TY - JOUR
T1 - Biologically inspired survival analysis based on integrating gene expression as mediator with genomic variants
AU - Youssef, Ibrahim
AU - Clarke, Robert
AU - Shih, Ie Ming
AU - Wang, Yue
AU - Yu, Guoqiang
N1 - Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2016/10/1
Y1 - 2016/10/1
N2 - 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.
AB - 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.
KW - Copy number alterations
KW - Gene expression
KW - Genomic data integration
KW - Survival analysis
KW - Survival feature selection
UR - http://www.scopus.com/inward/record.url?scp=84986550231&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986550231&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2016.08.020
DO - 10.1016/j.compbiomed.2016.08.020
M3 - Article
C2 - 27619193
AN - SCOPUS:84986550231
SN - 0010-4825
VL - 77
SP - 231
EP - 239
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
ER -