Inference of tumor phylogenies with improved somatic mutation discovery

Authors:
Raheleh Salari;Syed Shayon Saleh;Dorna Kashef-Haghighi;David Khavari;Daniel E. Newburger;Robert B. West;Arend Sidow;Serafim Batzoglou
Affiliations:
Department of Computer Science, Stanford University, Stanford, CA;Department of Computer Science, Stanford University, Stanford, CA;Department of Computer Science, Stanford University, Stanford, CA;Department of Computer Science, Stanford University, Stanford, CA;Biomedical Informatics Training Program, Stanford University, Stanford, CA;Department of Pathology, Stanford University School of Medicine, Stanford, CA;Department of Pathology, Stanford University School of Medicine, Stanford, CA and Department of Genetics, Stanford University School of Medicine, Stanford, CA;Department of Computer Science, Stanford University, Stanford, CA
Venue:
RECOMB'13 Proceedings of the 17th international conference on Research in Computational Molecular Biology
Year:
2013

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Abstract

Next-generation sequencing technologies provide a powerful tool for studying genome evolution during progression of advanced diseases such as cancer. Although many recent studies have employed new sequencing technologies to detect mutations across multiple, genetically related tumors, current methods do not exploit available phylogenetic information to improve the accuracy of their variant calls. Here, we present a novel algorithm that uses somatic single nucleotide variations (SNVs) in multiple, related tissue samples as lineage markers for phylogenetic tree reconstruction. Our method then leverages the inferred phylogeny to improve the accuracy of SNV discovery. Experimental analyses demonstrate that our method achieves up to 32% improvement for somatic SNV calling of multiple related samples over the accuracy of GATK's Unified Genotyper, the state of the art multisample SNV caller.