TY  - JOUR
A1  - Ha, Gavin
A1  - Roth, Andrew
A1  - Khattra, Jaswinder
A1  - Ho, Julie
A1  - Yap, Damian
A1  - Prentice, Leah M.
A1  - Melnyk, Nataliya
A1  - McPherson, Andrew
A1  - Bashashati, Ali
A1  - Laks, Emma
A1  - Biele, Justina
A1  - Ding, Jiarui
A1  - Le, Alan
A1  - Rosner, Jamie
A1  - Shumansky, Karey
A1  - Marra, Marco A.
A1  - Gilks, C. Blake
A1  - Huntsman, David G.
A1  - McAlpine, Jessica N.
A1  - Aparicio, Samuel
A1  - Shah, Sohrab P.
T1  - TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data
Y1  - 2014/11/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1881 
EP  - 1893 
DO  - 10.1101/gr.180281.114 
VL  - 24 
IS  - 11 
UR  - http://genome.cshlp.org/content/24/11/1881.abstract 
N2  - 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. 
ER  -