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Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes

Nature Cancer volume 4, pages 290–307 (2023)Cite this article

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An Author Correction to this article was published on 19 May 2023

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Abstract

We report a proteogenomic analysis of pancreatic ductal adenocarcinoma (PDAC). Mutation–phosphorylation correlations identified signaling pathways associated with somatic mutations in significantly mutated genes. Messenger RNA–protein abundance correlations revealed potential prognostic biomarkers correlated with patient survival. Integrated clustering of mRNA, protein and phosphorylation data identified six PDAC subtypes. Cellular pathways represented by mRNA and protein signatures, defining the subtypes and compositions of cell types in the subtypes, characterized them as classical progenitor (TS1), squamous (TS2–4), immunogenic progenitor (IS1) and exocrine-like (IS2) subtypes. Compared with the mRNA data, protein and phosphorylation data further classified the squamous subtypes into activated stroma-enriched (TS2), invasive (TS3) and invasive-proliferative (TS4) squamous subtypes. Orthotopic mouse PDAC models revealed a higher number of pro-tumorigenic immune cells in TS4, inhibiting T cell proliferation. Our proteogenomic analysis provides significantly mutated genes/biomarkers, cellular pathways and cell types as potential therapeutic targets to improve stratification of patients with PDAC.

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Fig. 1: SMGs in PDAC.
Fig. 2: Mutation–phosphorylation correlation in apoptosis and actin cytoskeleton pathways.
Fig. 3: Prognostic biomarkers predicted from mRNA–protein abundance correlation.
Fig. 4: RNA clusters and application of mRNA signatures to other PDAC cohorts.
Fig. 5: PDAC subtypes defined by proteogenomic analysis.
Fig. 6: Cellular pathways and cell types associated with PDAC subtypes.
Fig. 7: Network model for TS4 and pan-omics analysis.
Fig. 8: Suppression of T cell proliferation in TS4 by PMN-MDSCs.

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Data availability

The exome and mRNA sequencing data generated in this study are available in dbGaP (https://www.ncbi.nlm.nih.gov/gap/; accession ID: phs002347.v1.p1). Mass spectrometry-based global and phosphoproteomic data are available in the PDC (https://pdc.cancer.gov; accession ID: PDC000248 for global proteome and PDC000249 for phosphoproteome). Previously published somatic mutation, mRNA expression and clinical data for PDAC cohorts that were reanalyzed here were downloaded from the GDC data portal (https://portal.gdc.cancer.gov/) and the ICGC data portal (https://dcc.icgc.org/). Previously published scRNA-seq data for PDAC patients reanalyzed here are available under accession code GSE127471. SwissProt protein sequence data (v.2018.01) was downloaded from the UniProt database (https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/release-2018_01/knowledgebase/) to construct the multiplexed unified protein database. Source data are provided with this paper. All other relevant data supporting the findings of this study are available from the corresponding authors on reasonable request.

Code availability

All of the custom codes used for the analyses included in our manuscript were uploaded to GitHub repository with instructions for users: https://github.com/doyoungh/Hyeon_et_al_PDAC_Nature_Cancer.

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Acknowledgements

This work was supported by grants from the Collaborative Genome Program for Fostering New Post-Genome Industry through the National Research Foundation (grant no. NRF-2017M3C9A5031397) funded by the Korean Ministry of Science and ICT. This work was also supported by grants from the Creative Research Initiative Program (grant no. NRF-2017R1A3B1023387 to S.H.B.) and the National Research Foundation of Korea (grant no. NRF-2020R1C1C1015062 to K.J., grant no. NRF-2022M3H9A2086450 to S.-W.L. and grant no. 2022R1A2C2011122 to J.-Y.J.) and the Korea Health Industry Development Institute (grant no. HI16C2037 to T.P.) funded by the Ministry of Health & Welfare. This work was conducted under the auspices of a Memorandum of Understanding between Korea University’s Center for Proteogenome Research and the US National Cancer Institute’s International Cancer Proteogenome Consortium (ICPC). The ICPC encourages international cooperation among institutions and nations in proteogenomic cancer research in which proteogenomic datasets are made available to the public. This study was also conducted in collaboration with the US National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium. The biological specimens used in this study were provided by the Biobank at Seoul National University Hospital (SNUH), a member of the Korean Biobank Network (KBN4_A03), and SNUH Cancer Tissue Bank.

Author information

Author notes

  1. These authors contributed equally: Do Young Hyeon, Dowoon Nam, Youngmin Han, Duk Ki Kim, Gibeom Kim, Daeun Kim.

Authors and Affiliations

  1. School of Biological Sciences, Seoul National University, Seoul, Republic of Korea

    Do Young Hyeon, Gibeom Kim, Chang Rok Kim, Seon Ah Choi, Yong Ryoul Kim, Sung Hee Baek & Daehee Hwang

  2. Department of Chemistry and Center for Proteogenome Research, Korea University, Seoul, Republic of Korea

    Dowoon Nam, Jingi Bae, Seunghoon Back, Dong-Gi Mun, Inamul Hasan Madar, Hangyeore Lee, Su-Jin Kim, Hokeun Kim & Sang-Won Lee

  3. Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea

    Youngmin Han, Wooil Kwon, Young-Ah Suh, Hongbeom Kim & Jin-Young Jang

  4. Department of Anatomy and Cell Biology and Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea

    Duk Ki Kim, Juhee Jeong, Suwan Jeon, Yeon Woong Choo & Keehoon Jung

  5. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Seoul, Republic of Korea

    Duk Ki Kim

  6. Creative Research Initiatives Center for Epigenetic Code and Diseases, Seoul National University, Seoul, Republic of Korea

    Gibeom Kim, Chang Rok Kim, Seon Ah Choi, Yong Ryoul Kim & Sung Hee Baek

  7. Department of Biological Sciences, College of Natural Sciences and Department of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea

    Daeun Kim, Sangyeop Hyun & Daechan Park

  8. Department of Pathology, Seoul National University College of Medicine, Seoul, Korea

    Kyung Bun Lee

  9. Department of Computer Science, Hanyang University, Seoul, Republic of Korea

    Seunghyuk Choi & Eunok Paek

  10. Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea

    Taewan Goo

  11. Department of Statistics, Seoul National University, Seoul, Republic of Korea

    Taesung Park

  12. Korean Cell Line Bank, Laboratory of Cell Biology, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea

    Ja-Lok Ku

  13. Department of New Biology, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea

    Min-Sik Kim

  14. Institute of Allergy and Clinical Immunology, Seoul National University Medical Research Center, Seoul, Republic of Korea

    Keehoon Jung

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  1. Do Young Hyeon
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Contributions

S.-W.L., D.H., J.-Y.J., S.H.B., K.J. and D.P. designed and directed the integrated proteogenomic analysis. Y.H., W.K., Y.-A.S., Hongbeom K. and J.-Y.J. collected, characterized and processed the tumor samples. D.K., S.H., D.P., D.Y.H., D.H., T.G. and T.P. performed genomic analysis of tumor samples and analyzed the genomic data. S.-W.L. and D.H. designed the proteomic experiments. D.N., J.B., S.B., D.-G.M., I.H.M., H.L., S.-J.K., Hokeun K., S.-W.L., S.C., E.P. and M.-S.K. performed global proteome and phosphoproteome profiling experiments, unified database searches and analyzed the proteomic data. D.Y.H., D.H., D.K. and D.P. performed integrated analyses with genomic and proteomic data, as well as clustering, network and deconvolution analyses. J.-L.K. established primary cell lines from the human tumor samples. G.K., C.R.K., S.A.C., Y.R.K. and S.H.B. performed functional experiments to test the prognostic biomarkers. K.B.L. performed immunohistochemistry experiments to validate the markers for cell types and pathways in the network models. D.K.K., J.J., S.J., Y.W.C. and K.J. performed functional experiments to test the interactions between PMN-MDSCs and T cells. D.H., S.-W.L., K.J., S.H.B., J.-Y.J., D.P., D.Y.H., D.N., Y.H., D.K.K., G.K. and D.K. wrote the manuscript. All authors have read and approved the final version of the manuscript for publication.

Corresponding authors

Correspondence to Daechan Park, Keehoon Jung, Sung Hee Baek, Jin-Young Jang, Daehee Hwang or Sang-Won Lee.

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The authors declare no competing interests.

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Nature Cancer thanks Steven Gallinger, Karin Rodland and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Proteogenomic analysis of PDAC.

a, The workflow for proteogenomic analysis of PDAC. Exome-sequencing analysis of cancer tissues and blood samples and RNA-sequencing of cancer tissues were performed for tissue samples from 196 patients while mass spectrometry-based proteomic analyses (global proteome and phosphoproteome) were performed for high purity samples (cellularity ≥ 19%) from 150 patients. Distribution of tumor cellularity was shown for 196 samples (bottom left). Only the samples with cellularity ≥ 15% were used for the analysis. In the box plot, the center line indicates median value and the box limits indicate upper and lower quartiles. b, Numbers of non-redundant peptides identified from global proteome and phosphoproteome data. c, Numbers of protein-coding genes identified from mRNA sequencing and proteome data (global proteome and phosphoproteome). The average numbers of peptides and protein-coding genes are indicated in (b) and (c), respectively. d, Numbers of somatic mutations altering protein sequences and genes carrying the mutations identified from exome-sequencing data. e, Relationships among significantly mutated genes (SMGs) identified from our cohort and previous cohorts of the TCGA10, Bailey et al.11, Biankin et al.12, Waddell et al.13, and Witkiewicz et al.14.

Source data

Extended Data Fig. 2 Mutation-protein abundance and phosphorylation correlations reveal implication of apoptosis and the actin cytoskeleton in PDAC.

a-g, Association of somatic mutations with protein abundance or phosphorylation level in TP53 (a), RB1 (b), ATM (c), ARID1A (d), KMT2D (e), AHNAK2 (f), and FCGBP (g). Lollipop plots show the detected somatic mutations (circles) and phosphorylation sites (triangles) in the gene structures (top). The height of the lollipop indicates the number of patients with the corresponding mutations, and colours indicate the mutation types (see legend). The samples were sorted based on the somatic mutations. Protein abundance or intensity of the phosphorylated peptides was normalised relative to their median levels across all patients displayed in bar plots (bottom; red and blue, higher and lower than the median, respectively). Enriched mutation sites are indicated by arrows. h, Correlations of copy number variations (CNVs) with mRNA (left) and protein (right) expression levels (n = 93 patients). Red and blue indicate significant (FDR < 0.05 determined by multiOmicsViz64) positive and negative correlations, respectively. Diagonal and off-diagonal elements in the heat maps indicate cis- and trans-correlations between CNVs and expression levels of mRNA or protein. Along the chromosome, the numbers of CNVs that correlated specifically with either mRNA or protein expression levels and commonly with both mRNA and protein expression levels were displayed in blue (top) and black (bottom) bars, respectively.

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Extended Data Fig. 3 mRNA-protein abundance correlation suggests potential prognostic biomarkers.

a, Distribution of Spearman’s correlation coefficients of mRNA and protein abundances of individual genes across patients (n = 150 patients). Yellow and blue, positive and negative correlations, respectively. b, Differential association of the genes having high and low mRNA-protein abundance correlations with KEGG pathways (n = 150 patients). The genes involved in each KEGG pathway were indicated by yellow (positive correlation) and blue (negative correlation) bars. c, Cumulative density distributions of mRNA-protein correlations for the genes with significant (blue) and non-significant (red) survival differences between top and bottom 25% of patients with the highest and the lowest mRNA expression levels, respectively. p = 1.4 × 10−18 by two-sided Kolmogorov-Smirnov test. d, Distributions of survival differences (Chi-square statistic values) for the genes with significant (FDR < 0.01) and non-significant (FDR > 0.1) mRNA–protein correlations. p = 3.2 × 10−18 by two-sided Student’s t-test. In violin plots, the line indicates the median value. n = 150 and 196 patients for mRNA-protein correlation and survival analysis, respectively (c-d). e, Cellular pathways represented by the genes showing negative mRNA-survival correlations. The enrichment significance of each pathway is indicated as –log10 (p-value), where p-value is the p-value for enrichment from ConsensusPathDB. Two dotted lines for p = 0.05 and 0.01. f, Selection of prognostic biomarkers for functional experiments. Among 19 prognostic biomarkers with significant negative (hazard ratio>1) mRNA-survival correlations in the four PDAC cohorts, the candidates with desirable survival curve patterns (red and blue lines, top and bottom 25% of patients with the highest and the lowest mRNA expression levels) in at least 3 PDAC cohorts were selected, resulting in 16 prognostic biomarkers. Among them, 7 prognostic biomarkers that have not been previously reported in pancreatic cancer were selected. Finally, among them, the 5 prognostic biomarkers having expression levels larger than the median expression (log2-FPKM = 3.11) of expressed genes (FPKM > 1) in AsPC1 cells. Among them, KRT19 was excluded due to its involvement in the broad spectrum of functions. The mRNA expression profile of AsPC1 cells was obtained from Cancer Cell Line Encyclopedia (CCLE)65. FPKM, fragments per kilobase of transcript per million.

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Extended Data Fig. 4 Functional validation of potential prognostic biomarkers by cell-based assays in PANC1 cells.

a, Results of cell-based assays including proliferation curve, 3D spheroid formation, transwell migration, and matrigel invasion using PANC1 cells are summarized in the table. Each value written in the table corresponds to log2 value of fold change. Fold change was calculated as a relative quantification of results from respective assays comparing proliferation/migration of cells expressing each shRNA to that of cells expressing control shRNA (shCtrl). b, Knock-down efficiency of each shRNA targeting potential prognostic biomarkers was measured with immunoblot analysis. Knock-down efficiency was validated three times independently. c, Proliferation curves of PANC1 cells were drawn from the result of cell counting upon the expression of shCtrl or the indicated shRNA targeting prognostic biomarkers (n = 3 independent experiments). P < 0.0001 for all shRNAs. d, 3D spheroid volume of PANC1 cells after 7 days is presented as bar graph with representative images (n = 5 independent spheroids). Scale bar, 500 μm. P < 0.0001 for all shRNAs. e, Representative images from the transwell migration with quantification of average number of migrated PANC1 cells expressing the indicated shRNAs in each image are shown (n = 5 independent assays). Scale bar, 200 μm. P = 7.0 × 10−4 for shPPL-2 and P < 0.0001 for the others. f, Representative images from the matrigel invasion assay with quantification of average number of migrated PANC1 cells expressing the indicated shRNAs in each image are shown (n = 5 independent assays). Scale bar, 200 μm. P = 4.0 × 10−4 for shTPI1-1, P = 1.0 × 10−3 for shTPI1-2, P = 5.2 × 10−3 for shDCBLD2-1, and P < 0.0001 for the others. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 by two-way (c) and one-way (d-f) analysis of variance (ANOVA) with Dunnett’s post hoc correction. All data are shown as mean ± SEM (c-f).

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Extended Data Fig. 5 Clustering of samples using mRNA, protein, or phosphorylation data.

a, Two-stage clustering scheme. See Methods. b, Scatter plots comparing the four cellularity measures pairwise (n = 196, 196, 196, and 134 samples for qpure, KRAS VAF, ESTIMATE, and Histological, respectively). Spearman’s correlations and two-sided adjusted P-values (P) by random permutation experiments are shown. c, Two-stage clustering results of mRNA data (n = 98 and 196 samples for 1st and 2nd clustering, respectively). In the 1st (left) or 2nd (right) clustering, cophenetic correlation coefficient plots show how the coefficients vary with different numbers of clusters and also when different numbers of the molecules selected with multiple percentages (10 to 30%) of median absolute deviations (MADs) were used for clustering. The heat maps show the sample consensus obtained from pair-wise clustering. Blue-to-red gradient denotes the percentage of agreement in the clustering results in 100 clustering trials with a determined number of clusters. d-e, Two-stage clustering results of protein (d) and phosphorylation (e) data (n = 95 and 150 samples for 1st and 2nd clustering, respectively). See the legend in c. Colored bars in the bottom represent the samples belonging to the clusters: hi-RNA1-2 and RNA1-3 (c); hi-Prot1-3 and Prot1-5 (d); and hi-Phos1-3 and Phos1-5 (e).

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Extended Data Fig. 6 Molecular signatures and cell types define the characteristics of the subtypes.

a, How mRNA and protein signatures that defined Sub6 are selected and used for GSEA are shown as an example (n = 150 tumors). Integrative clustering (top left) shows that Sub6 was defined by RNA3, Prot5, and Phos5 clusters. Based on this information, 702 genes (rna3) defining RNA3 were selected as mRNA signatures (S6-G), and 1022 proteins (prot5) and 657 phosphopeptides (phos5) defining Prot5 and Phos5, respectively, were selected as protein signatures (S6-P) (bottom and top right). After mapping the phosphopeptides into phorphorylated proteins and combining them with 1022 proteins (prot5), the resulting proteins were used for GSEA. b, UMAP plot showing 9 clusters of the cell types indicated. Color coding was used to distinguish clusters. c, Genes specifically up-regulated in the 9 cell clusters. The heat map shows increased (yellow) and decreased (purple) expression (Z-score) of the genes up-regulated (row) in at least one cluster with respect to the median expression across all cells (column). Z-score was obtained from an inverse normal distribution of mRNA expression level. The color bar represents the gradient of the normalized Z-score. d, Violin plots showing distributions of mRNA expression levels for the selected representative genes across the 9 cell clusters. e, Distributions of tumor cellularity measurements determined by the methods indicated for TS1–4 and IS1–2 (n = 37, 18, 14, 33, 26, and 22 tumors for TS1-4 and IS1-2, respectively, in qpure cellularity, KRAS VAF, and ESTIMATE cellularity; n = 33, 15, 13, 30, 24, and 19 tumors for TS1-4 and IS1-2, respectively, in Histological cellularity). In violin plots, the line indicates the median value.

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Extended Data Fig. 7 Immunohistochemistry (IHC) analysis of markers for cell types and pathways in network models.

a-f, Representative IHC images (top), quantification of protein expression by IHC staining (bottom left), and protein expression levels from global proteome data (bottom right) for AGR2 (a), ITGA2 (b), FAP (c), FZD1 (d), PCNA (e), and MCM4 (f) (n = 12, 10, 10, and 13 samples for TS1-4, respectively). H-scores were used for quantification. However, as quantification measures, the percentage of positive cells in each tumor was used for PCNA, and a fraction of positive samples in each TS was used for MCM4. Scale bars = 100 μm. AGR2, P = 3.3 × 10−7 for TS1 vs. other TSs; ITGA2, P = 3.8 × 10−2 for TS2-4 vs. TS1; FAP, P = 2.6 × 10−3 for TS2 vs. other TSs; FZD1 (tumor), P = 8.5 × 10−3 for TS2 vs. other TSs; FZD1 (stroma), P = 1.4 × 10−2 for TS3 vs. other TSs; PCNA, P = 3.2 × 10−6 for TS4 vs. other TSs; and MCM4, P = 2.9 × 10−3 for TS4 vs. other TSs. In violin plots, the line indicates the median value. *, P < 0.05; **, P < 0.01; ***, P < 0.001 by one-sided Student’s t-test (a-e) and Fisher’s exact test (f).

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Extended Data Fig. 8 PDAC subtypes are associated with distinct cellular networks.

a, A network model showing interactions between the genes and proteins involved in immune-related pathways (top) and pancreatic secretion (bottom) associated with IS1–2. Node colours (centre and boundary) indicate whether the corresponding gene and protein were selected as signatures for IS1–2 (green for IS1 and dark green for IS2). Presence of a circled P on a node indicates phosphorylated peptides that define the corresponding subtypes. Nodes with blue label indicate kinases predominantly activated in IS1-2. Arrows, activation; inhibition symbols, inhibition; solid arrows, direct activation; dotted arrows, indirect activation; grey lines, protein-protein interactions. b, Heat map showing activities of the indicated kinases for the samples grouped according to the subtypes identified (n = 37, 18, 14, 33, 26, and 22 samples for TS1-4 and IS1-2, respectively). Color bar, gradient of normalized enrichment score (NESij) from GSEA, which indicates activity of kinase i in sample j. Subtypes associated with predominant kinase activation are indicated in parenthesis. c, Subtype distributions of the samples that harbored somatic mutations of TP53 or ARID1A and had altered protein abundance or phosphorylation level of the corresponding protein-encoding gene (n = 150 samples). The color in the heat maps below the bar plots indicate the subtype of patients. d, The number of patients with or without mutations for which protein abundances or intensities of phosphorylated peptides are higher (positive) or lower (negative) than the median across patients. The color in the stacked bar graphs indicate the subtype of patients.

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Extended Data Fig. 9 PMN-MDSCs inhibit T cell proliferation.

a, mRNA/protein levels of immune cell markers across TS1-4 and IS1-2. The heat maps show mRNA (left) and protein (right) Z-scores of the markers in TS1-4 and IS1-2. In each subtype, for each marker, Z-scores were computed by auto-scaling the median level in the subtype using the median and standard deviation of levels across TS1-4 and IS1-2. b, Distributions of the marker levels for T cell (CD4 and CD8A) and neutrophil (CXCL1, CXCL8, and LCN2) in TS1-4 and IS1-2 (n = 37, 18, 14, 33, 26, and 22 tumors for TS1-4 and IS1-2, respectively). In violin plots, the median levels (center line) of the markers were shown. c, Schematic procedure for development of orthotropic PDAC models. d, Representative ultrasound images of SNU3608 and SNU3573 tumors at Day 8, 22, and 36. Dotted circles represent the tumors. e, Gross images of SNU3608 and SNU3573 tumors at Day 42. f, SNU3608 and SNU3573 tumor weights at Day 42 (n = 10 samples/group). P = 5.5 × 10−2 by two-sided Student’s t-test. g, FACS gating scheme for myeloid population and chemokine receptors. Contour lines, cell density distributions; solid lines, indicated cell populations; red arrow heads, FACS gating flow. h-j, Percentages of indicated immune cells in SNU3608 and SNU3573 tumors in blood (CD11b: Naїve vs. SNU3608, P = 1.8 × 10−3; Naive vs. SNU3573, P = 6.0 × 10−3; PMN-MDSSC: Naive vs. SNU3608, P = 2.2 × 10−3; Naive vs. SNU3573, P = 7.5 × 10−3; Neutrophils: Naive vs. SNU3608, P = 8.6 × 10−3; Naive vs. SNU3573, P = 1.6 × 10−2) (h), spleen (i), and bone marrow (BM, j). PMN- and M-MDSC, polymorphonuclear and monocytic myeloid-derived suppressor cells, respectively. k-n, Numbers or percentages of indicated PMN-MDSC groups in SNU3608 and SNU3573 tumors (P < 0.0001) (k), as well as in blood (Naїve vs SNU3608, P < 0.001; Naїve vs SNU3573, P = 2.0 × 10−4) (l), spleen (m), and BM (P = 9.0 × 10−4) (n) of Balb/c-nu mice carrying SNU3608 and SNU3573 tumors. Naїve, n = 3 samples of blood, n = 4 samples of spleen and BM; SNU3608, n = 8 samples; SNU3573, n = 10 samples (h-n). *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 by two-way ANOVA with Sidak’s posthoc corrections (h-n). All data are shown as mean ± SEM (f and h-n).

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Extended Data Fig. 10 Higher level of infiltration of PMN-MDSCs is observed in TS4 tumors than in IS2 tumors.

a, Comparison of tumor volumes over time measured by ultrasonography in orthotopic PDAC models grafted with cells derived from TS4 (SNU3608) and IS2 (SNU3573 and SNU3752) tissues (SNU3608-2 vs. SNU3573-2, P = 0.0001; SNU3608 vs. SNU752-2, P < 0.0001). b-c, Comparison of volume (SNU3608-2 vs. SNU3573-2, P = 1.0 × 10−3; SNU3608-2 vs. SNU3752-2, P < 0.001) (b) and weights (P < 0.001) (c) of SNU3608, SNU3573, and SNU3752 tumors at the end point. d, Gross photos of SNU3608 (TS4), SNU3573 (IS2) and SNU3752 (IS2) tumors taken on Day 42. e-f, Percentages of immune cell subsets infiltrating SNU3608, SNU3573, and SNU3752 tumors (****, P < 0.0001; PMN-MDSC: SNU3573-2 vs. SNU3752-2, P = 3.2 × 10−3; Neutrophil: SNU3573-2 vs. SNU3752-2, P = 3.8 × 10−3) (e) and blood (****, P < 0.0001; CD11b: SNU3608-2 vs. SNU3573-2, P = 1.2 × 10−3; SNU3573-2 vs. SNU3752-2, P = 4.0 × 10−4; PMN-MDSC: SNU3573-2 vs. SNU3752-2, P = 8.0 × 10−4; Neutrophil: SNU3573-2 vs. SNU3752-2, P = 2.4 × 10−2) (f). g-h, Percentages of the four indicated groups of PMN-MDSCs defined based on expression levels of CXCR2 and CXCR4 measured in SNU3608, SNU3573 and SNU3752 tumors (****, P < 0.0001; CXCR2−CXCR4−: SNU3608-2 vs. SNU3573-2, P = 1.7 × 10−2; SNU3573-2 vs. SNU3752-2, P = 3.6 × 10−3) (g) and blood (P < 0.0001) (h). n = 9, 5, and 9 samples for SNU3608, SNU3573, and SNU3752, respectively (a-h). *, P < 0.05, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001 by two-way analysis of variance (ANOVA) with Sidak’s post hoc corrections (a and e-h) and one-way ANOVA with Dunnett’s post hoc correction (b-c). All data are shown as mean ± SEM (a-c and e-h).

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Supplementary Table 1–7

Legends of supplementary tables are included in the first sheet of Supplementary_Tables.

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Hyeon, D.Y., Nam, D., Han, Y. et al. Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes. Nat Cancer 4, 290–307 (2023). https://doi.org/10.1038/s43018-022-00479-7

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