Personalized genomic analyses for cancer mutation discovery and interpretation

作者：Siân Jones, Valsamo Anagnostou, Karli Lytle, Sonya Parpart-Li, Monica Nesselbush, David R. Riley, Manish Shukla, Bryan Chesnick, Maura Kadan, Eniko Papp, Kevin Galens, Derek Murphy, Theresa Zhang, Lisa Kann, Mark Sausen, Samuel V. Angiuoli, Luis A. Díaz, Victor E. Velculescu · 发表于：Science Translational Medicine · 年份：2015 · DOI：10.1126/scitranslmed.aaa7161 · 被引用次数：436 · 研究领域：Cancer Genomics and Diagnostics、Genomics and Rare Diseases、Genetic factors in colorectal cancer

Massively parallel sequencing approaches are beginning to be used clinically to characterize individual patient tumors and to select therapies based on the identified mutations. A major question in these analyses is the extent to which these methods identify clinically actionable alterations and whether the examination of the tumor tissue alone is sufficient or whether matched normal DNA should also be analyzed to accurately identify tumor-specific (somatic) alterations. To address these issues, we comprehensively evaluated 815 tumor-normal paired samples from patients of 15 tumor types. We identified genomic alterations using next-generation sequencing of whole exomes or 111 targeted genes that were validated with sensitivities >95% and >99%, respectively, and specificities >99.99%. These analyses revealed an average of 140 and 4.3 somatic mutations per exome and targeted analysis, respectively. More than 75% of cases had somatic alterations in genes associated with known therapies or current clinical trials. Analyses of matched normal DNA identified germline alterations in cancer-predisposing genes in 3% of patients with apparently sporadic cancers. In contrast, a tumor-only sequencing approach could not definitively identify germline changes in cancer-predisposing genes and led to additional false-positive findings comprising 31% and 65% of alterations identified in targeted and exome analyses, respectively, including in potentially actionable genes. These data suggest tha...