Biologically inspired survival analysis based on integrating gene expression as mediator with genomic variants.
Accurately linking cancer molecular profiling with survival can lead to improvements in the clinical management of cancer. However, existing survival analysis relies on statistical evidence from a single level of data, without paying much attention to the integration of interacting multi-level data and the underlying biology. Advances in genomic techniques provide unprecedented power of characterizing the cancer tissue in a more complete manner than before, offering the opportunity to design biologically informed and integrative approaches for survival data analysis. Human cancer is characterized by somatic copy number alternation and unique gene expression profiles. However, it remains largely unclear how to integrate the gene expression and genetic variant data to achieve a better prediction of patient survival and an improved understanding of disease progression. Consistent with the biological hierarchy from DNA to RNA, we prioritize each survival-relevant feature with two separate scores, predictive and mechanistic. For mRNA expression levels, predictive features are those mRNAs whose variation in expression levels is associated with survival outcome, and mechanistic features are those mRNAs whose variation in expression levels is associated with genomic variants. Further, we simultaneously integrate information from both the predictive model and the mechanistic model through our new approach, GEMPS (Gene Expression as a Mediator for Predicting Survival). Applied on two cancer types (ovarian and glioblastoma multiforme), our method achieved better prediction power (p-value: 6.18E-03-5.15E-11) than peer methods (GE.CNAs and GE.CNAs. Lasso). Gene set enrichment analysis confirms that the genes utilized for the final survival analysis are biologically important and relevant.
Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA; Biomedical Engineering Department, Cairo University, Giza 12613, Egypt.
Ibrahim
I
Youssef
Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC 20057, USA.
Robert
R
Clarke
Departments of Gynecology and Obstetrics, Pathology, and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA.
Ie-Ming
IeM
Shih
Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
Yue
Y
Wang
Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. Electronic address: yug@vt.edu.
Guoqiang
G
Yu
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D056915:DNA Copy Number Variations; D020869:Gene Expression Profiling / Q000379:methods*; D016678:Genome; D006801:Humans; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism / Q000401:mortality; D016019:Survival Analysis
Copy number alterations; Gene expression; Genomic data integration; Survival analysis; Survival feature selection