A transcripto me-based precision oncology platform for patient-therapy alignment in a diverse set of treatment-resistant malignancies Journal Article
| Authors: | Mundi, P. S.; Dela Cruz, F. S.; Grunn, A.; Diolaiti, D.; Mauguen, A.; Rainey, A. R.; Guillan, K.; Siddiquee, A.; You, D.; Realubit, R.; Karan, C.; Ortiz, M. V.; Douglass, E. F.; Accordino, M.; Mistretta, S.; Brogan, F.; Bruce, J. N.; Caescu, C. I.; Carvajal, R. D.; Crew, K. D.; Decastro, G.; Heaney, M.; Henick, B. S.; Hershman, D. L.; Hou, J. Y.; Iwamoto, F. M.; Jurcic, J. G.; Kiran, R. P.; Kluger, M. D.; Kreisl, T.; Lamanna, N.; Lassman, A. B.; Lim, E. A.; Manji, G. A.; McKhann, G. M.; McKiernan, J. M.; Neugut, A. I.; Olive, K. P.; Rosenblat, T.; Schwartz, G. K.; Shu, C. A.; Sisti, M. B.; Tergas, A.; Vattakalam, R. M.; Welch, M.; Wenske, S.; Wright, J. D.; Canoll, P.; Hibshoosh, H.; Kalinsky, K.; Aburi, M.; Sims, P. A.; Alvarez, M. J.; Kung, A. L.; Califano, A. |
| Article Title: | A transcripto me-based precision oncology platform for patient-therapy alignment in a diverse set of treatment-resistant malignancies |
| Abstract: | 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. |
| Keywords: | liver; targeted therapy; mutations; identification; inhibition; generation; models; network; cancer xenografts; stromal-epithelial tumor |
| Journal Title: | Cancer Discovery |
| Volume: | 13 |
| Issue: | 6 |
| ISSN: | 2159-8274 |
| Publisher: | American Association for Cancer Research |
| Date Published: | 2023-06-01 |
| Start Page: | 1386 |
| End Page: | 1407 |
| Language: | English |
| ACCESSION: | WOS:001010852900001 |
| DOI: | 10.1158/2159-8290.Cd-22-1020 |
| PROVIDER: | wos |
| PMCID: | PMC10239356 |
| PUBMED: | 37061969 |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Corresponding author is MSK author: Andrew L. Kung -- Source: Wos |
