With the release of multiple fully-sequenced tumor and matched normal genomes from The Cancer Genome Atlas (TCGA), there is great need for tools that can efficiently analyze these enormous datasets. To this end, we developed BamBam, a method that interfaces with the SAMtools library to simultaneously analyze a patient's tumor and germline genomes using short-read alignments stored in SAM/BAM-formatted files. BamBam enables an efficient calculation of regional copy number, loss of heterozygosity (LOH), and the discovery of both somatic and germline sequence variants and structural rearrangements. Chromosomal regions that experience single copy loss or copy neutral LOH are used to phase heterozygous sites and construct germline haplotypes.

We used BamBam to analyze the short-read sequencing data of several glioblastoma multiforme and ovarian serous carcinoma tumors produced by the TCGA genome sequencing centers at the Broad Institute and Washington University in St. Louis. In each tumor, BamBam identified somatic homozygous and heterozygous small indels and single nucleotide variants, inferred regions exhibiting large-scale loss of heterozygosity, estimated relative copy number, and identified sites of structural variation. The intra- and inter-chromosomal rearrangements we infer are often found at the boundary of copy number alterations, implicating homology-driven mechanisms. We identified variants shared by more than one tumor as well as events restricted to individual tumors. Strikingly, in each tumor processed thus far, distinct regions of localized chromosomal instability were observed. The inferred haplotypes share substantial regions of identity with the germline haplotypes of the other TCGA samples and phased SNPs from samples of 11 HapMap populations, indicating they have high accuracy.

BamBam is a computationally efficient method for surveying large sequencing datasets to produce a set of high-quality genomic events that occur within each tumor relative to its germline. These results provide a glimpse into the chromosomal dynamics of tumors, improving our understanding of these tumors’ final states and the events that led to them.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 89.