Abstract
BackgroundDetection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; however, high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting.MethodologyWe developed and implemented an optimized mutation profiling platform (“OncoMap”) to interrogate ∼400 mutations in 33 known oncogenes and tumor suppressors, many of which are known to predict response or resistance to targeted therapies. The performance of OncoMap was analyzed using DNA derived from both frozen and FFPE clinical material in a diverse set of cancer types. A subsequent in-depth analysis was conducted on histologically and clinically annotated pediatric gliomas. The sensitivity and specificity of OncoMap were 93.8% and 100% in fresh frozen tissue; and 89.3% and 99.4% in FFPE-derived DNA. We detected known mutations at the expected frequencies in common cancers, as well as novel mutations in adult and pediatric cancers that are likely to predict heightened response or resistance to existing or developmental cancer therapies. OncoMap profiles also support a new molecular stratification of pediatric low-grade gliomas based on BRAF mutations that may have immediate clinical impact.ConclusionsOur results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of “actionable” cancer gene mutations. In the future, this type of approach may be incorporated into both cancer epidemiologic studies and clinical decision making to specify the use of many targeted anticancer agents.
Highlights
Many tumors contain hallmark mutations within oncogenes or tumor suppressor (TS) genes that may confer a heightened susceptibility to targeted anticancer therapies
Our results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of ‘‘actionable’’ cancer gene mutations
Patients and Tumor Tissue Collection Anonymized tumor specimens were obtained from the Cooperative Human Tissue Network (CHTN), Surgical Oncology University of Siena, Italy, and Dana-Farber Cancer Institute; human glioma samples were obtained from the clinical archives of the departments of Pathology at Children’s Hospital Boston and Brigham and Women’s Hospital. (The required tumor content was .70%; necrosis,10%.) Institutional review board (IRB) exemption was obtained for all samples from the Dana-Farber/Partners
Summary
Many tumors contain hallmark mutations within oncogenes or tumor suppressor (TS) genes that may confer a heightened susceptibility to targeted anticancer therapies. Well-established examples include KIT mutations in gastrointestinal stromal tumors (GISTs) that predict response to imatinib or nilotinib, and non-small cell lung cancers with EGFR mutations that are sensitive to erlotinib[1,2,3]. Lung and colorectal cancers that harbor mutations in the KRAS oncogene are unresponsive to treatment with anti-EGFR agents[4], and inactivating. Generating a comprehensive profile of target-able or otherwise ‘‘actionable’’ tumor DNA mutations in the clinical arena remains challenging. Detection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting
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