Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction

• 成果类型: Article
• 署名作者: Rashid, Nairn U.; Li, Quefeng; Yeh, Jen Jen; Ibrahim, Joseph G.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1671197
• 发表日期: 2020
• 页码: 1125-1138
• 关键词: VARIABLE SELECTION; expression; cancer; quantification; algorithms; likelihood; regression; package

摘要

In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach to select gene signatures from multiple datasets whose effects are consistently nonzero and account for between-study heterogeneity. We build our model upon some rank-based quantities, facilitating integration over different genomic datasets. A high-dimensional penalized generalized linear mixed model is used to select gene signatures and address data heterogeneity. We compare our method to some commonly used strategies that select gene signatures ignoring between-study heterogeneity. We provide asymptotic results justifying the performance of our method and demonstrate its advantage in the presence of heterogeneity through thorough simulation studies. Lastly, we motivate our method through a case study subtyping pancreatic cancer patients from four gene expression studies. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.