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

Details

• Alternative Title:
  Cancer Discov
• Personal Author:
  Mundi, Prabhjot S. ; Dela Cruz, Filemon S. ; Grunn, Adina ; Diolaiti, Daniel ; Mauguen, Audrey ; Rainey, Allison R. ; Guillan, Kristina ; Siddiquee, Armaan ; You, Daoqi ; Realubit, Ronald ; Karan, Charles ; Ortiz, Michael V. ; Douglass, Eugene F. ; Accordino, Melissa ; Mistretta, Suzanne ; Brogan, Frances ; Bruce, Jeffrey N. ; Caescu, Cristina I. ; Carvajal, Richard D. ; Crew, Katherine D ; Decastro, Guarionex ; Heaney, Mark ; Henick, Brian S ; Hershman, Dawn L ; Hou, June Y. ; Iwamoto, Fabio M. ; Jurcic, Joseph G. ; Kiran, Ravi P. ; Kluger, Michael D ; Kreisl, Teri ; Lamanna, Nicole ; Lassman, Andrew B. ; Lim, Emerson A. ; Manji, Gulam A. ; McKhann, Guy M ; McKiernan, James M. ; Neugut, Alfred I ; Olive, Kenneth P. ; Rosenblat, Todd ; Schwartz, Gary K. ; Shu, Catherine A ; Sisti, Michael B. ; Tergas, Ana ; Vattakalam, Reena M ; Welch, Mary ; Wenske, Sven ; Wright, Jason D. ; Hibshoosh, Hanina ; Kalinsky, Kevin ; Aburi, Mahalaxmi ; Sims, Peter A. ; Alvarez, Mariano J. ; Kung, Andrew L. ; Califano, Andrea
• Description:
  Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a 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 hyperconnected 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 precision cancer medicine approaches, as also illustrated by a case study.|Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling 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. This article is highlighted in the In This Issue feature, p. 1275.
• Subjects:
  Antineoplastic Agents Article Humans Medical Oncology Neoplasms Precision Medicine Transcriptome
• Source:
  Cancer Discov. 13(6):1386-1407
• Pubmed ID:
  37061969
• Pubmed Central ID:
  PMC10239356
• Document Type:
  Journal Article
• Funding:
  S10 OD012351/CD/ODCDC CDC HHSUnited States/ ; S10 OD021764/CD/ODCDC CDC HHSUnited States/ ; U54 CA209997/CA/NCI NIH HHSUnited States/ ; P30 CA013696/CA/NCI NIH HHSUnited States/ ; U01 CA217858/CA/NCI NIH HHSUnited States/ ; S10 OD021764/OD/NIH HHSUnited States/ ; S10 OD012351/OD/NIH HHSUnited States/ ; P30 CA008748/CA/NCI NIH HHSUnited States/
• Volume:
  13
• Issue:
  6
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:0e40ea8e77099d8adb94379fa83f24f4e8be1ab17f143e5788d96fbab3062584
• Download URL:
  https://stacks.cdc.gov/view/cdc/129436/cdc_129436_DS1.pdf
• File Type:
  [PDF - 3.87 MB ]