Predicting response to preoperative chemotherapy agents by identifying drug action on modeled microRNA regulation networks.
Identifying patients most responsive to specific chemotherapy agents in neoadjuvant settings can help to maximize the benefits of treatment and minimize unnecessary side effects. Metagene approaches that predict response based on gene expression signatures derived from an associative analysis of clinical data can identify chance associations caused by the heterogeneity of a tumor, leading to reproducibility issues in independent validations. In this study, to incorporate information from drug mechanisms of action, we explore the potential of microRNA regulation networks as a new feature space for identifying predictive markers. We introduce a measure we term the CoMi (Context-specific-miRNA-regulation) pattern to represent a descriptive feature of the miRNA regulation network in the transcriptome. We examine whether the modifications to the CoMi pattern on specific biological processes are a useful representation of drug action by predicting the response to neoadjuvant Paclitaxel treatment in breast cancer and show that the drug counteracts the CoMi network dysregulation induced by tumorigenesis. We then generate a quantitative testbed to investigate the ability of the CoMi pattern to distinguish FDA approved breast cancer drugs from other FDA approved drugs not related to breast cancer. We also compare the ability of the CoMi and metagene methods to predict response to neoadjuvant Paclitaxel treatment in clinical cohorts. We find the CoMi method outperforms the metagene method, achieving area under curve (AUC) values of 0.78 and 0.66 respectively. Furthermore, several of the predicted CoMi features highlight the network-based mechanism of drug resistance. Thus, our study suggests that explicitly modeling the drug action using network biology provides a promising approach for predictive marker discovery.
School of Computer Science, Wuhan University, Wuhan, P. R. China.
Lida
L
Zhu
School of Computer Science, Wuhan University, Wuhan, P. R. China.
Juan
J
Liu
State Key Lab of Space Medicine Fundamentals and Application (SMFA), China Astronaut Research and Training Center (ACC), Beijing, P. R. China.
Fengji
F
Liang
Key Laboratory of Agricultural and Environmental Microbiology, Wuhan Institute of Virology, Wuhan, China.
Simon
S
Rayner
State Key Lab of Space Medicine Fundamentals and Application (SMFA), China Astronaut Research and Training Center (ACC), Beijing, P. R. China; The CUHK-ACC Space Medicine Centre on Health Maintenance of Musculoskeletal System, The Chinese University of Hong Kong Shenzhen Research Institute, Shenzhen, China.
Jianghui
J
Xiong
D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000378:metabolism; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000378:metabolism*; D002986:Clinical Trials as Topic; D015331:Cohort Studies; D019295:Computational Biology; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D008727:Methotrexate / Q000627:therapeutic use; D035683:MicroRNAs / Q000378:metabolism*; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D017239:Paclitaxel / Q000627:therapeutic use*; D012372:ROC Curve; D016896:Treatment Outcome
D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor; D035683:MicroRNAs; D017239:Paclitaxel; D008727:Methotrexate