The partner had a pipeline molecule that was under clinical development. The interest area was to identify biomarkers indicative of drug-response in patients and further utilize the biomarkers for patient stratification in clinical trials.
The focus was on analysing the proprietary gene-expression data of 118 cell-lines that were treated with the drug. Furthermore, after prediction of drug-response biomarkers, gene expression profiles of 11 patients was shared by the partner to retrospectively classify them into responders and non-responders.
Machine learning models were built using three different methods to prioritize biomarkers associated with drug-response. Pathway enrichment analysis was performed to understand the role of the biomarkers in disease pathophysiology. Stratification of patients based on these biomarkers resulted in correct prediction of drug response in 8 out of 11 patients.