Systematic efforts are underway to decipher the genetic changes associated with tumor initiation and progression. However, widespread clinical application of this information is hampered by an inability to identify critical genetic events across the spectrum of human tumors with adequate sensitivity and scalability. Here, we have adapted high-throughput genotyping to query 238 known oncogene mutations across 1,000 human tumor samples. This approach established robust mutation distributions spanning 17 cancer types. Of 17 oncogenes analyzed, we found 14 to be mutated at least once, and 298 (30%) samples carried at least one mutation. Moreover, we identified previously unrecognized oncogene mutations in several tumor types and observed an unexpectedly high number of co-occurring mutations. These results offer a new dimension in tumor genetics, where mutations involving multiple cancer genes may be interrogated simultaneously and in 'real time' to guide cancer classification and rational therapeutic intervention.

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.

The classification of human tumors based on molecular criteria offers tremendous clinical potential; however, discerning critical and