Abstract A41: Somatic mutations in human cancer cell lines predict sorafenib response

It is now widely accepted that a spectrum of somatic alterations occur in human cancer and that particular alterations are associated with response or lack of response to anti-cancer therapies. Here, we undertook a systematic correlation analysis of somatic mutations, copy number aberrations and drug response across 300+ human cancer cell lines. We studied the RAF and VEGFR inhibitor, sorafenib, evaluating associations between sorafenib response and somatic mutations in 35 common cancer genes and high-level amplifications and homozygous deletions in more than 100 minimal common regions of gain and loss, which include many known oncogenes and tumor suppressor genes. Notably, we found that NRAS mutations, among other alterations, were significantly associated with sensitivity to sorafenib (odds ratio = 7, p-value = 0.003), whereas activating BRAF mutations were strongly associated with a lack of response (odds ratio = 4.9, p-value = 0.012). Specifically, 64% (7 of 11) of NRAS mutant cell lines were sensitive to sorafenib, while only 20% (34 of 170) of NRAS wild-type lines were sensitive. On the contrary, just 6.7% (2 of 30) of BRAF mutant lines were sensitive to sorafenib. Because NRAS mutations occur frequently in leukemia and no leukemia cell lines were included in the original analysis, we selected 5 NRAS mutant and 5 NRAS wild-type leukemia cell lines for further evaluation. Consistent with our previous observation, the four most sensitive leukemia cell lines were all NRAS mutant, each line exhibiting greater than 50% growth inhibition after 72 hours of exposure to 4μM sorafenib. These results confirm the original association and suggest that across both solid tumors and hematologic malignancies, NRAS mutations may identify a subset of cancers that are likely to respond sorafenib. In addition, this work demonstrates that systematic analysis of somatic alterations and drug response across a large collection of cancer cell lines is capable of discovering novel predictive biomarkers. Citation Information : Clin Cancer Res 2010;16(7 Suppl):A41