TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data.
TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data.
复制标题
DOI：
10.1101/gr.180281.114
复制
发表时间：
2014-11
期刊：
影响因子：
7
通讯作者：
Shah SP
中科院分区：
文献类型：
--
作者：
Ha G;Roth A;Khattra J;Ho J;Yap D;Prentice LM;Melnyk N;McPherson A;Bashashati A;Laks E;Biele J;Ding J;Le A;Rosner J;Shumansky K;Marra MA;Gilks CB;Huntsman DG;McAlpine JN;Aparicio S;Shah SP
单个肿瘤内癌症基因组的进化产生了具有不同体细胞突变景观的混合细胞群体。肿瘤亚群的推断不成比例地集中在体细胞点突变的评估上，而针对全基因组测序数据中拷贝数改变（CNA）和杂合性丢失（洛）的进化动力学的计算方法仍然不发达。我们提出了一种新的概率模型，TITAN，推断CNA和洛事件，同时占细胞群体的混合物，从而估计窝藏每个事件的细胞的比例。我们评估TITAN的理想化的混合物，模拟克隆群体从全基因组序列从基因组异质性卵巢肿瘤网站收集从同一个病人。此外，我们在23个乳腺肿瘤的全基因组中显示，使用TITAN对CNA和洛的推断至关重要地告知了群体结构和不断发展的癌症基因组的性质。最后，我们实验验证了亚克隆预测使用荧光原位杂交（FISH）和单细胞测序卵巢癌患者样本，从而概括了TITAN的关键建模假设。
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.
登录
查看更多内容
影响因子：
7.3
作者：
Ahmed, Ashour Ahmed;Etemadmoghadam, Dariush;Temple, Jillian;Lynch, Andy G.;Riad, Mohamed;Sharma, Raghwa;Stewart, Colin;Fereday, Sian;Caldas, Carlos;DeFazio, Anna;Bowtell, David;Brenton, James D.
通讯作者：
Brenton, James D.
核苷酸分辨率上杂合性丧失和单相表达的全基因组丧失的综合分析揭示了三阴性乳腺癌的途径中断。
影响因子：
7
作者：
Ha G;Roth A;Lai D;Bashashati A;Ding J;Goya R;Giuliany R;Rosner J;Oloumi A;Shumansky K;Chin SF;Turashvili G;Hirst M;Caldas C;Marra MA;Aparicio S;Shah SP
通讯作者：
Shah SP
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
64.8
作者：
Greaves, Mel;Maley, Carlo C.
通讯作者：
Maley, Carlo C.
DOI：
10.1093/bioinformatics/btr670
发表时间：
2012-02-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Boeva V;Popova T;Bleakley K;Chiche P;Cappo J;Schleiermacher G;Janoueix-Lerosey I;Delattre O;Barillot E
通讯作者：
Barillot E