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Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma

Abstract

Intratumoral heterogeneity (ITH) is a fundamental property of cancer; however, the origins of ITH remain poorly understood. We performed single-cell transcriptome profiling of peritoneal carcinomatosis (PC) from 15 patients with gastric adenocarcinoma (GAC), constructed a map of 45,048 PC cells, profiled the transcriptome states of tumor cell populations, incisively explored ITH of malignant PC cells and identified significant correlates with patient survival. The links between tumor cell lineage/state compositions and ITH were illustrated at transcriptomic, genotypic, molecular and phenotypic levels. We uncovered the diversity in tumor cell lineage/state compositions in PC specimens and defined it as a key contributor to ITH. Single-cell analysis of ITH classified PC specimens into two subtypes that were prognostically independent of clinical variables, and a 12-gene prognostic signature was derived and validated in multiple large-scale GAC cohorts. The prognostic signature appears fundamental to GAC carcinogenesis and progression and could be practical for patient stratification.

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Fig. 1: A single-cell transcriptome map of PC and the inferred tumor cell lineages.
Fig. 2: The diversity in tumor cell lineage compositions links to ITH at transcriptomic, genotypic and molecular levels.
Fig. 3: 17q copy number gain is prevalent in cells of stomach origin and significantly associated with inferior survival.
Fig. 4: Molecular pathway-based dissection of the transcriptomic ITH and correlation with tumor cell lineage and patient survival.
Fig. 5: Identification and validation of the 12-gene prognostic signature.

Data availability

All single-cell RNA-sequencing data generated by this study have been be deposited in the European Genome-Phenome Archive (EGA, https://ega-archive.org/). The data can be accessed under the accession number EGAS00001004443. Bulk mRNA-seq expression data (normalized) generated by The Cancer Genome Atlas (TCGA) on primary stomach adenocarcinoma were downloaded from NCI Cancer Genomic Data Commons (NCI-GDC: https://gdc.cancer.gov). Three large-scale primary GAC datasets (GSE62254 (ref. 28) and GSE15459 (refs. 27,71), GSE84437 (ref. 72)) were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/).

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Acknowledgements

This study was supported by the start-up research fund provided to L.W. by the UT MD Anderson Cancer Center (MDACC); the Andrew Sabin Family Fellowship Program to L.W. by the Andrew Sabin Family Foundation; the DOD grants no. CA150334 and no. CA160445 to J.A.A.; the DOD grants no. CA160433 and no. CA170906 to S.S.; and the generous support from the Caporella, Dallas, Sultan, Park, Smith, Frazier, Oaks, Vanstekelenberg, Planjery, McNeil, Hyland and Cantu families; as well as from the Schecter Private Foundation, the Rivercreek Foundation, the Kevin Fund, the Myer Fund, the Stupid Strong Foundation, the V. Foundation, the Dio Fund, the Milrod Fund and the MDACC multidisciplinary grant programs. This study was also supported by the NIH grant no. 1S10OD024977-01 Award to the Advanced Technology Genomics Core (ATGC) and the Core grant no. CA016672 (ATGC). We thank E. J. Thompson and D. P. Pollock from the ATGC for their excellent technical assistance. We thank all of the patients who participated in this study.

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L.W. and J.A.A. conceived and jointly supervised the study. S.S., K.H., M.P.P., M.Z., G.T., N.S., A.A.F.A., B.D.B. and M.B.M. contributed to sample collection and processing and collection of patient clinical information. A.J.L., J.S.E. and S.R.-C. contributed to pathology review. Y.L. reviewed the CT images. L.W. supervised the bioinformatics data analysis, data integration and interpretation. R.W. contributed to sequencing data processing, integrative analyses and generation of figures and tables for the manuscript. M.D., G.H., F.W., S. Zhang., D.H., S. Zhao., Y.W., X.S., Y.C., J.Z., M.L. and K.C. assisted with data processing and analysis. L.W., J.A.A. and R.W. wrote the manuscript. L.W., J.A.A., R.W., A.J.L., A.F., S.H., G.A.C. and G.P. revised the manuscript.

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Correspondence to Jaffer A. Ajani or Linghua Wang.

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The authors declare no competing interests.

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Peer review information Javier Carmona was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 A single cell transcriptome map of PC.

a, t-SNE (t-distributed stochastic neighbor embedding) plots showing unbiased clustering analysis of 45,048 single cells that passed quality control in this study. Each dot represents a single cell. Cells are color coded for (left to right): the associated cell types, cell clusters, the corresponding patient origins, and survival status. b, t-SNE as in a, showing expression of canonical marker genes used for cell types assignment.

Extended Data Fig. 2 Relationships between tumor cell clusters and correlation with patient survival.

a, the UMAP (uniform manifold approximation and projection) plot of PC tumor cells, showing the global data structure. Tumor cell clusters from short-term survivors appeared closer to each other on the UMAP plot than to cell clusters from long-term survivors. b, the dendrogram showing relationships between tumor cell clusters. c, the Bhattacharyya pairwise distance between tumor cell clusters from samples of long and short-term survivors. Overall, the pairwise distance between clusters of long and short survivors was significantly larger than that within the clusters of Short or Random, indicating distinct transcriptomic profiles associated with survival. Each dot represents one sampling, in totally 100 times. Box, median ± interquartile range. Whiskers, the minimum and maximum values. P values were calculated by a two-sided Wilcoxon rank sum test with Benjamini-Hochberg correction. P < 2.2e-16 represents a P value approaching 0.

Extended Data Fig. 3 Cell lineage assignment was not confounded by differences in cell cycle states.

The histograms showing tumor cell lineage compositions before (top) and after (bottom) regressing out cell cycle-related genes, respectively.

Extended Data Fig. 4 Unsupervised clustering analysis revealed inter-patient and intra-tumoral transcriptome heterogeneity in PC tumor cells.

The UMAP plots showing unsupervised clustering analysis of tumor cells (using Seurat) from 14 samples underwent HCL mapping and cell lineage inference as in Fig. 1g. Cells are colored by their corresponding cluster IDs (left) and sample origins (right). Dashed circles highlight samples that formed two or more tumor cell clusters (related to Fig. 1g).

Extended Data Fig. 5 SC3 unsupervised clustering analysis of PC tumor cells by patient.

SC3 results of 3 representative patients are shown. Each column represents a cell. The lineage annotation is shown in the top annotation track. The fractions of intestinal cells (IP-067, IP-073) or stomach pit cells (IP-009) in each SC3 defined cell clusters are labelled at the top. Some of the representative marker genes of intestine and stomach origins are labelled on the right. Two-sided proportion tests were performed between C1 and C4 (IP-067), C1 and C3 (IP-073), and C1 and C2 (IP-009), and all are significant (P < 2.2e-16).

Extended Data Fig. 6 The Bhattacharyya distance between and within inferred cell lineages.

The Bhattacharyya pairwise distance between different tumor cell lineages was computed as previously described (see Methods). Only the major lineages that had 500 or more cells were included in the analysis. The Bhattacharyya distance between cells of the same lineage and the Bhattacharyya distance between cells randomly sampled independent of lineage annotation (Random) was also computed to provide background distributions for statistical comparison. Each dot represents one sampling, in total 100 times. Box, median ± interquartile range. Whiskers, the minimum and maximum values. P values were calculated by a two-sided Wilcoxon rank-sum test with Benjamini-Hochberg correction. P < 2.2e-16 represents a P value approaching 0.

Extended Data Fig. 7 Representative examples of somatic variants identified on 3’UTR using scRNA-seq data.

Integrative Genomics Viewer (IGV) was used for visualization of the QC-passed somatic variants. The Bam files of Monocle defined cell clusters C1, C2, C3 of sample IP-067 were loaded to IGV and snapshots of 3’UTR mutations are shown for representative events: somatic mutations shared by PC tumor cells from all three clusters (top); mutations shared by only two of the three clusters (bottom left and middle), and mutations that were unique to one of the three clusters (bottom right) are shown. For each representative mutation across Monocle cell clusters, the gene name, chromosome, start position, base change, total read coverage, and tumor variant allele fraction (TAF) are shown. Total_dp: total read depth.

Extended Data Fig. 8 Prognostic significance of 12-gene signature in TCGA primary gastric cancer cohort and correlation with molecular subtypes and clinical variables.

a, Disease-specific survival (DSS, left) and progression-free interval (PFI, right) of patients whose PCs were in the GI-mixed and gastric- dominant groups defined by expression of the 12-gene signature. The analyses were performed with the Kaplan–Meier estimates and two-sided log-rank tests. Twenty-five out of 411 patients whose DSS information were not available were excluded from survival analysis. b, the alluvial plots display relationships between the PC subtypes defined by the 12-gene signature (left strip) and the molecular subtypes defined by TCGA multi-omic analysis (left), tumor stages (middle), histology types (right), and presence of local recurrence and/or distant metastasis (c). N.S., not statistically significant. P value for alluvial plots were calculated by a two-sided Fisher’s Exact test.

Extended Data Fig. 9 Validation of the 12-gene signature in a large-scale localized GAC cohort from Cristescu R, et al.

a, The multivariate Cox proportional hazard model analysis. The 12-gene signature, clinical and histopathological variables as well as the molecular signatures defined by the original study were included. For each variable, the reference level is the first one. Block in center of error bars represent the weighted mean. Whiskers of error bars represent the 95% confidence interval. b, (left) Alluvial plot shows the relationships between the PC subtypes (left strip) and the molecular signatures (right strip). The two-sided Fisher’s Exact test was used to calculate the P values and asterisks indicate significant enrichment events. (right) The 12-gene signature scores were calculated and compared across the four molecular groups defined by the original the study. Box, median ± interquartile range. Whiskers, 1.5X interquartile range. P value was calculated by one-way Kruskal-Wallis rank-sum test.

Extended Data Fig. 10 Validation of the 12-gene signature in a large-scale localized GAC cohort from Ooi CH, et al.

a, The multivariate Cox proportional hazard model analysis. The 12-gene signature, clinical and histopathological variables as well as the molecular signatures defined by the original study were included. For each variable, the reference level is the first one. Block in center of error bars represent the weighted mean. Whiskers of error bars represent the 95% confidence interval. b, (left) Alluvial plot shows the relationships between the PC subtypes (left strip) and the molecular signatures (right strip). The two-sided Fisher’s Exact test was used to calculate the P values and asterisks indicate significant enrichment events. (right) The 12-gene signature scores were calculated and compared across the four molecular groups defined by the original the study. Box, median ± interquartile range. Whiskers, 1.5X interquartile range. P value was calculated by one-way Kruskal-Wallis rank-sum test.

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Wang, R., Dang, M., Harada, K. et al. Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma. Nat Med 27, 141–151 (2021). https://doi.org/10.1038/s41591-020-1125-8

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