Methods and kits for determining subtypes of pancreatic ductal adenocarcinoma

Abstract

The present invention relates to a method for distinguishing subtypes of pancreatic ductal adenocarcinoma patients by analyzing PDAC genetic proteins. [Solution] A method for discriminating subtypes of pancreatic cancer according to one embodiment of the present invention includes the steps of (1) crushing diseased tissue of pancreatic ductal adenocarcinoma isolated from a patient with pancreatic ductal adenocarcinoma, (2) extracting and digesting proteins from the diseased tissue to obtain a patient peptide sample, (3) measuring the expression levels of representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma from the patient peptide sample, and (4) comparing the expression levels of representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma to discriminate the subtype of the patient with pancreatic ductal adenocarcinoma.

Description

Technical Field

[0001] The present invention relates to a method for discriminating subtypes of pancreatic ductal adenocarcinoma and a subtype discrimination kit. More specifically, the present invention relates to a method for discriminating the subtype of a patient's pancreatic ductal adenocarcinoma and a subtype discrimination kit using subtype information of pancreatic ductal adenocarcinoma hierarchized by analysis of a genetic proteome by integration of genomic, mRNA, and protein data.

Background Art

[0002] Pancreatic cancer ranks ninth in terms of incidence among cancers occurring in the Republic of Korea, but many diagnosed patients die, and it is a cancer with a mortality rate ranking fifth. In the United States, currently, pancreatic cancer is the fourth leading cause of cancer-related deaths and is predicted to be the second leading cause of cancer-related deaths in the United States by 2030. Since there is no extremely effective systemic treatment method for pancreatic cancer, complete cure can only be expected by surgery. However, due to its anatomical characteristics, it develops into major blood vessel invasion and systemic metastasis, and is found in a state where complete cure is impossible in 80% of patients. Even in patients within stage 2 who are eligible for surgery (about 20% of all patients), even if surgery and anticancer treatment are actively performed, about 70% of patients relapse, and the 5-year survival rate is only about 20%, making it the least treated tumor. That is, only about 5 to 8% of all patients with pancreatic cancer can be completely cured, and the remaining more than 90% of patients are tumors that are refractory to both surgery and anticancer treatment, which are the current treatment methods. Therefore, efforts to overcome pancreatic cancer through research on its mechanism and selective treatment using the same are urgent.

[0003] Traditionally, pancreatic cancer has been treated with anticancer chemotherapy based on 5-fluouracil (5-FU) or gemcitabine, but the response rate is low, and there is still no anticancer agent that consistently shows a prominent effect. Existing clinical diagnostic methods such as imaging and pathological examinations cannot predict treatment responsiveness / resistance, the possibility of early recurrence, and prognosis. Therefore, a new approach that can classify pancreatic cancer based on its biological mechanism and enable appropriate treatment and prognosis prediction is essential.

[0004] Recent research on genetic proteins in various cancers has shown that integrated genetic protein data provides more precise cancer subtype information than genetic protein data alone, and offers more complete information on the pathogenesis of each classified subtype. Therefore, by using a pancreatic cancer subtype discrimination technology based on the pathogenesis of each subtype in a genetic protein database to identify the subtype of pancreatic cancer patients, it will be possible to develop precision medical technologies for pancreatic cancer that enable optimal treatment for each subtype by developing individualized therapeutic agents tailored to each subtype. For example, Patent Document 1 presents a method for determining the subtype of pancreatic tumors, classifying pancreatic cancer subtypes into four types using TPI1, GAPDH, ENO1, LDHA, and PGK1. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] International Public Publication No. 2020-205993 (October 8, 2020) [Overview of the project] [Problems that the invention aims to solve]

[0006] This invention aims to provide a method for determining the subtype of pancreatic ductal adenocarcinoma in a patient, and a subtype determination kit, using hierarchical subtype information of pancreatic ductal adenocarcinoma. [Means for solving the problem]

[0007] One embodiment of the present invention provides a method for determining a subtype of pancreatic ductal adenocarcinoma, comprising the following steps (1) to (4).

[0008] (1) A step of fragmenting the lesional tissue of pancreatic ductal adenocarcinoma isolated from a patient with pancreatic ductal adenocarcinoma, (2) The step of extracting and digesting proteins from the lesional tissue to obtain patient-specific peptide samples, (3) A step of measuring the expression levels of representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma from the extracted patient-specific peptide samples, wherein the representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma are one or more selected from the group consisting of the following: Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, and BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, and CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, and ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, and BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, and EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIPRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, and SEC11C (4) A step of determining the subtype of pancreatic ductal adenocarcinoma patient by comparing the expression levels of representative genes of subtypes 1 to 6 of the pancreatic ductal adenocarcinoma.

[0009] Another embodiment of the present invention provides a kit for determining subtypes of pancreatic ductal adenocarcinoma. The pancreatic ductal adenocarcinoma subtype determination kit comprises a preparation for measuring the expression level of representative genes of pancreatic ductal adenocarcinoma subtypes 1 to 6, wherein the representative genes of pancreatic ductal adenocarcinoma subtypes 1 to 6 may be one or more selected from the group consisting of the following.

[0010] Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, and BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, and CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, and ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, and BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, and EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIPRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, and SEC11C Yet another embodiment of the present invention provides a method for predicting the prognosis of a patient with pancreatic ductal adenocarcinoma, comprising the following steps (1) to (5).

[0011] (1) A step of fragmenting the lesional tissue of pancreatic ductal adenocarcinoma isolated from a patient with pancreatic ductal adenocarcinoma, (2) The step of extracting and digesting proteins from the lesional tissue to obtain patient-specific peptide samples, (3) A step of measuring the expression levels of representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma from the extracted patient-specific peptide samples, wherein the representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma are one or more selected from the group consisting of the following: Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, and BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, and CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, and ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, and BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, and EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIPRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, and SEC11C (4) Comparing the expression levels of the representative genes of subtypes 1 to 6 of the pancreatic ductal adenocarcinoma to discriminate the subtype of the pancreatic ductal adenocarcinoma patient; (5) Predicting the prognosis based on the discrimination of the subtype.

Advantages of the Invention

[0012] The analysis of the genetic proteome according to an embodiment of the present invention can improve the understanding of PDAC and the stratification of PDAC patients, discriminate the subtypes of pancreatic ductal adenocarcinoma, and improve the treatment for pancreatic cancer patients.

[0013] According to an embodiment of the present invention, the subtype of a pancreatic ductal adenocarcinoma patient can be discriminated by analyzing the genetic proteome of PDAC. This will enable precision medicine technology for pancreatic cancer in the future, allowing for optimal treatment tailored to each individual subtype through the development of subtype-specific therapeutic agents.

[0014] According to an embodiment of the present invention, the subtype of pancreatic cancer can be discriminated and the prognosis can be predicted, enabling the development of customized new drugs according to the subtype.

Brief Description of the Drawings

[0015] [Figure 1A] It relates to the correlation of mutation-protein rich and phosphorylation, the association of cell death in PDAC, and the actin cytoskeleton. [Figure 1B] It relates to the correlation of mutation-protein rich and phosphorylation, the association of cell death in PDAC, and the actin cytoskeleton. [Figure 1C] It relates to the correlation of mutation-protein rich and phosphorylation, the association of cell death in PDAC, and the actin cytoskeleton. [Figure 1D] It relates to the correlation of mutation-protein rich and phosphorylation, the association of cell death in PDAC, and the actin cytoskeleton. [Figure 1E] It relates to the correlation of mutation-protein rich and phosphorylation, the association of cell death in PDAC, and the actin cytoskeleton. [Figure 1F] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 1G] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 1H] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 1I] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 2A] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 2B] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 2C] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 2D] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 2E] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 2F] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 2G] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 2H]This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 2I] This study concerns the correlation between mutation, protein-richness, and phosphorylation, and the link between cell death and the actin cytoskeleton in PDACs. [Figure 3A] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 3B] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 3C] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 3D] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 3E] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 3F] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 3G] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 3H] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 3I] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 4A] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 4B] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 4C]This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 4D] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 4E] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 4F] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 4G] This study concerns mRNA-protein-rich correlations and novel oncogenes and tumor suppressor candidates. [Figure 5A] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 5B] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 5C] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 5D] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 5E] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 5F] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 5G] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 5H] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 6A] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 6B] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 6C] This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 6D]This concerns the redefinition of PDAC subtypes through the analysis of genetic proteins. [Figure 7A] This concerns the association between PDAC subtypes and distinct cellular networks. [Figure 7B] This concerns the association between PDAC subtypes and distinct cellular networks. [Figure 7C] This concerns the association between PDAC subtypes and distinct cellular networks. [Figure 7D] This concerns the association between PDAC subtypes and distinct cellular networks. [Figure 7E] This concerns the association between PDAC subtypes and distinct cellular networks. [Figure 8A] This concerns the association between PDAC subtypes and distinct cellular networks. [Figure 8B] This concerns the association between PDAC subtypes and distinct cellular networks. [Figure 8C] This concerns the association between PDAC subtypes and distinct cellular networks. [Figure 9A] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9B] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9C] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9D] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9E] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9F] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9G] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9H] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9I] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9J] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9K] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9L] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 9M] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10A] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10B] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10C] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10D] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10E] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10F] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10G] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10H] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10I] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10J] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10K] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10L] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10M] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 10N] This relates to the suppression of T cell proliferation in pro-tumorigenic PMN-MDSCs. [Figure 11] This is a schematic diagram illustrating the performance of MRM-MS analysis on a mixture of proteins extracted from lesional tissue of pancreatic ductal adenocarcinoma patients and stable isotope-labeled peptides of representative genes of subtypes 1-6. [Figure 12] This shows the MRM signal intensity and contour maps between patient samples and representative peptides of each subtype. [Figure 13] This document describes the fragmentation process of pancreatic cancer tissue and a method for processing pancreatic cancer samples using ultra-high pressure circulation technology.

[0016] Figure 1 shows (A) mutations per megabase for individual patients (top panel), SMG mutation types for each patient (middle right); mutation frequency for each gene in each patient (middle left); and clinical parameters for each patient (bottom panel). (B) Comparison of somatic mutation frequencies for SMG. Red labeling indicates genes detected at significantly higher frequencies (p<0.05 by proportional test) in this cohort compared to other cohorts. (C) Phosphorylated peptides upregulated in tumors with somatic mutations in KRAS, SMAD4, and ARID1A. The color bar shows the slope of the log2-fold-change of phosphorylated peptide intensity compared to the mean intensity. (D) Cell treatments related to proteins whose phosphorylation levels correlate with somatic mutations in KRAS, SMAD4, and ARID1A. The heatmap shows the significance of enrichment in each process by proteins that show a confirmed mutation-phosphorylation correlation. Significance is -log 10(p) is shown, where p is the p-value relative to enrichment. (EG) Association between somatic mutations and protein-rich or phosphorylated levels in TP53(E), RB1(F), and ATM(G). The lollipop plot shows somatic mutations (circles) and phosphorylated sites (triangles) detected in the gene structure (top panel). The height of the lollipop indicates the number of patients with the mutation, and the color indicates the mutation type (see legend in A). Samples are classified based on somatic mutations. Phosphorylated peptide intensities or protein richness normalized to the mean level across all patients are shown as bar graphs (bottom panel; red and blue, higher or lower than mean, respectively). Rich mutation sites are indicated by arrows. (H) Network model illustrating the interactions between EMT-related proteins and strong mutant phosphorylation correlations (orange nodes) in KRAS, SMAD4, or ARID1A. Gray nodes indicate molecules that were added to the pathway to increase inter-node connectivity, but do not have a significant mutant-phosphorylation correlation. Solid arrows indicate direct activation; dotted arrows indicate indirect activation; gray lines indicate protein-protein interactions; and thick lines indicate the plasma membrane. (I) Correlation between protein-rich and phosphorylation levels and somatic mutations in ARID1A.

[0017] (A) Workflow for PDAC gene protein analysis in Figure 2. Exome sequencing analysis of tumor tissue and blood samples and RNA sequencing of tumor tissue were performed on tumors from 196 patients, while mass spectrometry-based protein analysis (global proteome and phosphorylated proteome) was performed on tumors from 150 patients. The distribution of tumor cellular fidelity is shown for the 196 tumors (lower left). Tumors with cellular fidelity exceeding 15% were used for gene protein analysis, and tumors with cellular fidelity exceeding 19% were used for protein analysis. (B) Number of non-duplicate peptides identified from global proteome and phosphorylated proteome data. (C) Number of protein-coding genes identified from mRNA sequencing and proteome data (global proteome and phosphorylated proteome). The average number of peptides and protein-coding genes is shown in (B) and (C), respectively. (D) Number of somatic mutations that alter the protein sequence and gene carrying mutations, as confirmed from exome sequencing data. (E) Relationships between significantly mutated genes (SMGs) confirmed from this cohort and previous cohorts of TCGA (Cancer Genome Atlas Research Network, Electronic address and Cancer Genome Atlas Research, 2017), Bailey et al. (Bailey et al., 2016), Biankin et al. (Biankin et al., 2012), Witkiewicz et al. (Witkiewicz et al., 2015), and Waddell et al. (Waddell et al., 2015). (FH) Association between protein-rich or phosphorylated peptide levels and somatic mutations in KMT2D (F), AHNAK2 (G), and FCGBP (H). For each gene, the lollipop plot (top panel) shows the detected somatic mutations (circles) and phosphorylation sites (triangles). The height of the lollipop indicates the number of patients with the mutation, and the color indicates the mutation type shown in the legend.The intensities of protein-rich or phosphorylated peptides were normalized proportionally to the median across all patients shown in the bar plot (bottom panel, red and blue, above or below median). (I) Correlation between copy number variations (CNVs) and mRNA (left) and protein (right) expression levels. Red and blue indicate positive and negative correlations, respectively. Diagonal and off-diagonal elements of the heatmap indicate cis and trans correlations between CNVs and mRNA or protein expression levels. By chromosome, the number of CNVs that specifically correlate with mRNA or protein expression levels and those that generally correlate with all mRNA and protein expression levels are shown as blue (top panel) and black (bottom panel) bars, respectively.

[0018] Figure 3 (A) Distribution of survival differences (chi-squared statistic) for genes with significant (FDR<0.01) and non-significant (FDR>0.1) mRNA-protein correlations. p<0.01, Student's t-test. In the violin plot, the line represents the median. (B) Cellular processes represented by genes showing correlations with positive and negative mRNA survival. Significance of enrichment for each process is -log 10(P) is shown as p, where p is the p-value for enrichment. Red dotted line: p=0.05. (C) Selection of tumor gene and tumor suppressor candidate. Of the genes with significant (FDR<0.01) mRNA-protein correlations, 12 tumor suppressor candidates and 19 tumor gene candidates were identified with significant positive (risk ratio <1) and negative (risk ratio >1) mRNA-survival correlations in all four PDAC cohorts. According to the criteria (Figure 7D), 6 tumor suppressors and 4 tumor genes were selected for further study. (DE) AsPC1 count (D) (n≧3 / condition) at the expression of the displayed shRNA targeting the tumor gene candidate to induce control group shRNA (shCtrl) or knockdown. Cell count (E) determined on day 5. (FG) Representative image (F) and quantification (G) (n≧2 / condition) of wound healing measured at 0 or 48 hours after scratching AsPC1 cells transduced with the displayed shRNA. Wound healing was quantified at 48 hours. AsPC1 count (H) after overexpression of the tumor suppressor candidate (HI) (n≧2 / condition). Cell count was determined at 3 days (I). Data are shown mean ± SEM. Dunnett's post-hoc corrected variance (ANOVA) was performed by bidirectional (D and H) and unidirectional (E, G and I) analyses, with p<0.05, **, p<0.01, ***, p<0.001, ****, p<0.0001.

[0019] (A) Distribution of mRNA-protein-rich Spearman correlation coefficients for individual genes across the entire patient population. Yellow and blue indicate positive and negative correlations, respectively. (B) Differential associations of genes with high or low mRNA-protein-rich correlations with the KEGG pathway. Genes associated with each KEGG pathway are indicated by yellow (positive correlation) and blue (negative correlation) bars. (C) Cumulative density distribution of mRNA-protein correlations for genes with significant (blue) and non-significant (red) survival differences between the top 25% and bottom 25% of patients with the highest mRNA expression levels and patients with the lowest mRNA expression levels, respectively. p<0.01 by Kolmogorov-Smirnov test. (D) Selection of tumor suppressor and oncogene candidate for functional experiments. In four PDAC cohorts, from 12 tumor suppressor (TS) candidates and 19 oncogene candidates with markedly positive (risk < 1) and negative (risk > 1) mRNA-survival correlations, candidates exhibiting favorable survival curve patterns (red and blue lines, representing the top and bottom 25% of patients with the highest and lowest mRNA expression levels) in three or more PDAC cohorts were selected, resulting in 10 TS and 16 oncogene candidates. Of these, 9 TS and 7 oncogene candidates that had not previously been reported in pancreatic cancer were selected. Finally, 7 TS and 5 oncogene candidates with expression levels lower than and higher than the median expression (log2-FPKM=3.11) of the gene expressed in AsPC1 cells (FPKM>1) were selected. Of these, IQGAP2 was excluded from functional studies due to its large gene size, and KRT19 was excluded due to its broad functional involvement. The mRNA expression profiles of AsPC1 cells were obtained from the Cancer Cell Line Encyclopedia (CCLE) (Ghandi et al., 2019). FPKM, fragments per kilobase of transcripts per million. (E) Relative mRNA expression levels of candidate tumor genes measured by quantitative RT-PCR analysis in AsPC1 cells after knockdown.mRNA levels were normalized by GAPDH, 18s RNA, and HPRT (n=3 per condition), and then normalized by the resulting values ​​of the target gene (e.g., shDCBLD2) in control shRNA-transduced cells (shCtrl). Student's t-test showed **, p<0.01; ***, p<0.001. (FG) Representative image obtained by immunoblotting (F) and relative protein levels of the tumor suppressor candidate in AsPC1 cells after overexpression (G). Protein levels of the target gene were normalized to α-actin levels.

[0020] Figure 5 (A) RNA1-3 clusters defined by mRNA signatures (rna1-3) in the TCGA, PACA-AU, and PACA-CA cohorts. The rna1-3 defining RNA1-3 are shown in the first heatmap, respectively. The number of mRNAs in ran1-3 is shown in parentheses. For each cohort, the heatmap shows tumors classified as RNA1-3 based on rna1-3 (left) and tumors unrelated to rna1-3 (right). The subtype bar plot shows subtypes predicted by molecular signatures defined by Moffitt et al. (Moffitt et al., 2015), Collisson et al. (Collisson et al., 2011), and Bailey et al. (Bailey et al., 2016). (B) Post-diagnosis survival of tumor patients belonging to the RNA1-3 cluster in the TCGA, PACA-AU, and PACA-CA cohorts. (CD) Protein signatures (prot1-5 and phos1-4) defining Prot1-5(C) and Phos1-4(D) based on global proteome and phosphorylated proteome data. The number of proteins and phosphorylated peptides is shown in parentheses. (E) Six subtypes (Sub1-6) identified by integrated clustering of mRNA, protein, and phosphorylation data. The heatmap shows the indicator vector for the cluster (row) identified from the clustering of individual data types. Red indicates membership of individual samples belonging to the cluster; upper color bar indicates Sub1-6; RNA1-3 cluster defined by rna1-3. (F) Survival time of patients with tumors in Sub1-6. (G) Somatic mutation distribution of SMGs in Sub1-6 and clinical parameters for each patient (lower panel). (H) Mutation distribution per somatic megabase identified from tumors in Sub1-6. In the violin plot, the line indicates the median. One-way ANOVA with Sidak post-hoc correction* showed p<0.05.

[0021] Figure 6(A) shows the clustering results of mRNA sequencing data. In the individual clustering results, the cophenetic correlation coefficient plot (left) shows how the coefficients are diversified into clusters of different numbers (k=2-6), and when different numbers of molecules selected by multiple percentages of median absolute deviation (MAD) (10-30%) are used for clustering. The silhouette width score plot (middle) shows the core samples with positive scores in the clusters identified from the individual type data. The heatmap (right) shows the sample consensus obtained from pair-wise clustering. The gradient from blue to red shows the agreement rate in clustering obtained by performing clustering 100 times with the determined number of clusters (k=3). The color bars in the lower panel show the samples belonging to the clusters: RNA1-3 (A); Prot1-5 (C); and Phos1-4 (D). (B) The proportions of subtypes defined by mRNA signature bases provided by Moffitt et al. (Moffitt et al., 2015), Collisson et al. (Collisson et al., 2011), and Bailey et al. (Bailey et al., 2016) for RNA1-3 (see legend for subtypes) and tumors that were not correlated in the PDAC cohort are shown. (CD) Global clustering results for proteome data (C) and phosphorylated proteome data (D). See legend for (A). The number of clusters k was determined to be k=5 and 4 for protein (C) and phosphorylation (D) data, respectively.

[0022] Figure 7(A) Cellular processes represented by genes (S1-G to S6-G) and proteins (S1-P to S6-P) that define Sub1-6. The heatmap shows the significance of enrichment of cellular processes by genes or proteins that define Sub1-6. Significance is -log 10(p), where p is represented as the p-value relative to enrichment. (BC) Network model showing the interactions between genes and proteins related to immune-associated processes (B, upper panel) and pancreatic secretion (B, lower panel) and RHOA signals associated with Sub5-6 (B) and Sub2-4 (C), respectively. The center and boundary colors of the nodes indicate whether the gene and protein were selected as signatures for Sub5-6 (green for Sub5, dark green for Sub6) and Sub2-4 (orange for Sub2, red for Sub3, dark red for Sub4). The circle P in the node indicates the phosphorylated peptide that defines the subtype. Arrows indicate activation; repression symbols indicate repression; solid arrows indicate direct activation; dotted arrows indicate indirect activation; gray lines indicate protein-protein interactions. (D) Distribution of tumor cellular fidelity in Sub1-6. The median of cell richness is shown by the red line. (E) Cell counts for cultured Sub4 (SNU3608) and Sub6 (SNU3573) tumors. Data are presented as mean ± SEM. Bidirectional ANOVA with Sidak post-correction for the entire time range yielded ****; p<0.0001.

[0023] Figure 8 shows the mRNA, protein, and phosphorylated signatures defining Sub1-6 for gene set enrichment analysis (GSEA), with Figure 8(A) illustrating how the mRNA and protein signatures defining Sub6 were selected and used for GSEA. Integrated clustering (upper left) shows that Sub6 is defined by RNA3, Prot5, and Phos4 clusters. Based on this information, 416 genes defining RNA3 (rna3) were selected as mRNA signatures (S6-G), and 945 proteins (prot5) and 1030 phosphorylated peptides (phos4) defining Prot5 and Phos5, respectively, were selected as protein signatures (S6-P) (lower and upper right). After mapping the phosphorylated peptides with phorphorylated proteins and conjugating them to 945 proteins (prot5), the resulting proteins were used for GSEA. (B) Subtype distribution of tumors with somatic mutations in TP53 or ARID1A, resulting in protein-rich mutations or altered phosphorylation levels of corresponding protein-coding genes. The color of the heatmap below the bar plot indicates the patient's subtype. (C) Number of patients with or without protein-rich mutations, or with phosphorylated peptide intensities above (positive) or below (negative) the median. The color of the cumulative bar plot indicates the patient's subtype.

[0024] Figure 9 (AB) Comparison of tumor volume over time (A) and at the final point (B) in orthotopic transplant PDAC models transplanted with cells derived from Sub4 (SNU3608) and Sub6 (SNU3573) tumors (n=10 / group). (C) Number of subgroups of immune cells infiltrating SNU3608 and SNU3573 tumors (n=10 / group). PMN-MDSCs, mononuclear MDSCs. (DE) Representative FACS data using indicated markers. Boxes indicate PMN-MDSCs. Superscripts H: high, L: low. (FG) Percentage (F) and ratio (G) of PMN-MDSCs in four indicated groups (n=8 / group) defined based on expression levels for CXCR2 and CXCR4. (H) Percentage of PMN-MDSCs showing high levels of CXCR2 and CXCR4. (I) Co-culture of T cells isolated from PMN-MDSCs, orthotopic transplant PDAC models, and naive Balb / c mice, respectively. A FACS scheme for CFSE analysis of CD8+ and CD4+ cells is shown. CFSE intensity distribution for (JM)CD8+(J) and CD4+ T cells(L). MFI of CFSE is determined (K and M; n=3-4 / group). Bidirectional ANOVA (A, C and F) and Student's t-test (B, H, K and M) with Sidak's post-hoc correction yielded results of *, p<0.05; ***, p<0.001; ****, p<0.0001.

[0025] Figure 10 (A) mRNA and protein expression patterns of immune cell markers across Sub1-6. The heatmap shows the Z-scores of marker mRNA (left) and protein (right) in Sub1-6. For each subtype, the Z-score for each marker was calculated in Sub1-6 by auto-sizing the median expression level in the subtype, using the median and standard deviation of expression levels across Sub1-6. (B) Expression level distribution of representative markers for T cells (CD4 and CD8A) and neutrophils (CXCL1, CXCL8, and LCN2) in Sub1-6. In the violin plot, the center line shows the median expression level for each subtype marker. (C) Outline procedure for developing orthotopic PDAC models transplanted with cells derived from Sub4 (SNU3608) and Sub6 (SNU3573) tumors in Balb / c-nu mice. (D) Representative ultrasound images of SNU3608 and SNU3573 tumors taken on days 8, 22, and 36. Dotted circles indicate tumors. (E) Total image of SNU3608 and SNU3573 tumors taken on day 42. (F) Weight comparison of SNU3608 and SNU3573 tumors measured on day 42. p=0.062 by Student's t-test. (G) FACS gating scheme for myeloid populations and chemokine receptors. Contours show cell density distribution. Solid lines show cell populations shown in individual plots. Red arrowheads show FACS gating flow. (HJ) Percentage of immune cells shown in SNU3608 and SNU3573 tumors measured in blood (H), spleen (I), and bone marrow (BM, J). PMN-MDSCs, polymorphonuclear myelo-derived suppressor cells; M-MDSCs, mononuclear cell MDSCs. The number or percentage of four represented PMN-MDSC groups, defined by the expression levels of CXCR2 and CXCR4 measured in the blood (L), spleen (M), and BM (N) of Balb / c-nu mice harboring not only (KN)SNU3608 and SNU3573 tumors (K), but also (N). n=3 or 4 (naive), 8-10 (SNU3608 or SNU3573).Bidirectional ANOVA with Sidak's post-hoc correction yielded results of *, p<0.05;**, p<0.01;****, and p<0.0001. [Modes for carrying out the invention]

[0026] Hereinafter, embodiments and examples of the present invention will be described in detail so that those with ordinary skill in the art to which the present invention pertains can easily implement it. However, the present invention can be realized in various different forms and is not limited to the embodiments and examples described herein.

[0027] While the present invention can take on various forms through numerous modifications, specific embodiments are described in detail below. However, it should be understood that this is not intended to limit the present invention to any particular disclosure, but rather to include all modifications, equivalents, or substitutions that fall within the spirit and technical scope of the present invention.

[0028] The terminology used in this application is used solely to describe specific embodiments and is not intended to limit the invention. In this application, terms such as “includes” or “having” are intended to specify the existence of features, steps, operations, components, or combinations thereof described in the specification, and should be understood not to preemptively exclude the possibility of the existence or addition of one or more other features, steps, operations, components, or combinations thereof.

[0029] Furthermore, unless otherwise specified, all terms used herein, including technical or scientific terms, have the same meaning as they would be generally understood by a person of ordinary skill in the art to which this invention pertains. Terms as defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art, and not as ideal or overly formal unless explicitly defined herein.

[0030] This invention relates to a method for determining subtypes of pancreatic ductal adenocarcinoma and a subtype determination kit.

[0031] The objective of this invention is to improve the stratification of patients with pancreatic ductal adenocarcinoma (PDAC) and to identify diagnostic markers for improving patient management of pancreatic cancer, which is a potential therapeutic target or a life-threatening disease.

[0032] This invention demonstrates that protein and genomic data are complementary. The availability of phosphorylation data provides information on signaling pathways with activity correlated with somatic mutations in SMG, suggesting a relationship between mutations and signaling pathways in pancreatic ductal adenocarcinoma (PDAC).

[0033] To select tumor genes and tumor suppressor candidates in PDAC, mRNA-protein-rich correlations were used. Furthermore, protein-rich and phosphorylation data were coupled with mRNA-rich data to more accurately define PDAC subtypes. GSEA and network analysis of mRNA and protein signatures defining PDAC subtypes reveal subtype characteristics. Analysis of genetic proteins through the effective integration of genomic, mRNA, and protein data provides useful information to elucidate the pathogenesis of PDAC, stratify PDAC patients, and potentially identify therapeutic targets.

[0034] This invention combines mRNA expression data from lesion tissue samples of pancreatic ductal adenocarcinoma with global protein data and phosphorylated protein data to perform proteogenomic analysis on PDAC samples. This allows for the identification of representative genes for all subtypes of pancreatic ductal adenocarcinoma, six subtypes of pancreatic ductal adenocarcinoma, and representative genes for each subtype.

[0035] Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIPRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, SEC11C Representative genes for all subtypes of pancreatic ductal adenocarcinoma (All Sub): KRT19, RAB27B, QSOX1, VILL, GNPAT, ABCC3, GP2, ETHE1, BPNT1, AGR2, PIGR, SRC, CTSE, JUP, RPL7, TSPAN8, SRM, VDAC1, SCP2, RPS3, AK4, RPL9, RDX, RPL3, RPL13A, RPL5, RPS9, HK2, RAB25, GNG2, RPL15, RPL37, RPS7, RPL8, RPL18A, RPL6, PABPC4, INF2, SLC25A24, MYH14, GALNT7, GOLM1, MCU, GSDMB, CYP2S1, HTATIP2, SDCBP2, SYTL2, PREB, MYO6, PKP3, SNTB2, S100A11 A method for determining subtypes of pancreatic ductal adenocarcinoma according to one embodiment of the present invention may include the following steps (1) to (4).

[0036] (1) A step of fragmenting the lesional tissue of pancreatic ductal adenocarcinoma isolated from a patient with pancreatic ductal adenocarcinoma, (2) The step of extracting and digesting proteins from the lesional tissue to obtain patient-specific peptide samples, (3) A step of measuring the expression levels of representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma from the patient-specific peptide samples, wherein the representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma are selected from the group consisting of the following: Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIPRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, SEC11C (4) A step of determining the subtype of pancreatic ductal adenocarcinoma patient by comparing the expression levels of representative genes of subtypes 1 to 6 of the pancreatic ductal adenocarcinoma.

[0037] According to one embodiment of the present invention, the expression levels of representative genes for subtypes 1 to 6 can be compared with the expression levels of representative genes (All Sub) for all subtypes of pancreatic ductal adenocarcinoma. This can improve the reliability of subtype discrimination. More specifically, the expression levels of the genes that contribute most significantly to differentiating subtypes 1 to 6 and all subtypes of pancreatic ductal adenocarcinoma described below can be combined and compared.

[0038] Representative genes for all subtypes of the aforementioned pancreatic ductal adenocarcinoma (All Sub) can be selected from the group consisting of KRT19, RAB27B, QSOX1, VILL, GNPAT, ABCC3, GP2, ETHE1, BPNT1, AGR2, PIGR, SRC, CTSE, JUP, RPL7, TSPAN8, SRM, VDAC1, SCP2, RPS3, AK4, RPL9, RDX, RPL3, RPL13A, RPL5, RPS9, HK2, RAB25, GNG2, RPL15, RPL37, RPS7, RPL8, RPL18A, RPL6, PABPC4, INF2, SLC25A24, MYH14, GALNT7, GOLM1, MCU, GSDMB, CYP2S1, HTATIP2, SDCBP2, SYTL2, PREB, MYO6, PKP3, SNTB2, and S100A11.

[0039] According to one embodiment of the present invention, the measurement and comparison of the expression levels of representative genes for all subtypes of pancreatic ductal adenocarcinoma and representative genes for subtypes 1 to 6 is performed by: constructing a stable isotope-labeled peptide panel representing representative genes for all subtypes of pancreatic ductal adenocarcinoma and genes for each subtype; mixing the patient-specific peptide sample with the stable isotope-labeled peptide panel; and analyzing the mixture by quantitative mass spectrometry to determine the subtype of the pancreatic ductal adenocarcinoma patient.

[0040] The quantitative mass spectrometry method may be MRM-MS (Multiple Reaction Monitoring-Mass Spectrometry), PRM-MS (Parallel Reaction Monitoring-Mass Spectrometry), DIA-MS (Data Independent Acquisition Mass Spectrometry), etc., but is not limited to these.

[0041] Figure 11 is a schematic diagram of the performance of MRM-MS analysis on a mixture of peptides extracted from lesional tissue of pancreatic ductal adenocarcinoma patients and a stable isotope-labeled peptide panel of representative genes of subtypes 1-6.

[0042] Multiple Reaction Monitoring / Mass Spectrometry (MRM-MS), using a Triple Quadrupole (QQQ) mass spectrometer, is a method that guides ions to a quadruple anode composed of four electrode columns and analyzes them based on the mass / charge ratio. A peptide with a mass / charge specific to the selected target protein (Precursor ion, MS1) is selected, and when this peptide is collided with the second quadrupole, a fragment with a characteristic mass / charge (Fragment ion, MS2) is selected from the resulting fragments. At this time, the precursor ion / fragment ion pairs obtained from MS1 and MS2, respectively, are called the specific transition of the target protein (the fingerprint of the specific mass of the target protein). By measuring all of these transitions using the multiple reaction monitoring method for all target proteins (100-300 proteins), the amount of all target proteins in the sample can be simultaneously analyzed relatively or absolutely in a short time using standard substances, which are peptides with the same amino acid sequence substituted with isotopes for which quantitative information is known. Based on this principle, MRM-MS can selectively detect and quantify only the target analyte with high sensitivity, thereby reducing the cost required for analysis.

[0043] Currently, the most commonly used method for quantitative analysis of proteins is antibody-dependent, such as ELISA assays. This method is costly and time-consuming, as it involves finding new antibodies and optimizing the analytical process.

[0044] According to one embodiment of the present invention, the MRM-MS analysis is performed by comparing the signal intensity of the patient-derived peptide with that of a representative peptide of the subtype, and the ratio of the signal intensity is represented by a contour map of the signal intensity for each peptide with peptide and peptide elution time as the two axes.

[0045] Figure 12 shows the MRM signal intensity and contour map between patient samples and representative peptides of each subtype.

[0046] These contour maps of representative peptide intensities for each subtype are used to compare patterns with contour maps of representative peptide intensities for each subtype obtained from endoscopic tissue of pancreatic cancer patients admitted to the hospital, thereby allowing for the determination of the patient's subtype.

[0047] According to one embodiment of the present invention, the disruption of the pancreatic cancer lesion tissue in step (1) may be carried out by cryogenic freezing. Fine tissue powder can be obtained by cryogenic freezing.

[0048] Cryogenic cryolysis is the optimal tissue sample processing technique for minimizing tumor tissue loss. While not limited to this, cryogenic temperatures may also be liquid nitrogen temperatures (-196°C).

[0049] To perform subtype differentiation in a large number of pancreatic cancer patients, peptide samples derived from patient tissues to be mixed with representative peptides of the subtype must be obtained rapidly. The first step in obtaining peptides from tissue is the tissue homogenization process, where the tissue can be processed at cryogenic temperatures for less than one minute to minimize tissue denaturation.

[0050] Cryogenic cryolipolysis is an optimal method for even minute amounts of pancreatic cancer patient samples because it does not involve any exposure to the outside during the process of tissue fragmentation into a powder, thus preventing sample loss.

[0051] According to one embodiment of the present invention, the step of extracting and digesting the protein in step (2) to obtain a peptide sample may be performed by a pressure circulation technique.

[0052] The pressure circulation technique involves cross-applying ultra-high pressure (45,000 psi) and low pressure (~15 psi) to fragmented pancreatic cancer tissue samples, thereby more effectively extracting and digesting proteins. This method allows for peptide acquisition from tissue in less than 3 hours. This is significantly faster than existing methods that take 30 hours and is a highly efficient technique that can apply to 16 tissue samples simultaneously. Therefore, it enables subtype discrimination in large numbers of patients.

[0053] Figure 13 shows the fragmentation process of pancreatic cancer tissue and a method for processing pancreatic cancer samples using ultra-high pressure circulation technology.

[0054] According to one embodiment of the present invention, subtypes 2 to 4 may have invasive properties, and subtypes 5 and 6 may have immunogenic properties.

[0055] Furthermore, subtype 4 possesses invasive properties and proliferative capacity, and can induce low T cell proliferation.

[0056] According to one embodiment of the present invention, subtypes 2 to 4 may be associated with epithelial-mesenchymal metastasis (EMT)-related processes.

[0057] Furthermore, subtypes 5 and 6 may be associated with immune-related processes.

[0058] The aforementioned subtype 1 may be involved in carbohydrate / lipid metabolism.

[0059] The aforementioned subtypes of PDAC include not only mRNA / protein signatures and cellular pathways for each subtype, but also anti-inflammatory immune cell profiles. PDAC patients can be further stratified by prognosis by classifying them into subtypes 2-4 (poor prognosis subtypes) or subtypes 1, 5, and 6 (good prognosis subtypes) based on the mRNA and protein signatures of their tumors.

[0060] Another embodiment of the present invention relates to a kit capable of distinguishing subtypes of pancreatic ductal adenocarcinoma. A pancreatic ductal adenocarcinoma subtype discrimination kit according to one embodiment of the present invention includes a formulation for measuring the expression levels of representative genes of pancreatic ductal adenocarcinoma subtypes 1 to 6, the representative genes of pancreatic ductal adenocarcinoma subtypes 1 to 6 can be selected from the group consisting of the following.

[0061] Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIPRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, SEC11C According to one embodiment of the present invention, the pancreatic ductal adenocarcinoma subtype discrimination kit includes a formulation for measuring the expression level of representative genes (All Sub) for all subtypes of pancreatic ductal adenocarcinoma, and the expression levels of the representative genes for subtypes 1 to 6 can be compared with the expression levels of representative genes for pancreatic ductal adenocarcinoma.

[0062] Representative genes for all subtypes of the aforementioned pancreatic ductal adenocarcinoma (All Sub) can be selected from the group consisting of KRT19, RAB27B, QSOX1, VILL, GNPAT, ABCC3, GP2, ETHE1, BPNT1, AGR2, PIGR, SRC, CTSE, JUP, RPL7, TSPAN8, SRM, VDAC1, SCP2, RPS3, AK4, RPL9, RDX, RPL3, RPL13A, RPL5, RPS9, HK2, RAB25, GNG2, RPL15, RPL37, RPS7, RPL8, RPL18A, RPL6, PABPC4, INF2, SLC25A24, MYH14, GALNT7, GOLM1, MCU, GSDMB, CYP2S1, HTATIP2, SDCBP2, SYTL2, PREB, MYO6, PKP3, SNTB2, and S100A11.

[0063] According to one embodiment of the present invention, a formulation for measuring the expression levels of representative genes for all subtypes of pancreatic ductal adenocarcinoma and representative genes for subtypes 1 to 6 may include a stable isotope-labeled peptide panel representing representative genes for all subtypes of pancreatic ductal adenocarcinoma and genes specific to each subtype.

[0064] A further embodiment of the present invention relates to a method for predicting the prognosis of a patient with pancreatic ductal adenocarcinoma, comprising the following steps (1) to (5).

[0065] (1) A step of fragmenting the lesional tissue of pancreatic ductal adenocarcinoma isolated from a patient with pancreatic ductal adenocarcinoma, (2) The step of extracting and digesting proteins from the lesional tissue to obtain patient-specific peptide samples, (3) A step of measuring the expression levels of representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma from the patient-specific peptide samples, wherein the representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma are selected from the group consisting of the following: Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIPRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, SEC11C (4) A step of determining the subtype of pancreatic ductal adenocarcinoma patient by comparing the expression levels of representative genes of subtypes 1 to 6 of the pancreatic ductal adenocarcinoma, (5) A step of predicting the prognosis by identifying the subtype.

[0066] According to one embodiment of the present invention, the expression levels of the representative genes of subtypes 1 to 6 can be compared with the expression levels of the representative genes (All Sub) of all subtypes of pancreatic ductal adenocarcinoma.

[0067] The representative genes for all subtypes of the aforementioned pancreatic ductal adenocarcinoma can be selected from the group consisting of KRT19, RAB27B, QSOX1, VILL, GNPAT, ABCC3, GP2, ETHE1, BPNT1, AGR2, PIGR, SRC, CTSE, JUP, RPL7, TSPAN8, SRM, VDAC1, SCP2, RPS3, AK4, RPL9, RDX, RPL3, RPL13A, RPL5, RPS9, HK2, RAB25, GNG2, RPL15, RPL37, RPS7, RPL8, RPL18A, RPL6, PABPC4, INF2, SLC25A24, MYH14, GALNT7, GOLM1, MCU, GSDMB, CYP2S1, HTATIP2, SDCBP2, SYTL2, PREB, MYO6, PKP3, SNTB2, and S100A11.

[0068] According to one embodiment of the present invention, subtypes 2 to 4 can be predicted to have a worse prognosis than subtypes 1, 5, and 6.

[0069] Furthermore, therapeutic strategies can be employed based on subtype and associated pathways and / or immune cell profiles. For example, Sub4 exhibits increased PMN-MDSCs that contribute to tumor cell proliferation by reducing high invasive activity and T cell activity. Such patterns suggest that when treating Sub4 tumors, both invasiveness and PMN-MDSCs must be addressed by simultaneously targeting invasive-associated RHOA and / or TGFB signaling and pro-tumorogenic PMN-MDSCs. Interestingly, despite the PDAC cohort not including acinar cell carcinomas, Sub6 exhibits low cellular fidelity and some endocrine characteristics. In tumors with low cellular fidelity, such characteristics are suggested to arise from tubular cell dedifferentiation (Martens et al., 2019), a high proportion of stromal cells (Bailey et al., 2016), or acinar cell contamination (Puleo et al., 2018). Genetic protein signatures are applicable to low cellular fidelity tumors classified as Sub6 that exhibit endocrine characteristics. However, whether these methods are applicable to acinar cell carcinoma needs to be investigated in large cohorts.

[0070] Numerous immune checkpoint molecules have been reported (Kalbasi and Ribas, 2020; Wei et al., 2018). mRNA expression levels of CEACAM1, PVR, and PVRL2 were higher in Sub2-4 than in Sub5-6, while levels of CD48, IGSF11, CD96, CD244, and BTLA were even higher in Sub5-6. Furthermore, CEACAM1, HMGB1, and CD274 showed the highest mRNA expression levels in Sub4 across all subtypes. Consistent with mRNA data, higher levels of the proteins CEACAM1 and PVR were detected in Sub1-4 than in Sub5-6, and the highest protein level of CD274 was detected in Sub4. CEACAM1, PVR, and CD274 suppress the activity of T cells and / or natural killer (NK) cells (Qin et al., 2019). This type of immunosuppression is observed in a variety of cancers, including PDAC (Dong et al., 2002; Feig et al., 2013; Nishiwada et al., 2015). The immune checkpoints identified in Sub5-6 tumors are not detectable by proteomic analysis. Furthermore, PMN-MDSCs, primarily from protumorigenic neutrophils, infiltrated Sub4 tumors at high levels. PMN-MDSC-mediated immunosuppression has been reported not only in lung cancer (Huang et al., 2013), colon cancer (Jung et al., 2017a; Jung et al., 2017b), breast cancer (Alizadeh et al., 2014), head cancer and neck cancer (Brandau et al., 2011), renal cancer (Rodriguez et al., 2009), and gastric cancer (Wang et al., 2013), but also in PDAC (Porembka et al., 2012). According to a human blood atlas (Uhlen et al., 2019), CEACAM1, PVR, and CD274 are expressed at high levels in PMN-MDSCs, suggesting a potential link between immune checkpoints and PMN-MDSCs.How these proteins are associated with anti-tumor immunity deficiency in Sub4 tumors can be investigated further through detailed functional studies.

[0071] The invention will be described in more detail below through the examples provided. However, these examples are for illustrative purposes only, illustrating one or more specific examples, and the scope of the present invention is not limited to these examples.

[0072] [Examples] To derive subtypes of PDAC patients based on genetic proteins, we first clustered patient tumor samples using mRNA expression data, global proteome data, and phosphorylated proteome data, respectively. This identified three patient clusters (RNA1-3), five (Prot1-5), and five (Phos1-5) from each data set. To understand the characteristics of each patient cluster, we selected signature genes (RNA1-3), proteins (Prot1-5), and phosphorylated peptides (Phos1-5) that showed significantly higher expression in patient samples within each cluster compared to the remaining patient samples, using statistical comparative analysis. Finally, we performed integrated clustering of 150 patient samples to derive six subtypes (Sub1-6).

[0073] To determine the cellular processes associated with each derived subtype, we first selected signature genes and proteins corresponding to each subtype. Subsequently, we confirmed these cellular processes through functional enrichment analysis of these genes and proteins. This revealed that Sub2-4 commonly exhibited high expression of epithelial-mesenchymal metastasis (EMT) related genes, but specifically, Sub2-3 showed high expression of the same EMT-related protein, while Sub4 showed high expression of cell cycle-related proteins. In the case of Sub5-6, common was high expression of immune-related genes, but specifically, Sub5 showed high expression of the same immune-related protein, while Sub6 showed high expression of exocrine-related proteins. Finally, in the case of Sub1, we confirmed high expression of carbohydrate / lipid metabolism-related genes and proteins, which are characteristic of classical precursor PDAC subtypes.

[0074] To derive representative subtype peptides for differentiating these six patient subtypes, Partial least squares (PLS) analysis was performed on previously selected signature proteins (prot1-5) and phosphorylated peptides (phos1-5). In the case of signature proteins, PLS analysis was performed after converting each protein to its corresponding sibling peptide. Using the log2-fold-change values ​​of the peptides in 150 patients from the PLS analysis, a model was generated that simultaneously predicts whether 150 patients belong to a specific subtype (Sub1-6) or the overall subtype of all 150 patients. Furthermore, the degree to which individual peptides contribute to predicting patient subtypes was quantified using variable importance in projection (VIP) values.

[0075] To derive representative phosphorylated peptides for each of the six subtypes, we first selected phosphorylated peptides that met the following criteria for that subtype: 1) identified by signature, 2) VIP value greater than 1.5, 3) VIP value greater than that of other subtypes, and 4) detected in 80% or more of patients. For representative phosphorylated peptides predicting the overall subtype, we selected phosphorylated peptides that met the following criteria: 1) VIP value greater than 1.5, and 2) detected in 80% or more of all patients. Subsequently, we selected peptides from these that contained only one phosphorylation molecule and were suitable for use in MRM-MS analysis (considering peptide length, presence or absence of signal peptides, presence or absence of missed cleavage, etc.), ultimately deriving 16 phosphorylated peptides.

[0076] Next, to derive representative global peptides for each of the six subtypes, we first selected proteins that were 1) identified by the signature protein and 2) detected in more than 80% of patients in that subtype. From the sibling peptides of the selected proteins, we selected peptides that met the following criteria in that subtype: 1) VIP value greater than 1.15, 2) VIP value greater than that of other subtypes, and 3) detected in more than 80% of patients. For representative global peptides predicting the overall subtype, we selected peptides from the sibling peptides of proteins detected in more than 80% of all patients that met the following criteria: 1) VIP value greater than 1.15, and 2) detected in more than 80% of all patients. Subsequently, we selected up to two peptides per signature protein suitable for use in MRM-MS analysis, ultimately deriving 132 global peptides.

[0077] By adding two KRAS mutant protein peptides that show expression differences between subtypes to the 16 phosphorylated peptides and 132 global peptides finally derived through the above process, a final total of 150 subtype representative peptides were derived.

[0078] Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIPRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, SEC11C Representative genes for all subtypes of pancreatic ductal adenocarcinoma (All Sub): KRT19, RAB27B, QSOX1, VILL, GNPAT, ABCC3, GP2, ETHE1, BPNT1, AGR2, PIGR, SRC, CTSE, JUP, RPL7, TSPAN8, SRM, VDAC1, SCP2, RPS3, AK4, RPL9, RDX, RPL3, RPL13A, RPL5, RPS9, HK2, RAB25, GNG2, RPL15, RPL37, RPS7, RPL8, RPL18A, RPL6, PABPC4, INF2, SLC25A24, MYH14, GALNT7, GOLM1, MCU, GSDMB, CYP2S1, HTATIP2, SDCBP2, SYTL2, PREB, MYO6, PKP3, SNTB2, S100A11 All 150 derived representative gene peptide samples were mixed to form a representative peptide sample for each subtype, which was then mixed with a peptide sample derived from pancreatic cancer patients to identify the subtype of the pancreatic cancer patient. A pressure-circulating Barocycler apparatus was used to obtain the pancreatic cancer patient-derived peptide sample. First, ultra-high pressure of 45,000 psi and low pressure of 15 psi were cross-applied to microtubes containing tissue samples and lysis buffer to effectively disintegrate the cell walls, after which proteins were extracted. Subsequently, the digestive enzymes Lys-C and Trypsin were added to digest the proteins, and ultra-high pressure of 20,000 psi and low pressure of 15 psi were cross-applied to obtain peptide samples from 16 pancreatic cancer tissue samples within a total of 3 hours. Next, the obtained pancreatic cancer patient peptide samples underwent a C18 spin column-based desalting process, and finally, BCA quantification was performed to obtain the pancreatic cancer patient-derived peptide sample containing quantitative information.

[0079] Next, to identify subtypes in pancreatic cancer patients, a subtype-discriminating peptide sample was constructed by mixing a patient-derived peptide sample with a representative peptide sample containing representative gene information for 150 subtypes. For each peptide, y-ions corresponding to charge states +2 and +3 were selected using Top-3 transition analysis, which is reproducible and allows for stable MRM analysis.

[0080] The subtype information, gene symbols, and protein names for all 150 representative subtype peptides are shown in Table 1 below.

[0081] [Table 1(1)] [Table 1(2)] [Table 1(3)] [Table 1(4)] [Table 1(5)] Table 1(6)

Claims

1. A method for distinguishing subtypes of pancreatic ductal adenocarcinoma, including the following steps (1) to (4). (1) A step of fragmenting the lesional tissue of pancreatic ductal adenocarcinoma isolated from a patient with pancreatic ductal adenocarcinoma, (2) The step of extracting and digesting proteins from the lesional tissue to obtain patient-specific peptide samples, (3) A step of measuring the expression levels of representative genes for subtypes 1 to 6 of pancreatic ductal adenocarcinoma from the patient-specific peptide samples by quantitative mass spectrometry, wherein the representative genes are identified by quantifying the contribution of each peptide to subtype discrimination via Partial least squares (PLS) analysis, and are evaluated as genes that have a significant contribution to subtype discrimination by having a variable importance in projection (VIP) value exceeding a threshold, and the representative genes for subtypes 1 to 6 of pancreatic ductal adenocarcinoma are one or more selected from the group consisting of the following: Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, and BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, and CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, and ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, and BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, and EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, and SEC11C (4) A step of determining the subtype of a pancreatic ductal adenocarcinoma patient by comparing the contour map and pattern of the signal intensity of the representative peptide of each subtype with the contour map and pattern of the signal intensity generated for each peptide, wherein the signal intensity and pattern are represented by a contour map of the signal intensity for each peptide with peptide and peptide elution time as two axes.

2. The method for determining a subtype of pancreatic ductal adenocarcinoma according to claim 1, wherein the comparison of the expression levels of representative genes of subtypes 1 to 6 is performed by combining and comparing the expression levels of the genes that contribute most significantly to distinguishing between subtypes 1 to 6 and all subtypes of pancreatic ductal adenocarcinoma described below. Representative genes (All Sub) for all subtypes of pancreatic ductal adenocarcinoma: KRT19, RAB27B, QSOX1, VILL, GNPAT, ABCC3, GP2, ETHE1, BPNT1, AGR2, PIGR, SRC, CTSE, JUP, RPL7, TSPAN8, SRM, VDAC1, SCP2, RPS3, AK4, RPL9, RDX, RPL3, RPL13A, RPL5, RPS9, HK2, R AB25, GNG2, RPL15, RPL37, RPS7, RPL8, RPL18A, RPL6, PABPC4, INF2, SLC25A24, MYH14, GALNT 7, GOLM1, MCU, GSDMB, CYP2S1, HTATIP2, SDCBP2, SYTL2, PREB, MYO6, PKP3, SNTB2, and S100A11

3. The method for determining a subtype of pancreatic ductal adenocarcinoma according to claim 1, wherein the disruption of the lesional tissue of pancreatic cancer in step (1) is performed by cryogenic cryolipolysis.

4. The method for determining a subtype of pancreatic ductal adenocarcinoma according to claim 1, wherein the protein extraction in step (2) is performed by pressure circulation technology.

5. The method for determining a subtype of pancreatic ductal adenocarcinoma according to claim 1, wherein the representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma in step (3) above are identified by analysis of gene proteins obtained by combining mRNA data and global protein data and phosphorylated protein data from a lesion tissue sample of pancreatic ductal adenocarcinoma.

6. The measurement and comparison of the expression levels of representative genes for all subtypes of the aforementioned pancreatic ductal adenocarcinoma and representative genes for subtypes 1 to 6 were performed as follows: The steps include constructing a stable isotope-labeled peptide panel representing representative genes for all subtypes of pancreatic ductal adenocarcinoma and genes specific to each subtype, The steps include mixing the patient-specific peptide sample with the stable isotope-labeled peptide panel, The process involves analyzing the mixture using quantitative mass spectrometry to determine the subtype of pancreatic ductal adenocarcinoma patients, and A method for determining a subtype of pancreatic ductal adenocarcinoma according to claim 1 or 2, including the above.

7. The method for determining a subtype of pancreatic ductal adenocarcinoma according to claim 6, wherein the quantitative mass spectrometry method is performed by comparing the signal intensity of a patient-extracted peptide with that of a stable isotope-labeled peptide.

8. A kit for determining subtypes of pancreatic ductal adenocarcinoma, comprising a preparation for measuring the expression level of representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma, wherein the preparation for measuring the expression level comprises a stable isotope-labeled peptide corresponding to the representative gene, the representative gene is identified by quantifying the contribution of each peptide to subtype determination via Partial least squares (PLS) analysis, and is evaluated as a gene having a significant contribution to subtype determination by having a variable importance in project (VIP) value exceeding a threshold, and the representative genes of subtypes 1 to 6 of pancreatic ductal adenocarcinoma are one or more selected from the group consisting of the following. Representative genes of subtype 1 (Sub1): CLDN18, EPS8L3, CAPN5, GMDS, BCAS1, IDH1, DDAH1, SOD1, VIL1, GPX2, AOC1, LGALS4, MICU2, POF1B, MICU1, PLS1, and BDH1 Representative genes of subtype 2 (Sub2): UNC5B, PPP1R3G, IGFBP3, EDIL3, CLSTN1, COL11A1, P4HA1, PDLIM4, ST5, FSTL1, PPP1R13L, PLTP, PDLIM7, and CALU Representative genes of subtype 3 (Sub3): MYH9, FLNA, P4HA2, LOXL2, FN1, CD55, FLT1, ECM1, CCDC80, TSKU, HTRA1, COL12A1, SPON2, and ANGPTL2 Representative genes of subtype 4 (Sub4): PLEC, LPGAT1, NRDC, PRPF40A, CSDE1, IPO7, CDK1, HMGA1, DDX5, RASA1, ADSS, GMPS, CSE1L, PSME3, CAPRIN1, and BZW1 Representative genes of subtype 5 (Sub5): HSPB6, HSPA12A, ANXA6, VIM, UCHL1, PRPH, MAP1B, CD81, ANK2, AKAP12, ITSN1, RTN1, COL28A1, KCTD12, SPON1, SYNPO2, and EPB41L3 Representative genes of subtype 6 (Sub6): CTNND2, DTNA, REG1A, PRSS2, CPA1, CPB1, ACAT1, CPA2, PNLIRP1, PRDX4, SNTB1, PDCD4, CTRC, FKBP11, and SEC11C

9. The pancreatic ductal adenocarcinoma subtype discrimination kit according to claim 8, comprising a preparation for measuring the expression level of one or more representative genes of all subtypes of pancreatic ductal adenocarcinoma selected from the group consisting of the following, wherein the preparation for measuring the expression level comprises a stable isotope-labeled peptide corresponding to the representative gene and is compared with the expression level of the representative genes of subtypes 1 to 6. Representative genes (All Sub) for all subtypes of pancreatic ductal adenocarcinoma: KRT19, RAB27B, QSOX1, VILL, GNPAT, ABCC3, GP2, ETHE1, BPNT1, AGR2, PIGR, SRC, CTSE, JUP, RPL7, TSPAN8, SRM, VDAC1, SCP2, RPS3, AK4, RPL9, RDX, RPL3, RPL13A, RPL5, RPS9, HK2, R AB25, GNG2, RPL15, RPL37, RPS7, RPL8, RPL18A, RPL6, PABPC4, INF2, SLC25A24, MYH14, GALNT 7, GOLM1, MCU, GSDMB, CYP2S1, HTATIP2, SDCBP2, SYTL2, PREB, MYO6, PKP3, SNTB2, and S100A11

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