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

By G. Ha, Andrew Roth, J. Khattra, Julie Ho, Damian Boon Siew Yap, Leah M Prentice, Nataliya Melnyk, Andrew W. McPherson, Ali Bashashati, E. Laks and 11 more. Genome Research, 2014

367 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Genetics

View Paper: https://www.semanticscholar.org/paper/a2fd5a323398c5277c2ca770b738aaf325f9570a

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Citations in the corpus

Tree inference for single-cell data (2016, methodology): “In the past years an abundance of tools have been developed to study subclone composition in mixed samples (Gerstung et al., 2012; Ha et al., 2014; Oesper et al., 2013; Roth et al., 2014; Shah et al., 2012; Zare et al., 2014).”
Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. (2017, positive): “While tools to infer copy number heterogeneity have also been developed (Ha et al., 2014; Shen and Seshan, 2016), more recent tools seek to combine copy number and mutational data (Fischer et al., 2014; Jiang et al., 2016) and furthermore, attempt to infer evolutionary relationships between…”
Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution (2017, methodology): “…themajority of copy-number tools rely on the relative coverage and variant allele frequency of single nucleotide polymorphisms (SNPs) in the tumor and matched normal across the genome or exome (Carter et al., 2012; Favero et al., 2015; Ha et al., 2014; Shen and Seshan, 2016; Van Loo et al., 2010).”
Cancer Evolution: Mathematical Models and Computational Inference (2014, methodology): “In the same spirit Ha et al. (2014) implemented a Hidden Markov Model-based approach for identification of subclonal copy number profiles.”
Organoid profiling identifies common responders to chemotherapy in pancreatic cancer (2018, methodology): “The final 591 average ploidy/purity was taken from the Titan (49) or ABSOLUTE (50) estimate that most closely matched 592 the manual calculation.”