An RNA-based precision oncology platform for patient-therapy alignment in a diverse set of treatment resistant malignancies

We use a Master Regular-based precision cancer medicine framework to predict drug sensitivity. The network based analysis uses RNASeq profiles of patient tumors, in conjunction with de novo drug mechanism analysis from high throughput drug screens with post-perturbation RNASeq profiles performed in cognate cell line models. The analysis aims to predict available antineoplastic drugs most likely to invert the aberrant protein activity state of individual tumors. Validation of predictions was performed in corresponding patient-derived xenograft (PDX) mice. Pharmacodynamic samples were obtained from PDX mice after the third dose for RNASeq and downstream analysis. RNASeq profiles of patient tumors and their respective xenografts for which drugs were predicted using the PCM tools OncoTarget and OncoTreat; RNASeq profiles of cognate cell line models; drug perturbational (DMSO controls and post-perturbation) profiles in these cell lines; RNASeq of on-treatment pharmacodynamic PDX samples.