Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics

Yang Ni, Francesco C. Stingo, Min Jin Ha, Rehan Akbani, Veerabhadran Baladandayuthapani

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, Bayesian hierarchical varying-sparsity regression (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein–gene relationships as well as induces sparsity in both protein–gene and protein–survival relationships, to select genomically driven prognostic protein markers at the patient-level. Simulation studies demonstrate the superior performance of BEHAVIOR against competing method in terms of both protein marker selection and survival prediction. We apply BEHAVIOR to The Cancer Genome Atlas (TCGA) proteogenomic pan-cancer data and find several interesting prognostic proteins and pathways that are shared across multiple cancers and some that exclusively pertain to specific cancers. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available online.

Original languageEnglish (US)
Pages (from-to)48-60
Number of pages13
JournalJournal of the American Statistical Association
Volume114
Issue number525
DOIs
StatePublished - Jan 2 2019

Keywords

  • Precision medicine
  • Prognostic biomarker
  • Threshold
  • Tumor heterogeneity
  • p-splines

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

MD Anderson CCSG core facilities

  • Biostatistics Resource Group
  • Functional Proteomics Reverse Phase Protein Array Core

Fingerprint

Dive into the research topics of 'Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics'. Together they form a unique fingerprint.

Cite this