Genomic and functional fidelity of small cell lung cancer patient-derived xenografts

Benjamin J. Drapkin, Julie George, Camilla L. Christensen, Mari Mino-Kenudson, Ruben Dries, Tilak Sundaresan, Sarah Phat, David T. Myers, Jun Zhong, Peter Igo, Mehlika H. Hazar-Rethinam, Joseph A. Licausi, Maria Gomez-Caraballo, Marina Kem, Kandarp N. Jani, Roxana Azimi, Nima Abedpour, Roopika Menon, Sotirios Lakis, Rebecca S. HeistReinhard Büttner, Stefan Haas, Lecia V. Sequist, Alice T. Shaw, Kwok Kin Wong, Aaron N. Hata, Mehmet Toner, Shyamala Maheswaran, Daniel A. Haber, Martin Peifer, Nicholas Dyson, Roman K. Thomas, Anna F. Farago

Research output: Contribution to journalArticlepeer-review

131 Scopus citations

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. In vivo treatment with etoposide and platinum (EP) in 30 PDX models demonstrated greater sensitivity in PDXs from EP-naive patients, and resistance to EP corresponded to increased expression of a MYC 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. SIGNIFICANCE: 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.

Original languageEnglish (US)
Pages (from-to)600-615
Number of pages16
JournalCancer discovery
Volume8
Issue number5
DOIs
StatePublished - May 2018
Externally publishedYes

ASJC Scopus subject areas

  • Oncology

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