Genomic and Functional Fidelity of Small Cell Lung Cancer Patient-Derived Xenografts.

TitleGenomic and Functional Fidelity of Small Cell Lung Cancer Patient-Derived Xenografts.
Publication TypeJournal Article
Year of Publication2018
AuthorsDrapkin BJ, George J, Christensen CL, Mino-Kenudson M, Dries R, Sundaresan T, Phat S, Myers DT, Zhong J, Igo P, Hazar-Rethinam MH, Licausi JA, Gomez-Caraballo M, Kem M, Jani KN, Azimi R, Abedpour N, Menon R, Lakis S, Heist RS, Büttner R, Haas S, Sequist LV, Shaw AT, Wong K-K, Hata AN, Toner M, Maheswaran S, Haber DA, Peifer M, Dyson N, Thomas RK, Farago AF
JournalCancer Discov
Volume8
Issue5
Pagination600-615
Date Published2018 May
ISSN2159-8290
Abstract

Small cell lung cancer (SCLC) patient-derived xenografts (PDX) can be generated from biopsies or circulating tumor cells (CTC), though scarcity of tissue and low efficiency of tumor growth have previously limited these approaches. Applying an established clinical-translational pipeline for tissue collection and an automated microfluidic platform for CTC enrichment, we generated 17 biopsy-derived PDXs and 17 CTC-derived PDXs in a 2-year timeframe, at 89% and 38% efficiency, respectively. Whole-exome sequencing showed that somatic alterations are stably maintained between patient tumors and PDXs. Early-passage PDXs maintain the genomic and transcriptional profiles of the founder PDX. treatment with etoposide and platinum (EP) in 30 PDX models demonstrated greater sensitivity in PDXs from EP-naïve patients, and resistance to EP corresponded to increased expression of a gene signature. Finally, serial CTC-derived PDXs generated from an individual patient at multiple time points accurately recapitulated the evolving drug sensitivities of that patient's disease. Collectively, this work highlights the translational potential of this strategy. Effective translational research utilizing SCLC PDX models requires both efficient generation of models from patients and fidelity of those models in representing patient tumor characteristics. We present approaches for efficient generation of PDXs from both biopsies and CTCs, and demonstrate that these models capture the mutational landscape and functional features of the donor tumors. .

DOI10.1158/2159-8290.CD-17-0935
Alternate JournalCancer Discov
PubMed ID29483136

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