Bayesian data integration and variable selection for pan‐cancer survival prediction using protein expression data

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of p...

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Bibliographic Details
Published in:Biometrics 2020-03, Vol.76 (1), p.316-325
Main Authors: Maity, Arnab Kumar, Bhattacharya, Anirban, Mallick, Bani K., Baladandayuthapani, Veerabhadran
Format: Article
Language:English
Subjects:
AFT regression
Annotations
Bayesian analysis
borrowing strength
Cancer
Computer applications
Computer simulation
Data analysis
Data integration
Failure times
horseshoe
Integration
Mathematical models
pan‐cancer model
Precision medicine
Protein arrays
Protein expression
Proteins
Proteomes
Regression analysis
Regression coefficients
Regression models
Statistical analysis
Survival
TCPA
Tumors
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Summary:Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse‐phase protein arrays–based high‐quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.13132