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Multi-omic profiling of peritoneal metastases in gastric cancer identifies molecular subtypes and therapeutic vulnerabilities

Nature Cancer volume 2, pages 962–977 (2021)Cite this article

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Abstract

Peritoneal metastasis, a hallmark of incurable advanced gastric cancer (GC), presently has no curative therapy and its molecular features have not been examined extensively. Here we present a comprehensive multi-omic analysis of malignant ascitic fluid samples and their corresponding tumor cell lines from 98 patients, including whole-genome sequencing, RNA sequencing, DNA methylation and enhancer landscape. We identify a higher frequency of receptor tyrosine kinase and mitogen-activated protein kinase pathway alterations compared to primary GC; moreover, approximately half of the gene alterations are potentially treatable with targeted therapy. Our analyses also stratify ascites-disseminated GC into two distinct molecular subtypes: one displaying active super enhancers (SEs) at the ELF3, KLF5 and EHF loci, and a second subtype bearing transforming growth factor-β (TGF-β) pathway activation through SMAD3 SE activation and high expression of transcriptional enhancer factor TEF-1 (TEAD1). In the TGF-β subtype, inhibition of the TEAD pathway circumvents therapy resistance, suggesting a potential molecular-guided therapeutic strategy for this subtype of intractable GC.

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Fig. 1: Landscape of molecular alterations in GC with malignant ascites.
Fig. 2: Driver gene alterations in tumor cells purified from ascites and patient-derived cell lines.
Fig. 3: EMT cluster in GC with peritoneal metastasis.
Fig. 4: Activation of the YAP–TAZ–TEAD pathway in the EMT cluster.
Fig. 5: Epigenetic control in GC with peritoneal metastasis.
Fig. 6: Augmentation of genes coding core regulatory transcription factors by genetic alterations.
Fig. 7: Molecular targets in GC with peritoneal metastasis.
Fig. 8: TEAD inhibition in GC in the EMT group.

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Data availability

Sequencing data have been deposited at the European Genome-phenome Archive (EGA) under accession no. EGAS00001004959. Data are controlled because they include patient information; access will be granted upon reasonable request to yotanaka@ncc.go.jp, based on EGA terms. Expression, ChIP–seq and methylation data have been deposited at Gene Expression Omnibus under accession nos. GSE162213, GSE162214 and GSE168999. Publicly available genomic data of the GC cohort were downloaded from the cBioPortal data portal. Publicly available cell line data were downloaded from DepMap. Source data for Figs. 2–5, 7 and 8 and Extended Data Figs. 1, 2 and 4–10 have been provided with the paper. The data supporting the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

No unpublished code was used in this study. Mutation calling was performed using the Genomon2 pipeline. To detect positively selected coding mutations, we used three independent approaches: MutSigCV13; dndscv14; and MutPanning15. For noncoding variants, we used the FunSeq2 software25. The HOMER pipeline v.4.10 (ref. 57) was used for the de novo motif discovery of noncoding hotspot genetic regions. Mutational signatures were analyzed using SigProfilerExtractor58. SVs were detected using GenomonSV. Copy number status and tumor purity were analyzed using FACETS61. Clonal composition was analyzed using PyClone v.0.13.0 (ref. 62). RNA-seq reads were quantified using TopHat2 and Cufflinks (refs. 63,64). Gene fusions were detected using the deFuse pipeline. GSEA was performed using GSEA v.4.0. ChIP–seq peaks were determined using MACS2 v.2.0 (ref. 67). SEs were detected using the ROSE method37,38. CRCmapper39 was used to extract candidate SE core regulatory circuit constituents.

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Acknowledgements

We thank K. Miyazono for the discussion and S. Sugaya for her technical assistance. We thank all the patients and families who contributed to this study. This study was supported in part by grants from the Project for Cancer Research and Therapeutic Evolution (P-CREATE) under grant no. JP20cm0106502 (to M. Kawazu), Funding for research to expedite effective drug discovery by Government, Academia and Private partnership (GAPFREE) under grant no. JP19ak0101043 (to Y.K. and H.S.) and Leading Advanced Projects for Medical Innovation under grant no. JP18am0001001 (to H. Mano) from the Japan Agency for Medical Research and Development. K-975 was kindly provided by Kyowa Kirin.

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Author notes

  1. These authors contributed equally: Yosuke Tanaka, Fumiko Chiwaki.

Authors and Affiliations

  1. Division of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan

    Yosuke Tanaka, Shinya Kojima, Masahito Kawazu, Toshihide Ueno, Satoshi Inoue & Hiroyuki Mano

  2. Department of Translational Oncology, National Cancer Center Research Institute, Tokyo, Japan

    Fumiko Chiwaki, Masayuki Komatsu & Hiroki Sasaki

  3. Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, Japan

    Shigeki Sekine & Yasushi Yatabe

  4. Kanamecho Hospital, Tokyo, Japan

    Keisuke Matsusaki

  5. Department of Laboratory Medicine, National Cancer Center Hospital, Tokyo, Japan

    Hiromichi Matsushita

  6. Division of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo, Japan

    Narikazu Boku

  7. Department of Pathology, Keio University School of Medicine, Tokyo, Japan

    Yae Kanai

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  1. Yosuke Tanaka
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Contributions

H. Mano and H.S. designed the study. Y.T., S.K. and T.U. performed the sequencing data analyses. Y.T. and Y.K. performed the methylation analyses. Y.T. and F.C. performed the functional assays. S.S. and Y.Y. performed the pathological analyses. F.C. and H.S. established the cell lines. F.C., K.M., H. Matsushita and N.B. collected the specimens. M. Kawazu, M. Komatsu, and S.I. performed sample preparation. Y.T. and H. Mano generated the figures and tables and wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Yosuke Tanaka, Hiroki Sasaki or Hiroyuki Mano.

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The authors declare no competing interests.

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Peer review information Nature Cancer thanks Razvan Cristescu, Stephen Meltzer 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 Genomic features of tumour cells purified from ascites and patient-derived cell lines.

a, Composition of clones in tumour cells purified from ascites calculated by the PyClone model. The vertical axis shows the proportion of each clone. b, Pairwise comparison of mutations in tumour cells purified from ascites and those in patient-derived cell lines. The number of shared mutations, mutations specific to malignant ascitic fluid, and mutations specific to patient-derived cell lines are depicted in blue, green, and yellow, respectively. c, Pairwise comparison of copy number alterations between tumour cells purified from ascites and patient-derived cell lines. The vertical axis shows the log R ratio. d, SBS mutational signature generated from samples from 98 patients. The corresponding reference signatures are shown above each graph. e, Comparison of TMB between our cohort (n = 98) and 4 subtypes in the TCGA GC cohort (EBV: n = 30, MSI: n = 73, GS: n = 50, CIN: n = 223). Box plots show medians (lines), interquartile ranges (IQRs; boxes) and ±1.5× IQRs (whiskers). Wilcoxon rank-sum test (two-tailed). All the samples are biologically independent.

Source data

Extended Data Fig. 2 Driver gene alterations in tumour cells purified from malignant ascites and patient-derived cell lines.

a, Distribution of RHOA mutations in 98 samples. b, Pattern of mutant allele imbalance for the allele harbouring the RHOA mutation. c, Reads of aberrantly spliced transcripts of RNA-seq at the RHOA locus. The red arrow indicates the mutation locus. d, Cooccurrence of mutations in PIGR, genes in the IL-17, Toll-like receptor, and the NF-κB proinflammatory signalling pathways, and driver genes of DGC in 98 samples. e, CTCF-binding motif identified by de novo motif analyses in the noncoding SNV hotspot. f, Higher gene expression levels in samples with gene amplification. Box plots show medians (lines), interquartile ranges (IQRs; boxes) and ±1.5× IQRs (whiskers). Number of cases with and without amplifications are as follows; KRAS (n = 19 and n = 73), FGFR2 (n = 11 and n = 81), MET (n = 7 and n = 85), ERBB2 (n = 5 and n = 87), EGFR (n = 4 and n = 88), CCND1 (n = 6 and n = 86), MYC (n = 7 and n = 85), CD44 (n = 12 and n = 80), and GATA6 (n = 5 and n = 87). Wilcoxon rank-sum test (two-tailed). All the samples are biologically independent. g, A model of the RP2-ARHGAP6 fusion gene (top) and copy number change around the fusion genes (bottom) is depicted. The vertical axis of the bottom panel shows the log R ratio.

Source data

Extended Data Fig. 3 Pathways with gene alterations in tumour cells purified from malignant ascites and patient-derived cell lines.

a, Biological pathways dysregulated by gene alterations in 98 patients. Mutual exclusivities and associations between genes in the pathways are annotated. b, Comparison of the frequencies of driver gene amplifications in the TCGA GC cohort using the definition of high-level amplification in the TCGA study (total: n = 282, diffuse-type: n = 68, GS: n = 57).

Extended Data Fig. 4 EMT cluster in GC with peritoneal metastasis.

a, Hierarchical clustering analysis of the expression data of 200 genes belonging to the gene set ‘epithelial mesenchymal transition’ in 59 patient-derived cell lines. Each row represents an individual gene, and each column represents a cell line. Each cell in the matrix represents the expression levels of transcripts in an individual cell line. Red reflects high expression, and blue reflects low expression. b, Expression levels of genes characteristic of EMT in patient-derived cell lines in the EMT group (orange, n = 26) and the non-EMT group (green, n = 33). c, Hierarchical clustering analysis of the expression data of 200 genes belonging to the gene set ‘epithelial mesenchymal transition’ in the TCGA cohort (n = 265). Each row represents an individual gene, and each column represents an individual case. Each cell in the matrix represents the transcript expression levels in an individual case. The identified cluster with high expression is marked with red. d, Survival analysis of patients in the TCGA cohort (two-sided log-rank test). e, Relationship of the expression levels of SMAD3 between purified tumour cells in ascites and corresponding cell lines. f, Hierarchical clustering analysis of the expression data of the 1000 most variable noncoding RNAs in 59 patient-derived cell lines. Each row represents an individual gene, and each column represents a cell line. Each cell in the matrix represents the expression levels of transcripts in an individual cell line. Pink-coloured samples belong to the EMT group, and green-coloured samples belong to the non-EMT group. g, Expression levels of noncoding RNAs in patient-derived cell lines in the EMT group (orange, n = 26) and the non-EMT group (green, n = 33). h, Number of exonic mutations (left) and SVs (right) in patient-derived cell lines in the EMT group (orange, n = 26) and the non-EMT group (green, n = 33). b, g, h, Box plots show medians (lines), interquartile ranges (IQRs; boxes) and ±1.5× IQRs (whiskers). Wilcoxon rank-sum test (two-tailed). All the samples are biologically independent.

Source data

Extended Data Fig. 5 Activation of the YAP/TAZ/TEAD pathway in the EMT cluster.

a, Relationship of the expression levels of TEAD1, WWTR1, and YAP1 between purified tumour cells in ascites and corresponding cell lines. The Pearson correlation coefficient is indicated. b, Relationships between the expression levels of SMAD3 and those of TEAD1, YAP1, and WWTR1 in cell lines. The Pearson correlation coefficient is indicated. c, Immunohistochemistry of malignant ascitic fluid samples in the representative cases from the EMT group using antibodies against TEAD1 (brown). Scale bar, 10 μm. Representative images of two independent experiments. d, Expression levels of genes in the YAP/TAZ/TEAD pathway in the EMT-high group (orange, n = 73) and the other group (green, n = 192) from the TCGA cohort. e, Expression levels of genes downstream of the YAP/TAZ/TEAD pathway in patient-derived cell lines in the EMT group (orange, n = 26) and the non-EMT group (green, n = 33). d, e, Box plots show medians (lines), interquartile ranges (IQRs; boxes) and ±1.5× IQRs (whiskers). Wilcoxon rank-sum test (two-tailed). All the samples are biologically independent.

Source data

Extended Data Fig. 6 Super-enhancer analyses in GC with peritoneal metastasis.

a, Fragmentation of SEs for the calculation of recurrent SE fragments. b, IGV plots of H3K27ac ChIP-seq profiles. Plots of the representative cases from the EMT group and the non-EMT group are coloured in pink and green, respectively. c, SMAD3 mRNA knockdown in representative cell lines from the EMT group. Transfected cell lines were sampled after incubation for 96 hours. The vertical axis shows the relative expression levels compared to GAPDH. Values represent biological triplicate experiments (mean ± s.d.). d, Expression levels of genes with SEs in patient-derived cell lines in the EMT group (orange, n = 26) and the non-EMT group (green, n = 33). e, Frequencies of usage of TFs that form core regulatory circuits in the whole cohort of patient-derived cell lines. TFs specific to the EMT group and the non-EMT group are coloured pink and green, respectively. f, Bar graphs of the ratio of samples with recurrent usage of TFs in the EMT group and the non-EMT group (left vertical axis). The line graph shows the significance of the bias between the ratio in the EMT group and that in the non-EMT group. The P value was calculated by two-sided Fisher’s exact test (right vertical axis). g, Expression levels of genes encoding TFs in patient-derived cell lines in the EMT group (orange, n = 26) and the non-EMT group (green, n = 33). d, g, Box plots show medians (lines), interquartile ranges (IQRs; boxes) and ±1.5× IQRs (whiskers). Wilcoxon rank-sum test (two-tailed). All the samples are biologically independent.

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Extended Data Fig. 7 Methylation analyses in GC with peritoneal metastasis.

a, Relationship between the β value of the representative probe position (chr16:68,771,035) in the CpG island and the expression level of CDH1. b, Ratio (EMT/non-EMT) of the median expression of the annotated genes of the probe positions with significant methylation differences between the EMT group (22 genes) and the non-EMT group (45 genes). Box plots show medians (lines), interquartile ranges (IQRs; boxes) and ±1.5× IQRs (whiskers). c, β value of the representative probe positions in the CpG island of the genes with differential methylation levels (VIM, chr10:17,271,006; WWTR1, chr3:149,374,761; ZEB1, chr10:31,608,663) between the EMT group (orange, n = 16) and the non-EMT group (green, n = 28). Box plots show medians (lines), interquartile ranges (IQRs; boxes) and ±1.5× IQRs (whiskers). Wilcoxon rank-sum test (two-tailed). All the samples are biologically independent.

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Extended Data Fig. 8 Disruption of the TAD boundary by genetic alterations.

a, Relationship of the genetic position on the Hi-C map for IMR90 cells, SVs, copy number changes, SE, and IGV plots of H3K27ac ChIP-seq profiles from patient-derived cell lines and the original purified tumour cells. The vertical axis of the copy number plot shows the log R ratio. b, Relationship between the dependency of CCLE GC cell lines (n = 27) on JUP and EIF1 (vertical axis) and the expression level of each gene (horizontal axis).

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Extended Data Fig. 9 Molecular targets in GC with peritoneal metastasis.

a-b, Relationship of cell viability after drug administration and gene expression levels. Each drug was administered at a fixed concentration as indicated: ERBB2 (neratinib, 120 nM, n = 53) and KRAS (binimetinib, 50 nM, n = 52). The vertical axis shows viability calculated by cell number. Data represent the mean of at least 3 independent experiments. The Pearson correlation coefficient is indicated. c, Macroscopic slides of mouse peritoneum (top) and bioluminescence images (bottom). Dissemination of cancer cells (mock) and clearance by each drug were observed. d, HE staining of mouse peritoneum tissues. Dissemination of cancer cells (mock) and clearance by each drug were observed. Scale bar, 100 μm for samples treated with an ALK inhibitor;100 μm for samples treated with a MET inhibitor; 50 μm for samples treated with an FGFR2 inhibitor. Representative images of two independent experiments.

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Extended Data Fig. 10 TEAD inhibition in GC in EMT group.

a, Relationship between the dependency of CCLE cell lines on SMAD3 (vertical axis) and the expression level of SMAD3 (horizontal axis). Red circles represent GC cell lines (n = 27) and grey circles represent other cancer cell lines (n = 744). b, K-975 administration experiments in the representative cell line from the EMT group. The treated cell line was sampled after incubation for 96 hours. The vertical axis shows the relative expression levels compared to that of GAPDH. Values represent biological triplicate experiments (mean ± s.d.). c, Comparison of cell viability after binimetinib administration (50 nM) between the EMT group (orange, n = 22) and the non-EMT group (green, n = 30). d, Expression levels of BCL2L1 in patient-derived cell lines in the EMT group (orange, n = 26) and the non-EMT group (green, n = 33). e, f, Relationship of cell viability after the administration of binimetinib (left) and binimetinib combined with K-975 (right) in patient-derived cell lines in the EMT group (e) and the non-EMT group (f). The vertical axis shows viability calculated by cell number. Data represent the mean of at least 3 independent experiments. g, Expression levels of SMAD3 in CDKN2B-deleted samples (orange, n = 26) and the other samples (green, n = 239) from the TCGA cohort. c, d, g, Box plots show medians (lines), interquartile ranges (IQRs; boxes) and ±1.5× IQRs (whiskers). Wilcoxon rank-sum test (two-tailed). All the samples are biologically independent.

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Tanaka, Y., Chiwaki, F., Kojima, S. et al. Multi-omic profiling of peritoneal metastases in gastric cancer identifies molecular subtypes and therapeutic vulnerabilities. Nat Cancer 2, 962–977 (2021). https://doi.org/10.1038/s43018-021-00240-6

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