American Association for Cancer Research
Browse
cd-22-1020_table_s8_suppst8.xlsx (11.53 kB)

Table S8 from A Transcriptome-Based Precision Oncology Platform for Patient–Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies

Download (11.53 kB)
dataset
posted on 2023-06-02, 08:20 authored by Prabhjot S. Mundi, Filemon S. Dela Cruz, Adina Grunn, Daniel Diolaiti, Audrey Mauguen, Allison R. Rainey, Kristina Guillan, Armaan Siddiquee, Daoqi You, Ronald Realubit, Charles Karan, Michael V. Ortiz, Eugene F. Douglass, Melissa Accordino, Suzanne Mistretta, Frances Brogan, Jeffrey N. Bruce, Cristina I. Caescu, Richard D. Carvajal, Katherine D. Crew, Guarionex Decastro, Mark Heaney, Brian S. Henick, Dawn L. Hershman, June Y. Hou, Fabio M. Iwamoto, Joseph G. Jurcic, Ravi P. Kiran, Michael D. Kluger, Teri Kreisl, Nicole Lamanna, Andrew B. Lassman, Emerson A. Lim, Gulam A. Manji, Guy M. McKhann, James M. McKiernan, Alfred I. Neugut, Kenneth P. Olive, Todd Rosenblat, Gary K. Schwartz, Catherine A. Shu, Michael B. Sisti, Ana Tergas, Reena M. Vattakalam, Mary Welch, Sven Wenske, Jason D. Wright, Peter Canoll, Hanina Hibshoosh, Kevin Kalinsky, Mahalaxmi Aburi, Peter A. Sims, Mariano J. Alvarez, Andrew L. Kung, Andrea Califano

List of available interactomes (gene regulatory networks) that have been generated from RNASeq datasets of patient tumor samples using the ARACNe algorithm.

Funding

National Cancer Institute (NCI)

United States Department of Health and Human Services

Find out more...

NIH Office of the Director (OD)

CureSearch for Children's Cancer (CureSearch)

History

ARTICLE ABSTRACT

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

Usage metrics

    Cancer Discovery

    Categories

    Keywords

    cancer

    Licence

    CC BY

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC