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Single-cell analyses reveal increased intratumoral heterogeneity after the onset of therapy resistance in small-cell lung cancer

Nature Cancer volume 1, pages 423–436 (2020)Cite this article

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Abstract

The natural history of small-cell lung cancer (SCLC) includes rapid evolution from chemosensitivity to chemoresistance, although mechanisms underlying this evolution remain obscure due to the scarcity of post-relapse tissue samples. We generated circulating tumor cell (CTC)-derived xenografts from patients with SCLC to study intratumoral heterogeneity (ITH) via single-cell RNA sequencing of chemosensitive and chemoresistant CTC-derived xenografts and patient CTCs. We found globally increased ITH, including heterogeneous expression of therapeutic targets and potential resistance pathways, such as epithelial-to-mesenchymal transition, between cellular subpopulations following treatment resistance. Similarly, serial profiling of patient CTCs directly from blood confirmed increased ITH post-relapse. These findings suggest that treatment resistance in SCLC is characterized by coexisting subpopulations of cells with heterogeneous gene expression leading to multiple, concurrent resistance mechanisms. These findings emphasize the need for clinical efforts to focus on rational combination therapies for treatment-naïve SCLC tumors to maximize initial responses and counteract the emergence of ITH and diverse resistance mechanisms.

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Fig. 1: SCLC CDXs mimic patient disease at the single-cell transcriptional level and by platinum response.
Fig. 2: Platinum-resistant disease is associated with increased ITH.
Fig. 3: Platinum resistance is associated with heterogeneous expression of therapeutic targets or EMT-related genes within specific clusters.
Fig. 4: Serial single-cell RNA-Seq analysis of patient CTCs revealed similar transcriptional heterogeneity to a paired CDX.
Fig. 5: Increased ITH and emergence of cell populations with EMT signatures occur following cisplatin relapse.
Fig. 6: Resistance to DNA-damaging targeted therapies resulted in the emergence of new, therapy-specific clusters.

Data availability

The single-cell and bulk RNA-Seq data have been deposited in the NCBI Gene Expression Omnibus database with accession number GSE138474. Source data for Figs. 1–6 and Extended Data Figs. 1–6 are provided with the paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The bioinformatics analyses were performed using open-source software, including BWA-MEM version 0.7.9a49, VARSCAN2 version 2.3.9 (ref. 50), TOPHAT2 version 2.0.13 (ref. 51), HTSEQ version 0.9.1 (ref. 52), EdgeR version 3.7 (ref. 53), GSEA version 3.0 (ref. 42), ANNOVAR version 2018Apr16 (ref. 54), Seurat version 2.3 (ref. 55) and Cell Ranger version 2.0, as well as in-house R script that is available upon request.

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Acknowledgements

We thank the patients who participated in this study, as well as their families. We also thank M. Vasquez for obtaining consent from the patients, E. Roarty for scientific input and editing, and K. Ramkumar for general laboratory assistance. This work was supported by NIH/NCI Cancer Center Support Grant P30-CA016672 (to the Bioinformatics Shared Resource), NIH/NCI T32 Award CA009666 (to C.M.G.), The University of Texas Southwestern Medical Center and MD Anderson Cancer Center Special Program of Research Excellence (5 P50 CA070907), NIH/NCI award R01-CA207295 (to L.A.B.), NIH/NCI award U01-CA213273 (to J.V.H. and L.A.B.), NIH/NCI award U01 CA231844 (to T.G.O.), award P30CA042014 (to the Huntsman Cancer Institute), the Department of Defense award LC170171 (to L.A.B.), the ASCO Young Investigator Award (to C.M.G.), generous philanthropic contributions to The University of Texas MD Anderson Cancer Center Moon Shots Program (to J.V.H., J.W. and L.A.B.), The University of Texas MD Anderson Cancer Center Small Cell Lung Cancer Working Group, Abell Hangar Foundation Distinguished Professor Endowment (to L.A.B. and B.G.), The University of Texas MD Anderson Cancer Center Physician Scientist Award (to L.A.B.), The Hope Foundation SWOG/ITSC Pilot Program (to P.R. and L.A.B.), an Andrew Sabin Family Fellowship (to L.A.B.) and Rexanna’s Foundation for Fighting Lung Cancer (to J.V.H. and L.A.B.).

Author information

Author notes

  1. These authors contributed equally: C. Allison Stewart, Carl M. Gay, Yuanxin Xi.

Authors and Affiliations

  1. Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    C. Allison Stewart, Carl M. Gay, Patrice M. Hartsfield, Hai Tran, Jianjun Zhang, Bonnie Glisson, John V. Heymach & Lauren Averett Byers

  2. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Yuanxin Xi & Jing Wang

  3. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

    Santhosh Sivajothi, V. Sivakamasundari, Mohan Bolisetty & Paul Robson

  4. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Junya Fujimoto & Ignacio Wistuba

  5. Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Veerakumar Balasubramaniyan & John de Groot

  6. Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA

    Milind D. Chalishazar & Trudy G. Oliver

  7. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Cesar Moran, Neda Kalhor & John Stewart

  8. Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Stephen G. Swisher & Jack A. Roth

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Contributions

C.A.S., C.M.G. Y.X. and L.A.B. conceived of the project, analyzed and interpreted the data and wrote the manuscript. S.S., V.S., V.B., P.R., J.Z., B.G., J.d.G., S.G.S., J.A.R., M.D.C., T.G.O. and J.V.H. contributed to acquiring the data. J.F., C.M., N.K., J.S. and I.W. performed the pathology review and analysis. M.B. and J.W. contributed to analysis and interpretation of the data. P.M.H. collected liquid biopsies from the patients. H.T. coordinated the patient protocols. P.R., J.Z., B.G., J.d.G., S.G.S., J.A.R., M.D.C., T.G.O. and J.V.H. provided administrative and/or material support. All authors contributed to the writing, reviewing and/or revising of the manuscript.

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Correspondence to Lauren Averett Byers.

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Competing interests

L.A.B. serves on advisory committees for AstraZeneca, AbbVie, Genmab, BerGenBio, Pharma Mar SA, Sierra Oncology, Merck, Bristol-Myers Squibb, Genentech and Pfizer and has research support from AbbVie, AstraZeneca, Genmab, Sierra Oncology and Tolero Pharmaceuticals. J.V.H. serves on advisory committees for AstraZeneca, Boehringer Ingelheim, Exelixis, Genentech, GlaxoSmithKline, Guardant Health, Hengrui, Lilly, Novartis, Spectrum, EMD Serono and Synta, and has research support from AstraZeneca, Bayer, GlaxoSmithKline and Spectrum and royalties and licensing fees from Spectrum. Otherwise, there are no pertinent financial or non-financial conflicts of interest to report.

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Extended data

Extended Data Fig. 1 CDXs exhibit common SCLC markers and mutations that are maintained over multiple generations.

a, Histological analysis of CDX tumors are consistent with SCLC. Scale bar = 100 µM. b, Patient expression of NCAM and TTF1 by staff pathologist review of diagnostic sample matches CDXs. c, Presence of parenchymal brain metastasis, confirmed by staff neuroradiologist and treating physician review, in the cerebellum (indicated by dashed circle) of the patient from which MDA-SC39 was derived. d, Genomic alterations in CDXs. Top panel: mutation load; middle panel: somatic mutations and genomic gain/loss status; lower panel: type of base-pair substitution. e, Mutational status of common SCLC genes and others unique to each CDX are maintained over multiple CDX passages in three separate models. f, Expression heatmap for ASCL1- and NEUROD1-associated genes. b, CDX and PDX models derived from patient SC49 exhibit similar patterns of expression for common SCLC markers, including loss of TTF1 expression. These experiments were repeated in three independent tumors from each model. Scale bar = 100 µM.

Source data

Extended Data Fig. 2 ITH among SCLC molecular subtypes.

a, t-SNE visualization of NE gene expression status in all CDXs. b, t-SNE visualization of cell populations from biological replicates of MDA-SC39s and MDA-SC16r obtained from tumors grown in the same passage, but different mice. Note mixing of cell populations indicate that clustering is not due to variations in replicate. c, Heatmap analysis of NE gene expression indicating that all CDXs are considered high neuroendocrine subtypes. d, Expression of ASCL1 and NEUROD1 in all CDXs by both violin plot to indicate range in expression and feature plot to show abundance. e, Violin plots indicating expression of MYC family members in the CDXs. Each dot represents one cell and the violin curve represent the density of the cells at different expression levels. f, EMT score is elevated within MDA-SC39s and MDA-SC49r, which corresponds with increased expression of VIM and decreased EPCAM. In a, b, d, e, and f, n=2,000 cells each.

Source data

Extended Data Fig. 3 Validation of cluster calls and visualization.

a, Silhouette analysis to determine cluster number in each of the eight CDXs. b, UMAP visualization of the clusters in all CDXs. c, Barplot of variations of absolute normalized enrichment scores (NES) for hallmark pathways in GSEA analysis in sensitive clusters (blue) and resistant clusters (red). The variation of pathway enrichment is higher in resistant clusters than sensitive clusters by one-sided Wilcoxon rank sum test (P=2.9e-6; n=21 pathways).

Source data

Extended Data Fig. 4 CDX copy number and expression of DNA repair genes between clusters.

a,b, Inferred copy number between clusters in MDA-SC16r (a) and MDA-SC49r (b). c, Expression heatmap of genes associated with DNA repair in all CDX clusters. d, Violin plots indicating range of expression of several therapeutic targets within individual clusters. AURKA, AURKB and DLL3 were relatively unchanged between clusters. MDA-SC4s: n=978, 1,022 cells for clusters 1-2; MDA-SC39s: n=1172, 828 cells for clusters 1-2; MDA-SC68s: n=733, 704, 563 cells for clusters 1-3; HCI-008s: n=596, 1,404 cells for clusters 1-2; MDA-SC49r: n=683, 317, 652, 348 cells for clusters 1-4. Each dot represents one cell and the violin curve represent the density of the cells at different expression levels.

Source data

Extended Data Fig. 5 Validation of CTC identification within a patient liquid biopsy by positive expression of epithelial, NE and SCLC genes.

Percentage of cells expressing epithelial, NE genes (for example, UCHL1, NCAM1, SYP, and CHGA) or SCLC lineage-specific genes (for example, ASCL1, NEUROD1, etc.) in the CTC population and non-CTC populations.

Source data

Extended Data Fig. 6 Emergence of a mesenchymal cell cluster following cisplatin-treatment.

Violin plot of VIM (a) and EXPCAM (b) expression in the clusters of MDA-SC68s vehicle and cisplatin-treated CDXs. MDA-SC68 vehicle: n=733, 704, 563 cells for clusters 1-3; MDA-SC68 cisplatin: n=635, 489, 71, 467, 338 cells for clusters 1-5. Each dot represents one cell and the violin curve represent the density of the cells at different expression levels Source data.

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Stewart, C.A., Gay, C.M., Xi, Y. et al. Single-cell analyses reveal increased intratumoral heterogeneity after the onset of therapy resistance in small-cell lung cancer. Nat Cancer 1, 423–436 (2020). https://doi.org/10.1038/s43018-019-0020-z

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