Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

Gal Dinstag*, Eldad D. Shulman, Efrat Elis, Doreen S. Ben-Zvi, Omer Tirosh, Eden Maimon, Isaac Meilijson, Emmanuel Elalouf, Boris Temkin, Philipp Vitkovsky, Eyal Schiff, Danh Tai Hoang, Sanju Sinha, Nishanth Ulhas Nair, Joo Sang Lee, Alejandro A. Schäffer, Ze'ev Ronai, Dejan Juric, Andrea B. Apolo, William L. DahutStanley Lipkowitz, Raanan Berger, Razelle Kurzrock, Antonios Papanicolau-Sengos, Fatima Karzai, Mark R. Gilbert, Kenneth Aldape, Padma S. Rajagopal, Tuvik Beker*, Eytan Ruppin*, Ranit Aharonov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. Methods: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. Findings: Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. Conclusions: ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. Funding: This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Original languageEnglish
Pages (from-to)15-30.e8
JournalMed
Volume4
Issue number1
DOIs
StatePublished - 13 Jan 2023

Funding

FundersFunder number
Israeli Innovation Authority
U.S. Government
National Institutes of Health
U.S. Department of Health and Human Services
National Cancer Institute

    Keywords

    • Foundational research
    • clinical trial design
    • immunotherapy
    • patient stratification
    • personalized medicine
    • precision oncology
    • synthetic lethality
    • targeted therapy
    • transcriptomics
    • translational medicine
    • treatment matching

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