Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling

TLDR

Kinase inhibitors constitute an important new class of cancer drugs, whose selective efficacy is largely determined by underlying tumor cell genetics. The study aimed to develop a high‑throughput platform profiling 500 diverse epithelial cancer cell lines for sensitivity to 14 kinase inhibitors and to demonstrate that genetically defined cancer subsets predict response. The authors used a high‑throughput assay to measure sensitivity of 500 cell lines to 14 kinase inhibitors. The screen revealed that most inhibitors were ineffective except against small, nonoverlapping subsets whose sensitivity correlated with activating mutations or amplifications of the target, uncovered low‑frequency drug‑sensitizing genotypes in previously unassociated tumor types, and showed that drugs targeting the same kinase differed markedly, suggesting predictive value for clinical efficacy.

Abstract

Kinase inhibitors constitute an important new class of cancer drugs, whose selective efficacy is largely determined by underlying tumor cell genetics. We established a high-throughput platform to profile 500 cell lines derived from diverse epithelial cancers for sensitivity to 14 kinase inhibitors. Most inhibitors were ineffective against unselected cell lines but exhibited dramatic cell killing of small nonoverlapping subsets. Cells with exquisite sensitivity to EGFR, HER2, MET, or BRAF kinase inhibitors were marked by activating mutations or amplification of the drug target. Although most cell lines recapitulated known tumor-associated genotypes, the screen revealed low-frequency drug-sensitizing genotypes in tumor types not previously associated with drug susceptibility. Furthermore, comparing drugs thought to target the same kinase revealed striking differences, predictive of clinical efficacy. Genetically defined cancer subsets, irrespective of tissue type, predict response to kinase inhibitors, and provide an important preclinical model to guide early clinical applications of novel targeted inhibitors.