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.

Automated summary

We evaluated two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to oncology drugs. The predicted drugs showed significantly higher disease control rates compared to unpredicted controls, suggesting these methods have potential for complementing existing precision cancer medicine approaches. This study introduces scalable systems biology tools for predicting drug response in vivo.