TY - JOUR
T1 - A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles
AU - Wang, Lin
AU - Li, Fuhai
AU - Sheng, Jianting
AU - Wong, Stephen T.C.
N1 - Funding Information:
This work is supported by CPRIT RP110532, NIH U54 CA149196, TT & WF Chao Center for BRAIN, and John S Dunn Research Foundation.
Publisher Copyright:
© 2015 Wang et al.; licensee BioMed Central Ltd.
PY - 2015/6/11
Y1 - 2015/6/11
N2 - Background: Personalized genomics instability, e.g., somatic mutations, is believed to contribute to the heterogeneous drug responses in patient cohorts. However, it is difficult to discover personalized driver mutations that are predictive of drug sensitivity owing to diverse and complex mutations of individual patients. To circumvent this problem, a novel computational method is presented to discover potential drug sensitivity relevant cancer subtypes and identify driver mutation modules of individual subtypes by coupling differentially expressed genes (DEGs) based subtyping analysis with the driver mutation network analysis. Results: The proposed method was applied to breast cancer and lung cancer samples available from The Cancer Genome Atlas (TCGA). Cancer subtypes were uncovered with significantly different survival rates, and more interestingly, distinct driver mutation modules were also discovered among different subtypes, indicating the potential mechanism of heterogeneous drug sensitivity. Conclusions: The research findings can be used to help guide the repurposing of known drugs and their combinations in order to target these dysfunctional modules and their downstream signaling effectively for achieving personalized or precision medicine treatment.
AB - Background: Personalized genomics instability, e.g., somatic mutations, is believed to contribute to the heterogeneous drug responses in patient cohorts. However, it is difficult to discover personalized driver mutations that are predictive of drug sensitivity owing to diverse and complex mutations of individual patients. To circumvent this problem, a novel computational method is presented to discover potential drug sensitivity relevant cancer subtypes and identify driver mutation modules of individual subtypes by coupling differentially expressed genes (DEGs) based subtyping analysis with the driver mutation network analysis. Results: The proposed method was applied to breast cancer and lung cancer samples available from The Cancer Genome Atlas (TCGA). Cancer subtypes were uncovered with significantly different survival rates, and more interestingly, distinct driver mutation modules were also discovered among different subtypes, indicating the potential mechanism of heterogeneous drug sensitivity. Conclusions: The research findings can be used to help guide the repurposing of known drugs and their combinations in order to target these dysfunctional modules and their downstream signaling effectively for achieving personalized or precision medicine treatment.
UR - http://www.scopus.com/inward/record.url?scp=84969256308&partnerID=8YFLogxK
U2 - 10.1186/1471-2164-16-S7-S6
DO - 10.1186/1471-2164-16-S7-S6
M3 - Article
C2 - 26099165
AN - SCOPUS:84969256308
SN - 1471-2164
VL - 16
JO - BMC genomics
JF - BMC genomics
IS - 7
M1 - S6
ER -