A Transcripto me-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 a fi rst-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clini-cally 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-specifi c drug predictions. Both OncoTarget, which identifi es high-affi nity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identi-fi es drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly signifi cant 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 signifi cantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study. SIGNIFICANCE: Complementary precision cancer medicine paradigms are needed to broaden the clini-cal benefi t realized through genetic profi ling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo . OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials.