Abstract

Chromosomal aneuploidy and segmental copy number changes are common genomic aberrations in -cancer. Copy number alterations (CNAs) arise from deletions, insertions, or duplications resulting in -chromosomal aberrations and aneuploidy. Genomes of normal cells also exhibit variable copy number called germline copy number variants (CNVs). CNVs in the general population tend to confound interpretation of predictions when attempting to extract relevant driver somatic events in cancer. In large studies of CNAs in cancer patients, it becomes necessary to accurately identify and separate CNAs and CNVs so as to prioritize candidate tumor suppressors and oncogenes. We have developed a probabilistic approach, HMM-Dosage, for segmenting and distinguishing CNAs and CNVs as separate, discrete events in cancer SNP genotyping array data. We outline the steps and computer code for the analysis of whole-genome cancer DNA hybridized to SNP genotyping arrays, focusing on distinguishing somatic CNA and germline CNVs, and describe the combined approach of HMM-Dosage for probabilistic inference and classification of somatic and germline copy number changes.