A transcriptome-based precision oncology platform for patient-therapy alignment in a diverse set of treatment resistant malignancies.

Predicting in vivo response to antineoplastics remains an elusive challenge. We performed first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism-of-action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual Master Regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyper-connected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing PCM approaches, as also illustrated by a case study.