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Science. 2014 Dec 19;346(6216):1480-6. doi: 10.1126/science.1254721. Epub 2014 Nov 13.

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

Author information

1
Massachusetts General Hospital Cancer Center, Department of Medicine and Harvard Medical School, Boston, MA 02114, USA.
2
Dana-Farber Cancer Institute, Department of Biological Chemistry and Molecular Pharmacology and Harvard Medical School, Boston, MA 02115, USA. Chemical Kinomics Research Center, Korea Institute of Science and Technology, Seoul, 136-791, South Korea.
3
Massachusetts General Hospital Cancer Center, Department of Pathology and Harvard Medical School, Boston, MA 02114, USA.
4
Massachusetts General Hospital Cancer Center, Department of Medicine and Harvard Medical School, Boston, MA 02114, USA. jengelman@partners.org cbenes@partners.org.

Abstract

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.

PMID:
25394791
PMCID:
PMC4388482
DOI:
10.1126/science.1254721
[Indexed for MEDLINE]
Free PMC Article

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