ABSTRACT
Purpose The majority of cancer patients receive treatments that are minimally informed by omics data. Our goal was to develop a precision medicine computational framework (PANOPLY: Precision cancer genomic report: single sample inventory) to identify and prioritize drug targets and cancer therapy regimens.
Methods The PANOPLY approach integrates clinical data with germline and somatic features obtained from multi-omics platforms, and apply machine learning, and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow employs four steps (i) selection of matched controls to the case of interest (ii) identification of case-specific genomic events (iii) identification of suitable drugs using the driver-gene network and random forest analyses and (iv) provide an integrated multi-omics case report of the patient with prioritization of anti-cancer drugs.
Results The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study which collected clinical and genomic data as well as patient-derived xenografts (PDXs) to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant PDX model, we found that that the prioritized drug, olaparib, was more effective than placebo at treating the tumor (P < 0.05). We also applied PANOPLY to in-house and publicly accessible multi-omics tumor datasets with therapeutic response or survival data available.
Conclusion In summary, PANOPLY prioritizes drugs based on both clinical and multi-omics data, and it can aid oncologists in their decision-making to effectively treat an individual patient.