A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.
A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.
复制标题
使用泛组学数据的泛癌症生存分层尖峰和平板模型。
DOI：
10.1186/s12859-022-04770-3
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发表时间：
2022-06-17
影响因子：
3
通讯作者：
Lock, Eric F.
中科院分区：
文献类型：
--
作者：
Samorodnitsky, Sarah;Hoadley, Katherine A.;Lock, Eric F.
关键词：
泛组学、泛癌分析促进了我们对癌症分子异质性的理解。然而，这种分析在使用来自多个数据来源的信息的能力方面受到限制（例如，组学平台）和多个样品组（例如，癌症类型）来预测临床结果。我们解决了多个高维数据源和样本集的预测问题，通过使用BIDIFAC+，一种二维链接矩阵的综合降维方法，在贝叶斯分层模型中识别的分子模式。我们的模型通过在聚类数据中借用信息的spike-and-slab先验进行变量选择。我们使用该模型来预测癌症基因组图谱中患者的总体生存期，该图谱包含来自29种癌症类型和4个组学来源的数据，并使用模拟来表征分层尖峰和平板先验的性能。我们发现，在所有或大多数癌症中共享的分子模式在很大程度上不能预测生存。然而，我们的模型选择了癌症亚群特有的模式，这些模式区分了具有显著不同生存结局的临床肿瘤亚型。这些亚型中的一些是以前建立的，如子宫体子宫内膜癌的亚型，而其他亚型可能是新的，如一组肾癌中的亚型。通过模拟，我们发现，层次尖峰和板先验表现最好的变量选择精度和预测能力时，借用信息是有利的，但也提供了竞争力的表现时，它不是。我们通过在患者总体生存率的贝叶斯分层模型中使用BIDIFAC+的结果来解决多个数据源的预测问题。通过整合借用癌症信息的尖峰和平板先验，我们确定了在单一癌症和一组癌症中区分临床肿瘤亚型的分子模式。我们还证实了使用钉和板先验作为贝叶斯变量选择方法的灵活性和性能。
Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior. We found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not. We address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.
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影响因子：
64.8
作者：
通讯作者：
--
DOI：
10.1214/12-aoas597
发表时间：
2013-03-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Lock EF;Hoadley KA;Marron JS;Nobel AB
通讯作者：
Nobel AB
DOI：
10.1214/21-aoas1495
发表时间：
2022-03
期刊：
The annals of applied statistics
影响因子：
--
作者：
通讯作者：
--
影响因子：
30.8
作者：
Weinstein, John N.;Collisson, Eric A.;Mills, Gordon B.;Shaw, Kenna R. Mills;Ozenberger, Brad A.;Ellrott, Kyle;Shmulevich, Ilya;Sander, Chris;Stuart, Joshua M.
通讯作者：
Stuart, Joshua M.
影响因子：
64.5
作者：
Liu J;Lichtenberg T;Hoadley KA;Poisson LM;Lazar AJ;Cherniack AD;Kovatich AJ;Benz CC;Levine DA;Lee AV;Omberg L;Wolf DM;Shriver CD;Thorsson V;Cancer Genome Atlas Research Network;Hu H
通讯作者：
Hu H