Method for diagnosing or treating cancer using cancer-related fibroblasts

Abstract

The present invention relates to a method for diagnosing or treating cancer using cancer-related fibroblasts. According to the method according to the present invention, cancer, particularly breast cancer and pancreatic cancer, can be more accurately diagnosed by using AKR1C1+ inflammatory fibroblasts and / or mesothelioma-derived fibroblasts, and early treatment is possible on the basis thereof. Thus, the method is expected to be widely used in the medical field.

Description

Method for diagnosing or treating cancer using cancer-related fibroblasts

[0001] The present invention relates to cancer-related fibroblasts, specifically AKR1C1 + The present invention relates to a method for diagnosing or treating cancer using inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts.

[0002] Cancer has a high mortality rate worldwide, and in Western societies, it is the second most common cause of death after cardiovascular disease. In particular, the incidence of stomach, colon, breast, and prostate cancers is steadily increasing due to factors such as an aging population, the widespread consumption of high-fat diets resulting from Westernized diets, a rapid increase in environmental pollutants, and increased alcohol consumption. Pancreatic cancer, in particular, is the seventh leading cause of cancer-related death worldwide, and ranks even higher, ranking between the second and fifth leading causes in developed countries. Despite its significant role in cancer-related death, pancreatic cancer is often difficult to detect in its early stages due to subtle symptoms and limited research on specific tumor markers. This lack of early diagnostic methods means that only 5 to 22% of pancreatic cancer cases are diagnosed early enough to be surgically resected. With a five-year survival rate of only 12.6% (2018 National Cancer Registry Statistics), it is one of the most lethal cancers.

[0003] Currently, pancreatic cancer is commonly diagnosed clinically using imaging techniques such as ultrasound, CT scans, MRI, angiography, endoscopic retrograde cholangiopancreatography, and endoscopic ultrasound; as well as tumor markers (biomarkers). Carbohydrate antigen 19-9 (CA19-9) is the only pancreatic cancer biomarker known to have a proven diagnostic effect. However, this biomarker lacks sufficient sensitivity and specificity to effectively diagnose pancreatic cancer early. While CA19-9 is a useful tumor marker for predicting prognosis and tracking treatment progress in pancreatic cancer, its usefulness as a screening test is generally considered low due to the low incidence of pancreatic cancer. Therefore, to increase the survival rate of pancreatic cancer patients, the discovery of novel pancreatic cancer-specific biomarkers that enable early diagnosis with higher accuracy is urgently needed.

[0004] Meanwhile, the existence of cancer-associated fibroblasts (CAFs) that initiate remodeling of the extracellular matrix within the tumor microenvironment or promote tumorigenic properties by secreting cytokines has been identified, and it has been suggested that CAFs may act differently in each cancer type to affect cancer cell growth, metastasis, and immune response. Therefore, it is necessary to understand the function of CAFs through research on each cancer type and develop customized diagnosis and treatment strategies based on this.

[0005] Therefore, the present invention has been devised to solve the above problems, and cancer-related fibroblasts, specifically AKR1C1 + The present invention relates to a method for diagnosing or treating cancer using inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts. The method of the present invention is expected to be widely utilized in the medical field, as it enables more accurate diagnosis of cancer, particularly breast cancer and pancreatic cancer, and early treatment based on this diagnosis.

[0006] The present invention has been devised to solve the problems in the conventional technology as described above, and is a cancer-related fibroblast, specifically, AKR1C1 + The present invention relates to a method for diagnosing or treating pancreatic cancer using inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts.

[0007] However, the technical problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

[0008] Hereinafter, various embodiments described herein will be described with reference to the drawings. In the following description, various specific details, such as specific configurations, compositions, and processes, are set forth to provide a thorough understanding of the present invention. However, certain embodiments may be practiced without one or more of these specific details, or in conjunction with other known methods and configurations. In other instances, well-known processes and manufacturing techniques have not been described in specific detail so as not to unnecessarily obscure the present invention. Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, configuration, composition, or characteristic described in connection with the embodiment is included in one or more embodiments of the present invention. Thus, the appearances of "in one embodiment" or "an embodiment" in various places throughout this specification do not necessarily refer to the same embodiment of the present invention. Additionally, the particular features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments.

[0009] Unless otherwise specifically defined in the specification, all scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0010] Throughout the specification, whenever a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise stated.

[0011] In one embodiment of the present invention, AKR1C1 is isolated from a biological sample from a cancer patient. + Inflammatory fibroblasts (AKR1C1 + A method for providing information regarding the diagnosis of cancer is provided, comprising the step of determining the presence of inflammatory fibroblasts or mesothelium-derived fibroblasts.

[0012] In another aspect of the present invention, AKR1C1 + Inflammatory fibroblasts (AKR1C1 + A composition for diagnosing cancer is provided, comprising a preparation capable of measuring inflammatory fibroblasts or a preparation capable of measuring mesothelium-derived fibroblasts.

[0013] In another aspect of the present invention, AKR1C1 + Inflammatory fibroblasts (AKR1C1 + A kit for diagnosing cancer is provided, comprising a composition for diagnosing cancer, comprising an agent capable of measuring inflammatory fibroblasts or an agent capable of measuring mesothelium-derived fibroblasts.

[0014] In another aspect of the present invention, AKR1C1 + Inflammatory fibroblasts (AKR1C1 + Provided is a pharmaceutical composition for treating cancer, comprising an inflammatory fibroblasts inhibitor or a mesothelium-derived fibroblasts inhibitor as an active ingredient.

[0015] Hereinafter, in the specification of the present invention, the tumor microenvironment (TME) encompasses the constituent cell populations, such as vascular cells, immune cells, and stromal cells, present within a tumor, and their environments. It is the overall environment in which cancer cells proliferate and evolve. It is known to directly and indirectly influence tumor growth and progression. The TME forms a physical and signaling barrier around the tumor, hindering the penetration of immune cells and drugs. Consequently, it causes non-responsiveness and drug resistance to anticancer drugs such as targeted therapies and immunotherapies, contributing to cancer growth and metastasis.

[0016] In the present specification, cancer-associated fibroblasts (CAF) refer to fibroblasts surrounding cancer cells, i.e., within the tumor-like environment (TME). They are known to promote cancer cell invasion and metastasis, block drug effects on cancer cells, increase angiogenesis in cancer tissue, and secrete various cytokines, thereby promoting cancer progression and metastasis. They also bind to the immune protein immunoglobulin A to suppress immune responses.

[0017] Recently, it has been suggested that CAFs may act differently in different cancer types, affecting cancer cell growth, metastasis, and immune responses. Therefore, it is necessary to understand the function of CAFs through research on each cancer type and develop customized diagnosis and treatment strategies based on this. Accordingly, the inventors of the present invention tracked CAFs that specifically promote cancer development in breast and pancreatic cancers and identified AKR1C1. + Inflammatory fibroblasts (AKR1C1 + inflammatory fibroblasts), and / or mesothelium-derived fibroblasts.

[0018] In the present invention, the AKR1C1 +Inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts may exist in a tumor microenvironment and may promote cancer development, wherein the cancer may be at least one selected from the group consisting of breast cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colon cancer, pancreatic cancer, stomach cancer, gallbladder cancer, ovarian cancer, lymphoma, osteosarcoma, uterine cancer, oral cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, thyroid cancer, parathyroid cancer, ureteral cancer, adenocarcinoma, and thymic cancer, and preferably may be breast cancer or pancreatic cancer, and more preferably may be triple-negative breast cancer or pancreatic cancer, but is not limited thereto.

[0019] In addition, in the present invention, the AKR1C1 + Inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts may actively express TIGIT (T-cell innunoreceptor with immunoglobulin and ITIM domain) and / or IL-6 (interleukin-6), wherein TIGIT or IL-6 may function as a type of immune checkpoint inhibitor.

[0020] In the present invention, the immune checkpoint inhibitor is a type of cancer immunotherapy, and refers to a drug that blocks immune checkpoint proteins expressed in cancer cells and some immune cells. It is also called an immuno-oncology agent, and may be a CTLA-4 (Cytotoxic T-lymphocyte-associated antigen-4) inhibitor, a PD-1 (Programmed cell death protein 1) inhibitor, a PD-L1 (Programmed death-ligand 1) inhibitor, a KIR (Killer-cell immunoglobulin-like receptor) inhibitor, a LAG3 (Lymphocyte Activation Gene-3) inhibitor, a CD137 inhibitor, an OX40 inhibitor, a CD47 inhibitor, a CD276 inhibitor, a CD27 inhibitor, a GITR (Glucocorticoid-induced tumor necrosis factor receptor-related protein) inhibitor, a TIGIT (T-cell innunoreceptor with immunoglobulin and ITIM domain) inhibitor, and an IL-6 (interleukin-6) inhibitor, but is not limited thereto. Immune checkpoint inhibitors have proven effective in various cancers such as gastric cancer, kidney cancer, bladder cancer, and lung cancer. Among them, PD-1 inhibitors such as Pembrolizumab (Keytruda) and Nivolumab (Opdivo), PD-L1 inhibitors such as Atezolizumab (Tecentriq) and Durvalumab (Imfinzi), and CTLA-4 inhibitors such as Ipilimumab (Yervoy) have been developed and approved and are being used. However, research on drugs targeting TIGIT and patient targeting related to TIGIT is still insufficient.

[0021] According to the present invention, by implementing the AND gating algorithm, the characteristic genes that are repeatedly up- or down-regulated in tumors compared to normal tissues in the major cell types that constitute the TME of various organs were systematically characterized, and as a result, CD8 of pancreatic tumor tissues+ T cells did not show upregulation of PDCD1 and LAG3, which may explain the current inapplicability of immune checkpoint inhibitors in pancreatic cancer (PAAD) unlike other cancer types. In addition, when the expression levels of immune checkpoint protein-related ligands in cancer cells themselves were compared by cancer type, pancreatic cancer showed a uniformly high expression of PVR, Nectin2, and Nectin4, which are ligands for TIGIT. This suggests that specific CAFs (AKR1C1) are present in pancreatic cancer, for which immune checkpoint inhibitors are currently not applicable. + This suggests that inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts may be a clue for anti-TIGIT immunotherapy.

[0022] In the present invention, diagnosis means confirming the presence or characteristics of a pathological condition. For the purpose of the present invention, the diagnosis means confirming the presence or possibility of developing cancer, particularly breast cancer or pancreatic cancer, by detecting AKR1C1 in a biological sample isolated from a cancer patient. + Inflammatory fibroblasts (AKR1C1 + The method may be performed by a method including a step of determining the presence of inflammatory fibroblasts, or mesothelium-derived fibroblasts.

[0023] Accordingly, the diagnostic composition in the specification of the present invention is AKR1C1 + Inflammatory fibroblasts (AKR1C1 + It may include an agent capable of measuring inflammatory fibroblasts, or an agent capable of measuring mesothelium-derived fibroblasts, and the AKR1C1 +The agent capable of measuring inflammatory fibroblasts or mesothelial cell-derived fibroblasts may be an agent capable of measuring TIGIT (T-cell innunoreceptor with immunoglobulin and ITIM domain) or an agent capable of measuring IL-6 (interleukin-6), wherein TIGIT or IL-6 may be recognized as a type of biomarker for diagnosing breast cancer or pancreatic cancer. In addition, the agent capable of measuring TIGIT or the agent capable of measuring IL-6 may be an agent capable of measuring the expression level of the TIGIT protein or the gene encoding it, or an agent capable of measuring the expression level of the IL-6 protein or the gene encoding it.

[0024] In the present invention, the agent for measuring the expression level of the protein may include at least one selected from the group consisting of antibodies, oligopeptides, ligands, peptide nucleic acids (PNAs), and aptamers that specifically bind to the protein. In the present invention, the antibody refers to a substance that specifically binds to an antigen and causes an antigen-antibody reaction. For the purposes of the present invention, the antibody refers to an antibody that specifically binds to a protein that functions as the biomarker. The antibodies of the present invention include polyclonal antibodies, monoclonal antibodies, and recombinant antibodies. The antibodies can be easily produced using techniques well known in the art. For example, polyclonal antibodies can be produced by methods well known in the art, including a process of injecting an antigen of the biomarker protein into an animal and collecting blood from the animal to obtain serum containing the antibody. Such polyclonal antibodies can be produced from any animal, such as a goat, rabbit, sheep, monkey, horse, pig, cow, or dog. In addition, monoclonal antibodies can be produced using hybridoma methods or phage antibody library technology widely known in the art. Antibodies produced by the above methods can be separated and purified using methods such as gel electrophoresis, dialysis, salt precipitation, ion exchange chromatography, and affinity chromatography. In addition, the antibody of the present invention includes not only a complete form having two full-length light chains and two full-length heavy chains, but also a functional fragment of the antibody molecule. A functional fragment of an antibody molecule means a fragment that has at least an antigen-binding function, and includes Fab, F(ab'), F(ab')2, and Fv. In the present invention, the PNA (Peptide Nucleic Acid) refers to an artificially synthesized polymer similar to DNA or RNA.While DNA has a phosphate-ribose sugar backbone, PNA has a repeating N-(2-aminoethyl)-glycine backbone linked by peptide bonds, which greatly increases binding affinity and stability to DNA or RNA, and is used in molecular biology, diagnostic analysis, and antisense therapy. In the present invention, the aptamer is an oligonucleotide or peptide molecule.

[0025] In addition, in the specification of the present invention, the agent for measuring the expression level of the gene may include at least one selected from the group consisting of a primer, a probe, and an antisense nucleotide that specifically binds to the gene. In the present invention, the primer is a fragment that recognizes a target gene sequence, and includes a pair of forward and reverse primers, but is preferably a pair of primers that provide analysis results with specificity and sensitivity. When the nucleic acid sequence of the primer is a sequence that does not match a non-target sequence present in the sample, so that the primer only amplifies the target gene sequence containing the complementary primer binding site and does not cause non-specific amplification, high specificity can be provided. In the present invention, the probe refers to a substance that can specifically bind to a target substance to be detected in a sample, and refers to a substance that can specifically confirm the presence of the target substance in the sample through the binding. The type of the probe is not limited to a substance commonly used in the art, but may preferably be PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA, or DNA, and most preferably PNA. More specifically, the probe includes a biomaterial derived from or similar to a living organism or manufactured in vitro, for example, an enzyme, a protein, an antibody, a microorganism, an animal or plant cell and organ, a nerve cell, DNA, and RNA. DNA includes cDNA, genomic DNA, and oligonucleotides, RNA includes genomic RNA, mRNA, and oligonucleotides, and examples of proteins include antibodies, antigens, enzymes, and peptides. In the present invention, the LNA (Locked nucleic acids) refers to a nucleic acid analog containing a 2'-O, 4'-C methylene bridge.LNA nucleosides contain the common nucleic acid bases of DNA and RNA and can form base pairs according to the Watson-Crick base pairing rules. However, due to the molecular 'locking' caused by the methylene bridge, LNAs cannot form an ideal shape in Watson-Crick bonds. When LNAs are included in DNA or RNA oligonucleotides, LNAs can pair more quickly with complementary nucleotide chains, thereby increasing the stability of the double helix. In the present invention, the antisense oligomer refers to an oligomer having a sequence of nucleotide bases and an intersubunit backbone that allows the antisense oligomer to hybridize with a target sequence in RNA through Watson-Crick base pairing, typically allowing the formation of an mRNA and RNA:oligomer heteroduplex within the target sequence. The oligomer may have exact sequence complementarity or approximate complementarity to the target sequence.

[0026] Since the biomarker protein according to the present invention or the information on the gene encoding the same is known, a person skilled in the art will be able to easily design a primer, probe or antisense nucleotide that specifically binds to the gene encoding the protein based on this.

[0027] In addition, in the specification of the present invention, a biological sample isolated from a cancer patient includes cancer tissue, or tissue containing cancer cells, cells, cell extracts, whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial It may be at least one selected from the group consisting of bronchial aspirate, synovial fluid, joint aspirate, organ secretions, and cerebrospinal fluid, and more preferably, it may include a tumor microenvironment.

[0028] In the present invention, the kit refers to a kit comprising the diagnostic composition of the present invention described above. According to the present invention, the kit may be an RT-PCR kit, a DNA chip kit, an ELISA kit, a protein chip kit, a rapid kit, or an MRM (Multiple reaction monitoring) kit. In addition, the kit of the present invention may further comprise one or more other component compositions, solutions, or devices suitable for an analysis method. For example, the kit of the present invention may further comprise essential elements required for performing a reverse transcription polymerase reaction. The reverse transcription polymerase reaction kit comprises a primer pair specific for a gene encoding a marker protein. The primers are nucleotides having a sequence specific for the nucleic acid sequence of the gene, and may have a length of about 7 bp to 50 bp, more preferably about 10 bp to 30 bp. In addition, it may include a primer specific for the nucleic acid sequence of a control gene. Other reverse transcription polymerase reaction kits may include test tubes or other suitable containers, reaction buffers (with varying pH and magnesium concentrations), deoxynucleotides (dNTPs), enzymes such as Taq polymerase and reverse transcriptase, DNase, RNase inhibitor DEPC-water, sterile water, etc. In addition, the kit of the present invention may include essential elements necessary for performing a DNA chip. The DNA chip kit may include a substrate to which cDNA or oligonucleotides corresponding to a gene or a fragment thereof are attached, and reagents, agents, enzymes, etc. for producing a fluorescently labeled probe. In addition, the substrate may include cDNA or oligonucleotides corresponding to a control gene or a fragment thereof. In addition, the kit of the present invention may include essential elements necessary for performing an ELISA. The ELISA kit includes an antibody specific for the protein.Antibodies are monoclonal, polyclonal, or recombinant antibodies that have high specificity and affinity for a marker protein and little cross-reactivity with other proteins. ELISA kits may also include antibodies specific for a control protein. ELISA kits may also include reagents capable of detecting bound antibodies, such as labeled secondary antibodies, chromophores, enzymes (e.g., conjugated to antibodies), and their substrates or other substances capable of binding to antibodies.

[0029] In the specification of the present invention, the pharmaceutical composition is used for the prevention or treatment of cancer, and here, prevention means suppressing the occurrence of a disease or disease in a subject who has not been diagnosed as having the disease or disease but is likely to have such a disease or disease, and treatment means all actions that improve symptoms caused by the target disease or benefit the subject by using the effective ingredient of the present invention, and means an attempt to obtain useful results or desirable results including clinical results. For the purpose of the invention, the pharmaceutical composition is for the prevention or treatment of cancer, particularly breast cancer or pancreatic cancer, AKR1C1 + Inflammatory fibroblasts (AKR1C1 + It may contain an inflammatory fibroblasts inhibitor, or a mesothelium-derived fibroblasts inhibitor as an active ingredient, and the AKR1C1 +The inflammatory fibroblast or mesothelial cell-derived fibroblast inhibitor may be a TIGIT (T-cell innunoreceptor with immunoglobulin and ITIM domain) inhibitor or an IL-6 (interleukin-6) inhibitor, wherein the TIGIT or IL-6 may be recognized as a target for the prevention or treatment of breast cancer or pancreatic cancer. In addition, the TIGIT inhibitor may be an expression inhibitor of the TIGIT protein or a gene encoding it, or the IL-6 inhibitor may be an expression inhibitor of the IL-6 protein or a gene encoding it.

[0030] In the present invention, the protein expression inhibitor may include an antibody specific for the target protein, or an antigen-binding fragment thereof. Inhibition of protein activity here means inhibiting the activity of the target protein with a protein-specific antibody or an antigen-binding fragment thereof. The antibody, as a term known in the art, refers to a specific protein molecule directed against an antigenic site. Such antibodies can be produced by cloning each gene into an expression vector according to a conventional method to obtain a protein encoded by the marker gene, and then producing the obtained protein by a conventional method. This also includes partial peptides that can be produced from the protein, and the partial peptides of the present invention include at least 7 amino acids, preferably 9 amino acids, and more preferably 12 or more amino acids. The form of the antibody of the present invention is not particularly limited, and polyclonal antibodies, monoclonal antibodies, or any antibody having antigen-binding properties, as well as a portion thereof, are included in the antibody of the present invention, including all immunoglobulin antibodies. Furthermore, the antibody of the present invention also includes specialized antibodies such as humanized antibodies. The antibody of the present invention includes a complete form having two full-length light chains and two full-length heavy chains, as well as functional fragments of the antibody molecule. A functional fragment of an antibody molecule means a fragment that possesses at least an antigen-binding function, such as Fab, F(ab'), F(ab') 2, and Fv.

[0031] Additionally, in the present invention, the gene expression inhibitor may be at least one selected from the group consisting of antisense oligonucleotides, siRNAs, shRNAs, and microRNAs specific to the target gene. The antisense oligonucleotides are short synthetic DNA strands (or DNA analogs) that are antisense (or complementary) to a specific DNA or RNA target, and are used to achieve gene-specific inhibition both in vivo and in vitro. Antisense oligonucleotides have been proposed to block the expression of proteins encoded by DNA or RNA targets by binding to the target and halting expression at the transcription, translation, or splicing stages. Antisense oligonucleotides have also been successfully utilized in cell culture and animal models of disease. Additional modifications of antisense oligonucleotides to make them more stable and resistant to degradation are known and understood by those skilled in the art. The antisense oligonucleotides used herein include double-stranded or single-stranded DNA, double-stranded or single-stranded RNA, DNA / RNA hybrids, DNA and RNA analogs, and oligonucleotides with base, sugar, or backbone modifications. The oligonucleotides are modified by methods known in the art to increase stability and resistance to nuclease degradation. These modifications include, but are not limited to, modifications of the oligonucleotide backbone, modifications of the sugar moiety, or modifications of the bases, all of which are known in the art. The siRNA (small interfering RNA) is a nucleic acid molecule capable of mediating RNA interference or gene silencing, and is used as an efficient gene knockdown method or gene therapy method because it can suppress the expression of a target gene.When siRNA molecules are used in the present invention, they may have a double-stranded structure in which the sense strand and the antisense strand are positioned opposite each other, or a single-stranded structure with self-complementary sense and antisense strands. siRNA is not limited to a double-stranded RNA portion in which RNA pairs with each other, and may include a portion that does not pair due to a mismatch (corresponding bases are not complementary), a bulge (lack of a corresponding base in one strand), etc. The siRNA terminal structure can be either a blunt end or a cohesive end, as long as it can suppress the expression of a target gene through the RNA interference (RNAi) effect. The cohesive end structure can be either a 3'-end overhang structure or a 5'-end overhang structure. The siRNA molecule is not limited thereto, but may have a total length of 15 to 30 bases, preferably 19 to 21 bases. The shRNA (short hairpin RNA) is a single-stranded RNA having a length of 45 to 70 nucleotides. After synthesizing oligo DNA that connects a 3-10 base linker between the sense strand of the target gene siRNA base sequence and the complementary antisense strand, cloning it into a plasmid vector or inserting the shRNA into a retrovirus such as a lentivirus or adenovirus and expressing it, an shRNA with a hairpin structure having a loop is created and converted into siRNA by dicer in the cell to exhibit an RNAi effect. The microRNA regulates various biological processes such as development, differentiation, proliferation, preservation, and apoptosis. MicroRNA generally regulates the expression of a gene encoding a target mRNA by destabilizing the target mRNA or interfering with its translation.Regulatory sequences useful for the expression construct / vector having the antisense oligonucleotide, siRNA, shRNA or microRNA (e.g., constitutive promoter, inducible promoter, tissue-specific promoter or a combination thereof) can also be appropriately selected from those known in the art, and the expression inhibitor of the gene can be any substance that inhibits the expression of the gene in addition to the antisense oligonucleotide, siRNA, shRNA or microRNA.

[0032] The pharmaceutical compositions of the present invention are not limited thereto, but may be formulated and used in the form of oral dosage forms such as powders, granules, capsules, tablets, and aqueous suspensions, as well as in the form of topical preparations, suppositories, and sterile injectable solutions, according to conventional methods. The pharmaceutical compositions of the present invention may include pharmaceutically acceptable carriers. Pharmaceutically acceptable carriers may include binders, lubricants, disintegrants, excipients, solubilizers, dispersants, stabilizers, suspending agents, coloring agents, and fragrances for oral administration, and may include buffers, preservatives, analgesics, solubilizers, isotonic agents, and stabilizers for injections. For topical administration, bases, excipients, lubricants, and preservatives for topical administration may be used. The formulations of the pharmaceutical compositions of the present invention may be prepared in various ways by mixing them with the pharmaceutically acceptable carriers described above. For example, when administered orally, it can be manufactured in the form of tablets, troches, capsules, elixirs, suspensions, syrups, wafers, etc., and when administered as an injection, it can be manufactured in the form of unit dose ampoules or multiple doses. In addition, it can be formulated as a solution, suspension, tablet, capsule, sustained-release preparation, etc. Meanwhile, examples of carriers, excipients, and diluents suitable for formulation include lactose, dextrose, sucrose, sorbitol, mannitol, xylitol, erythritol, malditol, starch, acacia gum, alginate, gelatin, calcium phosphate, calcium silicate, cellulose, methyl cellulose, microcrystalline cellulose, polyvinylpyrrolidone, water, methyl hydroxybenzoate, propyl hydroxybenzoate, talc, magnesium stearate, or mineral oil. Additionally, fillers, anti-coagulants, lubricants, humectants, fragrances, emulsifiers, preservatives, etc. may be included.Here, the term administration in this specification means directly administering a therapeutically effective amount of the composition of the present invention to a subject so that the same amount is formed in the body of the subject, and includes introducing the composition of the present invention to a patient by any appropriate method, and the route of administration of the composition of the present invention may be administered through any general route as long as it can reach the target tissue. Oral administration, intraperitoneal administration, intravenous administration, intramuscular administration, subcutaneous administration, intradermal administration, intranasal administration, intrapulmonary administration, rectal administration, intracavitary administration, intraperitoneal administration, and intrathecal administration may be performed, but is not limited thereto. In the present invention, the effective amount may be adjusted according to various factors including the type of disease, the severity of the disease, the type and content of the active ingredient and other ingredients contained in the composition, the type of formulation, and the patient's age, body weight, general health condition, sex, and diet, administration time, administration route, and secretion rate of the composition, treatment period, and concurrently used drugs. For adults, the therapeutic pharmaceutical composition can be administered into the body in an amount of 50 ml to 500 ml at a time, and in the case of a compound, it can be administered in a dose of 0.1 ng / kg to 10 mg / kg, and in the case of a monoclonal antibody, it can be administered in a dose of 0.1 ng / kg to 10 mg / kg. The dosing interval can be 1 to 12 times a day, and in the case of administering 12 times a day, it can be administered once every 2 hours. In addition, the pharmaceutical composition of the present invention can be administered alone or together with other treatments known in the art, such as chemotherapy, radiation, and surgery, for the treatment of the target cancer. In addition, the pharmaceutical composition of the present invention can be administered in combination with other treatments designed to enhance the immune response, such as adjuvants or cytokines (or nucleic acids encoding cytokines) known in the art. Other standard delivery methods, such as biolistic delivery or ex vivo treatment, can also be used.In ex vivo treatment, for example, antigen-presenting cells (APCs), dendritic cells, peripheral blood mononuclear cells, or bone marrow cells can be obtained from a patient or a suitable donor, activated in vitro with the pharmaceutical composition, and then administered to the patient. In addition, the term therapeutically effective amount in this case means the content of the composition containing a pharmacological ingredient in the composition sufficient to provide a therapeutic or preventive effect to a subject to whom the pharmaceutical composition of the present invention is to be administered, and includes a preventive effective amount.

[0033] According to the method of the present invention, AKR1C1 + Using inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts, cancer, especially breast cancer and pancreatic cancer, can be diagnosed more accurately and early treatment can be conducted based on this.

[0034] Figure 1 presents the results of characterizing genes that are repeatedly up- or down-regulated in tumors compared to normal tissues in the major cell types that make up the TME of various organs.

[0035] Figure 2 shows the results of gene ontology (GO) analysis in the major cell types that make up the TME of various organs.

[0036] Figure 3 shows the results of comparing the ligand expression levels in cancer cells themselves by cancer type.

[0037] Figure 4 shows the results of identifying several fibroblast subtypes exhibiting immune-related gene expression.

[0038] Figure 5 is AKR1C1 + We present the results of spatial transcriptomic analysis to investigate the co-localization patterns of inflammatory fibroblasts.

[0039] Figure 6 shows the results of deconvolution of the bulk transcriptome of samples treated with immune checkpoint inhibitors in a cohort of various cancer types.

[0040] Figure 7 shows the expression patterns of immune checkpoint inhibitor-related ligands in fibroblasts classified in detail from single-cell transcriptome data of breast and pancreatic cancer.

[0041] Figure 8 shows the expression patterns of immune checkpoint inhibitor-related ligands in independent patient data of breast cancer and pancreatic cancer.

[0042] Figure 9: AKR1C1 by breast cancer subtype + It shows the activity pattern of inflammatory fibroblasts or mesothelial cell-derived fibroblasts.

[0043] Figure 10 shows the expression pattern of PVR or IL-6 by breast cancer subtype.

[0044] Figure 11 AKR1C1 in breast cancer TME + Inflammatory fibroblasts, cytotoxic T cells, and CD8 + Indicates the relevance of T cells.

[0045] Figure 12 shows the expression pattern of PVR or TIGIT in mesothelial cell-derived fibroblasts of pancreatic cancer TME.

[0046] Figure 13: AKR1C1 in pancreatic cancer TME + The expression pattern of IL-6 in inflammatory fibroblasts or mesothelial cell-derived fibroblasts is shown.

[0047] Figure 14 AKR1C1 in pancreatic cancer TME + Inflammatory fibroblasts or mesothelial cell-derived fibroblasts and cytotoxic T cells and CD8 + Indicates the relevance of T cells.

[0048] The expression patterns of each ligand were examined in breast and pancreatic cancer cells, classified in detail from single-cell transcriptome data. As a result, PVR, which was underexpressed in breast cancer cells, was found to be significantly higher than AKR1C1. + It was observed that PVR was specifically and strongly expressed in inflammatory fibroblasts. In pancreatic cancer, PVR was highly expressed not only in cancer cells but also in mesothelial cell-derived fibroblasts.

[0049] To validate the results obtained from the single-cell transcriptome data at the protein level in independent patient data, transcriptome and proteome data for pancreatic cancer and breast cancer, respectively, were obtained from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the expression levels of each ligand were confirmed. As a result, it was confirmed that PVR, Nectin2, and Nectin4 were all expressed significantly higher than PD-L1 at the protein level in both breast and pancreatic cancers.

[0050] Meanwhile, when breast cancer was divided into subtypes according to the PAM50 classification system, AKR1C1 was found in Basal (TNBC) breast cancer. + We confirmed that inflammatory fibroblasts showed the strongest activity. Mesothelial cell-derived fibroblasts also showed somewhat stronger activity in basal (TNBC). In the single-cell transcriptome data above, AKR1C1 + Since PVR and IL-6 were strongly expressed in inflammatory fibroblasts, the expected pattern was confirmed in the validation data when PVR and IL-6 were highly expressed in basal (TNBC) breast cancer. In addition, AKR1C1 + The higher the activity of inflammatory fibroblasts, the more cytotoxic T cells and CD8 cells infiltrated into cancer tissue. + Lower T cell counts have also been observed in breast cancer.

[0051] In the case of pancreatic cancer, PVR was expressed in both mesothelial cell-derived fibroblasts and cancer cells in the single-cell transcriptome data. On the other hand, in the validation data, PVR was not specifically high in the patient group with strong activity of mesothelial cell-derived fibroblasts. This is presumably because the validation data is not expression data at the single-cell level and therefore appears mixed with the expression level in cancer cells. However, TIGIT, a receptor for PVR, was specifically expressed significantly higher in proportion to the high activity of mesothelial cell-derived fibroblasts. In the single-cell transcriptome data, in the case of IL-6, AKR1C1 + It was strongly expressed in inflammatory fibroblasts and at a significant level in mesothelial cell-derived fibroblasts, which was confirmed in the validation data. In addition, AKR1C1 was expressed in pancreatic cancer. + The higher the activity of inflammatory fibroblasts and mesothelial cell-derived fibroblasts, the more cytotoxic T cells and CD8 cells infiltrated into cancer tissue. + It was observed that the amount of T cells was low. In the case of IL-6 blockade, there is a study result that it can reduce the side effects that usually occur when performing immune checkpoint inhibitor treatment while maintaining the therapeutic effect (Cancer Cell 40:509 (2022)). Therefore, the above results indicate that AKR1C1 in pancreatic cancer + This may provide a clue that blocking IL-6 through a method of suppressing inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts may enhance the therapeutic effect of immune checkpoint inhibitors.

[0052] Hereinafter, the present invention will be described in detail with reference to the following examples. However, the following examples are merely illustrative of the present invention, and the content of the present invention is not limited to the following examples.

[0053] Example

[0054] [Implementation Method]

[0055] 1. Immunotherapy cohort data

[0056] The transcriptome data of the immunotherapy cohort used in the present invention are from Kim et al. (Nat. Genet. 55, 221-231 (2023))(n=335), Van Allen et al. (Science350, 207-211 (2015))(n=75), Gide et al. (Cancer Cell35, 238-255.e6 (2019))(n=73), Riaz et al. (Cell171, 934-949.e16 (2017))(n=46), Hugo et al. (Cellvol. 165 35-44 (2016))(n=25), Mariathasan et al. (Nature554, 544-548 (2018))(n=347), McDermott et al. (Nat. Med.24, 749-757 (2018))(n = 165), Miao et al. (Bioinformatics30, 2114-2120 (2014))(n = 33).

[0057] 2. Single-cell data inclusion criteria and data collection

[0058] For data selection, we first searched relevant 10x scRNA-seq datasets to homogenize and minimize batching issues caused by various chemicals. Data sets were searched and downloaded from PubMed, Google Scholar, Gene Expression Omnibus, Single Cell Portal (https: / singlecell.broadinstitute.org / single_cell), COVID-19 Cell Atlas (https: / www.covid19cellatlas.org / ), and Curated Cancer Cell Atlas (https: / www.weizmann.ac.il / sites / 3CA / ).

[0059] Studies generated from the 10x-genome reagent kit and included cancer, precancerous, benign tumor, and normal samples, and among the normal control samples, non-malignant tissues derived from cancer patients (annotated as adjacent normal) and tissues from healthy normal individuals (annotated as normal) were collected separately. Cells labeled (e.g., CD45 + Studies that included only body fluid samples (e.g., ascites, cerebrospinal fluid, or PBMCs), cell line cultures, mouse studies, and studies generated from nuclei-seq were excluded. Consequently,

[0060] We obtained single-cell transcriptome data for 1,070 cancer tissues and 493 normal tissues for more than 30 cancer types, and classified various cell types (cancer cells, immune cells, fibroblasts, etc.) in detail to confirm gene expression in each.

[0061] 3. Analysis of single-cell RNA sequencing data

[0062] The gene columns in each dataset were realigned to the GRCh38 human reference genome (official Cell Ranger reference, version 2020-A). Cells with UMI counts less than 2000 and 500 detected genes in each dataset were considered empty droplets and removed from the dataset. Cells with more than 7000 detected genes were also considered potential doublets and removed from the dataset. The cell-gene count matrix was loaded and analyzed using the Scanpy (v. 1.8.2) Python package, and clustering, annotation, and downstream analysis were performed using tools from the Scanpy package and some custom code. Scrublet was used for doublet detection. Additionally, to reduce the computational burden and accelerate downstream analysis, a geometric sketch was used to select cell subsets for each dataset that reflect transcriptional diversity and preserve rare cell types.

[0063] 4. Cell type annotation and batch editing

[0064] In this invention, we merged all tumor-normal scRNA-seq data and divided the dataset to reduce computational burden, visualize cells, and annotate them. We used BBKNN as a batch-effect correction algorithm to generate a connected graph structure, obtained UMAP at a global scale, and then annotated the data based on cell type-specific marker genes. We then examined and refined the key cell type annotations for each dataset.

[0065] 5. Copy number variation inference for malignant cell identification

[0066] Large-scale copy number variations (CNVs) in malignant cells were inferred using inferCNVpy (available at https: / / github.com / icbi-lab / infercnvpy) with the default window size and gencode v29 as the genomic location reference. In this study, infercnvpy.tl.infercnv was used to infer CNVs, and normal immune cells or fibroblasts were selected as reference normal cells depending on each cancer type. Cells were visualized in CNV UMAP (infercnvpy.tl.umap) based on dimensionality reduction (infercnvpy.tl.pca) and clustering based on CNV profiles (infercnvpy.tl.leiden), and CNV scores were calculated using infercnvpy.tl.cnv_score. Cells were considered malignant if they formed separate clusters, a known characteristic of malignant cells, or had a high CNV score compared to known normal cell types (normal epithelial cells, fibroblasts, or immune cells, depending on the cancer type).

[0067] 6. AND gating algorithm for differential expression of genes to identify characteristic features of genes.

[0068] In this study, we applied the AND gating algorithm to extract tumor-enriched or immunotherapy-advantageous gene signatures for each cell type in various cancers. Cells from specific organs were divided into subsets and differential expression analysis was performed to identify genes that were upregulated in the cell type of interest compared to other cell types (log2 fold change > 0), or highly expressed in tumor tissue (or immunotherapy responders) compared to normal tissue (or immunotherapy non-responders; log2 fold change > 0.5 and adjusted p-value < 0.05). Genes that met both criteria were retained to generate tumor-specific / immunotherapy-advantageous gene signatures. P-values ​​were calculated using a two-tailed t-test on the log-normalized gene matrix and adjusted using the Benjamini-Hochberg method (Python packages scipy.stats v. 1.10.0 and statsmodels.stats v. 0.13.5). After obtaining the gene signature derived from AND-gating for each cell type and organ, the gene signatures from multiple organs were integrated to identify characteristic gene signatures for each cell type.

[0069] 7. Biological annotation of characteristic gene signatures in the tumor-normal ecosystem.

[0070] In this study, Enrichr was used to annotate the biological functions of tumor-specific hallmark gene signatures across various cell types. GO terms from MsigDB Hallmark 2020, GO Biological Process 2023, and GO Molecular Function 2023 were used, and only terms with an adjusted p-value less than 0.05 were considered significant.

[0071] 8. NMF Preprocessing and Visualization

[0072] After cell type annotation, NMF was performed separately for each individual tissue, taking into account each cell type category and tissue origin, to generate cell states that contribute to the heterogeneity within each individual. Starting from a log-normalized centered expression matrix of all genes, negative values ​​were set to zero. The sklearn.decomposition.NMF method was applied with default parameters implemented in the scikit-learn Python package v1.0.2. Considering that NMF requires a K parameter that influences the results, we ran NMF with different values ​​(K=5, 6, 7, 8, and 9), generating 35 modules for each individual. Next, we clustered and visualized the NMF modules graphically. First, all modules derived from each cell type were max-normalized and merged to anndata objects. After highly variable gene selection, low-quality modules (modules with NMF weights less than 10–20 or greater than 150–170, depending on the cell type) were removed. Dimensionality reduction was performed using principal components and UMAP visualization, and small module clusters with fewer than 150 modules were removed. Leiden clustering was then performed to derive a list of the top 50 genes for each cluster. Clusters enriched in ribosomal protein genes or mitochondrial encoding genes, composed of NMF modules from a single study, and suspected of reflecting a soup effect based on high similarity to expression profiles in doublet cells or other cell types were removed.

[0073] 9. Automated removal of doublet or soup effect clusters.

[0074] To identify and remove NMF module clusters (cell states) exhibiting doublet cells or soup-effect effects, we developed an algorithm to automate the detection of doublet or soup-effect clusters. Specifically, we identified two organs (only one organ if the difference in the number of NMF modules between the two dominant organs was greater than twofold) that comprised the majority of NMF clusters of interest and subdivided these organs from a geometrically sketched anndata of the tumor-normal meta-atlas. We then scored each cell type category in the subdivided anndata using the sc.tl.score function in the Scanpy package, using the top 50 genes derived from these clusters. Clusters were defined as doublet or soup-effect clusters and subsequently removed if their scores were greater than 0.2 in other cell types (e.g., T cells) compared to the cell type of interest (e.g., mesenchymal cells). To prevent the removal of EMT states, clusters with higher mesenchymal scores were excluded from the epithelial cell state.

[0075] 10. Defining and annotating cell states

[0076] After visualizing the NMF modules and clustering the states, we identified the top 50 genes with weighted averages for each state. For genes that overlapped between cell states, we orthogonally assigned the gene to the state with the higher NMF-weighted average. Azimuth (https: / / azimuth.hubmapconsortium.org / ), The Human Protein Atlas (https: / / www.proteinatlas.org / ), and Enrichr71 were used as primary references for annotating cell states. Furthermore, to validate cell states, we compared gene signatures obtained from other studies using Pearson correlation.

[0077] 11. Building a reference component using cell states

[0078] To assess the correspondence between cell states and cell subtypes, cells were projected using cell-type-specific cell state profiles as reference components. For each cell state category, orthogonal genes with weighted averages were identified for each state (see Cell State Definition and Annotation). Subsequently, cell cycle and cell state-derived features derived from surrounding RNA or doublets were removed, and genes constituting the remaining states were selected as variable genes from the log-normalized scRNA-seq dataset. The reference components were constructed by performing matrix multiplication between the scRNA-seq anndata and the cell state-weighted average (RCA = anndata. X.dot(cell state-weighted average)). These reference components were used to replace the principal components, followed by BBKNN using the dataset as the batch key. Final cell type annotations were then created based on the cell type-specific marker genes.

[0079] 12. Cell status score distribution measurement and concordance analysis

[0080] To determine which cell states are enriched in the cells of each individual, we used the sc.tl.score_genes function to score each individual by their orthologous genes for cell states, resulting in a cell state score. For the eight cancer types that comprise the majority of the pan-cancer atlas (BRCA, CRC, HCC, HNSC, LC, OV, PAAD, and RCC), we calculated the average score for each individual and performed a Pearson correlation between cell states to measure concordance. For each concordance between cell states, we calculated adjacency using the WGCNA package (v. 1.71) and plotted a Circos plot using the circlize package (v. 0.4.15). The thickness of the line in the Circos plot corresponds to the adjacency between cell states.

[0081] 13. Ratio of observed and expected cell states

[0082] To quantify the tissue or organ preference of a cell state, we calculated the ratio of the observed to expected (Ro / e). To quantify tissue Ro / e, we created a 3 x 2 contingency table by counting the occurrence of tissue origin (i.e., normal, adjacent normal, and tumor) of NMF modules in the cell state of interest and other cell states. To simultaneously consider tissue and organ origin when calculating Ro / e, we first extracted the NMF modules of the cell states, determined the proportion of organ origin within these modules, and then filtered out those derived from organs that comprised less than 3% of the total. We then created a contingency table by counting the organ origin occurrence of NMF modules in the cell state of interest and other cell states for each tissue origin. The expected counts were derived using chi-square analysis, and Ro / e was calculated using Equation 1 below.

[0083] [Mathematical Formula 1]

[0084]

[0085] If Ro / e > 0 or Ro / e < 0, the cell state was considered to be enriched or depleted in the specific tissue / organ.

[0086] 14. Ligand-receptor interaction analysis

[0087] AKR1C1 + To understand the functional properties of inflammatory fibroblasts and mesothelial cell-derived fibroblasts, we used cell-cell interaction inference tools such as CellPhoneDB to identify AKR1C1 + We investigated potential cellular interactions between inflammatory fibroblasts and / or mesothelial-derived fibroblasts and other cell types, focusing on gene expression programs specific to these two fibroblasts. The strength of the interaction was calculated by multiplying the normalized expression values ​​of the ligand and receptor for each cell-cell pair.

[0088] 15. Survival analysis using bulk transcriptomes

[0089] To assess the prognostic value of cell status and hallmark features in each cancer type, survival analysis was performed using TCGA RNA-seq data. Upper-quartile normalized FPKM data were collected across 28 cancer types at UCSC Xena. TCGA clinical data (OS) were obtained from the TCGA Pan-Cancer clinical data resource. Enrichment of cell status and hallmark features was calculated for each TCGA primary cancer sample using the single-sample gene set enrichment analysis (ssGSEA) function implemented in the Corto package (v. 1.1.10). Patients were grouped into depleted and enriched groups based on the mean cell status score of the analyzed samples. Kaplan-Meier curves were plotted using the ggsurvplot function, and statistical significance was quantified using the log-rank test, with multiple testing corrected using the Benjamini-Hochberg method. To assess the prognostic significance of cell status in the relevant organ, the Ro / e filtering threshold for each cell type was determined using Equation 2 below.

[0090] [Equation 2]

[0091]

[0092] Cell states were then identified as rare within an organ if the tumor-derived Ro / e value did not exceed a filtering threshold. This prevents deconvolution of rare cell states that are irrelevant to survival analysis of a specific organ.

[0093] 16. Building a network using cell states

[0094] We constructed an undirected network using cell states to visualize co-occurrence patterns. Each tissue network was constructed using cell states identified from the corresponding tissue origin. After calculating the co-occurrence of cell states, we calculated an adjacency matrix using the WGCNA package (v. 1.71). The adjacency values ​​of cell state pairs with a p-value greater than 0.05 were set to 0 to minimize false positives. The adjacency matrix was then imported into gephi (v. 0.10.1) to construct a connected network. Community detection was performed with default parameters, nodes were colored by modularity class, and nodes were scaled by average weight. ForceAtlas2 was selected for graph embedding.

[0095] 17. Collection and processing of transcriptome data from the immunotherapy cohort.

[0096] We collected large-scale transcriptomes from cohorts (eight cohorts across four cancer types) that received immunotherapy. Transcriptome data for the cohorts were collected from Kim et al. (n = 335), Van Allen et al. (n = 75), Gide et al. (n = 73), Riaz et al. (n = 46), Hugo et al. (n = 25), Mariathasan et al. (n = 347), McDermott et al. (n = 165), and Miao et al. (n = 33). Raw FASTQ files were obtained from all cohorts and processed using an integrated pipeline. First, adapter sequences in FASTQ files were trimmed with Trimmomatic (v. 0.39), and SortMeRNA (v. 2.1b) was used to filter rRNA. The filtered reads were aligned to the hg38 reference genome with STAR aligner (v. 2.7.6a) in two-pass default mode with gencode annotation (v. 35). The aligned reads were aligned with samtools (v. 1.7) and then read counts were calculated with HTSeq (v. 0.12.4). Read counts were normalized to TPM values ​​to quantify gene expression.

[0097] 18. Immunotherapy Cohort Data Analysis

[0098] We performed ssGSEA using Gseapy (v. 0.10.8) to score cell status per sample, and used the normalized enrichment scores for analysis. For the pan-cancer analysis, only samples with both response and survival data were included. Patients with a durable clinical benefit (complete response, partial response, stable disease with PFS > 6 months or OS > 1 year) were classified as responders, while other patients were classified as non-responders. We then performed a meta-analysis to examine the association between clinical response to immunotherapy and cell status across cohorts. First, the scaled signature score was fitted to clinical response in each cohort using logistic regression. The calculated estimates and standard errors were pooled across cohorts using the metagen function in the meta package. Finally, a random-effects model was established to estimate the effect of cell status, accounting for heterogeneity between studies. The overall estimates, standard errors, and p-values ​​were obtained from the random-effects model.

[0099] 19. Precancerous spatial transcriptome analysis

[0100] Spatial transcriptomic analysis of 137 cancer datasets across 11 cancer types was performed using cell2location94 with default parameters to quantify the spatial distribution of cell types. For each spatial transcriptomic cancer type, we used the corresponding cancer scRNA-seq dataset from the Pan-Cancer Single-Cell Atlas as a reference. We then quantified spatial colocalization patterns using spot-wise Pearson correlations, along with estimated cell type abundances, similar to published data. A high positive Pearson correlation indicates similar spatial distributions between two cell types, whereas a negative Pearson correlation suggests distinct spatial distributions between the two cell types.

[0101] [Implementation Results]

[0102] 1. Identification of universal signature gene signatures in the tumor-normal ecosystem.

[0103] By implementing an AND gating algorithm, we systematically characterized the characteristic genes that are repeatedly up- or down-regulated in tumors compared to normal tissues in the major cell types that constitute the tumor microenvironment (TME) of various organs. CD8 + For T cells, co-stimulatory molecule (CD27) and immune checkpoint or exhaustion markers such as CXCL13, PDCD1, TIGIT, CTLA4, LAG3, and TNFRSF9 were generally increased in tumors, whereas IL7R, PTGER2, and PTGER4 were increased in normal tissues (Fig. 1). Of note, CD8 + T cells did not show upregulation of PDCD1 and LAG3, which may explain the current inapplicability of immune checkpoint inhibitors in pancreatic cancer (PAAD) unlike other cancer types. Similarly, tumor-associated NK cells were marked by ZNF683 and KRT81. Tumor-infiltrating Treg upregulate genes with regulatory functions such as RBPJ, CXCR3, and ZBED2, whereas normal tissue Treg upregulate CCR7 and CXCR5, indicating distinct mechanisms for immune cell recruitment and infiltration. Notably, tumor-infiltrating macrophages universally expressed immune checkpoint (IL4I1), M2 polarization-related (SPP1), and inflammatory genes (CCL7, ADAMDEC1, and SLAMF9), whereas tumor-infiltrating dendritic cells showed increased expression of CCL19 and LAMP3, which are associated with inflammatory and migratory functions (Figure 1). Gene ontology (GO) analysis revealed that tumor-infiltrating macrophages, dendritic cells, and CD8 +Genes upregulated in T cells were found to be enriched in relevant functions and pathways, including defense responses to viruses, responses to type II interferons, inflammatory responses, chemotaxis of lymphocytes, and cytokine-mediated signaling pathways. In non-immune cell types, cancer cells universally expressed GO terms associated with protein serine / threonine kinase activity (PRKCA, GSK3B, and CAMKK2), glycolysis (PLOD1, EGLN3, and P4HA1), mTORC1 signaling (SLC2A1, GMPS, and PDK1), and positive regulation of cell cycle processes (E2F7, E2F8, and KIF23) (Figure 2). Cancer-associated fibroblasts (CAFs) expressed well-known markers, including FAP, COL1A1, COL10A1, MMP11, and CTHRC1, as well as other genes, such as INHBA, SLC12A8, F2R, and COL12A1, in various organs, while tumor endothelial cells upregulated angiogenesis-related genes, including CHST1, FOLH1, and MMP15 (Figure 1). Both tumor-associated fibroblasts and endothelial cells were enriched for aspects related to extracellular matrix organization, cell migration regulation, cell-matrix adhesion, and filamin binding (Figure 2). Overall, these results illustrate the characteristic dysregulated nature of all TME components.

[0104] 2. Comparison of ligand expression levels by cancer type

[0105] The expression levels of ligands in cancer cells themselves were compared by cancer type. PD-L1, a ligand for PD-1, showed the highest expression in lung cancer cells, whereas PVR, Nectin2, and Nectin4, ligands for TIGIT, showed high expression in pancreatic and breast cancer cells (Fig. 3). Bladder cancer was the cancer type with the highest expression of Nectin4, but the small number of patients studied made it difficult to draw a definitive conclusion, and the expression of PVR and Nectin2 was low. On the other hand, pancreatic cancer showed high expression of all three ligands, and breast cancer showed the same high expression of Nectin4 and Nectin2 as pancreatic cancer, but the expression of PVR was low.

[0106] Recently, numerous studies have shown that cancer-associated fibroblasts (CAFs), particularly inflammatory CAFs (iCAFs), alter the immune environment of cancer tissues and inhibit T cell activation, thereby aiding the development and progression of cancer. Therefore, these results suggest that specific CAFs may provide a clue for anti-TIGIT immunotherapy in pancreatic cancer, where immune checkpoint inhibitors are currently ineffective.

[0107] 3. Characterization of fibroblasts by subtype

[0108] Fibroblasts are a highly heterogeneous population with diverse functions, including collagen deposition, angiogenesis, and cytokine secretion, and play a central role in shaping the TME. Fibroblasts promote inflammation and modulate the tissue microenvironment toward immunosuppression in the context of cancer, but the diversity of inflammatory fibroblasts has not been extensively explored in previous pan-cancer studies. Therefore, projecting a mesenchymal cell population to defined states, we identified several fibroblast subtypes exhibiting immune-related gene expression (Figure 4). Distinct patterns of interaction between fibroblast subtypes were identified. Accordingly, we hypothesized that different microenvironmental environments in each cancer type induce distinct phenotypes of specific fibroblasts. To investigate their colocalization patterns, we analyzed the spatial transcriptome. + Inflammatory fibroblasts include cancer cells, neutrophils, and CTSK + Significant colocalization with macrophages, DC1, and PRR-induced mo-DCs (Fig. 5).

[0109] 4. Determining cell status to predict immunotherapy according to cancer type

[0110] After identifying the diversity and dynamics of interferon-enriched and tumorigenic communities in tumors, adjacent normal tissues, and healthy normal tissues, we leveraged these cell states to deconvolve the bulk transcriptomes of samples treated with immune checkpoint inhibitors in a cohort of diverse cancers. The clinical benefits of checkpoint blockade were studied in the context of exhausted CD8 + T cells, mesenchymal-derived interferon, CXCL9 + Macrophage, CD160 + Intraepithelial lymphocytes, Tregs, DC1, ISG15 + Macrophage, XCL1 + / CD16 + NK cells, IFIT1 + Interferon signaling, Tfh, GCB, LAMP3 + DC, pDC, CD16 +Monocyte-derived macrophages, CCL19 + Fibroblasts and plasma cell precursor states were highlighted (Fig. 6). Fibroblasts, osteoblasts, mesothelial cell-derived fibroblasts, and CTSK + Macrophage cell status was associated with poor response to immunotherapy across the cohort, many of which belonged to the tumor-initiating community (Figure 6). These pro-tumorigenic component statuses (i.e., fibroblasts, osteoblasts, mesothelial cell-derived fibroblasts, and CTSK) + Given that macrophages negatively impact immunotherapy responses across various cancer types, alternative treatment strategies should be pursued for patients with a pro-tumor ecosystem.

[0111] 5. Transcriptome analysis of single fibroblasts in breast or pancreatic cancer.

[0112] The expression patterns of each ligand were examined in breast and pancreatic cancer cells in fibroblasts classified in detail from single-cell transcriptome data. As a result, PVR, which was underexpressed in breast cancer cells, was found to be significantly higher than AKR1C1. + It was observed that PVR was specifically and strongly expressed in inflammatory fibroblasts. In pancreatic cancer, PVR was highly expressed not only in cancer cells but also in mesothelial cell-derived fibroblasts (Fig. 7).

[0113] To validate the results obtained from the single-cell transcriptome data at the protein level in independent patient data, transcriptome and proteome data for pancreatic cancer and breast cancer from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were obtained to confirm the expression levels of each ligand. As a result, it was confirmed that PVR, Nectin2, and Nectin4 were all expressed significantly higher than PD-L1 at the protein level in both breast and pancreatic cancers (Fig. 8).

[0114] Meanwhile, when breast cancer was divided into subtypes according to the PAM50 classification system, AKR1C1 was found in Basal (TNBC) breast cancer. + We confirmed that inflammatory fibroblasts showed the strongest activity. Mesothelial cell-derived fibroblasts also showed somewhat stronger activity in basal (TNBC) cells (Fig. 9). In the single-cell transcriptome data above, AKR1C1 + Since PVR and IL-6 were strongly expressed in inflammatory fibroblasts, the expected pattern was confirmed in the validation data when PVR and IL-6 were highly expressed in basal (TNBC) breast cancer (Fig. 10). In addition, AKR1C1 + The higher the activity of inflammatory fibroblasts, the more cytotoxic T cells and CD8 cells infiltrated into cancer tissue. + Lower levels of T cells were also observed in breast cancer (Fig. 11).

[0115] In the case of pancreatic cancer, PVR was expressed in both mesothelial cell-derived fibroblasts and cancer cells in the single-cell transcriptome data. On the other hand, in the validation data, PVR was not specifically high in the patient group with strong activity of mesothelial cell-derived fibroblasts. This is presumably because the validation data is not expression data at the single-cell level and therefore appears mixed with the expression level in cancer cells. However, TIGIT, a receptor for PVR, was specifically expressed significantly higher in proportion to the high activity of mesothelial cell-derived fibroblasts (Fig. 12). In the single-cell transcriptome data, in the case of IL-6, AKR1C1 + It was strongly expressed in inflammatory fibroblasts and at a significant level in mesothelial cell-derived fibroblasts, which was confirmed in the validation data (Fig. 13). In addition, AKR1C1 was expressed in pancreatic cancer. + The higher the activity of inflammatory fibroblasts and mesothelial cell-derived fibroblasts, the more cytotoxic T cells and CD8 cells infiltrated into cancer tissue.+ A low amount of T cells was observed (Fig. 14). There are research results showing that IL-6 blockade can reduce the side effects that usually occur when performing immune checkpoint inhibitor treatment while maintaining the therapeutic effect (Cancer Cell 40:509 (2022)). Therefore, the above results suggest that AKR1C1 in pancreatic cancer + This may provide a clue that blocking IL-6 through a method of suppressing inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts may enhance the therapeutic effect of immune checkpoint inhibitors.

[0116] In addition, it has been revealed that IL-6 produced in the cancer microenvironment can interact with specific KRAS mutations and affect cancer progression (Cancer Cell 19:456 (2011)). Based on these results, pancreatic cancer and breast cancer can be selected as targets for anti-TIGIT and anti-IL-6 compared to other cancer types. In the case of breast cancer, a higher therapeutic effect can be expected, especially in the Basal (TNBC) subtype, and in the case of pancreatic cancer, AKR1C1 + A better therapeutic effect can be expected when the activity of inflammatory fibroblasts and mesothelial cell-derived fibroblasts is high.

[0117] While specific aspects of the present invention have been described in detail above, it should be apparent to those skilled in the art that these specific descriptions are merely preferred embodiments and do not limit the scope of the present invention. Therefore, the substantial scope of the present invention is defined by the appended claims and their equivalents.

[0118] The present invention relates to cancer-related fibroblasts, specifically AKR1C1 +The present invention relates to a method for diagnosing or treating cancer using inflammatory fibroblasts and / or mesothelial cell-derived fibroblasts. The method of the present invention is expected to be widely utilized in the medical field, as it enables more accurate diagnosis of cancer, particularly breast cancer and pancreatic cancer, and early treatment based on this diagnosis.

Claims

1. AKR1C1 in biological samples isolated from cancer patients + Inflammatory fibroblasts (AKR1C1 + A method for providing information regarding the diagnosis of cancer, comprising the step of determining the presence of inflammatory fibroblasts or mesothelium-derived fibroblasts.

2. In paragraph 1, A method wherein the cancer is breast cancer or pancreatic cancer.

3. In paragraph 2, A method wherein the above breast cancer is triple-negative breast cancer.

4. In paragraph 1, A method wherein the biological sample comprises a tumor microenvironment. 5.AKR1C1 + Inflammatory fibroblasts (AKR1C1 + A composition for diagnosing cancer, comprising a preparation capable of measuring inflammatory fibroblasts or a preparation capable of measuring mesothelium-derived fibroblasts.

6. In paragraph 5, AKR1C1 above + A composition wherein the agent capable of measuring inflammatory fibroblasts or mesothelial cell-derived fibroblasts is an agent capable of measuring TIGIT (T-cell innunoreceptor with immunoglobulin and ITIM domain) or an agent capable of measuring IL-6 (interleukin-6).

7. In paragraph 5, A composition wherein the cancer is breast cancer or pancreatic cancer.

8. In paragraph 7, A composition wherein the above breast cancer is triple-negative breast cancer.

9. In paragraph 5, AKR1C1 above + A composition wherein inflammatory fibroblasts or mesothelial cell-derived fibroblasts are isolated from a tumor microenvironment.

10. A cancer diagnosis kit comprising the cancer diagnosis composition of Article 5.

11. In paragraph 10, The above cancer is breast cancer or pancreatic cancer, kit. 12.AKR1C1 + Inflammatory fibroblasts (AKR1C1 + A pharmaceutical composition for preventing or treating cancer, comprising an inflammatory fibroblasts inhibitor or a mesothelium-derived fibroblasts inhibitor as an active ingredient.

13. In paragraph 12, AKR1C1 above + A pharmaceutical composition wherein the inflammatory fibroblast or mesothelial cell-derived fibroblast inhibitor is a TIGIT (T-cell innunoreceptor with immunoglobulin and ITIM domain) inhibitor or an IL-6 (interleukin-6) inhibitor.

14. In paragraph 13, The above TIGIT inhibitor is an inhibitor of the expression of the TIGIT protein or the gene encoding it, A pharmaceutical composition wherein the IL-6 inhibitor is an inhibitor of the expression of the IL-6 protein or a gene encoding the same.

15. In paragraph 12, A pharmaceutical composition wherein the cancer is breast cancer or pancreatic cancer.

Images