TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data

Abstract

The evolution of cancer genomes within a single tumor creates mixed cell populations with divergent somatic mutational landscapes. Inference of tumor subpopulations has been disproportionately focused on the assessment of somatic point mutations, whereas computational methods targeting evolutionary dynamics of copy number alterations (CNA) and loss of heterozygosity (LOH) in whole-genome sequencing data remain underdeveloped. We present a novel probabilistic model, TITAN, to infer CNA and LOH events while accounting for mixtures of cell populations, thereby estimating the proportion of cells harboring each event. We evaluate TITAN on idealized mixtures, simulating clonal populations from whole-genome sequences taken from genomically heterogeneous ovarian tumor sites collected from the same patient. In addition, we show in 23 whole genomes of breast tumors that the inference of CNA and LOH using TITAN critically informs population structure and the nature of the evolving cancer genome. Finally, we experimentally validated subclonal predictions using fluorescence in situ hybridization (FISH) and single-cell sequencing from an ovarian cancer patient sample, thereby recapitulating the key modeling assumptions of TITAN.

Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing

“…TITAN (17) and THetA (18) focus on estimating cell population structure and recovering clonal evolutionary history for the case where somatic CNAs and loss of heterozygosity (LOH) distinguish subpopulations. These methods use allelic read coverage at germline heterozygous SNP loci to distinguish clonal versus subclonal CNA events.…”

“…For example, ABSOLUTE (13), one of the earliest methods, classifies mutations as clonal or subclonal after adjusting for the estimated purity and ploidy of the sample. Most approaches for the detection of subclonal mutations treat point mutations and copy number aberrations separately (15)(16)(17)(18). In the case of point mutations, that is, single-nucleotide alterations (SNAs) and small insertions and deletions (indels), most methods rely on mixture models for the variant allele frequency (VAF) under the assumption that mutations carried by the same set of cells have the same VAF.…”

Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future

“…Eleveld, T.F., Oldridge, D.A., Bernard, V., Koster, J., Daage, L.C., Diskin, S.J., Schild, L., Bentahar, N.B., Bellini, A., Chicard, M., et al (2015). Relapsed neuroblastomas show frequent RAS-MAPK pathway mutations.…”

Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution

“…Analogously, we analyzed clonal dynamics using CNAs as clonal marks, applying a probabilistic model (TITAN 19 ) that infers CNA and LOH from WGSS data, accounting for mixtures of tumour and normal cells and reporting estimates of mutation cellular prevalence and mutation cluster membership (Table S10). Despite conservation of complex disruptions, such as chromothripsis in SA429 ( Figure S8) and breakage-fusion-bridge cycles in SA429 and SA494 ( Figure S9, Figure S10), we identified substantial differences in copy number architecture between tumour and xenograft in all cases (Extended Figure E2c, Figure S5c).…”