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Accurate Estimation of Genomic Deletions and Normal Cell Contamination by Bayesian Analysis of Mixtures

Copy number change is an important form of structural variation in human genomes. Somatic copy number alterations can cause the acquisition of oncogenes and loss of tumor suppressor genes in tumorigenesis. Recent development of SNP array technology facilitates studies on copy number changes in a genome-wide scale with high resolution. However, tumor samples often consist of mixed cancer and normal cells. Such tissue heterogeneity poses as a serious hurdle to analyzing copy number changes and could confound subsequent marker identification and diagnostic classification rooted in specific cells. We report here a statistically-principled in silico approach to accurately estimate genomic deletions and normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. We tested the proposed method on three simulation and one real datasets and obtained highly promising results validated by the ground truth and figure of merit. We expect this newly developed method to be a useful tool in routine copy number analysis of heterogeneous tissues.