Inferring clonal composition from multiple sections of a breast cancer.
Inferring clonal composition from multiple sections of a breast cancer.
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DOI：
10.1371/journal.pcbi.1003703
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发表时间：
2014-07
影响因子：
4.3
通讯作者：
Noble WS
中科院分区：
文献类型：
--
作者：
Zare H;Wang J;Hu A;Weber K;Smith J;Nickerson D;Song C;Witten D;Blau CA;Noble WS
癌症起源于连续的突变和选择，产生大小、突变含量和药物反应性各不相同的克隆群体。因此，确定肿瘤的克隆组成对预后和治疗都很重要。由下一代测序（NGS）得出的突变计数和频率可能反映肿瘤的克隆组成；然而，反卷积NGS数据来推断肿瘤的克隆结构提出了一个主要挑战。我们提出了一个从单个肿瘤的多个亚段获得的NGS数据的生成模型，并描述了使用该模型估计克隆基因型和相对频率的期望最大化过程。我们通过模拟证明了该方法的有效性，然后使用我们的算法来评估原发性乳腺癌和相关转移性淋巴结的克隆组成。在将肿瘤分成亚段后，我们对每个子段进行外显子组测序以评估突变含量，然后进行深度测序以精确计数每个子段中的正常和变异等位基因。通过量化17个体细胞变异的频率，我们证明了我们的算法预测克隆关系在系统发育和空间上都是合理的。将这种方法应用于更大量的肿瘤，将有助于揭示癌症在空间和时间上的克隆进化。癌症是由一系列随时间发生的突变引起的。因此，随着肿瘤的生长，每个细胞都继承了一种独特的基因型，这种基因型由所有体细胞突变的集合定义，这些体细胞突变将肿瘤细胞与正常细胞区分开来。确定这些基因型模式，并确定哪些与癌症的生长及其转移能力有关，可以潜在地为临床医生提供如何治疗癌症的见解。在这项工作中，我们描述了一种推断单个肿瘤内主要基因型的方法。该方法要求对肿瘤进行切片，并对每个切片进行高通量测序。由此产生的突变及其在每个肿瘤切片内的相关频率然后被用作概率模型的输入，该模型推断出潜在的基因型及其在肿瘤内的相对频率。我们使用模拟数据来证明该方法的有效性，然后我们将我们的算法应用于原发性乳腺癌和相关转移性淋巴结的数据。我们证明，我们的算法预测基因型是一致的进化模型和肿瘤本身的物理拓扑结构。将这种方法应用于更多的肿瘤，将有助于了解癌症在空间和时间上的演变。
Cancers arise from successive rounds of mutation and selection, generating clonal populations that vary in size, mutational content and drug responsiveness. Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy. Mutation counts and frequencies resulting from next-generation sequencing (NGS) potentially reflect a tumor's clonal composition; however, deconvolving NGS data to infer a tumor's clonal structure presents a major challenge. We propose a generative model for NGS data derived from multiple subsections of a single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model. We demonstrate, via simulation, the validity of the approach, and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node. After dividing the tumor into subsections, we perform exome sequencing for each subsection to assess mutational content, followed by deep sequencing to precisely count normal and variant alleles within each subsection. By quantifying the frequencies of 17 somatic variants, we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time. Cancers arise from a series of mutations that occur over time. As a result, as a tumor grows each cell inherits a distinctive genotype, defined by the set of all somatic mutations that distinguish the tumor cell from normal cells. Acertaining these genotype patterns, and identifying which ones are associated with the growth of the cancer and its ability to metastasize, can potentially give clinicians insights into how to treat the cancer. In this work, we describe a method for inferring the predominant genotypes within a single tumor. The method requires that a tumor be sectioned and that each section be subjected to a high-throughput sequencing procedure. The resulting mutations and their associated frequencies within each tumor section are then used as input to a probabilistic model that infers the underlying genotypes and their relative frequencies within the tumor. We use simulated data to demonstrate the validity of the approach, and then we apply our algorithm to data from a primary breast cancer and associated metastatic lymph node. We demonstrate that our algorithm predicts genotypes that are consistent with an evolutionary model and with the physical topology of the tumor itself. Applying this method to larger numbers of tumors should cast light on the evolution of cancers in space and time.
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影响因子：
64.8
作者：
通讯作者：
--
影响因子：
7.3
作者：
Bashashati, Ali;Ha, Gavin;Tone, Alicia;Ding, Jiarui;Prentice, Leah M.;Roth, Andrew;Rosner, Jamie;Shumansky, Karey;Kalloger, Steve;Senz, Janine;Yang, Winnie;McConechy, Melissa;Melnyk, Nataliya;Anglesio, Michael;Luk, Margaret T. Y.;Tse, Kane;Zeng, Thomas;Moore, Richard;Zhao, Yongjun;Marra, Marco A.;Gilks, Blake;Yip, Stephen;Huntsman, David G.;McAlpine, Jessica N.;Shah, Sohrab P.
通讯作者：
Shah, Sohrab P.
影响因子：
64.8
作者：
Navin N;Kendall J;Troge J;Andrews P;Rodgers L;McIndoo J;Cook K;Stepansky A;Levy D;Esposito D;Muthuswamy L;Krasnitz A;McCombie WR;Hicks J;Wigler M
通讯作者：
Wigler M
影响因子：
7
作者：
Navin, Nicholas;Krasnitz, Alexander;Wigler, Michael
通讯作者：
Wigler, Michael
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel