Subclonal variant calling with multiple samples and prior knowledge.

Abstract

Targeted resequencing of cancer genes in large cohorts of patients is important to understand the biological and clinical consequences of mutations. Cancers are often clonally heterogeneous, and the detection of subclonal mutations is important from a diagnostic point of view, but presents strong statistical challenges. Here we present a novel statistical approach for calling mutations from large cohorts of deeply resequenced cancer genes. These data allow for precisely estimating local error profiles and enable detecting mutations with high sensitivity and specificity. Our probabilistic method incorporates knowledge about the distribution of variants in terms of a prior probability. We show that our algorithm has a high accuracy of calling cancer mutations and demonstrate that the detected clonal and subclonal variants have important prognostic consequences.