Large-scale pharmacologic profiling of cell lines uncovers predictors of drug response.

  • Major finding: Large-scale pharmacologic profiling of cell lines uncovers predictors of drug response.

  • Impact: Publicly available cell line databases may guide drug development and clinical testing.

  • Clinical relevance: Inhibitors of topoisomerase and PARP1 may be effective in Ewing sarcoma.

There is an urgent need to identify biomarkers that predict which patients are most likely to respond to treatment. Systematic efforts to correlate tumor mutational data with biologic dependencies may facilitate the translation of somatic mutation catalogs into meaningful biomarkers for patient stratification. To identify genomic features associated with drug sensitivity, 2 teams integrated mutation, DNA copy number, and gene expression data for hundreds of cell lines with their responses to targeted and cytotoxic therapies. Barretina and colleagues established the Cancer Cell Line Encyclopedia, which includes genomic data for 947 cell lines as well as pharmacologic profiles for 24 anticancer drugs across a subset of the cell lines. As part of the Genomics of Drug Sensitivity in Cancer Project, Garnett and colleagues gathered genomic data from 639 cell lines and pharmacologic profiles for 130 compounds. Both groups analyzed the effects of each drug on cell viability after 72 hours of treatment and used a linear modeling technique called elastic net regression to analyze approximately 50,000 drug–cell line combinations and identify cellular features that predicted drug sensitivity. These large-scale screening strategies were validated by the identification of oncogenic mutations as top predictors of sensitivity to their corresponding targeted therapies, and several shared findings, such as the correlation between high NQO1 expression and sensitivity to the HSP90 inhibitor 17-AAG, showed the robustness of these approaches. The different cell lines and compounds used also led to complementary findings, as Ewing sarcoma cell lines were found to be specifically sensitive to inhibitors of both topoisomerase and PARP1. Together, these databases represent potentially useful resources to uncover unappreciated biologic relationships and identify biomarkers of drug sensitivity, some of which may have immediate clinical relevance.

Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity. Nature 2012;483:603–7.

Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 2012;483:570–5.

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