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A Gaussian Mixture-Model Exploiting Pathway Knowledge for Dissecting Cancer Heterogeneity.
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 3.6 ) Pub Date : 2018-09-24 , DOI: 10.1109/tcbb.2018.2869813
Rajan Kapoor , Aniruddha Datta , Chao Sima , Jianping Hua , Rosana Lopes , Michael L. Bittner

In this work, we develop a systematic approach for applying pathway knowledge to a multivariate Gaussian mixture model for dissecting a heterogeneous cancer tissue. The downstream transcription factors are selected as observables from available partial pathway knowledge in such a way that the subpopulations produce some differential behavior in response to the drugs selected in the upstream. For each subpopulation, each unique (drug, observable) pair is considered as a unique dimension of a multivariate Gaussian distribution. Expectation-maximization (EM) algorithm with hill-climbing is then used to rank the most probable estimates of the mixture composition based on the log-likelihood value. A major contribution of this work is to examine the efficacy of the EM based approach in estimating the composition of experimental mixture sets from cell-by-cell measurements collected on a dynamic cell imaging platform. Towards this end, we apply the algorithm on hourly data collected for two different mixture compositions of A2058, HCT116 and SW480 cell lines for three scenarios: untreated, Lapatinib-treated and Temsirolimus-treated. Additionally, we show how this methodology can provide a basis for comparing the killing rate of different drugs for a heterogeneous cancer tissue. This obviously has important implications for designing efficient drugs for treating heterogeneous malignant tumors.

中文翻译:

用于剖析癌症异质性的高斯混合模型开发途径知识。

在这项工作中,我们开发了一种系统途径,将途径知识应用于解剖异质癌组织的多元高斯混合模型。从可获得的部分途径知识中选择可观察到的下游转录因子,以使亚群响应于上游选择的药物而产生一些不同的行为。对于每个亚人群,每个唯一(药物,可观察到的)对都被视为多元高斯分布的唯一维度。然后使用具有爬坡的期望最大化(EM)算法基于对数似然值对混合物成分的最可能估计值进行排名。这项工作的主要贡献是检查基于EM的方法在从动态细胞成像平台上收集的逐细胞测量结果估计实验混合物组组成方面的功效。为此,我们将算法应用于三种情况下未经采集,经拉帕替尼处理和经坦罗西莫司处理的A2058,HCT116和SW480细胞系的两种不同混合物组成的每小时数据收集。此外,我们展示了该方法学如何为比较不同药物对异质癌组织的杀灭率提供基础。这显然对于设计用于治疗异质性恶性肿瘤的有效药物具有重要意义。我们将算法应用到以下三种情况下每小时收集的A2058,HCT116和SW480细胞系的两种不同混合物组成的数据中:未处理,拉帕替尼处理和坦罗莫司处理。此外,我们展示了该方法学如何为比较不同药物对异质癌组织的杀灭率提供基础。这显然对于设计用于治疗异质性恶性肿瘤的有效药物具有重要意义。我们将算法应用到以下三种情况下每小时收集的A2058,HCT116和SW480细胞系的两种不同混合物组成的数据中:未处理,拉帕替尼处理和坦罗莫司处理。此外,我们展示了该方法学如何为比较不同药物对异质癌组织的杀灭率提供基础。这显然对于设计用于治疗异质性恶性肿瘤的有效药物具有重要意义。
更新日期:2020-04-22
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