TY - JOUR
T1 - Cancer-specific high-throughput annotation of somatic mutations
T2 - Computational prediction of driver missense mutations
AU - Carter, Hannah
AU - Chen, Sining
AU - Isik, Leyla
AU - Tyekucheva, Svitlana
AU - Velculescu, Victor E.
AU - Kinzler, Kenneth W.
AU - Vogelstein, Bert
AU - Karchin, Rachel
PY - 2009/8/15
Y1 - 2009/8/15
N2 - Large-scale sequencing of cancer genomes has uncovered thousands of DNA alterations, but the functional relevance of the majority of these mutations to tumorigenesis is unknown. We have developed a computational method, called Cancerspecific High-throughput Annotation of Somatic Mutations (CHASM), to identify and prioritize those missense mutations most likely to generate functional changes that enhance tumor cell proliferation. The method has high sensitivity and specificity when discriminating between known driver missense mutations and randomly generated missense mutations (area under receiver operating characteristic curve, >0.91; area under Precision-Recall curve, >0.79). CHASM substantially outperformed previously described missense mutation function prediction methods at discriminating known oncogenic mutations in P53 and the tyrosine kinase epidermal growth factor receptor. We applied the method to 607 missense mutations found in a recent glioblastoma multiforme sequencing study. Based on a model that assumed the glioblastoma multiforme mutations are a mixture of drivers and passengers, we estimate that 8% of these mutations are drivers, causally contributing to tumorigenesis.
AB - Large-scale sequencing of cancer genomes has uncovered thousands of DNA alterations, but the functional relevance of the majority of these mutations to tumorigenesis is unknown. We have developed a computational method, called Cancerspecific High-throughput Annotation of Somatic Mutations (CHASM), to identify and prioritize those missense mutations most likely to generate functional changes that enhance tumor cell proliferation. The method has high sensitivity and specificity when discriminating between known driver missense mutations and randomly generated missense mutations (area under receiver operating characteristic curve, >0.91; area under Precision-Recall curve, >0.79). CHASM substantially outperformed previously described missense mutation function prediction methods at discriminating known oncogenic mutations in P53 and the tyrosine kinase epidermal growth factor receptor. We applied the method to 607 missense mutations found in a recent glioblastoma multiforme sequencing study. Based on a model that assumed the glioblastoma multiforme mutations are a mixture of drivers and passengers, we estimate that 8% of these mutations are drivers, causally contributing to tumorigenesis.
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U2 - 10.1158/0008-5472.CAN-09-1133
DO - 10.1158/0008-5472.CAN-09-1133
M3 - Article
C2 - 19654296
AN - SCOPUS:69249115697
SN - 0008-5472
VL - 69
SP - 6660
EP - 6667
JO - Cancer Research
JF - Cancer Research
IS - 16
ER -