Abstract
With unprecedented progress of cancer research, the world is now prepared with versatile arsenal of drugs to combat cancer. However, individual’s response to any drug or combination treatment stands as a major challenge and hence there exists the sheer need for personalized medication. Identification of drug response biomarkers from a wholistic tumor microenvironment analysis would guide researchers to develop custom-tailored treatment regimen.
In this study, a fast and robust method has been developed to identify drug response biomarkers from entire transcriptomics data analysis in a data-driven manner. The biomarkers which were identified by the method, were able to stratify patients between responders vs non-responders population. Furthermore, bayesian network (BN) analysis, done on the data, brought forth a mechanistic insight into the role of identified biomarkers in regulating drug’s efficacy.
The importance of this work lies with the protocol that is time saving and requires less computation power, yet analyzes a whole system data and helps the researchers to take a step forward towards the development of personalized care in effective cancer treatment.
Highlights
Supervised machine learning approach to analyze gene expression data.
Drug response biomarker identification.
Categorization of samples for their drug response with the help of identified biomarkers.
Functional enrichment to understand the biomarkers association with biological processes.
Bayesian network analysis to develop causal structure among identified biomarkers and drug targets.
Time and cost-effective pipeline for fast and robust prediction of drug response biomarkers.
