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The identification of somatic genetic alterations that confer sensitivity to pharmacologic inhibitors has led to new cancer therapies. To identify mutations that confer an exceptional dependency, shRNA-based loss-of-function data were analyzed from a dataset of numerous cell lines to reveal genes that are essential in a small subset of cancer cell lines. Once these cell lines were determined, detailed genomic characterization from these cell lines was utilized to ascertain the genomic aberrations that led to this extreme dependency. This method, in a large subset of lung cancer cell lines, yielded a single lung adenocarcinoma cell line, NCI-H1437, which is sensitive to RNA interference of MAP2K1 expression. Notably, NCI-H1437 is the only lung cancer cell line included in the dataset with a known activating mutation in MAP2K1 (Q56P). Subsequent validation using shRNA and CRISPR-Cas9 confirmed MAP2K1 dependency. In vitro and in vivo inhibitor studies established that NCI-H1437 cells are sensitive to MEK1 inhibitors, including the FDA-approved drug trametinib. Like NCI-H1437 cells, the MAP2K1-mutant cell lines SNU-C1 (colon) and OCUM-1 (gastric) showed decreased viability after MAP2K1 depletion via Cas9-mediated gene editing. Similarly, these cell lines were particularly sensitive to trametinib treatment compared with control cell lines. On the basis of these data, cancers that harbor driver mutations in MAP2K1 could benefit from treatment with MEK1 inhibitors. Furthermore, this functional data mining approach provides a general method to experimentally test genomic features that confer dependence in tumors.

Identification of an “Exceptional Responder” Cell Line to MEK1 Inhibition: Clinical Implications for MEK-Targeted Therapy