Cancer-associated fibroblast inhibitors

Abstract

Provided is a cancer-associated fibroblast inhibitor, particularly a cancer-associated fibroblast inhibitor capable of enhancing the anticancer effect of an anticancer agent. A cancer-associated fibroblast inhibitor containing a PDGFR inhibitor.

Description

Technical Field

[0001] This invention relates to cancer-associated fibroblast inhibitors, etc. Background Technology

[0002] Molecular targeted therapy and immunotherapy have had a significant impact on refractory cancers, but most tumors eventually acquire chemotherapy resistance. This resistance stems from intratumoral heterogeneity, allowing some cancer cells to survive and proliferate even after treatment. Notably, cancer cells acquire chemotherapy resistance by forming cellular networks with non-tumor cells such as cancer-associated fibroblasts (CAFs) (Non-Patent Literature 1). Therefore, to understand the biological basis of resistance, it is crucial to grasp the overall situation of intratumoral heterogeneity and the intercellular networks through which drug-resistant cancer cells survive.

[0003] Existing technical documents Patent documents Non-patent literature 1: Nat Rev Clin Oncol 18, 792-804. 10.1038 / s41571-021-00546-5. Summary of the Invention

[0004] The problem that the invention aims to solve The subject of this invention is to provide a cancer-associated fibroblast inhibitor, particularly a cancer-associated fibroblast inhibitor that can enhance the anticancer effect of anticancer agents.

[0005] Technical solutions for solving the problem The inventors of this invention conducted in-depth research on the aforementioned issues and discovered that PDGFR inhibitors can inhibit cancer-associated fibroblasts and enhance the anticancer effects of anticancer agents. Based on this insight, the inventors of this invention conducted further research and completed this invention. That is, this invention includes the following embodiments.

[0006] Item 1. A cancer-associated fibroblast inhibitor containing a PDGFR inhibitor.

[0007] Item 1A. A method for inhibiting cancer-associated fibroblasts, comprising administering a PDGFR inhibitor to a subject (particularly a subject for which inhibition of cancer-associated fibroblasts is desired).

[0008] Item 1B. PDGFR inhibitors for use in the inhibition of cancer-associated fibroblasts.

[0009] Item 1C. Use of PDGFR inhibitors in the manufacture of cancer-associated fibroblast inhibitors.

[0010] Item 1D. Use of PDGFR inhibitors in the suppression of cancer-associated fibroblasts.

[0011] Item 2. The cancer-associated fibroblast inhibitor as described in Item 1, wherein the PDGFR inhibitor is selected from at least one of PDGFR function inhibitors and PDGFR expression inhibitors.

[0012] Item 3. The cancer-associated fibroblast inhibitor as described in Item 1 or 2, wherein the PDGFR inhibitor is selected from at least one of low molecular weight compounds, PDGFR-targeted polynucleotides, expression cassettes of the polynucleotides, peptides, proteins, and antibodies.

[0013] Item 4. The cancer-associated fibroblast inhibitor as described in any one of items 1 to 3, wherein the PDGFR inhibitor is a low molecular weight compound and the low molecular weight compound is a kinase inhibitor.

[0014] Item 5. The cancer-associated fibroblast inhibitor as described in Item 4, wherein the kinase inhibitor is selected from at least one of lipitinib, ponatinib, erdatinib, dovirtinib, lenvatinib, furatinib, ENMD-2076, PP121, and cediranib.

[0015] Item 6. The cancer-associated fibroblast inhibitor as described in any one of items 1 to 5, wherein the cancer-associated fibroblasts are cells from ovarian cancer tissue, breast cancer tissue, or colorectal cancer tissue.

[0016] Item 7. The cancer-associated fibroblast inhibitor as described in any one of items 1 to 6, wherein the cancer-associated fibroblasts are cells in ovarian cancer tissue.

[0017] Item 8. The cancer-associated fibroblast inhibitor as described in any one of Items 1 to 7, for use in combination with an anticancer agent.

[0018] Item 9. The cancer-associated fibroblast inhibitor as described in Item 8, wherein the anticancer agent is a platinum preparation.

[0019] Item 10. An enhancer of the anticancer effect of an anticancer agent, comprising a PDGFR inhibitor.

[0020] Item 10A. A method for enhancing the anticancer effect of an anticancer agent, comprising administering a PDGFR inhibitor to a subject (particularly a subject for which inhibition of cancer-associated fibroblasts is required).

[0021] Item 10B. A PDGFR inhibitor for use in enhancing the anticancer effect of an anticancer agent.

[0022] Item 10C. Use of PDGFR inhibitors as enhancers of the anticancer effect in the manufacture of anticancer agents.

[0023] Item 10. The use of PDGFR inhibitors in enhancing the anticancer effects of anticancer agents.

[0024] Item 11. A cancer prevention or treatment agent containing a PDGFR inhibitor, wherein the cancer is selected from at least one of ovarian cancer, breast cancer, colorectal cancer, pancreatic cancer, urothelial carcinoma, prostate cancer, esophageal cancer, liver cancer, kidney cancer, endometrial cancer, gastric cancer, glioblastoma, lung cancer, and melanoma.

[0025] Item 11A. A method for the prevention or treatment of cancer, comprising the step of administering a PDGFR inhibitor to a subject (particularly a subject requiring cancer prevention or treatment, wherein the cancer is selected from at least one of ovarian cancer, breast cancer, colorectal cancer, pancreatic cancer, urothelial carcinoma, prostate cancer, esophageal cancer, liver cancer, kidney cancer, endometrial cancer, gastric cancer, glioblastoma, lung cancer, and melanoma), wherein the cancer is selected from at least one of ovarian cancer, breast cancer, colorectal cancer, pancreatic cancer, urothelial carcinoma, prostate cancer, esophageal cancer, liver cancer, kidney cancer, endometrial cancer, gastric cancer, glioblastoma, lung cancer, and melanoma.

[0026] Item 11B. A PDGFR inhibitor for use in the prevention or treatment of cancer, wherein the cancer is selected from at least one of ovarian cancer, breast cancer, colorectal cancer, pancreatic cancer, urothelial carcinoma, prostate cancer, esophageal cancer, liver cancer, kidney cancer, endometrial cancer, gastric cancer, glioblastoma, lung cancer, and melanoma.

[0027] Item 11C. Use of PDGFR inhibitors in the manufacture of cancer prevention or treatment agents, wherein the cancer is selected from at least one of ovarian cancer, breast cancer, colorectal cancer, pancreatic cancer, urothelial carcinoma, prostate cancer, esophageal cancer, liver cancer, kidney cancer, endometrial cancer, gastric cancer, glioblastoma, lung cancer, and melanoma.

[0028] Item 11D. PDGFR inhibitors for the prevention or treatment of cancer, wherein the cancer is selected from at least one of ovarian cancer, breast cancer, colorectal cancer, pancreatic cancer, urothelial carcinoma, prostate cancer, esophageal cancer, liver cancer, kidney cancer, endometrial cancer, gastric cancer, glioblastoma, lung cancer, and melanoma.

[0029] Item 12. The preventive or therapeutic agent as described in Item 11, for use in combination with an anticancer agent.

[0030] Invention Effects Using this invention, it is possible to provide a cancer-associated fibroblast inhibitor, particularly a cancer-associated fibroblast inhibitor that can enhance the anticancer effect of anticancer agents. Attached Figure Description

[0031] Figure 1This indicates that the chemoresistant OCCC cell microenvironment can be reproduced in vitro when co-cultured with CAF. (A) Experimental design of the in vitro co-culture system. Cancer spheroid cells and CAF were obtained from surgical specimens of HIF-1α positive OCCC. The constructed cancer cells and CAF were labeled with GFP / Luc2 and mCherry / hRluc, respectively, and cultured alone or in combination for chemoresistance testing, scRNA-seq, or drug screening. (B) Bright-field image (top) and fluorescence image (bottom) of labeled cells cultured for 7 days under organoid conditions. Scale bar: 100 μm. (C) 7-day survival rate of CAF cultured alone or co-cultured with cancer cells. The cultured cells proliferated in the absence or presence of a specified concentration of carboplatin, and the cell survival rate was evaluated by measuring hRLuc activity. The p-value was determined by Student's t-test. (D) Western blot analysis of cancer cells sorted by FACS after 3 days of culture under alone or co-culture conditions. (E) Representative images of HIF-1α and α-SMA immunostaining in cancer cells and CAF after 3 days of co-culture. Scale bar: 100 μm. (F) Proliferation of cancer cells after 7 days of culture alone or co-culture with CAF. Cells were proliferated in the absence or presence of a specified concentration of carboplatin, and cancer cell proliferation was evaluated by measuring Luc2 activity. (G) UMAP plot of scRNA-seq data from cancer cells and CAF after 3 days of culture alone and co-culture. (H) Violin plot of characteristic scores for cancer cell subsets (cancer #1-6) proliferating under the culture alone and co-culture conditions in G. (I) Violin plot of characteristic genes shown in CAF proliferating under the culture alone and co-culture conditions shown in G: ***p < 0.001.

[0032] Figure 2This study demonstrates how cancer-derived PDGF-activated CAF mediates chemotherapy resistance and HIF-1α activation in cancer cells. (A) Western blot analysis of GFP-labeled cancer cells and mCherry-labeled CAF using the specified antibody. (B) Western blot analysis of CAF proliferated for 3 days under single or co-culture conditions. Note that under co-culture conditions, PDGFRB levels may decrease via negative feedback regulation. (C) Western blot analysis of CAF treated with 40 nM PDGFB for 3 days. (D) Relative proliferation of CAF treated with different concentrations of PDGFB for 7 days. (E) Western blot analysis of CAF knocked out using Cas9 / CRIPSR. (F) Relative proliferation of CAF transformed with the specified sgRNA and treated with 20 nM PDGFB for 7 days. (G) Western blot analysis of controls and PDGFRB-deficient CAF sorted by mCherry after 3 days of incubation with cancer cells. (H) Survival rates of controls and PDGFRB-deficient CAF cells incubated with cancer cells for 7 days. (I) Proliferation of controls or PDGFRB-deficient CAF cells cultured for 7 days in the presence of a specified concentration of carboplatin. (J) Western blot analysis of cancer cells sorted by FACS using GFP after 3 days of incubation with controls or PDGFRB-deficient CAF cells. P-values ​​were determined by Student's t-test. Statistically significant differences were expressed as: **p < 0.01, ***p < 0.001.

[0033] Figure 3This demonstrates the inhibition of ovarian cancer cells (OCCCs) by lipitinib in combination with carboplatin. (A) Inhibition of ovarian cancer-derived CAFs cultured for 7 days in the presence of a specified TKI or carboplatin (1 μM). (B) Inhibition of CAFs by lipitinib. CAFs co-cultured with cancer cells were treated with specified concentrations of lipitinib and carboplatin for 7 days. (C) Synergistic inhibition of co-cultured cancer cell proliferation by lipitinib and carboplatin. Cancer cells co-cultured with CAFs were treated with specified concentrations of lipitinib and carboplatin for 7 days. (D) Fluorescence images of cancer cells and CAFs co-cultured for 7 days in the presence or absence of 100 μM carboplatin and / or 5 μM lipitinib. Scale bar: 100 μm. (E) Treatment of cancer cells proliferating under culture-alone conditions with specified concentrations of lipitinib and carboplatin for 7 days. (F) Tumor volume (mean ± standard error) was measured weekly in xenograft mice treated with a specified combination of carboplatin and / or lipitinib. α represents the number of days post-transplantation of cancer cells. (G) HIF-1α immunostaining of xenograft tumors (78 days post-transplantation). The right panel shows a magnified image. Scale bar: 500 μm (right panel), 100 μm (left panel). (H) Box plot of the proportion of HIF-1α-positive cancer cells in the tumor tissue shown in G. α represents mean ± SEM. P-values ​​were determined by Student's t-test. Statistical significance is expressed as: *p < 0.05, **p < 0.01, ***p < 0.001.

[0034] Figure 4 Indicates inhibition of breast cancer-derived CAFs (A) and colorectal cancer-derived CAFs (B) cultured for 7 days in the presence of a specified TKI or carboplatin (1 μM). Values ​​are expressed as mean ± SEM. P-values ​​were determined using Student's t-test. Statistical significance was indicated as *p < 0.05, **p < 0.01, ***p < 0.001. Detailed Implementation

[0035] In this specification, expressions such as “containing” and “comprising” include concepts such as “containing”, “comprising”, “substantially constituted by” and “consisting solely of”.

[0036] In one embodiment, the present invention relates to an agent containing a PDGFR inhibitor, a cancer-associated fibroblast inhibitor, an enhancer of the anticancer effect of an anticancer agent, or a preventive or therapeutic agent for ovarian cancer and / or breast cancer (in this specification, they are sometimes collectively referred to as "the formulations of the present invention"). This will be described below.

[0037] (1) Active ingredients (1-1) Suppression target The PDGFR gene encodes the platelet-derived growth factor receptor (PDGFR), a tyrosine kinase. The PDGFR (PDGFR protein, PDGFR mRNA) targeted for inhibition is the expression product of the PDGFR gene, specifically the PDGFR protein or PDGFR mRNA expressed by the target organism or its cells (particularly cancer-associated fibroblasts) to which the formulation of this invention is applied. Therefore, the PDGFR protein and PDGFR mRNA targeted for inhibition can be adjusted as needed, depending on the species of the target organism. There are no particular limitations on the species; examples include animals such as humans, monkeys, mice, rats, dogs, cats, rabbits, pigs, horses, cattle, sheep, goats, deer, and many other mammals.

[0038] Examples of PDGFRs include PDGFRα and PDGFRβ. The amino acid sequences of PDGFR proteins and the base sequences of PDGFR mRNAs from various biological species are well-known. Specifically, for example, the human PDGFRα gene is identified by NCBI gene ID 5156, and the human PDGFRβ gene is identified by NCBI gene ID 5159; the amino acid and base sequences of various biological species can be obtained or inferred from this information. Furthermore, the splice variants mentioned above may also be included as PDGFR proteins and PDGFR mRNAs.

[0039] The PDGFR protein targeted for inhibition only needs to possess its original properties, namely PDGF binding and tyrosine kinase activity, and can have amino acid mutations such as substitution, deletion, addition, and insertion. From the viewpoint of minimizing damage to activity, substitution is preferred, and conserved substitution is more preferred.

[0040] The PDGFR mRNA to be inhibited can be any protein translated from the mRNA that retains its original properties, namely PDGF binding and tyrosine kinase activity. It can contain base mutations such as substitution, deletion, addition, or insertion. Preferably, the mutation is one that does not involve amino acid substitution or involves a conserved substitution of amino acids in the protein translated from the mRNA.

[0041] Preferred examples of PDGFR proteins that are targets for inhibition include proteins that consist of an amino acid sequence having 85-100% identity with the wild-type PDGFR protein and possessing PDGF binding and tyrosine kinase activity. More preferably, the identity is 90% or more; further preferably, 95% or more; and even more preferably, 98% or more.

[0042] Preferred examples of PDGFR mRNAs that are targets for inhibition include base sequences consisting of a base sequence having 85-100% identity with the wild-type PDGFR mRNA and encoding a protein having PDGF binding and tyrosine kinase activity. More preferably, the identity is 90% or more; further preferably, 95% or more; and even more preferably, 98% or more.

[0043] The "identity" of an amino acid sequence refers to the degree of similarity between two or more comparable amino acid sequences relative to each other. Therefore, the higher the similarity between two amino acid sequences, the higher their identity or similarity. The level of amino acid sequence identity can be determined, for example, using the sequence analysis tool FASTA with default parameters, or using the BLAST algorithm developed by Karlin and Altschul (Karlin S, Altschul SF. "Methods for assessing the statistical significance of molecular sequence features by using generalscoringschemes" Proc Natl Acad Sci USA. 87:2264-2268 (1990), Karlin S, Altschul SF. "Applications and statistics for multiple high-scoring segments in molecular sequences." Proc Natl Acad Sci USA. 90:5873-7 (1993)). A program called BLASTX was developed based on this BLAST algorithm. The specific steps of these analytical methods are publicly known and can be found on the website of the National Center for Biotechnology Information (NCBI) (http: / / www.ncbi.nlm.nih.gov / ). Furthermore, the "identity" of base sequences is defined as described above.

[0044] In this specification, "conservative substitution" refers to the substitution of an amino acid residue by an amino acid residue having a similar side chain. For example, the substitution of amino acid residues with basic side chains, such as lysine, arginine, and histidine, is considered a conserved substitution. Furthermore, the substitution of the following amino acid residues is also considered a conserved substitution: amino acid residues with acidic side chains, such as aspartic acid and glutamic acid; amino acid residues with non-electrolyte polar side chains, such as glycine, asparagine, glutamine, serine, threonine, tyrosine, and cysteine; amino acid residues with non-polar side chains, such as alanine, valine, leucine, isoleucine, proline, phenylalanine, methionine, and tryptophan; amino acid residues with β-branched side chains, such as threonine, valine, and isoleucine; and amino acid residues with aromatic side chains, such as tyrosine, phenylalanine, tryptophan, and histidine.

[0045] (1-2) Inhibitors PDGFR inhibitors are any components capable of inhibiting the function and / or expression of PDGFR, without particular limitations. Preferred PDGFR inhibitors include low-molecular-weight compounds, polynucleotides targeting PDGFR, expression cassettes of such polynucleotides, peptides, proteins, and antibodies. PDGFR inhibitors can be a single type or a combination of two or more.

[0046] (1-2-1) PDGFR function inhibitor PDGFR function inhibitors are not particularly limited as long as they can inhibit the function of PDGFR protein and / or mRNA expressed by the target organism or its cells (especially cancer-associated fibroblasts) to which the formulation of the present invention is applied. PDGFR function inhibitors can be used alone or in combination of two or more.

[0047] As a PDGFR function inhibitor, there are no particular restrictions as long as it can reduce tyrosine kinase activity and / or inhibit the binding of its ligand (PDGF). Specifically, tyrosine kinase inhibitors and antagonists can be listed.

[0048] PDGFR function inhibitors include not only substances that specifically act on PDGFR, but also substances that are specific to tyrosine kinases other than PDGFR (such as KIT, ABL, VEGFR, SRC, FGFR, FLT, and other tyrosine kinases homologous to PDGFR) but also act on PDGFR. Furthermore, PDGFR function inhibitors also include substances that act on multiple tyrosine kinases containing PDGFR (such as the aforementioned tyrosine kinases homologous to PDGFR).

[0049] Examples of PDGFR inhibitors include low molecular weight compounds (e.g., molecular weights below 1000, 800, 700, or 600; or molecular weights above 100, 150, or 200).

[0050] Low-molecular-weight compounds with PDGFR inhibitory activity are available in various commercial products and have been extensively reported in numerous publications. Examples of such low-molecular-weight tyrosine kinase inhibitors include ribretinib, ponatinib, erdafitinib, dovitinib, lenvatinib, foretinib, ENMD-2076, PP121, and cediranib. Among these, ribretinib is particularly preferred.

[0051] Other PDGFR function inhibitors include, for example, PDGFR antibodies. PDGFR antibodies are preferably antibodies that bind to the PDGF-binding region of PDGFR. The binding site can be determined based on known information and / or inferred based on known information (e.g., using docking models).

[0052] Antibodies include a subset of the aforementioned antibodies that have antigen-binding properties, such as polyclonal antibodies, monoclonal antibodies, chimeric antibodies, single-chain antibodies, or Fab fragments or fragments generated from Fab expression libraries. Antibodies that have antigen-binding properties against polypeptides consisting of at least 8 consecutive amino acids, typically 15 amino acids, more preferably 20 amino acids, in the amino acid sequence of PDGFR are also included in the antibodies of the present invention. These antibodies are readily available; for example, known as anti-PDGFR antibodies are ab67017 manufactured by abcam, STJ117738 manufactured by St Johns Laboratory, and LS-C766558-60 manufactured by LifeSpan Biosciences.

[0053] In addition to the above, any molecule that binds to PDGFR (preferably with specific binding) (e.g., peptides, proteins, artificial antibodies, aptamers, etc.) can be used as a PDGFR function inhibitor. Furthermore, when using antibodies or other proteins or peptides as PDGFR function inhibitors, their expression cassettes can be used instead of these molecules.

[0054] (1-2-2) PDGFR expression inhibitors PDGFR expression inhibitors are not particularly limited as long as they can inhibit the expression of PDGFR protein and / or PDGFR mRNA in the target organism or its cells (especially cancer-associated fibroblasts) to which the formulation of the present invention is applied. PDGFR expression inhibitors can be used alone or in combination of two or more.

[0055] Examples of PDGFR expression inhibitors include PDGFR-specific small interfering RNA (siRNA), PDGFR-specific microRNA (miRNA), PDGFR-specific antisense nucleic acid, their expression cassettes; PDGFR-specific ribozymes; and PDGFR gene editing agents using the CRISPR / Cas system.

[0056] In addition, expression inhibition refers to suppressing the expression levels of PDGFR protein, PDGFR mRNA, etc., to below 1 / 2, 1 / 3, 1 / 5, 1 / 10, 1 / 20, 1 / 30, 1 / 50, 1 / 100, 1 / 200, 1 / 300, 1 / 500, 1 / 1000, or 1 / 10000, and also includes reducing their expression levels to 0.

[0057] (1-2-2-1) siRNA, miRNA, antisense nucleic acid PDGFR-specific siRNAs are any double-stranded RNA molecules that specifically inhibit the expression of genes encoding PDGFR, and there are no particular limitations. In one embodiment, the siRNA is preferably 18, 19, 20, or 21 bases or longer. Alternatively, the siRNA is preferably 25, 24, 23, or 22 bases or shorter. It is assumed that the upper and lower limits of the siRNA length described herein can be combined arbitrarily.

[0058] There are no particular restrictions on the structure of siRNA. siRNA can be shRNA (small hairpin RNA), (Small hairpin RNA). siRNA can have additional bases at the 5' or 3' end. siRNA can have an overhang at the 3' end; specifically, for example, sequences with added dTdT (dT stands for deoxythymidine) can be listed.

[0059] The sequences of siRNA and / or shRNA can be retrieved using free search software available on various websites. Examples of such websites include: Ambion's siRNA Target Finder (http: / / www.ambion.com / jp / techlib / misc / siRNA_finder.html), pSilencer (registered trademark) expression vector insertion tool (http: / / www.ambion.com / jp / techlib / misc / psilencer_converter.html), and RNAi Codex's GeneSeer (http: / / codex.cshl.edu / scripts / newsearchhairpin.cgi).

[0060] PDGFR-specific miRNAs are arbitrary, as long as they can inhibit the translation of the gene encoding PDGFR. For example, instead of cleaving the target mRNA like siRNAs, the miRNA can pair with the 3' untranslated region (UTR) of the target to inhibit its translation. The miRNA can be any type of pri-miRNA, primary miRNA, pre-miRNA, or mature miRNA. There is no particular limitation on the length of the miRNA; pri-miRNAs are typically hundreds to thousands of bases long, pre-miRNAs are typically 50 to 80 bases long, and mature miRNAs are typically 18 to 30 bases long. In one embodiment, the PDGFR-specific miRNA is preferably a pre-miRNA or a mature miRNA, more preferably a mature miRNA. Such PDGFR-specific miRNAs can be synthesized using known methods or purchased from companies that supply synthetic RNA.

[0061] PDGFR-specific antisense nucleic acids (ANIs) are nucleic acids containing a base sequence complementary to or substantially complementary to the mRNA encoding the PDGFR gene, or a portion thereof. These ANIs bind to the mRNA through the formation of a specific and stable double strand, thereby inhibiting PDGFR protein synthesis. Antisense nucleic acids can be DNA, RNA, or any DNA / RNA chimera. When the antisense nucleic acid is DNA, the RNA:DNA hybrid formed by the target RNA and antisense DNA is recognized by endogenous ribonuclease H (RNase H), causing selective degradation of the target RNA. Therefore, when targeting antisense DNA that is degraded by RNase H, the target sequence can be not only a sequence in the mRNA but also a sequence in the intron region of the initial translation product of the PDGFR gene. Intron sequences can be determined by comparing the homology of the genomic sequence with the cDNA sequence of the PDGFR gene using BLAST, FASTA, or other homology searches. The target region of a PDGFR-specific antisense nucleic acid is not limited in length, as long as it can inhibit the translation of the PDGFR protein by hybridizing with the antisense nucleic acid. The PDGFR-specific antisense nucleic acid can be either the full sequence or a partial sequence of the mRNA encoding PDGFR. Considering ease of synthesis, antigenicity, and intracellular migration, oligonucleotides consisting of approximately 10 to 40 bases, particularly approximately 15 to 30 bases, are preferred, but not limited to these. More specifically, the 5' hairpin loop, 5' untranslated region, translation start codon, protein-coding region, ORF translation stop codon, 3' untranslated region, 3' palindromic region, or 3' hairpin loop of the PDGFR gene can be selected as preferred target regions for the antisense nucleic acid, but are not limited to these.

[0062] PDGFR-specific siRNAs, PDGFR-specific miRNAs, and PDGFR-specific antisense nucleic acids can be prepared by determining the target sequence of mRNA or initial transcript based on the cDNA sequence or genomic DNA sequence of the PDGFR gene, and synthesizing complementary sequences using commercially available automated DNA / RNA synthesizers. Alternatively, antisense nucleic acids with various modifications can also be chemically synthesized using any known method.

[0063] Regarding expression cassettes for PDGFR-specific siRNA, PDGFR-specific miRNA, or PDGFR-specific antisense nucleic acid, any polynucleotide capable of inserting PDGFR-specific siRNA, PDGFR-specific miRNA, or PDGFR-specific antisense nucleic acid into an expressible state is acceptable; there are no particular limitations. Typically, the expression cassette is a polynucleotide containing a promoter sequence and the coding sequence of the PDGFR-specific siRNA, PDGFR-specific miRNA, or PDGFR-specific antisense nucleic acid (and, if necessary, a transcription termination signal sequence), and may contain other sequences as needed.

[0064] In this specification, "nucleic acid" and "polynucleotide" are not particularly limited and include any natural or artificial form. Specifically, in addition to DNA, RNA, etc., products that have undergone known chemical modifications can also be included, as shown below. To prevent degradation by hydrolytic enzymes such as nucleases, the phosphate residues (phosphate groups) of each nucleotide can be replaced with chemically modified phosphate residues such as thiosulfate (PS), methylphosphonate, and dithiophosphate. In addition, the hydroxyl group at the 2-position of the sugar (ribose) of each ribonucleotide can be replaced with -OR (R, for example, represents CH3(2´-O-Me), CH2CH2OCH3(2´-O-MOE), CH2CH2NHC(NH)NH2, CH2CONHCH3, CH2CH2CN, etc.). Furthermore, the base moiety (pyrimidine, purine) can be chemically modified, for example, by introducing a methyl or cationic functional group at the 5-position of the pyrimidine base, or by replacing the carbonyl group at the 2-position with a thiocarbonyl group. In addition, products obtained by modifying the phosphate or hydroxyl moiety, for example, with biotin, amino, lower alkylamine, acetyl, etc., can be listed, but are not limited to these. Furthermore, products such as LNA (BNA) can be used, where the sugar portion is fixed in an N-form conformation by crosslinking the 2' oxygen and 4' carbon of the sugar portion of the nucleotide.

[0065] (1-2-2-2) Gene editing agents There are no particular restrictions on PDGFR gene editing agents, as long as they can inhibit PDGFR gene expression through a target sequence-specific nuclease system (such as the CRISPR / Cas system). Inhibition of PDGFR gene expression can be achieved, for example, by disrupting the PDGFR gene or by altering the PDGFR gene promoter to inhibit its activity.

[0066] As a PDGFR gene editing agent, typically, when using the CRISPR / Cas system, a vector containing a guide RNA expression cassette targeting the PDGFR gene or its promoter and a Cas protein expression cassette (PDGFR gene editing vector) can be used, but it is not limited thereto. In addition to the typical example, a combination of a vector containing a guide RNA targeting the PDGFR gene or its promoter and / or its expression cassette and a vector containing a Cas protein and / or its expression cassette can also be used as a PDGFR gene editing agent.

[0067] Guide RNA can be any RNA that can be used in the CRISPR / Cas system, without any particular restrictions. For example, various guide RNAs that can bind to target sites of genomic DNA (such as the PDGFR gene, its promoter, etc.) and bind to the Cas protein to guide the Cas protein to the target site of genomic DNA can be used.

[0068] In this specification, the target site refers to a region on the genomic DNA consisting of a PAM (Proto-spacer Adjacent Motif) sequence and a sequence of approximately 17–30 bases (preferably 18–25 bases, more preferably 19–22 bases, and particularly preferably 20 bases) adjacent to it on its 5′ side, and its complementary DNA strand (non-target strand).

[0069] Guide RNAs possess sequences (sometimes called crRNA (CRISPRRNA) sequences) that participate in binding to target sites on genomic DNA. These crRNA sequences bind complementary (preferably complementary and specific) sequences to PAM sequences other than the non-target strand, enabling the guide RNA to bind to the target site on the genomic DNA. Additionally, guide RNAs possess sequences (sometimes called tracrRNA (trans-activating crRNA) sequences) that participate in binding to Cas proteins. These tracrRNA sequences guide the Cas proteins to their target sites on the genomic DNA by binding to them.

[0070] There are no particular restrictions on the tracrRNA sequence. Typically, a tracrRNA sequence is an RNA of about 50 to 100 bases in length that can form multiple (usually 3) stem-loops, and its sequence varies depending on the type of Cas protein used. Various known sequences can be used as tracrRNA sequences depending on the type of Cas protein used.

[0071] Guide RNA typically contains the crRNA and tracrRNA sequences mentioned above. Guide RNA can be a single-stranded RNA (sgRNA) containing both crRNA and tracrRNA sequences, or it can be an RNA complex formed by the complementary binding of RNA containing crRNA and RNA containing tracrRNA sequences.

[0072] There are no particular restrictions on Cas proteins as long as they are used by the CRISPR / Cas system. For example, any protein that can bind to a target site on genomic DNA in a complexed state with guide RNA and cleave that target site is acceptable. Known Cas proteins are derived from various organisms, such as Cas9, with endogenous Cas9 proteins from the genus *Streptococcus* being more preferred. Information on the amino acid sequences and coding sequences of various Cas proteins can be easily obtained from various databases such as NCBI.

[0073] PDGFR gene-editing agents can be easily produced using well-known genetic engineering procedures. For example, they can be produced using techniques such as PCR, restriction endonuclease digestion, DNA ligation, in vitro transcription-translation, and recombinant protein production.

[0074] (2) Uses As illustrated in the examples described later, PDGFR expression inhibitors exhibit inhibitory effects on cancer-associated fibroblasts (proliferation inhibition and activation inhibition). Therefore, PDGFR expression inhibitors can be utilized as an effective component of cancer-associated fibroblast inhibitors.

[0075] There are no particular limitations on the cancerous tissue containing cancer-associated fibroblasts, and examples include ovarian cancer, breast cancer, uterine cancer, colorectal cancer, pancreatic cancer, urothelial carcinoma, prostate cancer, esophageal cancer, liver cancer, kidney cancer, endometrial cancer, gastric cancer, glioblastoma, lung cancer, melanoma, leukemia, and skin cancer. Among these, ovarian cancer, breast cancer, colorectal cancer, pancreatic cancer, urothelial carcinoma, prostate cancer, esophageal cancer, liver cancer, kidney cancer, endometrial cancer, gastric cancer, glioblastoma, lung cancer, and melanoma are particularly preferred, ovarian cancer, breast cancer, and colorectal cancer are even more preferred, ovarian cancer and breast cancer are even more preferred, and ovarian cancer is especially preferred.

[0076] There are no particular limitations on the definition of ovarian cancer. Examples include epidermal-epithelial-stromal malignant tumors (e.g., serous (cystic) adenocarcinoma, mucinous (cystic) adenocarcinoma, membranous adenocarcinoma, clear cell adenocarcinoma, adenocarcinoma fibroma (all of the above types), adenosarcoma, mixed mesodermal tumor, [Müller's duct mixed tumor] [carcinosarcoma], malignant Brenner tumor, transitional cell carcinoma, undifferentiated carcinoma, etc.), sex cord-stromal tumors (e.g., fibrosarcoma, Sertoli-stromal cell tumor (poorly differentiated type), etc.), germ cell tumors (e.g., dysgerminoma, yolk sac tumor [endodermal sinus tumor], embryonal carcinoma (fetal carcinoma), polyembryoma, choriocarcinoma, mature cystic teratoma with malignant transformation, immature teratoma (G3), etc.), carcinomas, sarcomas, malignant lymphomas (primary), and secondary [metastatic] tumors. Among these, adenocarcinoma is particularly preferred, with clear cell carcinoma being the most preferred.

[0077] The type of cancer targeted is chemotherapy-resistant cancer. The definition of chemotherapy-resistant cancer varies depending on the type of cancer. However, in one context, it is defined as cancer that has not responded to treatment with first-line anticancer agents (e.g., platinum-based agents in the case of ovarian cancer) (no confirmed reduction in cancer size), or where a temporary reduction in cancer size is observed followed by early recurrence of cancer growth. Additionally, the proportion of HIF-1α-positive cells in the cancerous tissue is used as an indicator; for example, a proportion exceeding 10% is considered chemotherapy-resistant cancer.

[0078] From the viewpoint of inhibiting chemotherapy resistance in cancer, myofibroblastic cancer-associated fibroblasts are particularly preferred as cancer-associated fibroblasts. Cancer-associated fibroblasts can be characterized by the expression or high expression of αSMA and / or collagen I. Furthermore, the aforementioned myofibroblastic cancer-associated fibroblasts can be characterized by the expression or high expression of FAP, TPM1, THBS2 (especially FAP-α), etc. The cancer targeted can, in one embodiment, be a cancer containing cancer-associated fibroblasts / myofibroblastic cancer-associated fibroblasts characterized by the expression of the aforementioned markers.

[0079] Chemotherapy resistance is caused by cancer-associated fibroblasts (CAFS). Therefore, by inhibiting CAFS using PDGFR expression inhibitors, the anticancer effect of anticancer agents against chemotherapy-resistant cancers can be enhanced. From this perspective, PDGFR expression inhibitors can be utilized as effective components to enhance the anticancer effect of anticancer agents. Furthermore, since chemotherapy resistance is caused by CAFS, inhibiting CAFS using PDGFR expression inhibitors can also suppress the development of chemotherapy resistance in cancer. Based on these considerations, PDGFR expression inhibitors are suitable for combined administration with anticancer agents.

[0080] The active ingredients of this invention can be used as pharmaceuticals, reagents, food compositions, oral compositions, health enhancers, nutritional supplements (health products, etc.), and can also be used in combination with anticancer agents as cancer-modifying compositions (e.g., pharmaceuticals, reagents, food compositions, oral compositions, health enhancers, nutritional supplements (health products, etc.)). The active ingredients of this invention can be used directly or in combination with commonly used ingredients to formulate various compositions for application to animals, humans, and various cells (e.g., drug administration, ingestion, inoculation, treatment, etc.).

[0081] There are no particular restrictions on the target animals for application. Among mammals, examples include humans, monkeys, mice, rats, dogs, cats, rabbits, pigs, horses, cattle, sheep, goats, and deer.

[0082] When the active ingredient of the present invention is used in combination with an anticancer agent, the combination includes not only simultaneous administration, but also administration at intervals (e.g., several minutes to several days apart (1 minute to 10 days, etc.)).

[0083] When the active ingredient of the present invention is used as a cancer prevention or treatment agent, the prevention or treatment agent may be, for example, a formulation for administration in combination with an anticancer agent. In this case, the prevention or treatment agent may contain an anticancer agent, and in this case, the active ingredient of the present invention and the anticancer agent may be contained in the same container, or the active ingredient of the present invention and the anticancer agent may be contained in their respective containers.

[0084] Examples of anticancer agents include platinum preparations, metabolic antagonists, alkylating agents, microtubule inhibitors, antibiotic anticancer agents, topoisomerase inhibitors, molecularly targeted drugs, hormonal drugs, and biological agents, with platinum preparations being particularly preferred.

[0085] Examples of platinum-based preparations include cisplatin, carboplatin, nedaplatin, oxaliplatin, ceterplatin, miplatin, lobaplatin, spiroplatin, tetraplatin, omaliplatin, and isopropylplatin.

[0086] Examples of metabolic antagonists include enoxabin, carmoflu, capecitabine, tegafur, tegafur-uracil, tegafur-gemcimethine-oteracil potassium, gemcitabine, cytarabine, cytarabine phosphate octadecyl ester, nerabine, fluorouracil, fludarabine, pemetrexed, pentostatin, methotrexate, cladribine, docefluuridine, hydroxyurea, mercaptopurine, etc.

[0087] Examples of alkylating agents include cyclophosphamide, ifosfamide, nitrosourea, dacarbazine, temozolomide, nimustine, busulfan, melphalan, procarbazine, and ramustine.

[0088] As microtubule inhibitors, examples include alkaloid anticancer agents such as vincristine, and taxane anticancer agents such as docetaxel and paclitaxel.

[0089] As antibiotic anticancer agents, examples include mitomycin C, doxorubicin, epirubicin, daunorubicin, bleomycin, actinomycin D, arubicin, idarubicin, pirarubicin, pepromycin, mitoxantrone, amorubicin, and fentostatin ester.

[0090] Examples of topoisomerase inhibitors include CPT-11, irinotecan, and notecan, which inhibit topoisomerase I; and etoposide and sobuzosen, which inhibit topoisomerase II.

[0091] Examples of hormone-containing drugs include dexamethasone, finasteride, tamoxifen, anastrozole, exemestane, ethinylestradiol, chlormadinone, goserelin, bicalutamide, flutamide, prednisolone, leuprolide, letrozole, estradiol, toremifene, phosmet, mitotane, medroxyprogesterone, and metronidazole.

[0092] Examples of biological agents include, for example, interferon α, β and γ, interleukin-2, ubenimex, dried BCG, etc.

[0093] The formulation of this invention may contain other ingredients as needed. These other ingredients are, for example, any ingredients that can be incorporated into pharmaceuticals, food compositions, oral compositions, health enhancers, nutritional supplements (health products, etc.), etc., without particular limitation. Examples include base agents, carriers, solvents, dispersants, emulsifiers, buffers, stabilizers, excipients, binders, disintegrants, lubricants, thickeners, humectants, colorants, fragrances, chelating agents, etc. Pharmaceutically acceptable carriers and additives include, for example, excipients such as sucrose and starch; binders such as cellulose and methylcellulose; disintegrants such as starch and carboxymethylcellulose; lubricants such as magnesium stearate and fumed silica; fragrances such as citric acid and menthol; preservatives such as sodium benzoate and sodium sulfite; stabilizers such as citric acid and sodium citrate; suspending agents such as methylcellulose and polyvinylpyrrolidone; dispersants such as surfactants; diluents such as water and physiological saline; base waxes, etc., but are not limited to these. The form of the formulation of the present invention is not particularly limited, and can be formed into the form commonly used in each application according to the intended use.

[0094] As a form, when used for pharmaceutical purposes, it can adopt any dosage form, such as oral preparations such as tablets (including intraorally disintegrating tablets, chewable tablets, effervescent tablets, lozenges, gel drops, etc.), pills, granules, fine granules, powders, hard capsules, soft capsules, dry syrups, liquids (including beverages, suspensions, syrups), gels, etc.; and non-oral preparations such as injectable preparations (e.g., infusion preparations (such as intravenous drip preparations, intravenous injections, intramuscular injections, subcutaneous injections, intradermal injections), topical preparations (e.g., ointments, plasters, lotions), suppositories, inhalers, eye drops, eye ointments, nasal drops, ear drops, liposome preparations, etc.

[0095] As for the administration route of the formulation of the present invention, it is not particularly limited as long as the desired effect can be obtained. Examples include intestinal administration such as oral administration, tube feeding, and enema administration; and non-oral administration such as intravenous administration, arterial administration, intramuscular administration, intracardiac administration, subcutaneous administration, intradermal administration, and intraperitoneal administration.

[0096] As for the form, when the purpose is to be a health enhancer, nutritional supplement (health product, etc.), for example, tablets (including oral disintegrating tablets, chewable tablets, effervescent tablets, lozenges, gel drops, etc.), pills, granules, fine granules, powders, hard capsules, soft capsules, dry syrups, liquids (including beverages, suspensions, syrups), gels, etc., are suitable for oral intake (oral preparation forms).

[0097] As a form, when used in food compositions, examples include liquid, gel, or solid foods, such as fruit juice, soft drinks, tea, soup, soy milk, salad oil, sauces, yogurt, jelly, pudding, rice seasoning, infant formula, cake mix, powdered or liquid dairy products, bread, biscuits, etc.

[0098] The content of the active ingredient in the formulation of this invention is affected by the type of active ingredient, its use, method of use, target of application, and state of the target of application, and is not limited thereto. For example, it can be set to 0.0001 to 100% by weight, preferably 0.001 to 50% by weight.

[0099] The dosage of the formulation of this invention (e.g., administration, ingestion, inoculation, etc.) is only the effective amount that produces the target effect, and there is no particular limitation. Generally, the weight of the active ingredient is 0.01 to 1000 mg / kg body weight per day. The above dosage can be administered once a day or divided into multiple doses (2 to 3 times), and can also be appropriately increased or decreased according to age, disease condition, and symptoms.

[0100] Example The present invention will now be described in detail based on embodiments, but the present invention is not limited to these embodiments.

[0101] 1. Test Methods 1-1. Isolation of the cell nucleus Frozen ovarian clear cell carcinoma (OCCC) samples were homogenized in 500 μl of pre-chilled Nuclei EZ Lysis buffer (NUC-101, Sigma-Aldrich) using a Kimble Dounce tissue homogenizer (D8938, Sigma-Aldrich), followed by the addition of 1 ml of lysis buffer. The homogenate was incubated on ice for 5 minutes. The homogenate was filtered through a 70 μm cell strainer (#352350, Corning) and centrifuged at 500 × g, 4 °C for 1 minute. The pellet was resuspended, washed with 1 ml of lysis buffer, and incubated on ice for 5 minutes. After one cycle of washing with lysis buffer, the pellet was washed twice with 1 ml of nuclear suspension buffer (1 × PBS, 1% BSA, 0.2% RNase inhibitor (2313A, Clontech / TaKaRa)). The nuclear pellet was resuspended in 1 ml of nuclear suspension buffer and filtered twice through a 35 μm cell strainer (#352235, Corning).

[0102] 1-2. Single-cell nuclear RNA-seq (snRNA-seq) To perform snRNA-seq on OCCC tissues, cDNA libraries were prepared from isolated cell nuclei (4000-8000 nuclei) on a Chromium controller (10X Genomics) using the Single Cell 3′ Reagent Kit v3 (PN-1000075). Next-generation sequencing of the cDNA libraries was performed on a HiSeq 2500 (Illumina) platform with an average sequencing depth of 65,124 reads / cell. Fastq files of the sequencing data were processed using the Cellranger pipeline (version 3.0.2, 10X Genomics) and mapped in the GRCh38 (pre-mRNA version 3.0.0) reference genome to generate a matrix of unique molecular identifiers (UMIs) and cell-associated barcodes.

[0103] 1-3. Spatial Transcriptomics Frozen OCCC samples were embedded in pre-chilled OCT embedding medium (#25608-930, Sakura Finetek Japan Co., Ltd.), refrozen on dry ice, and stored at -80°C. cDNA library preparation of tissue sections was performed using the Visium Spatial Gene Expression kit (10X Genomics) according to the manufacturer's instructions. Optimal parameters for permeabilization of OCCC tissue were determined using the Visium Spatial Tissue Optimization Kit (PN-1000193; 10xGenomics). Subsequently, 10 mm sections excised from the OCT-embedded samples were H&E stained, permeabilized for 20 minutes, and cDNA libraries were prepared from barcoded Visium spots. Next-generation sequencing was performed on a HiSeq 2500 (Illumina) platform. The Fastq files of the sequencing data were processed by the spaceranger pipeline (version 1.1.0, 10X Genomics) and mapped in the GRCh38 reference genome to generate a matrix of UMI and spot-related barcodes.

[0104] 1-4. Target genome sequencing Genomic DNA was extracted from frozen OCCC tissue using the DNeasy Blood & Tissue kit (#69504, Qiagen) before target sequence selection using the SureSelect NCC Oncopanel (v.4.0; Agilent Technologies). A library was then constructed using the SureSelectXT kit (Agilent Technologies). Paired-end sequencing (2 × 150 bp) was performed using NextSeq 500 (Illumina). Mutations (single-base mutations, short insertions, deletions), gene amplifications, and gene fusions were detected using the cisCall system.

[0105] 1-5. Bulk RNA-seq analysis Total RNA extraction and library preparation were performed according to previously reported methods. A brief summary is as follows: Total RNA was extracted from OCCC frozen samples (30 cases) using TRIzol (#15596026, Invitrogen), and cDNA libraries were prepared using the TruSeq Stranded mRNALibrary Prep Kit (RS-20020595, Illumina) according to the manufacturer's instructions. The cDNA libraries were then sequenced on an Illumina HiSeq 2500 platform using a 2×100 bp paired-end read module. The sequenced reads were mapped to the human genome reference sequence (UCSC hg19) using Basespace (Illumina).

[0106] 1-6. Construction of Tumor-Derived Globules and Cancer-Associated Fibroblasts (CAFs) The OCCC tissue obtained through surgical resection was immediately washed with PBS and cut into pieces of approximately 10 mm using surgical scissors. 3Small fragments of cells were dissociated using collagenase / hyaluronidase (#7912, Stem Cell Technologies) at 37°C for 2 hours. The dissociated cells were then filtered sequentially through 100μm and 70μm cell sieves (352350, BD Falcon) and purified by density gradient centrifugation with Histodenz (D2158, Sigma) in PBS. After lysing red blood cells with ACK lysis buffer (A1049201, ThermoFisher Scientific), the separated cells were cultured in ultra-low adhesion dishes (#3471 or #3262, Corning) to establish tumor spheroids. These were then cultured in STEMPRO hESC SFM (A1000701, ThermoFisher Scientific) supplemented with 8 ng / ml basic fibroblast growth factor (#AA10-155, ThermoFisher Scientific) and penicillin / streptomycin (37°C, 5% CO2). Established cancerous spheroids were passaged consecutively every two weeks using Accumax (AM105, Innovative Cell Technologies) to dissociate the spheroids. To establish CAF culture, Histodenz purified cells, after erythrocyte removal, were cultured in adherent culture dishes (#35003, Corning) containing 10% FBS (#10270106, Thermo Fisher Scientific) and penicillin / streptomycin (#12561-05, Thermo Fisher Scientific) at 37°C, 5% CO2. Established CAFs were passaged consecutively every two weeks using TripLE Express Enzyme (#12604013, Thermo Fisher Scientific) to dissociate adherent cells.

[0107] In addition, following the above method, CAFs were also established from breast cancer tissue and colorectal cancer tissue, respectively. CAFs derived from breast cancer tissue and colorectal cancer tissue were used only for... Figure 4 In the experiment.

[0108] 1-7. Plasmid Construction To prepare the pCDH-Luc2-T2A-copGFP plasmid, the Luc2-T2A-copGFP expression cassette was first constructed by ligating Luc2 (PCR amplified from pGL4.51[Luc2 / CMV / Neo] (Promega, E1320)) and copGFP (PCR amplified from pCDH-CMV-MCS-EF1α-copGFP (Systembiosciences, CD511B-1)) with a synthesized T2A sequence. Subsequently, the Luc2-T2A-TagBFP sequence in pCDH-Luc2-T2A-TagBFP was replaced with the Luc2-T2A-copGFP expression cassette via the EcoRI and SalI sites, thus completing the pCDH-Luc2-T2A-copGFP expression cassette. To prepare the pCDH-hRluc-T2A-mCherry plasmid, the synthesized T2A sequence was first ligated with hRluc (PCR amplified from pGL4.74[hRluc / TK] (Promega, E6921)) and mCherry (PCR amplified from pcDNA5-MTS-TagBFP-P2AT2A-EGFP-NLS-P2AT2A-mCherry-PTS1 (Addgene, #87829)) to construct the hRluc-T2A-mCherry expression cassette. Subsequently, using a similar construction strategy, pCDH-hRluc-T2A-mCherry was constructed from pCDH-Luc2-T2A-TagBFP. The pCDH-Luc2-T2A-copGFP and pCDH-hRluc-T2A-mCherry plasmids were used to prepare lentiviruses for transfection of cancer cells and CAF, respectively.

[0109] 1-8. In vitro co-culture detection OCCC spheroidal cells and CAFs were infected with lentiviruses expressing Luc2 and GFP (pCDH-Luc2-T2A-copGFP) and lentiviruses expressing hRLuc and mCherry (pCDH-hRLuc-T2A-mCherry), respectively. For co-culture, infected spheroidal cells and CAFs were mixed at a 1:1 ratio. Then, individually cultured cancer cells, individually cultured CAFs, or co-cultured cells were seeded into 96-well plates (1×10⁻⁶) coated with growth factor reducing (GFR) matrix gel (#356231, Corning). 4Cells / well were cultured in MEM-α containing 10% FBS for 6 hours. After removing floating dead cells, the remaining cells were covered with GFR matrix gel and then cultured in E medium (DMEM / F12-GlutaMAX (#10565-042, Thermo Fisher Scientific)) supplemented with penicillin-streptomycin, 10 mM HEPES (#15630106, Thermo Fisher Scientific), N-2 supplement (#17502-001, Thermo Fisher Scientific), B-27 supplement (#17504-001, Thermo Fisher Scientific), 1 mM N-acetylcysteine ​​(A7250, Sigma-Aldrich), and 50 ng / ml human EGF (PHG0313, Thermo Fisher Scientific). For chemosensitivity testing, cells cultured alone or in co-culture were treated with carboplatin (S1215, Selleck Chemicals). Cell proliferation was evaluated using a dual-luciferase reporter gene assay kit (E1960, Promega). For Western blot analysis, cultured cells were collected with cell recovery medium (#354253, Corning) and GFP-expressing cancer cells and mCherry-expressing CAF cells were sorted by flow cytometry (FACS Aria III, Beckton Dickinson, Franklin Lakes, NJ).

[0110] 1-9. Single-cell RNA-seq (scRNA-seq) of in vitro cultured cells Single-cell cDNA library preparation involved separate and co-culture of cancer cells and CAFs (after 3 days of culture). For this purpose, 3000-6000 cells were loaded into a Chromium controller (10X Genomics). Library construction was performed according to the manufacturer's instructions using the Single Cell 3' Reagent Kit v3 and 3' CellPlex Kit Set A (10X Genomics). Next-generation sequencing of the cDNA library was performed using a HiSeq 2500 (Illumina). The Fastq files of the sequencing data were processed using the cellranger workflow (version 6.1.2, 10X Genomics) with the "cellranger multi" command, mapped to the GRCh38 reference genome, and generated a matrix of UMI and cell-associated barcodes.

[0111] 1-10. In vitro proliferation detection of CAF To assess the chemosensitivity of CAFs, CAFs cultured in vitro for 7–10 days after passage were enzymatically dissociated for chemosensitivity detection. The following tyrosine kinase inhibitors (TKIs) were purchased from Selleck Chemicals and used in the experiments: Carboplatin (S1215), Lenvatinib (S1164), Cediranib (S1017), Ripretinib (S8757), Erdafitinib (S8401), Dovitinib (S1018), PP121 (S2622), ENMD-2076 (S1181), Foretinib (S1111), and Ponatinib (S1490). The effect of PDGF signaling on CAFs was investigated using recombinant human PDGFB (160-24033, Fujifilm and light). The effect of TKI or PDGFB on cell proliferation was quantified by measuring luciferase activity using the CellTiter-Glo luminescence assay kit (G7571, Promega) according to the manufacturer's instructions.

[0112] 1-11. Gene knockout via CRISPR / Cas9 CAF cells multiplied for 7-10 days after passage were enzymatically dissociated, and Cas9-mediated gene knockout was performed using the Neon transfection system (Thermo Fisher Scientific) according to the manufacturer's instructions. The sgRNA / Cas9 complex formed by mixing Cas9 protein (Invitrogen) with Edit-R Human Synthetic sgRNA pool for PDGFRB (SQ-003163-01-0002, Dharmacon-Horizon Discovery) or Edit-R Synthetic sgRNA Non-targeting Control #1 (U-009501-01-001p, Dharmacon-Horizon Discovery) was used for electroporation (1600V, 10ms, 2 pulses).

[0113] 1-12. Animal Experiments To investigate the synergistic effect of carboplatin and lipitinib on xenograft tumors, Luc2-GFP-labeled cancer spheroid cells and hRluc-mCherry-labeled CAF were dissociated and mixed at a 1:1 ratio. Then, 1×10⁻⁶ cells were added... 5One mixed cell line was suspended in 100 μl of E medium containing 50% GFR matrix gel and subcutaneously injected into the flank of NOG (NOD / Shi-scid IL-2Rγnull) mice (CLEA, Japan). On day 49 post-transplantation (tumor volume: ~100 mm), the tumor was observed. 3 Mice were randomly divided into four groups and administered carboplatin (40 mg / kg / week, intraperitoneal injection) and / or lipitinib (50 mg / kg / day, oral administration) for 28 days, respectively. Tumor volume was calculated weekly using the standard formula (length × width × height × π / 6). Tumor volume was evaluated based on luciferase activity using an IVIS Spectrum imaging system (Caliper Life Sciences). In this system, the total luminescence emitted from the abdominal region of mice was measured 10 minutes after intraperitoneal injection of 15 mg / ml D-luciferin potassium salt (10 ml / g body weight, photopure). 2 / sr). Data were analyzed using Living Image software (v. 4.2; Caliper LifeScience).

[0114] 1-13. Western blot analysis Western blot analysis was performed according to existing reports. Antibodies specific to the following biomarkers were purchased from designated suppliers: PAX8 (10336-1-AP, Proteintech; dilution 1:2000), cytokeratin 7 (M7018, Dako; 1:1000), α-SMA (ab7817, abcam; 1:3000), collagen I (ab138492, abcam; 1:1000), β-actin (A5316, Sigma-Aldrich; 1:1000), HIF-1α (ab51608, abcam; 1:1000), HIF-2α (ab199, abcam; 1:1000), PDGFB (ab23914, abcam; 1:1000), fibronectin (ab268020, abcam; 1:1000), and PDGFRB (#3169, Cell Signaling). Technology; 1:1000), PDGFRB (phospho Y1021; ab16868, abcam; 1:1000), FAP-α (ab53066, abcam; 1:1000).

[0115] 1-14. Immunofluorescence analysis of clinical samples To perform immunostaining on OCCC clinical samples, surgical samples were fixed in 10% formaldehyde, embedded in paraffin, and cut into 4mm sections. For histological examination, sections were stained with H&E. For immunofluorescence analysis, sections were stained with 10mM citrate buffer (pH 6.0) for antigen recovery, followed by blocking endogenous peroxidase activity with 0.3% hydrogen peroxide. For co-staining of PAX8, HIF-1α, and α-SMA, sections were stained sequentially with rabbit anti-PAX8 antibody (1:1000; Proteintech, 10336-1-AP), biotinylated goat anti-rabbit IgG (1:500; Vector Laboratories, BA-1000), Vectastain Elite ABC assay kit (Vector Laboratories, PK-6100), and Alexa Fluor™ 488 Tyramide reagent (Invitrogen, B40953). In sequential staining with anti-HIF-1α and anti-α-SMA antibodies, sections were boiled in 10 mM citrate buffer (pH 6.0) for at least 15 minutes to remove the PAX8-secondary antibody complex. Sections were then stained with rabbit anti-HIF-1α antibody (1:100; abcam, ab51608) and mouse anti-α-SMA antibody (1:600; abcam, ab7817), followed by staining with donkey anti-rabbit IgG AlexaFluor 750 labeled (1:1000; abcam, ab175728) or goat anti-mouse IgG AlexaFluor 555 labeled (1:1000; Invitrogen, A21424) as secondary antibodies. Finally, the immunostained sections were mounted with ProLong™ Diamond anti-quenching mounting medium containing DAPI (Invitrogen, P36971). Immunostaining with anti-KRT7 antibody, anti-α-SMA antibody, and anti-PDGFRB (phospho Y1021) antibody was performed using the same procedure. Antibodies specific to the following markers were purchased from the following suppliers: cytokeratin 7 (M7018, Dako, 1:100), α-SMA (ab7817, abcam, 1:600), and PDGFRB (phospho Y1021) (ab16868, abcam, 1:100). Immunofluorescence images were evaluated using a Vectra Polaris (Akoya Biosciences) imager.

[0116] 1-15. Immunofluorescence analysis of in vitro cultured cells Cancer cells cultured alone or in co-culture with CAF were seeded onto GFR-coated glass-bottomed culture dishes (D11140H, Matsunaga Glass Industry Co., Ltd.), fixed with cold methanol, and permeabilized with 0.1% Triton X (Sigma-Aldrich). After blocking with 5% BSA, the fixed cells were incubated with rabbit anti-HIF-1α (1:100; abcam, ab51608) and mouse anti-α-SMA (1:600; abcam, ab7817) antibodies, followed by incubation with donkey anti-rabbit IgG AlexaFluor 750 conjugate (1:1000; abcam, ab175728) or goat anti-mouse IgG AlexaFluor 555 conjugate (1:1000; Invitrogen, A21424). The cells were then immobilized with ProLong™ Diamond anti-quenching immobilizer containing DAPI (Invitrogen, P36971). Fluorescence images were taken using a Keyence BZ-8000 microscope (manufactured by Keyence).

[0117] 1-16. Immunohistochemical staining Clinical tumor samples and mouse xenograft tumors were fixed in neutral formalin and embedded in paraffin. Immunohistochemical staining was performed according to existing reports. A brief description follows: Sections were stained with H&E, or stained with primary antibodies against FAPα (ab53066, abcam, 1:100), HIF-1α (ab51608, abcam, 1:100), or α-SMA (ab7817, abcam, 1:500), followed by staining with biotinylated secondary antibody (Vector Laboratories), and then incubated with Vector Stain ABC kit (PK6100, Vector Laboratories) and 3,3'-diaminobenzidine (D12384, Sigma). For evaluation of HIF-1α staining, positive cells in four representative fields were counted using Hybrid Cell Count software (Keyence).

[0118] 1-17. Processing of snRNA-seq data The gene counting matrix was analyzed using Seurat software v3.2.2 running on R v3.6.0. Cells with more than 1% mitochondrial genes, more than 6000 unique feature counts, and fewer than 400 unique feature counts were removed from the dataset. The gene-barcode matrix of the filtered cells was normalized using the "LogNormalize" method. The top 2000 variable genes were then identified using the "vst" method in Seurat's FindVariableFeatures function. All cells from 10 OCCC samples were integrated using Seurat's FindIntegrationAnchors and IntegrateData functions. After cell filtering and data integration, a total of 62673 cells were scaled using Seurat's ScaleData function. PCA analysis was then performed on the scaled data using Seurat's RunPCA function with parameter npcs = 30. UMAP plots were generated using Seurat's RunUMAP function with dims = 1:30.

[0119] 1-18. Annotations on cell populations in OCCC To stratify cell populations from the integrated snRNA-seq data, low-resolution clustering at 0.2 was performed using the FindClusters function. To annotate the five identified cell populations, specific marker genes were used to identify the corresponding epithelial and non-tumor cell populations. The annotation of these cell populations was confirmed by studying the expression of multiple marker genes.

[0120] 1-19. Estimating based on copy number of sequencing data InferCNV (https: / / github.com / broadinstitute / inferCNV) was used to analyze large-scale chromosome copy number variations based on single-cell sequencing data. InferCNV patterns of each chromosome were detected in epithelial cells using non-tumor cells (CAF and endothelial cells) as a reference.

[0121] 1-20. Enrichment Analysis To perform ssGSEA on cancer cell subpopulations, ssGSEA based on single-cell RNA-seq data measured the characteristic scores of the set of HALLMARK genes expressed in each cancer cell subpopulation. ssGSEA was performed using escape (v1.8.0, http: / / www.bioconductor.org / packages / release / bioc / vignettes / escape / inst / doc / vignette.html) running on R v4.2.1. For GO enrichment analysis, differentially expressed genes were selected using Seurat's FindAllMarkers function. Subsequently, clusterProfiler (v4.2.2) identified the top 10 most significant GO terms in biological process categories using the differentially expressed genes from each subpopulation.

[0122] 1-21. Quantification of transcription factor activity The activities of major transcription factors in each cell were inferred by running VIPER (virtual inference of protein activity by enrichment of regulators) v1.30.0 on R v4.2.1. VIPER scores were calculated using transcription factor-target interactions classified as confidence A (DoRothEA v1.6.0). VIPER scores were visualized as violin plots and heatmaps. Transcription factor-target interactions in the Cancer#2 cluster were plotted using the igraph package in R.

[0123] 1-22. Prognostic Analysis The top 20 differentially expressed genes selected using Seurat's FindAllMarkers function were defined as characteristic genes of each cancer cell subpopulation. Bulk RNA-seq analysis was performed on surgical samples from 30 patients with progressive OCCC (stages II-IV), and patients were divided into two groups based on the mean expression of characteristic genes. Kaplan-Meier analysis was performed using R's "survival" package to evaluate the prognostic value of the cancer cell clusters. The p-values ​​for overall survival and progression-free survival were evaluated using a stratified log-rank test.

[0124] 1-23. Ligand-receptor interaction analysis Ligand-receptor interaction analysis based on snRNA-seq data was performed using NicheNet. Ligands and receptors were selected from differentially expressed genes in each cell population using Seurat's FindAllMarkers function. Based on the NicheNet ligand-receptor network, corresponding receptors were identified from differentially expressed genes in non-tumor cells using ligands selected from the Cancer#2 subset. The average expression of ligands and receptors in each population was visualized as a heatmap using Seurat's AverageExpression function. Subsequently, the interaction potential between the selected ligand-receptor pairs was estimated using NicheNet's weighted integration network.

[0125] 1-24. Spatial Transcriptomics Data Processing The Visium spot gene expression matrix and spatial information from the spatial transcriptomics data were imported into Seurat v3.2.0 for downstream analysis. The UMI counts for each spot were normalized using Seurat's Sctransform function. PCA analysis was performed on the target objects using Seurat's RunPCA function with parameter npcs = 20, and a UMAP map was generated using Seurat's RunUMAP function with dims = 1:20. Clustering of Visium spots was performed using the FindClusters function at a resolution of 20.

[0126] 1-25. Integration of snRNA-seq and spatial transcriptomics data This study integrates snRNA-seq and Visium data using the anchor-based integration method in Seurat v3.2.0. The merged snRNA-seq dataset is set as the reference, and one of the Visium datasets is set as the query. Seurat's FindTransferAnchors function is used to detect transfer anchors. After integration, Seurat's TransferData function is used to transfer the cluster labels from the snRNA-seq dataset to the spatial dataset, providing snRNA-seq cluster prediction scores for each spot (pixel).

[0127] 1-26. Processing of scRNA-seq data The gene counting matrix was imported into Seurat software v3.2.2 running on R v3.6.0. Cells with more than 10% mitochondrial gene counts, more than 6000 feature counts, and fewer than 200 feature counts were removed. The filtered gene-barcode matrix was normalized using Seurat's "LogNormalize" method. Then, the top 2000 variable genes were identified using the "vst" method in Seurat's FindVariableFeatures function. Data from co-cultured and individually cultured cells were merged using Seurat's merge function. After filtering and merging, a total of 8208 cells were used for the following analysis: the merged objects were scaled using Seurat's ScaleData function and PCA analysis was performed using Seurat's RunPCA function. UMAP plots were generated using Seurat's RunUMAP function, dims = 1:30.

[0128] 1-27. Image analysis of multiplex immunofluorescence Immunofluorescence intensity of tumor and non-tumor cells was measured using QuPath (version 0.2.1). After loading all images, segmentation was performed using StarDist, and fluorescence intensity was measured for each cell. Cancer cells and CAFs were then identified based on PAX8 and α-SMA expression, respectively. The same immunofluorescence signal threshold intensity was applied to all samples. The centroid distance between α-SMA(+) cells and PAX8(+) / HIF-1α(+) cells was estimated using the “Detect centroid distance 2D” command. After each cell was annotated, the data was exported to CytoMAP (version 1.4.21).

[0129] 2. Results 2-1. Identification of cancer cell subsets associated with chemotherapy resistance in OCCC To identify the intratumoral network leading to chemotherapy resistance in OCCC, frozen samples were obtained from surgical specimens, and an integrated analysis combining single-cell analysis and spatial transcriptomics was performed. The results were then expanded using multicolor quantitative immunostaining, in vitro co-culture, and mouse xenograft experiments.

[0130] To obtain single-cell transcriptome data from frozen specimens, chemotherapy-sensitive cases (n=5, OCC-S1-S5) and chemotherapy-resistant cases (n=5, OCC-R1-R5) were analyzed. The snRNA-seq data were then dimensionality-reduced using Uniform Manifold Approximation and Projection (UMAP). Clustering of single-cell nuclear data revealed by UMAP showed that cells were primarily stratified by clinical case, which may be due to batch effects. To eliminate batch effects and integrate the various datasets, an anchoring procedure was performed to compare cellular identity across samples.

[0131] After data fixation, it was found that the main cell types (epithelial cancer cells, CAF, endothelial cells, and immune cells) formed clear clusters in the UMAP visualization. Cells from chemotherapy-resistant and chemotherapy-sensitive cases were distributed within their respective cell types. (In EpCAM) + Based on copy number changes in the epithelial cell population, non-tumor cells were almost undetectable, presumably due to the careful removal of non-tumor tissue during sample preparation.

[0132] To investigate the potential association between chemotherapy resistance and oncogenic activation, genomic alterations in major oncogenes and tumor suppressor genes were evaluated using the NCC Oncopanel. As previously reported, ARID1A and PIK3CA mutations were identified in multiple samples (7 / 10 and 4 / 10, respectively). However, these mutations were present in both chemotherapy-sensitive and chemotherapy-resistant cases, and therefore no clear association with chemotherapy resistance was established.

[0133] Next, in order to investigate whether there are chemotherapy-resistant cancer cell subpopulations, EpCAM will be used. + The tumor population was stratified into six subgroups (cancer #1-6). Surprisingly, the proportion of cancer #2 was found to be higher in chemotherapy-resistant cases than in chemotherapy-sensitive cases. On the other hand, no significant difference in the number of chemotherapy-resistant and chemotherapy-sensitive cases was found in the assessment of non-tumor cell types.

[0134] 2-2. Chemotherapy resistance in OCCC is associated with HIF activation and poor prognosis. To determine the gene expression profiles of each cancer cell subpopulation, preferentially expressed characteristic genes were isolated. In progressive OCCC cases (n=30), the expression of characteristic genes was investigated, revealing that characteristic genes of cancer subpopulation #2 (rather than those of other subpopulations) were associated with shortened progression-free survival or overall survival, indicating a poor prognosis for cancer subpopulation #2 associated with chemotherapy resistance.

[0135] Next, to investigate the biological characteristics of each subpopulation, gene ontology (GO) enrichment analysis was performed. The results showed that cancer #2 was associated with hypoxia response and extracellular matrix. Subsequently, based on single-sample gene set enrichment analysis (ssGSEA) of Hallmark characteristic gene sets, the hypoxia pathway was specifically activated in cancer #2. On the other hand, enrichment analyses of other major subpopulations showed that cancer #1 and #3 were associated with the interferon response pathway and cell cycle-related pathway, respectively, indicating that cancer #3 is a circulating population.

[0136] Next, VIPER (virtual inference of protein activity by enrichment of regulators) analysis was performed to investigate transcriptional regulators associated with each cluster. Consistent with the finding of enhanced hypoxia response, the cancer #2 subgroup exhibited increased activity of HIF1A (HIF-1α) and EPAS1 (HIF-2α). Notably, the top five transcription factors activated in the cancer #2 subgroup (HIF1A, EGR-1, ATF-2, EPAS1 (HIF-2A), and SP-1) all mediated hypoxia response. This indicates that these transcription factors synergistically induce hypoxia response. Taken together, these results suggest that the #2 chemotherapy resistance subgroup is associated with poor prognosis and HIF-mediated hypoxia response.

[0137] 2-3. Chemotherapy-resistant cells are locally present in the CAF region of OCCC. Next, spatial transcriptomics analysis was performed to reveal the histological localization of the chemotherapy-resistant cancer cell subset in cancer #2. Visium spatial gene expression analysis was performed on surgical specimens from chemotherapy-resistant cases (OCC-R2) and chemotherapy-sensitive cases (OCC-S3). Specific markers of epithelial cancer cells, CAF, endothelial cells, and immune cells were used to determine the location of these cells within the tumor. Based on hematoxylin and eosin (H&E) staining of serial sections, the specimens were broadly divided into cancer-dominated and CAF-dominated regions. Consistent with this, the tissue distribution of cancer cells and CAF depicted by the Visium analysis was largely consistent with the cancer cell and CAF-dominated regions visualized by H&E staining. In fact, based on gene expression profiling, Visium spots could be divided into three groups: cancer-dominated, CAF-dominated, and a mixture of cancer and CAF. The distribution of these three spots was largely consistent with the distribution of cancer cells and CAF observed in H&E images.

[0138] Next, to localize the major cancer cell subpopulations (#1-5), an anchor-based integration of snRNA-seq and Visium data was performed, and a predictive score for each subpopulation within each Visium spot was calculated. In both chemotherapy-resistant and chemotherapy-sensitive cases, visualization of cancer cell subpopulations based on predictive scores showed that cancer #2 was predominantly localized in the cancer / CAF mixed spot. In contrast, cancer #1 and #3 were predominantly localized in the cancer-dominant spot.

[0139] Consistent with snRNA-seq data, the expression of the cancer #2 signature gene was higher in OCC-R2 than in OCC-S3, while this was not the case for other signature genes. Therefore, this supports the association between cancer cells carrying the cancer #2 signature gene and chemotherapy resistance.

[0140] 2-4. HIF-1α-induced cancer cells exist near CAF in chemotherapy-resistant OCCC. Next, to further investigate the location of the chemotherapy-resistant population in cancer #2 within the cancer / CAF mixed region, immunostaining was performed on HIF-1α-positive cancer cells. Surprisingly, co-immunostaining with specific antibodies against HIF-1α, PAX8 (an ovarian cancer cell marker), and α-SMA (a CAF marker) in chemotherapy-resistant tumors (OCC-R1-5) revealed a widespread distribution of PAX8-positive cancer cells co-expressing HIF-1α. In contrast, the proportion of cancer cells co-expressing detectable levels of HIF-1α was significantly lower in chemotherapy-sensitive tumors (OCC-S1-5) than in chemotherapy-resistant tumors. In fact, quantification of stained cells using QuPath showed that the proportion of the HIF-1α-positive population in chemotherapy-resistant tumors was, on average, three times higher than that in chemotherapy-sensitive tumors (33.2% vs. 11.0%).

[0141] In chemotherapy-resistant tumors, HIF-1α-positive cancer cells were observed to be mostly locally located near α-SMA-positive cells. In fact, CytoMAP analysis, evaluating the relative distances between HIF-1α-positive, HIF-1α-negative, and α-SMA-positive cells, confirmed the local presence of HIF-1α-positive cancer cells near α-SMA-positive cells. These data suggest that the local presence of HIF-1α-induced cancer cells near CAFs is a characteristic feature of chemotherapy-resistant OCCC.

[0142] 2-5. CAF in chemotherapy-resistant tumors exhibits a myofibroblast phenotype. The localized presence of CAFs in close contact with chemoresistant cells suggests that CAFs may play a functional role in enhancing chemoresistant OCCC. To investigate whether a unique CAF subset exists in chemoresistant OCCC, the CAF population mentioned in 2-1 above was stratified using snRNA-seq data. However, contrary to the stratification of the cancer population, no subset preferentially present in chemoresistant OCCC was found.

[0143] As another approach, snRNA-seq data were used to investigate whether cancer cells (CAFs) derived from chemotherapeutic cancers (OCCCs) are associated with specific biological characteristics. CAFs are known to consist of a heterogeneous population, including inflammatory CAFs (iCAFs), antigen-presenting CAFs (apCAFs), and myofibroblastic CAFs (myCAFs), with myCAFs present near cancer cells. Comparison of CAFs from chemotherapeutic and chemotherapeutic tumors using ssGSEA and GO-term analyses revealed that CAFs in chemotherapeutic OCCCs were associated with epithelial-mesenchymal transition (EMT) and extracellular matrix tissue organization, phenotypes associated with myCAFs. Surprisingly, CAFs from chemotherapeutic OCCCs exhibited myCAF gene signatures and elevated expression of myCAF-related genes FAP, TPM1, and THBS2. Furthermore, immunostaining studies showed that the level of FAP-α (a protein encoded by the FAP gene) was higher in chemotherapeutic tumors than in chemotherapeutic tumors. These data combined indicate that the myCAF population increases in chemotherapy-resistant cancers, and that HIF-1α-induced cancer cells and myCAF constitute a unique cancer microenvironment in chemotherapy-resistant cancers.

[0144] 2-6. Co-culturing chemotherapy-resistant OCCC cells and CAF cells in vitro can reproduce the chemotherapy-resistant microcirculation. territory The co-localization of chemotherapeutic subpopulations of cancer cells with cancer cells (CAFs) suggests crosstalk between these cells in a chemotherapeutic-resistant microenvironment. To investigate this potential crosstalk, an in vitro co-culture system was constructed. First, tumor spheroids and CAFs were constructed from fresh surgical OCCC specimens. Then, based on the widespread expression of HIF-1α in cancer cells from the original surgical specimens, spheroids derived from chemotherapeutic-resistant cancers were retrospectively selected. The identity of the selected tumor spheroids and CAFs was confirmed by the expression of specific markers: PAX8 and KRT7 in OCCCs, and αSMA and collagen I in CAFs. Subsequently, spheroids and CAFs were labeled with GFP and mCherry, respectively, and cultured individually or together (1:1 ratio) to study co-culture-induced cell proliferation and phenotypic changes. Figure 1 A and 1B). In fact, co-culture improved the survival rate of CAFs (A and B). Figure 1C), and induced the expression of HIF-1α and HIF-2α in tumor spheroid cells (C), Figure 1 D and 1E). Surprisingly, co-culture increased resistance to carboplatin chemotherapy (D and 1E). Figure 1 (F), indicating that the presence of CAF is involved in chemotherapy resistance in cancer.

[0145] Next, to investigate the gene expression changes induced by co-culture, single-cell RNA sequencing (scRNA-seq) was performed on cells cultured under both solitary and co-culture conditions. Figure 1 G). Comparison of gene expression profiles of cancer spheroids under co-culture and solitary culture conditions using ssGSEA revealed that four of the top five Hallmark features induced by co-culture (EMT, NF-κB-mediated TNF-α signaling, inflammatory response, and hypoxia) were identical to those upregulated in cancer #2 subset. Furthermore, co-culture with CAF led to a specific upregulation of cancer #2 gene signatures (G). Figure 1 H), and induced the activation of all the top 9 transcription factors activated in the cancer #2 subset. In summary, the cancer globule phenotypic changes induced by co-culture with CAF almost mimicked the unique characteristics of the cancer #2 subset.

[0146] Furthermore, the gene expression profiles of CAFs under co-culture and monoculture conditions were compared using ssGSEA. The EMT pathway, which is strongly upregulated in CAFs from chemotherapy-resistant OCCC, was induced under co-culture conditions. Additionally, TGF-β signaling was found to be strongly induced after co-culture. This suggests that TGF-β signaling, as a well-known signaling pathway promoting CAF formation, may be involved in the EMT phenotype of CAFs induced under co-culture conditions. Furthermore, co-culture upregulated the myCAF characteristic (…). Figure 1 I) and genes representing myCAF such as FAP, THBS2, and TPM1, this induction has also been observed in chemoresistant OCCC. Therefore, both cancer spheroids and CAFs undergo phenotypic changes associated with chemoresistant cancers when co-cultured. These data strongly suggest that the interaction between cancer cells and CAFs is associated with the formation of a chemoresistant microenvironment in cancer.

[0147] 2-7. Cancer-derived PDGF activates CAF-mediated chemotherapy resistance and activates HIF-1α in cancer cells. To better understand the underlying molecular mechanisms of chemoresistance mediated by cancer-CAF interactions, NicheNet was used to investigate potential ligand-receptor interactions between these cells. snRNA-seq data were analyzed, identifying 12 ligand-encoding genes (NAMPT, EFNA5, PGFFB, C3, ANXA1, SPP1, FN1, ITGB1, LAMC2, LAMB1, LAMA1, and RELN) highly expressed in the cancer #2 subset. Subsequently, the genes encoding their receptors were investigated for high expression in CAFs. Receptor-ligand analysis revealed that the interaction between PGFFB (the β subunit of PDGF) and PDGFRB (the β subunit of the PDGF receptor) is a strong candidate mediator of cell-cell signaling between cancer cells and CAFs.

[0148] Therefore, the functional significance of PDGF-PDGFR interaction in the co-culture system was investigated. As expected, PDGFB and PDGFRB were highly expressed by tumor spheroid cells and CAF, respectively. Figure 2 A). Co-culture led to the activation and phosphorylation of PDGFR (p-PDGFRB) and the expression of the myCAF marker (FAP-α) in CAF. Figure 2 B). In fact, treatment of CAF with purified PDGFB ligands can induce PDGFRB phosphorylation and FAP-α expression (B). Figure 2 C), and enhance the proliferation of CAF ( Figure 2 D). Conversely, PDGF-mediated proliferation was eliminated in CAF via CRISPR-mediated PDGFRB knockout (D). Figure 2 E and 2F) and FAP-α expression ( Figure 2 (G). This indicates that PDGF-induced proliferation and the expression of the myCAF-like phenotype are mediated by the activation of the PDGF receptor in CAFs.

[0149] Notably, in the immunostaining of chemotherapy-resistant OCCC, activated phosphorylation of PDGFR was observed in αSMA-positive CAF located near KRT7-positive cancer cells. This suggests that PDGFR signaling in CAF is activated by neighboring cancer cells in vivo.

[0150] Next, the functional role of PDGFR signaling in cancer chemotherapy resistance was investigated using a co-culture system. PDGFR knockout in CAF reduced survival ( Figure 2 Surprisingly, knockout of PDGFR in CAF inhibited the expression of HIF-1α, HIF-2α, and PDGFB in cancer cells (H). Figure 2 I). Furthermore, this knockout reduces resistance to carboplatin-based cancer chemotherapy ( Figure 2J). These data suggest that a positive feedback loop exists between cancer cells and CAF: PDGF expressed by OCCC induces CAF activation and survival through PDGFR, thereby enhancing HIF activation, PDGF expression, and chemotherapy resistance in cancer cells.

[0151] 2-8. The combined use of lipitinib and carboplatin to inhibit CAF can suppress cancer proliferation. Since the PDGF-PDGFR signaling axis plays a crucial role in CAF-mediated chemotherapy resistance, novel therapies targeting PDGFR signaling in CAF have been conceived. Carboplatin did not show significant effects on CAF proliferation. Figure 3 A, Figure 4 A, Figure 4 B). On the other hand, TKIs that can inhibit PDGFR showed varying degrees of proliferation inhibition (B). Figure 3 A, Figure 4 A, Figure 4 B). 1 μM lipitinib inhibited PDGFR activation phosphorylation and FAP-α expression within 24 hours. Subsequently, the inhibitory effect of lipitinib in the presence or absence of carboplatin was investigated in an in vitro co-culture system. As expected, even in the absence of carboplatin, lipitinib at concentrations of 1–5 μM effectively reduced CAF survival (…). Figure 3 B). Importantly, the inhibitory effect of carboplatin on cancer cell proliferation was significantly enhanced by lipitinib (B). Figure 3 C and 3D). In contrast, lipitinib did not show a significant enhancement of carboplatin-mediated inhibition when cancer cells were cultured alone. Figure 3 E). This indicates that lipitinib inhibits cancer growth by suppressing CAF.

[0152] Finally, the efficacy of carboplatin combined with lipitinib was investigated. Cancer spheroids and CAF used in the co-culture experiment were mixed in a 1:1 ratio and subcutaneously transplanted into immunodeficient NOG mice. Notably, the combination of lipitinib and carboplatin resulted in a significant inhibition of tumor growth. Figure 3 F).

[0153] Therefore, lipitinib inhibits the growth of chemotherapy-resistant cancers by suppressing CAF. Thus, it was predicted that a portion of HIF-1α-positive cancer cells would decrease in the presence of lipitinib. Indeed, a portion of HIF-1α-positive cancer cells were observed to decrease after treatment. Figure 3 G and 3H) and α-SMA-positive CAF were significantly reduced. These data suggest that CAF inhibition, when combined with standard chemotherapy drugs, is an effective treatment for eliminating chemotherapy-resistant cancers.

Claims

1. A cancer-associated fibroblast inhibitor, characterized in that: It contains a PDGFR inhibitor.

2. The cancer-associated fibroblast inhibitor as described in claim 1, characterized in that: The PDGFR inhibitor is selected from at least one of PDGFR function inhibitors and PDGFR expression inhibitors.

3. The cancer-associated fibroblast inhibitor as described in claim 1, characterized in that: The PDGFR inhibitor is selected from at least one of low molecular weight compounds, polynucleotides targeting PDGFR, expression cassettes of the polynucleotides, peptides, proteins, and antibodies.

4. The cancer-associated fibroblast inhibitor as described in claim 1, characterized in that: The PDGFR inhibitor is a low molecular weight compound, and the low molecular weight compound is a kinase inhibitor.

5. The cancer-associated fibroblast inhibitor as described in claim 4, characterized in that: The kinase inhibitor is selected from at least one of lipitinib, ponatinib, erdatinib, dovirtinib, lenvatinib, furitinib, ENMD-2076, PP121, and cediranib.

6. The cancer-associated fibroblast inhibitor according to any one of claims 1 to 5, characterized in that: The cancer-associated fibroblasts are cells found in ovarian cancer tissue, breast cancer tissue, or colorectal cancer tissue.

7. The cancer-associated fibroblast inhibitor according to any one of claims 1 to 5, characterized in that: The cancer-associated fibroblasts mentioned are cells found in ovarian cancer tissue.

8. The cancer-associated fibroblast inhibitor according to any one of claims 1 to 5, characterized in that: It is used in combination with anticancer agents.

9. The cancer-associated fibroblast inhibitor as described in claim 8, characterized in that: The anticancer agent is a platinum preparation.

10. An enhancer for the anticancer effect of an anticancer agent, characterized in that: It contains a PDGFR inhibitor.

11. A cancer prevention or treatment agent, characterized in that: Contains PDGFR inhibitors, The cancer is selected from at least one of the following: ovarian cancer, breast cancer, colorectal cancer, pancreatic cancer, urothelial carcinoma, prostate cancer, esophageal cancer, liver cancer, kidney cancer, endometrial cancer, gastric cancer, glioblastoma, lung cancer, and melanoma.

12. The preventive or therapeutic agent as claimed in claim 11, characterized in that: It is used in combination with anticancer agents for administration.

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