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Nature cancer
04, 2020;1 423-436.

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

C Allison Stewart, Carl M Gay, Yuanxin Xi, Santhosh Sivajothi, V Sivakamasundari, Junya Fujimoto, Mohan Bolisetty, Patrice M Hartsfield, Veerakumar Balasubramaniyan, Milind D Chalishazar, Cesar Moran, Neda Kalhor, John Stewart, Hai Tran, Stephen G Swisher, Jack A Roth, Jianjun Zhang, John de Groot, Bonnie Glisson, Trudy G Oliver, John V Heymach, Ignacio Wistuba, Paul Robson, Jing Wang, Lauren Averett Byers
Author Information
  1. C Allison Stewart: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  2. Carl M Gay: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  3. Yuanxin Xi: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  4. Santhosh Sivajothi: The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  5. V Sivakamasundari: The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  6. Junya Fujimoto: Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  7. Mohan Bolisetty: The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  8. Patrice M Hartsfield: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  9. Veerakumar Balasubramaniyan: Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  10. Milind D Chalishazar: Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA.
  11. Cesar Moran: Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  12. Neda Kalhor: Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  13. John Stewart: Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  14. Hai Tran: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  15. Stephen G Swisher: Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  16. Jack A Roth: Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  17. Jianjun Zhang: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  18. John de Groot: Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  19. Bonnie Glisson: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  20. Trudy G Oliver: Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA. ORCID
  21. John V Heymach: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  22. Ignacio Wistuba: Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  23. Paul Robson: The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA. ORCID
  24. Jing Wang: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  25. Lauren Averett Byers: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. lbyers@mdanderson.org. ORCID
PMID: 33521652 DOI: 10.1038/s43018-019-0020-z

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 scarcity of post-relapse tissue samples. We generated circulating tumor cell (CTC)-derived xenografts (CDXs) from SCLC patients to study intratumoral heterogeneity (ITH) via single-cell RNAseq of chemo-sensitive and -resistant CDXs and patient CTCs. We found globally increased ITH including heterogeneous expression of therapeutic targets and potential resistance pathways, such as EMT, between cellular subpopulations following treatment-resistance. Similarly, serial profiling of patient CTCs directly from blood confirmed increased ITH post-relapse. These data 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-na��ve SCLC tumors to maximize initial responses and counteract the emergence of ITH and diverse resistance mechanisms.

Keywords

CDX CTC SCLC intratumoral heterogeneity single-cell RNAseq

References

  1. Horn, L. et al. First-line atezolizumab plus chemotherapy in extensive-stage small-cell lung cancer. N. Engl. J. Med. 379, 2220���2229 (2018). [PMID: 30280641]
  2. H.R.733: Leech Lake Band of Ojibwe Reservation Restoration Act (Senate and House of Representatives of the United States of America in Congress, 2012). https://www.congress.gov/bill/112th-congress/house-bill/733
  3. Hodgkinson, C. L. et al. Tumorigenicity and genetic profiling of circulating tumor cells in small-cell lung cancer. Nat. Med. 20, 897���903 (2014). [PMID: 24880617]
  4. Drapkin, B. J. et al. Genomic and functional fidelity of small cell lung cancer patient-derived xenografts. Cancer Discov. 8, 600���615 (2018). [PMID: 29483136]
  5. Chalishazar, M. D. et al. MYC-driven small-cell lung cancer is metabolically distinct and vulnerable to arginine depletion. Clin. Cancer Res. 25, 5107���5121 (2019). [PMID: 31164374]
  6. Aggarwal, C. et al. Circulating tumor cells as a predictive biomarker in patients with small cell lung cancer undergoing chemotherapy. Lung Cancer 112, 118���125 (2017). [PMID: 29191584]
  7. Farago, A. F. et al. Combination olaparib and temozolomide in relapsed small-cell lung cancer. Cancer Discov. 9, 1372���1387 (2019). [PMID: 31416802]
  8. Zhang, J. et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346, 256���259 (2014). [PMID: 25301631]
  9. Mollaoglu, G. et al. MYC drives progression of small cell lung cancer to a variant neuroendocrine subtype with vulnerability to aurora kinase inhibition. Cancer Cell 31, 270���285 (2017). [PMID: 28089889]
  10. Huang, Y. H. et al. POU2F3 is a master regulator of a tuft cell-like variant of small cell lung cancer. Genes Dev. 32, 915���928 (2018). [PMID: 29945888]
  11. Cardnell, R. J. et al. Protein expression of TTF1 and cMYC define distinct molecular subgroups of small cell lung cancer with unique vulnerabilities to aurora kinase inhibition, DLL3 targeting, and other targeted therapies. Oncotarget 8, 73419���73432 (2017). [PMID: 29088717]
  12. Lim, J. S. et al. Intratumoural heterogeneity generated by Notch signalling promotes small-cell lung cancer. Nature 545, 360���364 (2017). [PMID: 28489825]
  13. Shue, Y. T., Lim, J. S. & Sage, J. Tumor heterogeneity in small cell lung cancer defined and investigated in pre-clinical mouse models. Transl. Lung Cancer Res. 7, 21���31 (2018). [PMID: 29535910]
  14. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-Seq. Science 352, 189���196 (2016). [PMID: 27124452]
  15. Skoulidis, F. et al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities. Cancer Discov. 5, 860���877 (2015). [PMID: 26069186]
  16. Rudin, C. M. et al. Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data. Nat. Rev. Cancer 19, 289���297 (2019). [PMID: 30926931]
  17. Misch, D. et al. Value of thyroid transcription factor (TTF)-1 for diagnosis and prognosis of patients with locally advanced or metastatic small cell lung cancer. Diagn. Pathol. 10, 21 (2015). [PMID: 25889870]
  18. Zhang, W. et al. Small cell lung cancer tumors and preclinical models display heterogeneity of neuroendocrine phenotypes. Transl. Lung Cancer Res. 7, 32���49 (2018). [PMID: 29535911]
  19. George, J. et al. Comprehensive genomic profiles of small cell lung cancer. Nature 524, 47���53 (2015). [PMID: 26168399]
  20. Jahchan, N. S. et al. Identification and targeting of long-term tumor-propagating cells in small cell lung cancer. Cell Rep. 16, 644���656 (2016). [PMID: 27373157]
  21. Singhi, A. D. et al. MYC gene amplification is often acquired in lethal distant breast cancer metastases of unamplified primary tumors. Mod. Pathol. 25, 378���387 (2012). [PMID: 22056952]
  22. Lee, H. Y. et al. c-MYC drives breast cancer metastasis to the brain, but promotes synthetic lethality with TRAIL. Mol. Cancer Res. 17, 544���554 (2019). [PMID: 30266755]
  23. Stewart, C. A. et al. Dynamic variations in epithelial-to-mesenchymal transition (EMT), ATM, and SLFN11 govern response to PARP inhibitors and cisplatin in small cell lung cancer. Oncotarget 8, 28575���28587 (2017). [>PMCID: ]
  24. Gardner, E. E. et al. Chemosensitive relapse in small cell lung cancer proceeds through an EZH2���SLFN11 axis. Cancer Cell 31, 286���299 (2017). [PMID: 28196596]
  25. Wagner, A. H. et al. Recurrent WNT pathway alterations are frequent in relapsed small cell lung cancer. Nat. Commun. 9, 3787 (2018). [PMID: 30224629]
  26. Byers, L. A. et al. Proteomic profiling identifies dysregulated pathways in small cell lung cancer and novel therapeutic targets including PARP1. Cancer Discov. 2, 798���811 (2012). [PMID: 22961666]
  27. Dammert, M. A. et al. MYC paralog-dependent apoptotic priming orchestrates a spectrum of vulnerabilities in small cell lung cancer. Nat. Commun. 10, 3485 (2019). [PMID: 31375684]
  28. Semenova, E. A. et al. Transcription factor NFIB is a driver of small cell lung cancer progression in mice and marks metastatic disease in patients. Cell Rep. 16, 631���643 (2016). [PMID: 27373156]
  29. Bottger, F. et al. Tumor heterogeneity underlies differential cisplatin sensitivity in mouse models of small-cell lung cancer. Cell Rep. 27, 3345���3358.e4 (2019). [PMID: 31189116]
  30. Wu, N. et al. NFIB overexpression cooperates with Rb/p53 deletion to promote small cell lung cancer. Oncotarget 7, 57514���57524 (2016). [PMID: 27613844]
  31. Klameth, L. et al. Small cell lung cancer: model of circulating tumor cell tumorospheres in chemoresistance. Sci. Rep. 7, 5337 (2017). [PMID: 28706293]
  32. Hamilton, G., Hochmair, M., Rath, B., Klameth, L. & Zeillinger, R. Small cell lung cancer: circulating tumor cells of extended stage patients express a mesenchymal���epithelial transition phenotype. Cell Adh. Migr. 10, 360���367 (2016). [PMID: 26919626]
  33. Yu, N., Zhou, J., Cui, F. & Tang, X. Circulating tumor cells in lung cancer: detection methods and clinical applications. Lung 193, 157���171 (2015). [PMID: 25690734]
  34. Tanaka, F. et al. Circulating tumor cell as a diagnostic marker in primary lung cancer. Clin. Cancer Res. 15, 6980���6986 (2009). [PMID: 19887487]
  35. Rudin, C. M. et al. Rovalpituzumab tesirine, a DLL3-targeted antibody���drug conjugate, in recurrent small-cell lung cancer: a first-in-human, first-in-class, open-label, phase 1 study. Lancet Oncol. 18, 42���51 (2017). [PMID: 27932068]
  36. Giffin, M. et al. Targeting DLL3 with AMG 757, a BiTE�� antibody construct, and AMG 119, a CAR-T, for the treatment of SCLC. J. Thorac. Oncol. 13, abstr. P3.12-03 (2018).
  37. Carbone, D. P. et al. Efficacy and safety of rovalpituzumab tesirine in patients with DLL3-expressing, ��� 3rd line small cell lung cancer: results from the phase 2 TRINITY study. J. Clin. Oncol. 36(Suppl.), 8507 (2018).
  38. Paz-Ares, L. et al. Overall survival with durvalumab plus etoposide���platinum in first-line extensive-stage SCLC: results from the CASPIAN study. J. Thorac. Oncol. 14, S7���S8 (2019).
  39. Nugent, J. L. et al. CNS metastases in small cell bronchogenic carcinoma: increasing frequency and changing pattern with lengthening survival. Cancer 44, 1885���1893 (1979). [PMID: 227582]
  40. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411���420 (2018). [PMID: 29608179]
  41. Jamieson, A. R. et al. Exploring nonlinear feature space dimension reduction and data representation in breast CADx with laplacian eigenmaps and t-SNE. Med. Phys. 37, 339���351 (2010). [PMID: 20175497]
  42. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545���15550 (2005).
  43. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288���4297 (2012). [PMID: 3378882]
  44. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139���140 (2010).
  45. Tong, P., Chen, Y., Su, X. & Coombes, K. R. SIBER: systematic identification of bimodally expressed genes using RNAseq data. Bioinformatics 29, 605���613 (2013). [PMID: 23303507]
  46. Wang, J., Wen, S., Symmans, W. F., Pusztai, L. & Coombes, K. R. The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data. Cancer Inform. 7, 199���216 (2009). [PMID: 19718451]
  47. Byers, L. A. et al. An epithelial���mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin. Cancer Res. 19, 279���290 (2013). [PMID: 23091115]
  48. R Core Development Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).
  49. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows���Wheeler transform. Bioinformatics 26, 589���595 (2010). [PMID: 2828108]
  50. Koboldt, D. C. et al. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25, 2283���2285 (2009). [PMID: 19542151]
  51. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013). [PMID: 23618408]
  52. Anders, S., Pyl, P. T. & Huber, W. HTSeq���a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166���169 (2015).
  53. Dai, Z. et al. edgeR: a versatile tool for the analysis of shRNA-Seq and CRISPR���Cas9 genetic screens. F1000Res 3, 95 (2014). [PMID: 24860646]
  54. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010). [PMID: 20601685]
  55. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888���1902.e21 (2019). [PMID: 31178118]

Grants

  1. U01 CA213273/NCI NIH HHS
  2. P30 CA016672/NCI NIH HHS
  3. U01 CA231844/NCI NIH HHS
  4. R01 CA207295/NCI NIH HHS
  5. P50 CA070907/NCI NIH HHS
  6. T32 CA009666/NCI NIH HHS
  7. P30 CA042014/NCI NIH HHS

MeSH Term

Humans
Lung Neoplasms
Neoplasm Recurrence, Local
Neoplastic Cells, Circulating
Single-Cell Analysis
Small Cell Lung Carcinoma
Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

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