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Cited by 573Single-cell and spatial transcriptomicsGastric Cancer Management and OutcomesCancer Cells and Metastasis

Single-cell atlas of lineage states, tumor microenvironment, and subtype-specific expression programs in gastric cancer

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Kumar, Vikrant, Ramnarayanan, Kalpana, Sundar, Raghav, Padmanabhan, Nisha, Srivastava, Supriya, Koiwa, Mayu, Yasuda, Tadahito, Koh, Vivien +16 more·2022·Cancer Discovery
DOI 10.1158/2159-8290.cd-21-0683PMC PMC9394383

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Abstract

Gastric cancer heterogeneity represents a barrier to disease management. We generated a comprehensive single-cell atlas of gastric cancer (>200,000 cells) comprising 48 samples from 31 patients across clinical stages and histologic subtypes. We identified 34 distinct cell-lineage states including novel rare cell populations. Many lineage states exhibited distinct cancer-associated expression profiles, individually contributing to a combined tumor-wide molecular collage. We observed increased plasma cell proportions in diffuse-type tumors associated with epithelial-resident KLF2 and stage-wise accrual of cancer-associated fibroblast subpopulations marked by high INHBA and FAP coexpression. Single-cell comparisons between patient-derived organoids (PDO) and primary tumors highlighted inter- and intralineage similarities and differences, demarcating molecular boundaries of PDOs as experimental models. We complemented these findings by spatial transcriptomics, orthogonal validation in independent bulk RNA-sequencing cohorts, and functional demonstration using in vitro and in vivo models. Our results provide a high-resolution molecular resource of intra- and interpatient lineage states across distinct gastric cancer subtypes. Significance: We profiled gastric malignancies at single-cell resolution and identified increased plasma cell proportions as a novel feature of diffuse-type tumors. We also uncovered distinct cancer-associated fibroblast subtypes with INHBA–FAP-high cell populations as predictors of poor clinical prognosis. Our findings highlight potential origins of deregulated cell states in the gastric tumor ecosystem. This article is highlighted in the In This Issue feature, p. 587

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  • Tools like CONICSmat first infer large-scale copy-number states from the expression matrix and then overlay these states onto the corresponding UMAP/t-SNE plots.

    “For example, tools like CONICSmat first infer large-scale copy-number states from the expression matrix and then overlay these states onto the corresponding UMAP/t-SNE plots.”

    From Genetic Insights into the Economic Toll of Cell Line Misidentification: A Comprehensive Review (2026) · cites this paper as [53]

    The Methods section (CNV Analysis; page not available in our copy) says the authors used CONICSmat to call CNVs from a gene-expression matrix. The Methods also describes UMAP visualization separately, but does not say CONICSmat overlays the inferred copy-number states on UMAP or t-SNE plots. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Integration of lpWGS CNV profiles with single-cell RNA-seq enables researchers to correlate structural variants with gene-expression changes in cell-line sub-clones, organoids or primary tissue samples.

    “This enables researchers to correlate structural variants with gene-expression changes in cell-line sub-clones, organoids or primary tissue samples [52,53].”

    From Genetic Insights into the Economic Toll of Cell Line Misidentification: A Comprehensive Review (2026) · cites this paper as [53]

    The Results section reports that CNV profiles inferred from scRNA-seq agreed with profiles from bulk whole-exome sequencing of the same gastric tumors, and that a cell subcluster with high oncogenic-signature expression also had many CNVs. This supports relating copy-number changes to expression patterns in tumor cells, but the paper uses WES rather than lpWGS and does not establish the claimed approach for cell-line sub-clones. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Authors
Kumar, V., Ramnarayanan, K., Sundar, R., Padmanabhan, N., Srivastava, S., Koiwa, M., Yasuda, T., Koh, V., Huang, K. K., Tay, S. T., Ho, S. W. T., Tan, A. L. K., Ishimoto, T., Kim, G., Shabbir, A., Chen, Q., Zhang, B., Xu, S., Lam, K.-P., Lum, H. Y. J., Teh, M., Yong, W. P., So, J. B. Y., & Tan, P.
Affiliations
  • Duke-NUS Medical School (SG)
  • National University of Singapore (SG)
  • International Research Center for Medical Sciences, Kumamoto University (JP)
  • Agency for Science, Technology and Research (SG)
  • National University Health System (SG)
Corresponding author
Tan, Patrick
ORCID
Venue
Cancer Discovery
Publisher
American Association for Cancer Research (AACR)
Published
2022-03-01
Volume
12
Issue
3
Pages
670-691
ISSN
  • 2159-8274 (ISSN-L)
  • 2159-8290
DOI
10.1158/2159-8290.cd-21-0683
PMC
PMC9394383
OpenAlex
W3206448904
Cited by
573
References
16
Citations by year
2026: 137 · 2025: 130 · 2024: 146 · 2023: 115 · 2022: 41 · 2021: 3
FWCI
24.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Cancer Discovery (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
Funders
  • NMRC, Singapore (NMRC/Fellowship/0059/2018, MOH-000627)
  • NMRC (NR13NMR111OM, NMRC/STaR/0026/2015, MOH-OFLCG18May-0003)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Gastric Cancer Management and Outcomes
  • Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
Keywords
gastric cancer, tumor heterogeneity, tumor microenvironment, diffuse-type gastric cancer, cancer-associated fibroblasts, plasma cells, KLF2, INHBA, FAP, patient-derived organoids, spatial transcriptomics, histological subtypes, prognosis, single-cell atlas
MeSH terms
Cancer-Associated Fibroblasts, Humans, Stomach Neoplasms, Ecosystem, Single-Cell Analysis, Tumor Microenvironment, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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