Statistical model-based testing to evaluate the recurrence of genomic aberrations.

Abstract

In cancer genomes, chromosomal regions harboring cancer genes are often subjected to genomic aberrations like copy number alteration and loss of heterozygosity. Given this, finding recurrent genomic aberrations is considered an apt approach for screening cancer genes. Although several permutation-based tests have been proposed for this purpose, none of them are designed to find recurrent aberrations from the genomic dataset without paired normal sample controls. Their application to unpaired genomic data may lead to false discoveries, because they retrieve pseudo-aberrations that exist in normal genomes as polymorphisms. We develop a new parametric method named parametric aberration recurrence test (PART) to test for the recurrence of genomic aberrations. The introduction of Poisson-binomial statistics allow us to compute small P-values more efficiently and precisely than the previously proposed permutation-based approach. Moreover, we extended PART to cover unpaired data (PART-up) so that there is a statistical basis for analyzing unpaired genomic data. PART-up uses information from unpaired normal sample controls to remove pseudo-aberrations in unpaired genomic data. Using PART-up, we successfully predict recurrent genomic aberrations in cancer cell line samples whose paired normal sample controls are unavailable. This article thus proposes a powerful statistical framework for the identification of driver aberrations, which would be applicable to ever-increasing amounts of cancer genomic data seen in the era of next generation sequencing. Our implementations of PART and PART-up are available from http://www.hgc.jp/~niiyan/PART/manual.html.