with anti-PD1, and thus can be a new cancer treatment that manages both the intrinsic characteristics of cancer cells and cancer immunity.

[0066] Figure 1a shows a schematic of an in vivo genome-wide CRISPR / Cas9 knockout screen to identify genes associated with anti-PD1 responsiveness.

[0067] Figure 1b shows the in vivo efficacy of pembrolizumab in humanized NSG mice bearing xenografts of MDA-MB-231 cells.

[0068] Figure 1c is a volcano plot showing the relative enrichment of sgRNAs in a genome-wide CRISPR / Cas9 knockout screen for anti-PD1 responsiveness.

[0069] Figure 1d shows the KEGG pathway analysis results for 918 sgRNA-depleted genes (left) and 777 sgRNA-enriched genes (right) in anti-DP1 treated tumors.

[0070] Figure 1e shows the results of STRING network analysis using 22 genes with multiple depleted sgRNAs in anti-PD1 treated mice (left) and the results of STRING network analysis using genes with multiple enriched sgRNAs (right).

[0071] Figure 2a shows the expression levels of IFNγ-responsive genes in TPST2-depleted MDA-MB-231 cells.

[0072] Figure 2b shows the phosphorylation level of STAT1 when TPST2-depleted MDA-MB-231 cells were treated with IFNγ at specific concentrations and times.

[0073] Figure 2c shows the levels of human leukocyte antigen (HLA) in TPST2-depleted MDA-MB-231 cells.

[0074] Figure 2d shows cell counts at specific time points in TPST2-depleted MDA-MB-231 cells (left) and cell counts by IFNγ treatment (right).

[0075] Figure 2e shows the expression levels of IFNγ-responsive genes in TPST2-overexpressed MDA-MB-231 cells.

[0076] Figure 2f shows the phosphorylation level of STAT1 when TPST2-overexpressing MDA-MB-231 cells were treated with IFNγ at specific times (left) and concentrations (right).

[0077] Figure 2g shows the level of human leukocyte antigen (HLA) in TPST2-overexpressed MDA-MB-231 cells.

[0078] Figure 2h shows the cell number by IFNγ treatment in TPST2-overexpressing MDA-MB-231 cells.

[0079] Figure 2i shows the expression levels of IFNγ-responsive genes in TPST1-depleted MDA-MB-231 cells.

[0080] Figure 2j shows the phosphorylation level of STAT1 when TPST1-depleted MDA-MB-231 cells were treated with IFNγ at specific times.

[0081] Figure 2k shows the levels of human leukocyte antigen (HLA) in TPST1-depleted MDA-MB-231 cells.

[0082] Figure 2l shows the cell number by IFNγ treatment in TPST1-depleted MDA-MB-231 cells.

[0083] Figure 3a is a volcano plot showing the relative mRNA expression levels of genes in RNA sequencing analysis of wild-type and TPST2 knockdown MBA-MD-231 cells.

[0084] Figure 3b shows the results of a hallmark gene set analysis of genes significantly increased by TPST2 knockdown.

[0085] Figure 3c shows the results of KEGG pathway analysis of genes significantly increased by TPST2 knockdown.

[0086] Figure 3d shows gene set enrichment analysis for RNA sequencing analysis of wild-type and TPST2 knockdown MBA-MD-231 cells (ES: enrichment score, NES: normalized enrichment score, NOM p-val: nominal P-value, FDR q-val: false discovery rate Q value, FWER q-val: family-wise error rate Q-value).

[0087] Figure 3e shows the results of confirming the expression level of MHC class I genes in TPST2 knockdown MBA-MD-231 cells.

[0088] Figure 4a shows the results of predicting the tyrosine sulfation site of the IFNGR1 protein as Sulfinator (top) and SulfoSite (bottom).

[0089] Figure 4b shows the results of detecting IFNGR1 tyrosine sulfation in MBA-MD-231 cells.

[0090] Figure 4c shows the results of confirming the level of tyrosine sulfation of IFNGR1 in TPST2 knockdown cells.

[0091] Figure 4d shows the results confirming the protein-protein interaction between TPST2 and IFNGR1.

[0092] Figure 4e is a mass spectrum of the IFNGR1 protein containing a tyrosine-modified region.

[0093] Figure 4f is a mass spectrum of the IFNGR1 protein containing the tyrosine-unmodified region.

[0094] Figure 4g shows the amino acid sequence of the IFNGR1 protein (underline: detected peptide, P: phosphorylation site, S: sulfation site).

[0095] Figure 4h shows the results of confirming the expression of IFNGR1 in IFNGR1 knockout MBA-MD 231 cells, wild type, and IFNGR1 Y397F mutant cells.

[0096] Figure 4i shows the results of confirming the level of tyrosine sulfation in wild-type and IFNGR1 Y397F mutant cells.

[0097] Figure 4j shows the expression levels of IFNγ-responsive genes upon IFNγ treatment in wild-type and IFNGR1 Y397F mutant cells.

[0098] Figure 4k shows the phosphorylation levels of STAT1 when wild-type and IFNGR1 Y397F mutant cells were treated with IFNγ at specific times.

[0099] Figure 4l shows the levels of human leukocyte antigen (HLA) in wild-type and IFNGR1 Y397F mutant cells.

[0100] Figure 4m shows the cell number by IFNγ treatment in wild-type and IFNGR1 Y397F mutant cells.

[0101] Figure 4n shows the results of confirming the protein stability of mutant IFNGR1 in wild-type and IFNGR1 Y397F mutant cells.

[0102] Figure 5a shows the results comparing the expression of the Tpst2 gene (left) and the growth of in vitro control MC38 and Tpst2 knockdown MC38 cells (right).

[0103] Figure 5b shows the expression levels of IFNγ-responsive genes in Tpst2 knockdown cells.

[0104] Figure 5c shows the phosphorylation level of STAT1 when Tpst2 knockdown cells were treated with IFNγ at a specific time.

[0105] Figure 5d shows the results of measuring the volume (left) and weight (right) of Tpst2 knockdown MC38 tumors in C57B6 / N mice following anti-PD1 treatment.

[0106] Figure 5e shows the results of measuring the volume of Tpst2 knockdown MC38 tumors in xenograft models of nude mice following anti-PD1 treatment.

[0107] Figure 5f shows the percentage of effector CD4+ T cells in the tumor-draining lymph nodes of the Tpst2 knockdown MC38 syngeneic mouse model following anti-PD1 treatment.

[0108] Figure 5g shows the results of confirming the proportion of NK cells in the tumor-draining lymph nodes of the Tpst2 knockdown MC38 syngeneic mouse model following anti-PD1 treatment.

[0109] Figure 5h shows the results of confirming the proportion of T cells in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0110] Figure 5i shows the results of confirming the ratio of CD4+ T cells and CD8+ T cells in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0111] Figure 5j shows the results of confirming the percentage of effector CD8+ T cells in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0112] Figure 5k shows the results of confirming the proportion of PD1+ effector CD8+ T cells in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0113] Figure 5l shows the results of measuring the volume (left) and weight (right) of Tpst2 knockdown MC38 tumors in a syngeneic mouse model following anti-PD1 treatment.

[0114] Figure 5m shows the results of confirming the proportion of type 1 dendritic cells (cDC1) in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0115] Figure 5n shows the results of confirming the proportion of plasmacytoid dendritic cells (pDCs) in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0116] Figure 5o shows the results of confirming the proportion of macrophages with M1 characteristics in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0117] Figure 6a shows the results of gene set enrichment analysis of TPST2 knockdown tumors in a winter mouse model.

[0118] Figure 6b shows the results of gene set enrichment analysis based on gene ontology biological process (NES: Normalized Enrichment Score, NOM p-val: Nominal p-value, FDR q-val: False Discovery Rate q-value), and FWER p-val (Family-Wise Error Rate p-value).

[0119] Figure 6c is a heatmap showing the expression levels of 11 genes involved in antigen processing and presentation processes commonly identified from the results of gene set enrichment analysis of TPST2 knockdown tumors.

[0120] Figure 6d is a dot plot showing key genes in antigen processing and presentation.

[0121] Figure 6e shows a differential gene expression volcano plot between the control + anti-PD1 group and the TPST2 knockdown + anti-PD1 group (left) and the results of STRING analysis focusing on genes upregulated in the TPST2 knockdown + anti-PD1 group compared to the control + anti-PD1 group (right).

[0122] Figure 6f is a single-cell violin plot for TPST2 expression in non-immune cells of human lung tumor tissue.

[0123] Figure 6g shows the results of gene set enrichment analysis of TPST2-associated genomic alterations.

[0124] Figure 6h shows the comparative results of gene expression related to antigen processing and presentation.

[0125] Figure 6i shows the results of a correlation analysis between TPST2 expression and genes related to antigen processing and presentation.

[0126] Figure 7a shows the proportion of patients with copy number alterations in the TPST2 gene in tumor samples from various cancer patients.

[0127] Figure 7b shows the results of confirming the expression of TPST2 mRNA in normal tissues and cancer tissues according to cancer type.

[0128] Figure 7c shows the proportion of patients with upregulated TPST2 mRNA in cancer tissues according to cancer type.

[0129] Figure 7d shows the results of survival analysis by TPST2 expression according to cancer type.

[0130] Figure 7e shows the results of a hallmark gene set analysis of genes positively correlated with TPST2 expression in breast cancer.

[0131] Figure 7f shows the results of a hallmark gene set analysis of genes negatively correlated with TPST2 expression in breast cancer.

[0132] Figure 7g is a list of significantly enriched gene sets in the TPST2_H group.

[0133] Figure 7h shows an enrichment plot of representative gene sets significantly enriched in the TPST2_H group.

[0134] Figure 7i is a list of gene sets significantly enriched in the TPST2_L group.

[0135] Figure 7j shows an enrichment plot of representative gene sets significantly enriched in the TPST2_L group.

[0136] Hereinafter, the present invention will be described in detail.

[0137] Hereinafter, preferred examples are presented to aid in understanding the present invention. However, the following examples are provided solely to facilitate a better understanding of the present invention, and the scope of the present invention is not limited by the following examples.

[0138] [Example]

[0139] Example 1. Identification of Tumor Cell-Intrinsic Pathways Associated with Anti-PD1 Responsiveness

[0140] 1-1. In vivo CRISPR screening

[0141] To identify tumor-specific factors that determine anti-PD1 responsiveness, an integrated loss-of-function genetic screen was performed using humanized mice in which human immune cells are recapitulated through transplantation of CD34+ hematopoietic stem cells. First, the Human GeCKOv2 pooled library was adopted for Vector System 1 (Feng Zhang, Addgene #1000000048) to generate a lentiviral library for genome-wide knockout screening. Specifically, MDA-MB-231 cells were infected with the lentiviral library encoding Cas9 and 123,411 sgRNAs targeting 19,050 genes at an MOI of 0.3-0.5 for 24 h and then treated with puromycin (1 μg / mL) for 3 days. Puromycin-resistant cells (at least 1 × 10 per mouse) were cultured for 24 h. 7 (Canine cells) were injected into the flank of 4-week-old NOD / SCID / IL-2γ receptor null (NSG) female mice with reconstituted human immune systems. Tumors were approximately 100 mm in size. 3 After reaching , the mice were randomly divided into two treatment groups consisting of two mice in each group, and were administered control IgG and PD-1 antibody (pembrolizumab) intraperitoneally at 5 mg / kg every 5 days for a total of 27 days, and the average tumor size of each group was indicated.

[0142] Figure 1b shows the in vivo efficacy of pembrolizumab in humanized NSG mice bearing xenografts of MDA-MB-231 cells.

[0143] As a result, as shown in Fig. 1b, it was confirmed that the increase in tumor volume was delayed in mice treated with PD-1 antibody (pembrolizumab) compared to control mice administered IgG.

[0144] 1-2. Frequency analysis of sgRNA in residual tumors

[0145] Because the presence of sgRNA in residual tumors inactivates the matching gene and increases the prevalence of sgRNAs that induce resistance to anti-PD1, we analyzed the frequency of sgRNAs in tumors treated with anti-PD1. Specifically, genomic DNA was purified from residual tumors using the Blood & Cell Culture Midi kit (Qiagen). sgRNA target sequence amplification for sequencing was performed according to a previously described method (Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelson T, Heckl D, Ebert BL, Root DE, Doench JG et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science. 2014;343(6166):84-7.) with some modifications. The first PCR amplified a total of 130 μg of DNA per tumor using Herculase II Fusion DNA Polymerase (Agilent). The primer sequences for amplifying lentiCRISPR sgRNA for the first PCR are as described in Table 1 below;

[0146] First PCR primer sequence Forward 5'-AATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCG-3' (SEQ ID NO: 1) Reverse 5'-TCTACTATTCTTTCCCCTGCACTGTTGTGGGCGATGTGCGCTCTG-3' (SEQ ID NO: 2)

[0147] Subsequently, a second PCR was performed using 5 μl of the first PCR product to attach the Illumina adapter and barcode. The primer sequences for the second PCR were the known sequences (Joung J, Konermann S, Gootenberg JS, Abudayyeh OO, Platt RJ, Brigham MD, Sanjana NE, Zhang F. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat Protoc. 2017;12(4):828-63.). The PCR amplicons were gel extracted and sequenced using a HiSeq 2500 instrument (Illumina) in single-end mode. The raw sequencing data were then preprocessed using the FASTX toolkit (http: / hannonlab.cshl.edu / fastx_toolkit / ), which removes low-quality reads using fastq_quality_filter. The resulting reads were trimmed to remove the constant portion of the sgRNA sequence using CRISPR.sgRNA_read_trimmer in the GenePattern Module Archive (http: / www.gparc.org / ), and then aligned to GeCKO v2 sgRNA using bowtie 1 (v1.1.1) with default settings. After alignment, the number of uniquely aligned reads for each library sequence was calculated by CRISPR.single_sgRNA_count in GParc. The raw read counts were normalized using Equation 1 below, and differentially enriched sgRNAs for each treatment group were calculated using a t-test.

[0148]

[0149] Figure 1c is a volcano plot showing the relative enrichment of sgRNAs in a genome-wide CRISPR / Cas9 knockdown screen for anti-PD1 responsiveness.

[0150] As a result, as shown in Fig. 1c, a total of 797 sgRNAs for 777 genes were enriched in anti-PD1-treated tumors compared to control tumors (P<0.1), indicating that loss of function of these genes contributed to anti-PD1 resistance. On the other hand, a total of 950 sgRNAs for 918 genes were depleted in anti-PD1-treated tumors compared to control tumors (p<0.1), suggesting that loss of function of these genes increased sensitivity to anti-PD1.

[0151] Figure 1d shows the KEGG pathway analysis results for 918 sgRNA-depleted genes (left) and 777 sgRNA-enriched genes (right) in anti-DP1 treated tumors.

[0152] As a result, as shown in Fig. 1d, the KEGG pathway analysis results for 918 genes depleted of sgRNA showed that several immune-related pathways, including 'inflammatory bowel disease' and 'Jak-STAT signaling pathway', were enriched (p<0.2), and in particular, 11 genes were found in the 'Jak-STAT signaling pathway'. In addition, the KEGG pathway analysis results using 777 genes whose function increases anti-PD1 resistance also showed that the 'Jak-STAT signaling pathway' and 'cytokine-cytokine receptor interaction' were enriched.

[0153] Figure 1e shows the results of STRING network analysis using 22 genes with multiple depleted sgRNAs in anti-PD1 treated mice (left) and the results of STRING network analysis using genes with multiple enriched sgRNAs (right).

[0154] As a result, as shown in Figure 1e, genes with multiple depleted sgRNAs demonstrated a highly connected network enriched in immune-related pathways, including 'Cell adhesion molecules (CAMs),' 'Human papillomavirus infection,' and 'Jak-STAT signaling pathway' (p<0.05). In addition, 'Protein processing in the endoplasmic reticulum' was enriched in the protein-protein interaction network. Genes with multiple enriched sgRNAs demonstrated a highly connected network enriched in immune-related pathways, including 'Human papillomavirus infection' and 'Jak-STAT signaling pathway.'

[0155] This suggests that the Jak-STAT signaling pathway and cytokine-cytokine receptor interaction are highly linked to anti-PD1 responsiveness. Furthermore, genome-wide CRISPR screening suggests that the Jak-STAT signaling pathway is closely related to anti-PD1 responsiveness, and that altered IFNγ signaling is involved in the molecular mechanisms underlying resistance to cancer immunotherapy.

[0156] Example 2. Confirmation of the expression effect of TPST on the IFNγ signaling pathway.

[0157] 2-1. Confirmation of TPST2 knockdown effect

[0158] To confirm the effect of TPST2 knockdown on the IFNγ signaling pathway, TPST2 knockdown MDA-MB-231 cells were generated using the CRISPR / Cas9 method. Specifically, human breast cancer cell lines (MDA-MB-231) (Korea Cell Line Bank) were resuscitated and passaged for 6 months in RPMI 1640 medium containing 10% FBS (Life technologies), penicillin (100 units / ml; Life Technologies), and streptomycin (100 units / ml; Life Technologies). Cells were maintained in a humidified incubator at 37°C and 5% CO2. Subsequently, human TPST2 knockdown cell lines were generated using the CRISPR method. sgRNAs for the target genes were cloned into lentiCRISPR v2 (Feng Zhang, Addgene plasmid # 52961). Lentiviruses were generated by transfecting 293FT cells with lentiCRISPR v2, pCMV-VSV-G, and psPAX2. Target cells were transduced with concentrated lentiviruses for 48 hours using conditioned media from transfected 293FT cells, and treated with 1 μg / mL puromycin for 3 days. After serum starvation for 24 hours, transfected 293FT cells were treated with 10 ng / mL IFNγ, and the expression levels of IFNγ-responsive genes were assessed by real-time PCR. The sgRNA sequences for each target gene are shown in Table 2, and the PCR primers are shown in Table 3. In addition, the phosphorylation level of STAT1 was confirmed using Western blotting. In addition, the expression level of IFNγ-responsive HLA was confirmed using flow cytometry. Additionally, after treating the cells with 1 or 10 ng / ml IFNγ, cell proliferation was confirmed using a trypan blue staining assay.

[0159] Gene sequence: Human TPST2-15'-CTCCTCGCCGCAGCGCACCT'-3' (SEQ ID NO: 3) Human TPST2-25'-CCCCAGGATCGAGCGGTCCA-3' (SEQ ID NO: 4)

[0160] GeneForward primerReverse primerHuman IRF15'-CTCTGAAGCTACAACAGATGAG-3' (SEQ ID NO: 5)5'-GTAGACTCAGCCCAATATCCC-3' (SEQ ID NO: 6)Human TAP15'-AGGTACTGCTCTCCATCTAC-3' (SEQ ID NO: 7)5'-AGTGTAAGGGAGTCAACAGA-3' (SEQ ID NO: 8)Human TAP25'-ACGGCTGAGCTCGGATACCAC-3' (SEQ ID NO: 9)5'-CCTCGGCCCCAAAACTGC-3' (SEQ ID NO: 10)Human TAPBP5'-ACCCTGGAGGTAGCAGGTCTTT-3' (SEQ ID NO: 11)5'-AATCCTTGCAGGTGGACAGGTAG-3' (SEQ ID NO: 12)

[0161] Figure 2a shows the expression levels of IFNγ-responsive genes in TPST2-depleted MDA-MB-231 cells.

[0162] As a result, as shown in Fig. 2a, cells with down-regulated TPST2 showed enhanced expression of IFNγ-responsive genes, including IRF1, TAP1, TAP2, and TAPBP, upon IFNγ treatment compared to control cells.

[0163] Figure 2b shows the phosphorylation level of STAT1 when TPST2-depleted MDA-MB-231 cells were treated with IFNγ at specific times (left) and concentrations (right).

[0164] As a result, as shown in Fig. 2b, cells with down-regulated TPST2 showed increased phosphorylation of STAT1 by IFNγ treatment compared to control cells.

[0165] Figure 2c shows the levels of human leukocyte antigen (HLA) in TPST2-depleted MDA-MB-231 cells.

[0166] As a result, as shown in Fig. 2c, cells with down-regulated TPST2 showed an enhanced expression level of IFNγ-responsive HLA by IFNγ treatment compared to control cells.

[0167] Figure 2d shows cell counts at specific time points in TPST2-depleted MDA-MB-231 cells (left) and cell counts by IFNγ treatment (right).

[0168] As a result, as shown in Fig. 2d, cells with downregulated TPST2 showed little effect on cell proliferation, similar to control cells. However, cell growth was inhibited in an IFNγ concentration-dependent manner.

[0169] 2-2. Confirmation of the effect of TPST2 overexpression

[0170] To compare the knockdown effect of TPST2 on the IFNγ signaling pathway, TPST2 was recombined to overexpress human breast cancer cell line (MDA-MB-231) (Korea Cell Line Bank), and then IFNγ-responsive gene expression, STAT1 phosphorylation level, HLA expression, and cell growth were confirmed using the same method as in Example 2-1.

[0171] Figure 2e shows the expression levels of IFNγ-responsive genes in TPST2-overexpressed MDA-MB-231 cells.

[0172] As a result, as shown in Fig. 2e, cells with upregulated TPST2 showed reduced expression of IFNγ-responsive genes, including IRF1, TAP1, TAP2, and TAPBP, upon IFNγ treatment compared to control cells.

[0173] Figure 2f shows the phosphorylation level of STAT1 when TPST2-overexpressing MDA-MB-231 cells were treated with IFNγ at specific times (left) and concentrations (right).

[0174] As a result, as shown in Fig. 2f, cells with upregulated TPST2 showed decreased phosphorylation of STAT1 by IFNγ treatment compared to control cells.

[0175] Figure 2g shows the level of human leukocyte antigen (HLA) in TPST2-overexpressed MDA-MB-231 cells.

[0176] As a result, as shown in Fig. 2g, cells with upregulated TPST2 showed a reduced expression level of IFNγ-responsive HLA by IFNγ treatment compared to control cells.

[0177] Figure 2h shows the cell number by IFNγ treatment in TPST2-overexpressing MDA-MB-231 cells.

[0178] As a result, as shown in Fig. 2h, cells with upregulated TPST2 showed reduced IFNγ-mediated cell suppression.

[0179] 2-3. Confirmation of TPST1 knockdown effect

[0180] In humans, two TPST isoforms, designated TPST1 and TPST2, are expressed, which share 64% amino acid sequence similarity and exhibit differential substrate specificity and tissue-specific expression. Therefore, a TPST1 knockdown breast cancer cell line was prepared in the same manner as in Example 2-1, except that the sgRNA sequences in Table 4 below were used, and the knockdown effect of TPST1 on the IFNγ signaling pathway was confirmed.

[0181] Gene sequence: Human TPST15'-TACGTTCCTCTATCCGGTGA-3' (SEQ ID NO: 13)

[0182] Figure 2i shows the expression levels of IFNγ-responsive genes in TPST1-depleted MDA-MB-231 cells. As a result, as shown in Figure 2i, cells with downregulated TPST2 showed suppressed expression of IFNγ-responsive genes, including IRF1, TAP1, TAP2, and TAPBP, upon IFNγ treatment compared to control cells.

[0183] Figure 2j shows the phosphorylation level of STAT1 when TPST1-depleted MDA-MB-231 cells were treated with IFNγ at specific times.

[0184] As a result, as shown in Fig. 2j, cells with down-regulated TPST1 showed decreased phosphorylation of STAT1 by IFNγ treatment compared to control cells.

[0185] Figure 2k shows the levels of human leukocyte antigen (HLA) in TPST1-depleted MDA-MB-231 cells.

[0186] As a result, as shown in Fig. 2k, cells with down-regulated TPST1 showed an expression level of IFNγ-responsive HLA similar to that of control cells upon IFNγ treatment.

[0187] Figure 2l shows the cell number by IFNγ treatment in TPST1-depleted MDA-MB-231 cells.

[0188] As a result, as shown in Fig. 2l, cells with down-regulated TPST2 showed cell levels similar to those of control cells, regardless of the IFNγ treatment concentration.

[0189] Evaluation of the role of TPST1 in IFNγ signaling revealed that although TPST1 knockdown did not increase IFNγ treatment, cellular responsiveness to IFNγ was somewhat suppressed compared to TPST2 in terms of IFNγ response gene expression, STAT1 phosphorylation, HLA expression, and cell growth. In other words, TPST1 plays a distinct role in IFNγ signaling compared to TPST2.

[0190] Example 3. Confirmation of the role of TPST2 by IFNγ treatment

[0191] 3-1. Confirmation of the transcriptome of TPST2 knockdown cells

[0192] To determine the role of TPST2 in global gene expression in response to IFNγ treatment, we examined the transcriptome of TPST2 knockdown cells after IFNγ stimulation using RNA sequencing. Specifically, TPST2 knockdown cells generated in Example 2-1 were treated with 1 ng / ml IFNγ for 8 hours using the RNeasy Plus Mini Kit (Qiagen), and total RNA was extracted from the cells. Subsequently, a full transcriptome expression profile was generated by RNA sequencing using Mapsplice and RSEM with the TCGA RNASeq v2 pipeline (https: / wiki.nci.nih.gov / display / TCGA / RNASeq+Version+2). Differentially expressed genes were estimated using the edgeR package. Mouse tumor RNA sequencing analysis was performed using kallisto software for pseudoalignment and quantification of transcript abundance. The process was divided into two main steps: generation and quantification. First, a kallisto index for the Mus musculus genome (GRCm39) was constructed by downloading the primary assembly fasta file from the Ensembl database (release 108) to facilitate efficient mapping of RNA-seq reads. Subsequently, the kallisto index was constructed with a k-mer size of 31. To ensure robust statistical analysis, paired-end reads for each sample were quantified against the constructed index using a bootstrapping value of 100. The RNA-sequencing data have been deposited in the European Nucleotide Archive (accession no. PRJEB73786).

[0193] Figure 3a is a volcano plot showing the relative mRNA expression levels of genes in RNA sequencing analysis of wild-type and TPST2 knockdown MBA-MD-231 cells.

[0194] As a result, as shown in Fig. 3a, when treated with IFNγ, TPST2 knockdown cells significantly up-regulated and down-regulated 2313 and 225 genes, respectively, compared to wild-type cells (p<0.05, Log2[fold change]>=1).

[0195] Figure 3b shows the results of a hallmark gene set analysis of genes significantly increased by TPST2 knockdown.

[0196] As a result, as shown in Fig. 3b, hallmark gene sets such as INFLAMMATORY_RESPONSE were found to be enriched among the 313 genes upregulated by TPST2 knockdown. Furthermore, the APOPTOSIS gene was found to be enriched in TPST2 knockdown cells, suggesting enhanced IFNγ signaling, as IFNγ signaling is known to exert a pro-apoptotic effect on cancer cells.

[0197] Figure 3c shows the results of KEGG pathway analysis of genes significantly increased by TPST2 knockdown.

[0198] As a result, as shown in Fig. 3c, it was confirmed that KEGG pathway gene sets such as KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION were enriched in the 313 genes up-regulated by TPST2 knockdown.

[0199] 3-2. Gene set enrichment analysis

[0200] To confirm the role of TPST2 in global gene expression induced by IFNγ treatment, gene set enrichment analysis (GSEA) was performed on wild-type and TPST2 knockdown cells generated in Example 2-1 after IFNγ stimulation. Specifically, GSEA was performed using the javaGSEA desktop application (GSEA v2.1.0)

[0012] . The enriched gene sets for each group were examined using a hallmark gene set. The P-value was calculated by permuting the data 1000 times to identify the enriched gene sets. The GSEA software generates an enrichment score (ES), normalized ES (NES), nominal P-value, and false discovery rate (FDR, Q-value), and gene sets up- or down-regulated with a P-value < 0.05 were considered significant.

[0201] Figure 3d shows gene set enrichment analysis for RNA sequencing analysis of wild-type and TPST2 knockdown MBA-MD-231 cells (ES: enrichment score, NES: normalized enrichment score, NOM p-val: nominal P-value, FDR q-val: false discovery rate Q value, FWER q-val: family-wise error rate Q-value).

[0202] As a result, as shown in Fig. 3d, TPST2 knockdown cells were found to be enriched in the hallmark gene set of IFNγ signaling-related genes, including 'INFLAMMATORY_RESPONSE', 'INTERFERON_GAMMA_RESPONSE', and 'APOPTOSIS', compared to the control cells. On the other hand, cell cycle-related genes, such as 'E2F_TARGETS', 'G2M_CHECKPOINT', 'MYC_TARGETS', and 'MITOTIC_SPINDLE', were found to be depleted.

[0203] That is, it suggests that IFNγ signaling was enhanced because IFNγ signaling is involved in the anti-proliferative effect of cancer cells.

[0204] 3-3. Confirmation of MHC class I gene expression in TPST2 knockdown cells

[0205] Proper execution of immune checkpoint therapy requires antigen presentation via HLA molecules, and IFNγ increases the amount and efficiency of antigen presentation via the HLA complex. Therefore, after treating TPST2 knockdown MBA-MD-231 cells generated in Example 2-1 with IFNγ, major histocompatibility complex (MHC) class° gene expression was confirmed. mRNA expression levels were estimated as fragments per kilobase of transcript per million (FPKM) values ​​from RNA sequencing data.

[0206] Figure 3e shows the results of confirming the expression level of MHC class I genes in TPST2 knockdown MBA-MD-231 cells.

[0207] As a result, as shown in Fig. 3e, in the presence of IFNγ, a series of MHC class I genes, including HLA-A, HLA-B, HLA-C, HLA-E, and HLA-H, were upregulated in TPST2 knockdown cells.

[0208] MHC class I molecules expressed by cancer cells are recognized by CD8+ cytotoxic T lymphocytes (CTLs), which play a key role in immune checkpoint therapy. Therefore, knockdown of TPST2 can enhance IFNγ-induced gene expression reprogramming at the transcriptomic level.

[0209] Example 4. Confirmation of the responsiveness of TPST2-mediated sulfation of IFNGR1 to IFNγ.

[0210] 4-1. Identification of tyrosine sulfation target candidates

[0211] To investigate the target substrates of TPST2 in the regulation of IFNγ signaling, two predicted models for protein tyrosine sulfation, Sulfinator (https: / web.expasy.org / sulfinator / ) and SulfoSite (http: / sulfosite.mbc.nctu.edu.tw / ), were analyzed. Subsequently, IFNGR1 protein was detected in MBA-MD-231 cells using immunoprecipitation, and co-immunoprecipitation experiments were performed to confirm the interaction between TPST2 and IFNGR1. Specifically, cells were washed twice with ice-cold PSB and lysed in lysis buffer (50 mM Tris-Cl, pH 8.0, 150 mM NaCl, 1% NP-40, protease inhibitor cocktail (Roche), phosphatase inhibitor cocktail (PhosSTOP; Roche)) for 30 min. The cell lysates were then centrifuged at 12,000 g for 20 min at 4°C, and the supernatants were incubated with anti-myc-tag (Cell Signaling Technology, Cat. No. 2276), anti-flag (Sigma-Aldrich Corporation, Cat. No. F1804), or normal IgG antibody (Cell Signaling Technology, Cat. No. 61656) conjugated to protein G-conjugated magnetic beads (Dynabeads; Thermo Scientific) for 16 h at 4°C. The beads were washed five times with lysis buffer, and the proteins were eluted in sample loading buffer by boiling for 10 min and detected by Western blotting.

[0212] Figure 4a shows the results of predicting the tyrosine sulfation site of the IFNGR1 protein as Sulfinator (top) and SulfoSite (bottom).

[0213] As a result, as shown in Fig. 4a, it was confirmed that IFNGR1 is a candidate target for tyrosine sulfation.

[0214] Figure 4b shows the results of detecting IFNGR1 tyrosine sulfation in MBA-MD-231 cells.

[0215] As a result, as shown in Fig. 4b, Myc-tagged IFNGR1 was overexpressed in MBA-MD-231 cells, immunoprecipitated by Myc antibody, and tyrosine sulfation of IFNGR1 was detected by antibody against sulfotyrosine.

[0216] Figure 4c shows the results of confirming the level of tyrosine sulfation of IFNGR1 in TPST2 knockdown cells.

[0217] As a result, as shown in Fig. 4c, the level of tyrosine sulfation of IFNGR1 was reduced in TPST2 knockdown cells. In addition, Myc-tagged IFNGR1 was overexpressed in control and TPST2 knockdown cells, immunoprecipitated with Myc antibody, and tyrosine sulfation of IFNGR1 was detected with an antibody against tyrosine sulfation.

[0218] Figure 4d shows the results confirming the protein-protein interaction between TPST2 and IFNGR1.

[0219] As a result, as shown in Fig. 4d, Flag-tagged TPST2 and / or myc-tagged IFNGR1 were overexpressed in MBA-MD-231 cells. Furthermore, complex formation of TPST2 and IFNGR1 was confirmed by co-immunoprecipitation of Flag-tagged TPST2 and myc-tagged IFNGR1, which was detected by mutual immunoprecipitation and Western blotting.

[0220] That is, it can be seen that IFNGR1 is a direct substrate of TPST2.

[0221] 4-2. Identification of IFNGR1 Target Residues for TPST2-Mediated Tyrosine Sulfation

[0222] IFNGR1 target residues of TPST2-mediated tyrosine sulfation were identified by mass spectrometry. For mass spectrometry, myc-tagged IFNGR1 samples were prepared according to a previously described method with some modifications. Briefly, elution buffer (2% SDS, 5 mM tris(2-carboxyethyl)phosphine (TCEP), 20 mM chloroacetamide (CAA) in 50 mM ammonium bicarbonate (ABC)) was added to the washed beads. To elute the immunoprecipitated proteins, the mixture was heated at 95°C for 15 min. The eluted proteins were digested using filter-aided sample preparation (FASP) as previously described. Briefly, the eluate was loaded onto a 30 K amicon filter (Millipore, Burlington, MA, USA). Buffer exchange was performed by centrifuging UA solution (8 M urea in 0.1 M Tris pH 8.5) at 14,000 × g for 15 min. After buffer exchange with 40 mM ammonium bicarbonate, protein digestion was performed overnight at 37°C using a trypsin / LysC mixture (Promega, Madison, WI, USA) at a protein-to-protease ratio of 100:1. Peptides generated by the digestion were collected by centrifugation. The filter unit was washed with 40 mM ammonium bicarbonate and then digested with trypsin (enzyme-to-substrate ratio [w / w] = 1:1000) at 37°C for 2 h. All generated peptides were acidified with 10% TFA and desalted using a homemade C18-SDB-RPS-StageTip column as described. The desalted sample was completely dried in a vacuum desiccator and then stored at -80°C.

[0223] LC-MS / MS analysis was performed using a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) connected to an Ultimate 3000 RSLC system (Dionex, Sunnyvale, CA, USA) via an Easy-Spray source, as previously described. Before sample injection, dried peptide samples were re-dissolved in solvent A (2% [v / v] acetonitrile and 0.1% [v / v] formic acid). Peptide samples were separated on a two-column system consisting of a trap column (300 μm ID × 5 mm, C18, 5 μm) and an analytic column (75 μm ID × 50 cm, C18, 1.9 μm, 100 Å) with a 120-min gradient from 6% to 30% acetonitrile at 200 nl / min, and analyzed by mass spectrometry. The column temperature was maintained at 60°C using an Easy spray column heater. Survey scans (350 to 1605 m / z) were acquired at a resolution of 60,000 at m / z 200. The top-15 method was used to select precursor ions with a 2 m / z separation window. MS / MS spectra were acquired on an HCD with a normalized collision energy of 28. The maximum ion injection times for MS1 and MS2 scans were 25 ms and 125 ms, respectively.

[0224] MS spectra were processed using MaxQuant software version 1.6.1.0 [7]. MS / MS spectra were searched against the myc-tagged IFNGR1 sequence and the Uniprot Human protein sequence database (version 12.2014, 88,657 entries), including forward and reverse sequences and common contaminants. A primary search was performed using a 6 ppm precursor ion tolerance for total protein level analysis. MS / MS ion tolerance was set to 20 ppm. Cysteine ​​(C) carbamidomethylation was set as a fixed modification, and N-acetylation of the protein, sulfation of tyrosine (Y), phosphorylation of serine (S), threonine (T), and tyrosine (Y), and oxidation of methionine (M) were set as variable modifications. In addition, enzyme specificity was set to complete tryptic digestion, and peptides with a minimum length of 6 amino acids and a maximum of 2 missed cleavages were considered as search parameters.

[0225] Figure 4e is a mass spectrum of the IFNGR1 protein containing a tyrosine-modified region.

[0226] Figure 4f is a mass spectrum of the IFNGR1 protein containing the tyrosine-unmodified region.

[0227] Figure 4g shows the amino acid sequence of the IFNGR1 protein (green: detected peptide, P: phosphorylation site, S: sulfation site).

[0228] As a result, as shown in FIGS. 4e to 4g, sulfation was detected at amino acid 397 (Y) in the purified IFNGR1 peptide, and phosphorylation was detected at various amino acids including amino acid 280.

[0229] 4-3. Confirmation of the effect of sulfated Y397 on IFNγ signaling

[0230] To verify the effect of sulfated Y397 on IFNγ signaling, IFNGR1 knockout MDA-MB-231 cells were generated using the same method as in Example 2-1. In addition, site-directed mutagenesis of IFNGR1 was induced according to the manual of the QuikChange site-directed mutagenesis kit (Stratagene, La Jolla, CA, USA). 5'-CGCTTTAAACTCGTTTCACTCCAGAAATTG-3' and 5'-CAATTTCTGGAGTGAAACGAGTTTAAAGCG-3 were used as IFNGR1 Y397F mutation primers, and the mutant sequences are underlined. Subsequently, IFNGR1 expression was confirmed using Western blotting. In addition, tyrosine sulfation was confirmed using the same method as in Example 4-1, and IFNγ-responsive gene expression, STAT1 phosphorylation levels, HLA expression, and cell growth were confirmed using the same method as in Example 2-1.

[0231] Figure 4h shows the results of confirming the expression of IFNGR1 in IFNGR1 knockout MBA-MD 231 cells, wild type, and IFNGR1 Y397F mutant cells.

[0232] As a result, as shown in Fig. 4h, it was confirmed that IFNGR1 was overexpressed in wild-type and Y397F mutant cells compared to IFNGR1 knockout MBA-MD 231 cells.

[0233] Figure 4i shows the results of confirming the level of tyrosine sulfation in wild-type and IFNGR1 Y397F mutant cells.

[0234] As a result, as shown in Fig. 4i, the level of tyrosine sulfation was reduced in IFNGR1 Y397F mutant cells compared to the wild type, but was not completely abolished.

[0235] Figure 4j shows the expression levels of IFNγ-responsive genes upon IFNγ treatment in wild-type and IFNGR1 Y397F mutant cells.

[0236] As a result, as shown in Fig. 4j, IFNGR1 Y397F mutant cells showed enhanced expression of IFNγ-responsive genes, including IRF1, TAP1, and TAP2, compared to control cells.

[0237] Figure 4k shows the phosphorylation levels of STAT1 when wild-type and IFNGR1 Y397F mutant cells were treated with IFNγ at specific times.

[0238] As a result, as shown in Figure 4k, IFNGR1 Y397F mutant cells showed increased phosphorylation of STAT1 by IFNγ treatment compared to control cells.

[0239] Figure 4l shows the levels of human leukocyte antigen (HLA) in wild-type and IFNGR1 Y397F mutant cells.

[0240] As a result, as shown in Fig. 4l, IFNGR1 Y397F mutant cells showed an enhanced level of expression of IFNγ-responsive HLA by IFNγ treatment compared to control cells.

[0241] Figure 4m shows the cell number by IFNγ treatment in wild-type and IFNGR1 Y397F mutant cells.

[0242] As a result, as shown in Fig. 4m, it was confirmed that cell proliferation inhibition was enhanced by IFNγ treatment in IFNGR1 Y397F mutant cells.

[0243] Figure 4n shows the results of confirming the protein stability of mutant IFNGR1 in wild-type and IFNGR1 Y397F mutant cells.

[0244] As a result, as shown in Figure 4n, there was no significant difference in protein half-life between wild-type and IFNGR1 Y397F mutant cells. These results suggest that TPST2-mediated sulfation of IFNGR1 in Y397 interferes with the responsiveness of cancer cells to IFNγ.

[0245] Example 5. Confirmation of the relationship between TPST2 depletion and anti-PD1 antibody-mediated anticancer immunity.

[0246] 5-1. Confirmation of the effects of TPST2 knockdown on tumor growth and the host immune system.

[0247] To determine the effects of TPST2 knockdown on tumor growth and the host immune system, a syngeneic mouse model using MC38 cells (mouse colon cancer cells) known to be sensitive to anti-PD1 therapy was used. First, knockdown Tpst2 MC38 cells were generated in the same manner as in Example 2-1, except that the mouse Tpst2 sgRNA in Table 5 and the PCR primers in Table 6 were used, and then IFNγ-responsive gene expression and STAT1 phosphorylation levels were determined. Next, for tumor growth experiments, 2 x 10 MC38 cells or knockdown Tpst2 MC38 cells were inoculated into female C57B6 / N mice (5 weeks old, Orient Bio) or nude mice (5 weeks old, Orient Bio). 5 was injected subcutaneously. One week after inoculation, 2 mg / kg anti-PD-1 mAb (clone RMP1-14, BioXCell, USA) or IgG isotype in PBS was injected intraperitoneally into the tumor-bearing mice on days 3, 7, 10, 14, and 17 to evaluate the tumor growth changes in the mice. Tumor size was measured three times a week until the endpoint, and tumor volume was calculated as length x width 2x 0.5 was calculated. All animal experiments were performed according to protocols approved by the GIST Institutional Animal Care and Use Committee (IACUC no. GIST-2020-085 and GIST-2023-011). All animals used were cared for and handled according to policies approved by GIST.

[0248] Gene sequence: Mouse Tpst25'-GATGCTCGGCGCCGACCACG-3' (SEQ ID NO: 14)

[0249] Gene Forward primer Reverse primer Mouse Tpst 2 5'-TGCCCGTGTACTATGAGCAG-3' (SEQ ID NO: 15) 5'-GCTCGATCTTGGACAAGGAG-3' (SEQ ID NO: 16) Mouse Tap 1 5'-CTGGCAACCAGCTACGGGT-3' (SEQ ID NO: 17) 5'-TGAGAAAGAGGATGTGGTGGG-3' (SEQ ID NO: 18) Mouse Tapbp 5'-ACAAGGCCCCCAGAGTGT-3' (SEQ ID NO: 19) 5'-GGAAGAAGTGGGATGCAAGA-3' (SEQ ID NO: 20) Mouse Cxcl 9 5'-GGAACCCTAGTGATAAGGAATGCA-3' (SEQ ID NO: 21) 5'-TGAGGTCTTTGAGGGATTTGTAGTG-3' (SEQ ID NO: 22) Mouse Cxcl105'-TCCTTGTCCTCCCTAGCTCA-3'(SEQ ID NO: 23)5'-ATAACCCCTTGGGAAGATGG-3'(SEQ ID NO: 24)

[0250] Figure 5a compares the expression of the Tpst2 gene (left) and the growth of in vitro control MC38 and Tpst2 knockdown MC38 cells (right). As shown in Figure 5a, Tpst2 knockdown cells showed relatively lower TPST2 expression compared to control cells, but similar cell numbers, confirming similar cell proliferation capabilities.

[0251] Figure 5b shows the expression levels of IFNγ-responsive genes in Tpst2 knockdown cells.

[0252] As a result, as shown in Fig. 5b, Tpst2 knockdown MC38 cells showed enhanced expression of IFNγ-responsive genes, including Cxcl9, Cxcl10, and Tapbp, upon IFNγ treatment.

[0253] Figure 5c shows the phosphorylation level of STAT1 when Tpst2 knockdown cells were treated with IFNγ at a specific time.

[0254] As a result, as shown in Fig. 5c, cells with down-regulated Tpst2 showed increased phosphorylation of STAT1 by IFNγ treatment compared to control cells.

[0255] Figure 5d shows the results of measuring the volume (left) and weight (right) of Tpst2 knockdown MC38 tumors in C57B6 / N mice following anti-PD1 treatment.

[0256] As a result, as shown in Figure 5d, Tpst2 knockdown alone significantly reduced tumor growth and significantly enhanced the efficacy of anti-PD-1 treatment. Furthermore, Tpst2 knockdown significantly reduced tumor weight, and PD-1 combination therapy showed a trend toward a decrease in tumor weight.

[0257] Figure 5e shows the results of measuring the volume of Tpst2 knockdown MC38 tumors in xenograft models of nude mice following anti-PD1 treatment.

[0258] As a result, unlike C57B6 / N mice, tumor volume continued to increase in nude mice regardless of Tpst2 knockdown, and it was confirmed that anti-PD1 treatment did not affect tumor growth due to depletion of mature T cells.

[0259] That is, tumors can be effectively prevented or treated by combining Tpst2 inhibition and anti-PD1 treatment.

[0260] 5-2. Confirming the effect of Tpst2 knockdown on T cell immune activation.

[0261] Given the known influence of TPST2 on IFN-γ signaling in vitro, we hypothesized that the observed in vivo tumor growth reduction was due to an immune-mediated mechanism. Therefore, tumor tissues were obtained from the syngeneic mouse model of Example 5-1, and the effect of Tpst2 knockdown on immune activation was confirmed by flow cytometry analysis. Specifically, tumor tissues and tumor-draining lymph nodes were obtained 15 days after MC83 tumor inoculation into the C57B6 / N mice of Example 5-1. Tumor tissues were cut into small pieces and transferred to RPMI 1640 medium (Corning Incorporated, Cat. No. 10-040-CV, Corning, New York, USA) supplemented with 2.5 mg / ml collagenase type 1 (Gibco, Cat. No. 17018-029), 1.5 mg / ml collagenase type 2 (Gibco, Cat. No. 17101-015), 1 mg / ml collagenase type 4 (Gibco, Cat. No. 17104-019), 50 μg / ml DNase type 1 (Merck, Cat. No. 11284932001), and 0.25 mg / ml hyaluronidase Type IV-S (Sigma Aldrich, Cat. No. H3884). The tumor samples were cultured at 37°C and 150 rpm for 40 minutes and then filtered through a 70-μm cell strainer (Falcon, Cat. No. 352350). Tumor-draining lymph nodes were filtered through a 70-μm cell strainer using a 3-mL syringe plunger. Subsequently, they were stained with anti-mouse CD16 / 32 antibodies for Fc receptor blocking. Anti-mouse CD45, CD3, CD4, CD8a, CD44, CD62L, CD25, Foxp3, and NK1.1, CD11c, CD11b, B220, F4 / 80, iNOS, and CD206 antibodies (Biolegend or BD Bioscience) were used to stain cells. Stained cells were acquired using CANTO II (BD Bioscience), and data analysis was performed using FlowJo software (TreeStar, San Carlos, CA, USA). In addition, the size and volume of the tumors were measured using the same method as in Example 5-1.

[0262] Figure 5f shows the percentage of effector CD4+ T cells in the tumor-draining lymph nodes of the Tpst2 knockdown MC38 syngeneic mouse model following anti-PD1 treatment.

[0263] As a result, as shown in Fig. 5f, effector CD4+ T cells significantly increased in the tumor-draining lymph nodes of the group treated with both Tpst2 knockdown and anti-PD1.

[0264] Figure 5g shows the results of confirming the proportion of NK cells in the tumor-draining lymph nodes of the Tpst2 knockdown MC38 syngeneic mouse model following anti-PD1 treatment.

[0265] As a result, as shown in Fig. 5g, NK cells were significantly increased in the tumor-draining lymph nodes of the group treated with both Tpst2 knockdown and anti-PD1 compared to the tumor-draining lymph nodes of the other groups.

[0266] Figure 5h shows the results of confirming the proportion of T cells in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0267] As a result, as shown in Fig. 5h, the number of T cells increased in the tumor tissues of mice treated with anti-PD1, and in particular, the number of T cells increased significantly when anti-PD1 was treated in Tpst2 knockdown mice.

[0268] Figure 5i shows the results of confirming the ratio of CD4+ T cells and CD8+ T cells in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0269] As a result, as shown in Fig. 5i, CD4+ T cells did not show a significant difference between the groups, but CD8+ T cells increased due to anti-PD1 treatment and were significantly increased in Tpst2 knockdown tumor tissues.

[0270] Figure 5j shows the results of confirming the percentage of effector CD8+ T cells in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0271] Figure 5k shows the results of confirming the proportion of PD1+ effector CD8+ T cells in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0272] As a result, as shown in Figures 5j and 5k, the Tpst2 knockdown tumor tissues treated in combination with anti-PD1 showed an increased proportion of effector CD8+ T cells compared to the IgG control group, and the PD1 expression level also increased significantly.

[0273] Figure 5l shows the results of measuring the volume (left) and weight (right) of Tpst2 knockdown MC38 tumors in a syngeneic mouse model following anti-PD1 treatment.

[0274] As a result, as shown in Figure 5l, the tumor volume and weight were significantly reduced by treating Tpst2 knockdown tumors with anti-PD1 compared to the control group.

[0275] Therefore, combination therapy with Tpst2 inhibition and anti-PD1 was found to enhance T cell immunity by increasing the number of total T cells, including CD4+ T cells and CD8+ T cells, as well as effector CD4+ and effector CD8+ T cells.

[0276] 5-3. Confirmation of the effect of Tpst2 knockdown on myeloid immune cells in tumor tissue.

[0277] The effects of Tpst2 knockdown on bone marrow interface immune cells within tumor tissues were investigated. Specifically, the proportions of type 1 dendritic cells (cDC1), plasmacytoid dendritic cells (pDC), and macrophages with M1 characteristics were measured in control MC38 and TPST2 knockdown MC38 mice using the same method as in Example 5-2.

[0278] Figure 5m shows the results of confirming the proportion of type 1 dendritic cells (cDC1) in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0279] Figure 5n shows the results of confirming the proportion of plasmacytoid dendritic cells (pDCs) in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0280] As a result, as shown in Figures 5m and 5n, the Tpst2 knockdown + anti-PD1 combination group showed an increase in type 1 dendritic cells (cDC1s) and a decrease in plasmacytoid dendritic cells (pDCs). Given that cDC1s are known to support anti-tumor responses and pDCs are involved in defense against viral pathogens, these results are consistent with previous studies showing that a higher cDC1 / pDC ratio correlates with better anti-PD1 responsiveness.

[0281] Figure 5o shows the results of confirming the proportion of macrophages with M1 characteristics in the Tpst2 knockdown MC38 tumor tissue of a syngeneic mouse model following anti-PD1 treatment.

[0282] As a result, as shown in Fig. 5o, macrophage population analysis did not reveal significant differences in overall percentages, but showed increased iNOS expression, indicating enhanced M1 polarization in the Tpst2 knockdown + anti-PD1 combination group. Thus, polarization is crucial for anti-tumor effects and anti-PD1 therapeutic efficacy, and is thought to enhance the potential of Tpst2 knockdown to improve anti-PD1 therapeutic outcomes.

[0283] Example 6. Confirming the mechanistic basis of TPST2 knockdown for enhancing antitumor immunity and the efficacy of anti-PD1 therapy.

[0284] 6-1. RNA sequencing and differential gene expression analysis of residual tumor tissue in a syngeneic mouse model

[0285] We identified the mechanism underlying TPST2 knockdown for enhancing anti-tumor immunity and the efficacy of anti-PD1 therapy. Specifically, tumor samples were obtained from the syngeneic mouse model of Example 5-1, and the genetic profiles of TPST2 knockdown tumors were compared with those of the control group using RNA sequencing data from mouse tumor tissues. To highlight the differences between the control group and TPST2, the control + IgG group and the TPST2 knockdown + IgG group, and the control + anti-PD1 group and the TPST2 knockdown + anti-PD1 group were compared. First, for RNA sequencing of cell lines, total RNA was extracted after treating cells with 1 ng / ml of IFNγ for 8 hours using the RNeasy Plus Mini Kit (Qiagen). The entire transcriptome expression profile was generated by RNA sequencing using Mapsplice and RSEM using the TCGA RNASeq v2 pipeline (https: / wiki.nci.nih.gov / display / TCGA / RNASeq+Version+2). Differentially expressed genes were estimated using the edgeR package, and similarity alignment and transcript abundance quantification were performed using the kallisto software for mouse tumor RNA sequencing analysis. First, to facilitate efficient mapping of RNA-seq reads, the primary assembly fasta file was downloaded from the ensembl database (release 108) and a Kallisto index for the Mus musculus genome (GRCm39) was constructed. Subsequently, a k-mer kallisto index with a k-mer size of 31 was constructed. To ensure robust statistical analysis, paired-end reads for each sample were quantified based on the constructed index using a bootstrapping value of 100. The RNA-seq analysis data were accessed from the European Nucleotide Archive (accession no.PRJEB73786).

[0286] Figure 6a shows the results of gene set enrichment analysis of TPST2 knockdown tumors in a winter mouse model.

[0287] Figure 6b shows the results of gene set enrichment analysis based on gene ontology biological process (NES: Normalized Enrichment Score, NOM p-val: Nominal p-value, FDR q-val: False Discovery Rate q-value), and FWER p-val (Family-Wise Error Rate p-value).

[0288] As a result, as shown in Figures 6a and 6b, we found that both IgG and anti-PD1 treatments enhanced the antigen processing and presentation pathways of Tpst2 knockdown tumors (FDR q-value < 0.05), indicating that Tpst2 plays an important role in regulating immune recognition mechanisms.

[0289] Figure 6c is a heatmap showing the expression levels of 11 genes involved in antigen processing and presentation processes commonly identified from the results of gene set enrichment analysis of TPST2 knockdown tumors.

[0290] Figure 6d is a dot plot showing key genes in antigen processing and presentation.

[0291] As a result, as shown in Figures 6c and 6d, in-depth investigation of 11 central genes during antigen processing and presentation revealed that Tpst2 knockdown tumors were significantly upregulated compared to the control group. This upregulation was significant in the anti-PD1 treatment group and the IgG control group. Thus, Tpst2 inhibition and anti-PD1 therapy may have a synergistic effect on the expression of immune-related genes.

[0292] Figure 6e shows a differential gene expression volcano plot between the control + anti-PD1 group and the TPST2 knockdown + anti-PD1 group (left) and the results of STRING analysis focusing on genes upregulated in the TPST2 knockdown + anti-PD1 group compared to the control + anti-PD1 group (right).

[0293] As a result, as shown in Figure 6e, 83 upregulated and 68 downregulated genes were highlighted in Tpst2 knockdown tumors after anti-PD-1 treatment. Furthermore, STRING analysis revealed that the upregulated genes were classified into gene clusters related to electron transport and immune interactions, with the latter highlighting antigen processing and presentation.

[0294] 6-2. Cohort Analysis

[0295] To extend the results of Example 6-1 above to cellular resolution, single-cell RNA sequencing of a public lung cancer cohort was performed. Specifically, using data from the public lung cancer cohort described in the referenced paper and downloaded from Code Ocean capsules (https: / / doi.org / 10.24433 / CO.0121060.v1), we performed comprehensive single-cell RNA sequencing (scRNA-seq) analysis of human lung tumor tissues to investigate the expression and associated changes of TPST2 at the single-cell level. Starting with the Seurat package, scRNA-seq data for each of the 12 patients were loaded, and each dataset was systematically organized and converted into a Seurat entity. To ensure data quality, individual patient datasets were merged, cell names were adjusted to maintain uniqueness across the entire dataset, and quality control was performed by filtering cells based on mitochondrial gene expression to retain cells with less than 10% mitochondrial gene content. After normalization, variable feature identification, and scaling, data integration was performed using Seurat's FindIntegrationAnchors and IntegrateData functions to minimize batch effects across patients. After integration, all 74,888 cells were categorized into immune cells (52,321) and non-immune cells (22,567) based on PTRC expression. For detailed analysis, we focused on non-immune cells and annotated cell clusters based on the expression of genes significantly associated with each cluster, as identified using the FindAllMarkers function. This approach allowed us to attribute functional identity to clusters based on the most significantly expressed genes within them. Furthermore, to investigate the influence of TPST2 expression, non-immune cells were divided into two groups: TPST2-positive cells (4,106) and TPST2-negative cells (18,461).This distinction was crucial for subsequent analyses, and the FindMarkers function of the Seurat R package was used to identify 436 genes differentially expressed between TPST2-positive and -negative non-immune cells. The goal of this differential expression analysis was to identify specific genes that were upregulated or downregulated in relation to TPST2 expression levels. To further identify enriched biological pathways in TPST2-negative non-immune cells, gene set enrichment analysis (GSEA) was performed using clusterProfiler, ReactomePA, and org.Hs.eg.db packages.

[0296] Figure 6f is a single-cell violin plot for TPST2 expression in non-immune cells of human lung tumor tissue.

[0297] Figure 6g shows the results of gene set enrichment analysis of TPST2-associated genomic alterations.

[0298] Figure 6h shows the comparative results of gene expression related to antigen processing and presentation.

[0299] Figure 6i shows the results of a correlation analysis between TPST2 expression and genes related to antigen processing and presentation.

[0300] As a result, as shown in Figures 6f and 6g, non-immune cells in lung tumor tissues were classified into TPST2-positive and TPST2-negative cell populations, and it was confirmed that the antigen processing and presentation pathway was prominent in TPST2-negative cells. In addition, as shown in Figure 6h, the expression levels of eight genes related to the antigen processing and presentation pathway were examined, and significantly higher expression was confirmed in TPST2-negative cells. In addition, as shown in Figure 6i, additional analysis at the individual patient level showed a negative correlation between the TPST2 expression level and the expression of central genes related to antigen processing and presentation.

[0301] That is, because IFN-γ significantly increases the expression of genes important for antigen processing and presentation, these in vivo results are directly consistent with the in vitro increase in IFN-γ signaling, suggesting that modulation of antigen processing and presentation by TPST2 knockdown significantly enhances immune recognition and tumor responses.

[0302] Example 7. Confirmation of the relationship between increased TSTP2 expression and prognosis and tumor immunity in cancer patients.

[0303] 7-1. Analysis of TPST2 genome alterations and expression

[0304] To understand the clinical significance of TPST2 in cancer patients, we analyzed TPST2 genomic alterations and expression using data sets from The Cancer Genome Atlas (TCGA) PanCancer Atlas study (http: / www.cbioportal.org). Survival analysis of each cancer type in the TCGA PanCancer Atlas study in relation to TPST2 expression was performed using the online bioinformatics tool Kaplan-Meier Plotter (https: / kmplot.com / analysis / ).

[0305] Figure 7a shows the proportion of patients with copy number alterations in the TPST2 gene in tumor samples from various cancer patients.

[0306] Figure 7b shows the results of confirming the expression of TPST2 mRNA in normal tissues and cancer tissues according to cancer type.

[0307] As a result, as shown in Figures 7a and 7b, an increase in the copy number or amplification of the TPST2 gene was observed in several types of cancer (Figure 7a), and the mRNA expression of TPST2 was significantly increased in several types of tumor tissues compared to normal tissues (P < 0.05, Figure 7b).

[0308] Figure 7c shows the proportion of patients with upregulated TPST2 mRNA in cancer tissues according to cancer type.

[0309] Figure 7d shows the results of survival analysis by TPST2 expression according to cancer type.

[0310] As a result, as shown in Fig. 7c and 7d, a subset of patients in each tumor type showed upregulation of TPST2 (z-score threshold of 2 from cBioPortal database (http: / www.cbioportal.org); Fig. 7c). In addition, patients with high TPST2 expression in breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSC), ovarian serous cystadenocarcinoma (OV), sarcoma (SARC), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC) showed a worse prognosis compared to patients with low TPST2 expression (Fig. 7d).

[0311] 7-2. TPST2 transcriptome data analysis

[0312] To investigate the role of TPST2 in breast cancer patients by analyzing transcriptome data, 482 genes positively correlated with TPST2 expression (Spearman's correlation coefficient ρ ≥ 0.3) and 323 genes negatively correlated with TPST2 expression (Spearman's correlation coefficient ρ ≤ -0.3) were selected from the cohort of TCGA Breast Invasive Carcinoma PanCancer Atlas, n = 1084. Examples of the selected genes are shown in Tables 7 and 8, respectively.

[0313] Correlated GeneCytobandSpearman's Correlationp-Valueq-ValueTFIP1122q12.10.5038725829.91E-712.00E-66SELENOM22q12.20.4964835552.06E-682.08E-64EFEMP211q13.10.4652824833.17E-592.13E-55UBTD110q24.1-q24.20.4627864281.57E-587.91E-55RARRES27q36.10.4573658814.83E-571.95E-53IFFO112p13.310.4570592825.86E-571.97E-53PLPP79q34.130.4516755451.65E-554.77E-52COPZ217q21.320.4500701764.43E-551.12E-51EMP319q13.330.4465148873.85E-548.63E-51GAS2L122q12.20.4424789334.34E-538.77E-50PCOLCE7q22.10.4374818048.35E-521.53E-48SERPINF117p13.30.4351274373.30E-515.56E-48MFRP11q23.30.4347487284.12E-516.39E-48CHRD3q27.10.4329100611.19E-501.66E-47TGFB119q13.20.4328504171.24E-501.66E-47TNFSF1217p13.10.4315811982.57E-503.18E-47HTRA34p16.10.4315083712.68E-503.18E-47ROR29q22.310.4302829255.41E-506.07E-47LOXL115q24.10.4263904084.96E-495.00E-46HTRA110q26.130.42622745.43E-495.22E-46PODNL119p13.120.4255086468.15E-497.48E-46TREM26p21.10.4239793271.93E-481.69E-45JDP214q24.30.4238779072.04E-481.71E-45PPM1M3p21.20.4209711391.03E-478.32E-45MMP216q12.20.4198285671.94E-471.51E-44TOM122q12.30.4196160182.18E-471.63E-44MVP16p11.20.4175580236.78E-474.89E-44CLEC11A19q13.330.4169563979.42E-476.56E-44SYNDIG120p11.210.4168044291.02E-466.89E-44VENTX10q26.30.4146732563.27E-462.13E-43SMPD111p15.40.4129077768.52E-465.21E-43GAS613q340.4125105151.06E-456.26E-43LILRA219q13.420.4115979741.72E-459.95E-43APOBR16p12.10.4106639572.85E-451.60E-42COX7A119q13.120.4105770932.98E-451.63E-42NAALADL111q13.10.4102258523.60E-451.91E-42CTSK1q21.30.4098427194.42E-452.29E-42PDLIM75q35.30.4092781385.97E-452.94E-42LAT27q11.230.4090352276.80E-453.27E-42ADAMTSL29q34.20.4071195031.88E-448.83E-42CAMK13p25.30.4069311972.08E-449.53E-42CYS12p25.10.4062444122.99E-441.34E-41PTGIR19q13.320.4061122973.20E-441.38E-41SCARF222q11.210.4060962563.23E-441.38E-41NTNG29q34.130.4060652523.28E-441.38E-41CD99L2Xq280.4055183014.38E-441.80E-41THY111q23.30.4045848917.16E-442.83E-41ALOX5AP13q12.30.4042083898.72E-443.38E-41DOK12p13.10.4039194551.01E-433.86E-41C4A6p21.330.4036559081.16E-434.35E-41PTH1R3p21.310.3998940318.23E-432.97E-40CLEC2B12p13.310.3992878391.13E-423.99E-40GLIS216p13.30.399172231.19E-424.12E-40NNMT11q23.20.3991592691.20E-424.12E-40COL1A117q21.330.3981761331.99E-426.60E-40LY866p25.10.397704472.54E-428.27E-40MSC8q13.30.3976014942.68E-428.45E-40.

[0314] Correlated GeneCytobandSpearman's Correlationp-Valueq-ValueTMPO12q23.1-0.4290432051.10E-491.17E-46U2SURP3q23-0.4136420285.73E-463.61E-43MSH22p21-p16.3-0.4092848735.95E-452.94E-42ATL22p22.2-p22.1-0.4052750714.98E-442.01E-41MAD2L14q27-0.4022562662.42E-438.87E-41TOP2A17q21.2-0.3990064761.30E-424.38E-40KNTC112q24.31-0.3976496942.61E-428.37E-40CPSF612q15-0.3971068253.45E-421.06E-39ARHGAP11A15q13.3-0.3966271654.41E-421.33E-39PPAT4q12-0.3961177825.72E-421.67E-39MTMR417q22-0.3954303578.12E-422.31E-39ATAD517q11.2-0.3950009981.01E-412.79E-39PARPBP12q23.2-0.3945646871.26E-413.39E-39MBTD117q21.33-0.394425511.35E-413.59E-39XRCC27q36.1-0.3912831756.59E-411.64E-38BRIP117q23.2-0.3910974157.24E-411.78E-38NOL1117q24.2-0.389395171.69E-404.02E-38CKAP511p11.2-0.386469087.23E-401.59E-37RFWD316q23.1-0.3844489671.95E-393.98E-37NEMP112q13.3-0.3834783823.14E-396.27E-37BLM15q26.1-0.3827947774.38E-398.46E-37CIP2A3q13.13-0.3815886847.86E-391.44E-36CENPE4q24-0.3809042061.10E-381.97E-36CASP27q34-0.3792243122.46E-384.25E-36LARP4B10p15.3-0.3790821952.64E-384.47E-36MMS22L6q16.1-0. 3784351933.60E-386.05E-36CIT12q24.23-0.3778274594.81E-387.97E- 36BUB1B15q15.1-0.3776184745.32E-388.65E-36TOPBP13q22.1-0.37676 34368.00E-381.26E-35NAA2512q24.13-0.3761343321.08E-371.68E-35P LK44q28.1-0.3755197381.45E-372.20E-35NUP2057q33-0.3754134451.52E-372.29E-35ZGRF14q25-0.3751620651.71E-372.56E-35UBA219q13.11-0.3746707692.16E-373.18E-35PRR1117q22-0.3731837024.36E-376.25E-35MTBP8q24.12-0.3726284725.67E-378.06E-35STIL1p33-0.37257038 95.82E-378.22E-35TDG12q23.3-0.3722377246.81E-379.48E-35RAD51AP112p13.32-0.371748888.56E-371.17E-34BUB12q13-0.3714215879.98E-371.35E-34SASS61p21.2-0.3707191451.39E-361.87E-34WDHD114q22.2-q22.3-0.3702746591.71E-362.22E-34KIF20B10q23.31-0.3702024531.7 6E-362.27E-34DCAF717q23.3-0.3697298232.20E-362.81E-34ASPM1q31. 3-0.3695889172.35E-362.94E-34SGO13p24.3-0.3693601292.61E-363.2 2E-34RRP1B21q22.3-0.3685380483.82E-364.62E-34KIF1110q23.33-0.3 680317144.83E-365.74E-34ARHGAP11B15q13.2-0.3672378346.97E-368.13E-34KIF18A11p14.1-0.3662575721.09E-351.25E-33ENOPH14q21.22-0.3648539622.08E-352.29E-33ATAD28q24.13-0.364076 6692.97E-353.22E-33TTK6q14.1-0.3637070313.52E-353.76E-33WDR432p23.2-0.3636943283.54E-353.76E-33LRPPRC2p21-0.36 33145734.20E-354.37E-33XKXp21.1-0.3632051484.42E-354.55E-33FANCI15q26.1-0.3629500914.96E-355.08E-33CABLES220q1 3.33-0.3627306325.48E-355.59E-33DNA210q21.3-0.362615335.77E-355.83E-33POLQ3q13.33-0.3625894235.84E-355.87E-33.

[0315] Figure 7e shows the results of a hallmark gene set analysis of genes positively correlated with TPST2 expression in breast cancer.

[0316] As a result, as shown in Fig. 7e, genes positively correlated with TPST2 were enriched in several immune-related hallmark gene sets including 'EPITHELIAL_MESENCHYMAL_TRANSITION', 'INFLAMMATORY_RESPONSE', 'ALLOGRAFT_REJECTION', 'INTERFERON_GAMMA_RESPONSE', and 'TGF_BETA_SIGNALING' (FDR Q < 0.001).

[0317] Figure 7f shows the results of a hallmark gene set analysis of genes negatively correlated with TPST2 expression in breast cancer.

[0318] As a result, as shown in Fig. 7f, genes negatively correlated with TPST2 were enriched in several cell cycle-related hallmark gene sets including 'G2M_CHECKPOINT', 'E2F_TARGETS', and 'MITOTIC_SPINDLE' (FDR Q < 0.001).

[0319] 7-3. Gene set enrichment analysis (GSEA)

[0320] GSEA was performed on microarray data of breast cancer tissues according to TPST2 expression using the javaGSEA desktop application (GSEA v2.1.0). Microarray data from 238 triple-negative breast cancer patients (GSE103091) were downloaded from the Gene Expression Omnibus (GEO) database (https: / / www.ncbi.nlm.nih.gov / geo). We selected the 20 samples with the highest TPST2 expression levels (TPST2_H) and the 20 samples with the lowest TPST2 expression levels (TPST2_L), and performed GSEA between these two groups. The enriched gene sets for each group were examined using the whole-matter gene set. P-values ​​were calculated by permuting the data 1,000 times to find enriched gene sets. The GSEA software generates an enrichment score (ES), a normalized ES (NES), a nominal P-value, and a false discovery rate (FDR; Q-value). Gene sets upregulated or downregulated with a P-value <0.05 were considered significant.

[0321] Figure 7g is a list of significantly enriched gene sets in the TPST2_H group.

[0322] Figure 7h shows an enrichment plot of representative gene sets significantly enriched in the TPST2_H group.

[0323] As a result, as shown in Fig. 7g and Fig. 7f, when applying the gene set of the gene ontology (GO) biological process, the gene set related to cancer immunity, such as 'ALLOGRAFT_REJECTION', 'INTERFERON_GAMMA_RESPONSE', 'INFLAMMATORY_RESPONSE', 'IL2_STAT5_SIGNALING', 'INTERFERON_ALPHA_RESPONSE', and 'IL6_JAK_STAT3_SIGNALING', was highly enriched in the TPST2 (TPST2_H) group.

[0324] Figure 7i is a list of gene sets significantly enriched in the TPST2_L group.

[0325] Figure 7j shows an enrichment plot of representative gene sets significantly enriched in the TPST2_L group.

[0326] As a result, as shown in Figures 7i and 7j, several gene sets related to cell cycle regulation, such as 'E2F_TARGETS', 'G2M_CHECKPOINT', and 'MITOTIC_SPINDLE', were enriched in the TPST2 (TPST2_L) group.

[0327] That is, the above results suggest that TPST2 is involved in tumor immunity, including IFNγ signaling, and cell cycle regulation in breast cancer tissues.

[0328] The foregoing description of the present invention is provided for illustrative purposes only. Those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

[0329] The TPST2 inhibitor according to the present invention was confirmed to be involved in the interferon gamma (INFγ) responsiveness of cancer cells, and it was confirmed that T cell immunity can be enhanced by combined treatment with anti-PD1. Therefore, it can be used in a new cancer treatment that manages both the intrinsic characteristics of cancer cells and cancer immunity, and thus has industrial applicability.

Claims

1. A pharmaceutical composition for preventing or treating cancer comprising a TPST2 (Tyrosylprotein sulfotransferase-2) inhibitor or an IFNGR1 (Interferon gamma receptor 1) oxidation inhibitor.

2. In paragraph 1, A pharmaceutical composition wherein the above IFNGR1 oxidation inhibitor inhibits the oxidation of tyrosine at position 397 of the IFNGR1 protein amino acid sequence.

3. In paragraph 1, A pharmaceutical composition wherein the above TPST2 inhibitor or IFNGR1 oxidation inhibitor increases signal transduction of interferon gamma (IFNγ).

4. In paragraph 1, A pharmaceutical composition, wherein the cancer is selected from the group consisting of bladder cancer, urothelial cancer, colon cancer, large intestine cancer, esophageal cancer, head and neck cancer, brain cancer, ovarian cancer, stomach cancer, testicular cancer, lung cancer, and thyroid cancer. 5.1) Step of treating a candidate substance to cancer cells or a cancer animal model; 2) A step of confirming the degree of TPST2 expression, the degree of sulfation of IFNGR1 protein, or the degree of sulfation of tyrosine 397 of the IFNGR1 protein amino acid sequence in the cancer cells or cancer animal model after treatment with the candidate substance; and 3) A step of selecting a candidate substance that reduces TPST2 expression, reduces sulfation of IFNGR1 protein, or reduces sulfation of tyrosine at position 397 of the IFNGR1 protein amino acid sequence compared to a control group that is not treated with the candidate substance; A method for screening a substance for the prevention or treatment of cancer, including:

6. In paragraph 5, 1) A step of confirming the degree of signal transduction of interferon gamma (IFNγ) in the cancer cells or cancer animal model after treating the candidate substance; and 2) A step of selecting a candidate substance that increases the signal transduction of interferon gamma (IFNγ) compared to a control group that was not treated with the candidate substance; A screening method comprising: 7.(1) PD-1 or PD-L1 inhibitors, (2) A pharmaceutical composition for preventing or treating cancer, which is used in combination with a TPST2 inhibitor or an IFNGR1 oxidation inhibitor.

8. In paragraph 7, A pharmaceutical composition, wherein the PD-1 inhibitor is selected from the group consisting of BGB-A317, Nivolumab, Pembrolizumab, PDR001, Pidilizumab, REGN-2810, PF-06801591, BGB-108, INCSHR1210, TSR-042, and AMP 514.

9. In paragraph 7, A pharmaceutical composition, wherein the PD-L1 inhibitor is selected from the group consisting of CX-072, WBP-3155, KN035, A167, Cosibelimab, Atezolizumab, Avelumab, Durvalumab, Adebrelimab, and BMS-936559.

10. A pharmaceutical composition according to claim 7, wherein the TPST2 inhibitor or the IFNGR1 sulfation inhibitor inhibits sulfation of the 397th tyrosine of the IFNGR1 protein amino acid sequence.

11. In paragraph 7, A pharmaceutical composition, wherein the cancer is selected from the group consisting of bladder cancer, urothelial cancer, colon cancer, large intestine cancer, esophageal cancer, head and neck cancer, brain cancer, ovarian cancer, stomach cancer, testicular cancer, lung cancer, and thyroid cancer.

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