Abstract

Hosted by Nitzan Rosenfeld

Genomic aberrations and chromosomal instability are hallmarks of malignant human cancers. These mutational abnormalities, which encompass copy number alterations (CNA) and loss of heterozygosity (LOH), can have a measurable effect on the structure and dosage of chromosomal regions. Tumour suppressors and oncogenes altered by CNAs can contribute to a phenotype of increased proliferation. Branched evolution throughout tumour progression results in genomic heterogeneity in which divergent clones with distinct aberrations are often present at diagnosis. Measuring and modeling subclonal CNA /LOH events can elucidate the abundance of specific clones in cell populations. This will enable the study of clonal evolution dynamics, which have far-reaching implications for understanding modes of selection, and the genetic basis of metastatic potential and therapeutic resistance. Whole genome sequencing (WGS) provides a high-resolution genome-wide assay for profiling the genomes of cancer cell populations. However, accurate and statistically robust computational methods for inferring CNA and LOH in these data remain under-developed.

I will present two probabilistic approaches that employ hidden Markov models (HMM) to analyze CNA and LOH in tumour WGS data. The first approach, APOLLOH , was developed to profile LOH in heterogeneous tumour-normal admixture data. We applied APOLLOH to analyze 23 triple negative breast cancers (TNBC), and investigated the contribution to allelic expression in matching transcriptome (RNAseq) data. The second approach, TITAN , simultaneously infers CNA /LOH and estimates their cellular prevalence in the tumour sample by accounting for multiple tumour populations. We evaluated TITAN on simulated tumour subclones using real intra-patient samples from an ovarian carcinoma. Finally, I will report preliminary results from the application of TITAN to analyze the clonal selection patterns in breast cancer patient xenograft tumours.