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Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach.

Publication ,  Journal Article
Zhang, X; Li, Y; Akinyemiju, T; Ojesina, AI; Buckhaults, P; Liu, N; Xu, B; Yi, N
Published in: Genetics
January 2017

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.

Duke Scholars

Published In

Genetics

DOI

EISSN

1943-2631

Publication Date

January 2017

Volume

205

Issue

1

Start / End Page

89 / 100

Location

United States

Related Subject Headings

  • Proportional Hazards Models
  • Prognosis
  • Predictive Value of Tests
  • Ovarian Neoplasms
  • Models, Genetic
  • Humans
  • Gene Expression Regulation, Neoplastic
  • Gene Expression Profiling
  • Female
  • Developmental Biology
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhang, X., Li, Y., Akinyemiju, T., Ojesina, A. I., Buckhaults, P., Liu, N., … Yi, N. (2017). Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach. Genetics, 205(1), 89–100. https://doi.org/10.1534/genetics.116.189191
Zhang, Xinyan, Yan Li, Tomi Akinyemiju, Akinyemi I. Ojesina, Phillip Buckhaults, Nianjun Liu, Bo Xu, and Nengjun Yi. “Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach.Genetics 205, no. 1 (January 2017): 89–100. https://doi.org/10.1534/genetics.116.189191.
Zhang X, Li Y, Akinyemiju T, Ojesina AI, Buckhaults P, Liu N, et al. Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach. Genetics. 2017 Jan;205(1):89–100.
Zhang, Xinyan, et al. “Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach.Genetics, vol. 205, no. 1, Jan. 2017, pp. 89–100. Pubmed, doi:10.1534/genetics.116.189191.
Zhang X, Li Y, Akinyemiju T, Ojesina AI, Buckhaults P, Liu N, Xu B, Yi N. Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach. Genetics. 2017 Jan;205(1):89–100.

Published In

Genetics

DOI

EISSN

1943-2631

Publication Date

January 2017

Volume

205

Issue

1

Start / End Page

89 / 100

Location

United States

Related Subject Headings

  • Proportional Hazards Models
  • Prognosis
  • Predictive Value of Tests
  • Ovarian Neoplasms
  • Models, Genetic
  • Humans
  • Gene Expression Regulation, Neoplastic
  • Gene Expression Profiling
  • Female
  • Developmental Biology