Faculty Collaboration Database

Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach. Genetics 2017 Jan;205(1):89-100

Date

01/05/2017

Pubmed ID

28049703

Pubmed Central ID

PMC5223526

DOI

10.1534/genetics.116.189191

Scopus ID

2-s2.0-85008477467 (requires institutional sign-in at Scopus site)   33 Citations

Abstract

Heterogeneity in terms of tumor characteristics, prognosis, and survival among cancer patients has been a persistent problem for many decades. Currently, prognosis and outcome predictions are made based on clinical factors and/or by incorporating molecular profiling data. However, inaccurate prognosis and prediction may result by using only clinical or molecular information directly. One of the main shortcomings of past studies is the failure to incorporate prior biological information into the predictive model, given strong evidence of the pathway-based genetic nature of cancer, i.e., the potential for oncogenes to be grouped into pathways based on biological functions such as cell survival, proliferation, and metastatic dissemination. To address this problem, we propose a two-stage approach to incorporate pathway information into the prognostic modeling using large-scale gene expression data. In the first stage, we fit all predictors within each pathway using the penalized Cox model and Bayesian hierarchical Cox model. In the second stage, we combine the cross-validated prognostic scores of all pathways obtained in the first stage as new predictors to build an integrated prognostic model for prediction. We apply the proposed method to analyze two independent breast and ovarian cancer datasets from The Cancer Genome Atlas (TCGA), predicting overall survival using large-scale gene expression profiling data. The results from both datasets show that the proposed approach not only improves survival prediction compared with the alternative analyses that ignore the pathway information, but also identifies significant biological pathways.

Author List

Zhang X, Li Y, Akinyemiju T, Ojesina AI, Buckhaults P, Liu N, Xu B, Yi N

Author

Akinyemi Ojesina MD, PhD Assistant Professor in the Obstetrics and Gynecology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Bayes Theorem
Breast Neoplasms
Databases, Genetic
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Models, Genetic
Ovarian Neoplasms
Predictive Value of Tests
Prognosis
Proportional Hazards Models