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

TLDR

Targeted cancer therapies achieve strong responses, yet most tumors ultimately develop resistance. The study presents a pharmacogenomic platform for rapidly discovering drug combinations that can overcome resistance and guide individualized therapy. Patient‑derived lung cancer cell cultures from biopsies of patients progressing on EGFR or ALK inhibitors were genetically profiled and screened with a pharmacological panel. The platform uncovered multiple effective combinations, such as ALK+MEK in ALK‑positive tumors with MAP2K1 mutations, EGFR+FGFR in EGFR‑mutant tumors with FGFR3 mutations, and ALK+SRC in several ALK‑driven models, many of which were not predicted by genetics alone.

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.