Patient-derived models of acquired resistance can identify effective drug combinations for cancer

Adam S. Crystal, Alice T. Shaw, Lecia V. Sequist, Luc Friboulet, Matthew J. Niederst, Elizabeth L. Lockerman, Rosa L. Frias, Justin F. Gainor, Arnaud Amzallag, Patricia Greninger, Dana Lee, Anuj Kalsy, Maria Gomez-Caraballo, Leila Elamine, Emily Howe, Wooyoung Hur, Eugene Lifshits, Hayley E. Robinson, Ryohei Katayama, Anthony C. FaberMark M. Awad, Sridhar Ramaswamy, Mari Mino-Kenudson, A. John Iafrate, Cyril H. Benes, Jeffrey A. Engelman

Résultats de recherche: Contribution à un journalArticleRevue par des pairs

600 Citations (Scopus)

Résumé

Targeted cancer therapies have produced substantial clinical responses, but most tumors develop resistance to these drugs. Here, we describe a pharmacogenomic platform that facilitates rapid discovery of drug combinations that can overcome resistance.We established cell culture models derived from biopsy samples of lung cancer patients whose disease had progressed while on treatment with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors and then subjected these cells to genetic analyses and a pharmacological screen. Multiple effective drug combinations were identified. For example, the combination of ALK and MAPK kinase (MEK) inhibitors was active in an ALK-positive resistant tumor that had developed a MAP2K1 activating mutation, and the combination of EGFR and fibroblast growth factor receptor (FGFR) inhibitors was active in an EGFR mutant resistant cancer with a mutation in FGFR3. Combined ALK and SRC (pp60c-src) inhibition was effective in several ALK-driven patient-derived models, a result not predicted by genetic analysis alone.With further refinements, this strategy could help direct therapeutic choices for individual patients.

langue originaleAnglais
Pages (de - à)1480-1486
Nombre de pages7
journalScience
Volume346
Numéro de publication6216
Les DOIs
étatPublié - 19 déc. 2014
Modification externeOui

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