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

Autor:	Dinstag G; Pangea Biomed Ltd., Tel Aviv, Israel. Electronic address: gal@pangeabiomed.com., Shulman ED; Pangea Biomed Ltd., Tel Aviv, Israel., Elis E; Pangea Biomed Ltd., Tel Aviv, Israel., Ben-Zvi DS; Pangea Biomed Ltd., Tel Aviv, Israel., Tirosh O; Pangea Biomed Ltd., Tel Aviv, Israel., Maimon E; Pangea Biomed Ltd., Tel Aviv, Israel., Meilijson I; Pangea Biomed Ltd., Tel Aviv, Israel; Tel Aviv University, Tel Aviv, Israel., Elalouf E; Pangea Biomed Ltd., Tel Aviv, Israel., Temkin B; Pangea Biomed Ltd., Tel Aviv, Israel., Vitkovsky P; Pangea Biomed Ltd., Tel Aviv, Israel., Schiff E; Pangea Biomed Ltd., Tel Aviv, Israel., Hoang DT; Biological Data Science Institute, College of Science, The Australian National University, Canberra, ACT, Australia., Sinha S; Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Nair NU; Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Lee JS; Department of Precision Medicine, School of Medicine & Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea., Schäffer AA; Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Ronai Z; Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA., Juric D; Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA., Apolo AB; Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Dahut WL; Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Lipkowitz S; Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Berger R; Cancer Center, Chaim Sheba Medical Center, Tel Hashomer, Israel., Kurzrock R; Worldwide Innovative Network (WIN) for Personalized Cancer Therapy, Chevilly-Larue, France., Papanicolau-Sengos A; Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Karzai F; Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Gilbert MR; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Aldape K; Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Rajagopal PS; Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Beker T; Pangea Biomed Ltd., Tel Aviv, Israel. Electronic address: tuvik@pangeabiomed.com., Ruppin E; Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. Electronic address: eytan.ruppin@nih.gov., Aharonov R; Pangea Biomed Ltd., Tel Aviv, Israel. Electronic address: ranit@pangeabiomed.com.
Jazyk:	angličtina
Zdroj:	Med (New York, N.Y.) [Med] 2023 Jan 13; Vol. 4 (1), pp. 15-30.e8. Date of Electronic Publication: 2022 Dec 12.
DOI:	10.1016/j.medj.2022.11.001
Abstrakt:	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. Competing Interests: Declaration of interests G.D., E.D.S., E. Elis, D.S.B.-Z., O.T., E.M., E. Elalouf, B.T., P.V., T.B., and R.A. are employees of Pangea Biomed. I.M. is a paid consultant of Pangea Biomed. E.S. is the Chairman of the Board of Pangea Biomed. E.R. is a co-founder of MedAware, Metabomed, and Pangea Biomed (divested) and an unpaid member of Pangea Biomed’s scientific advisory board. Z.R. is a co-founder of Pangea Biomed and an unpaid member of its scientific advisory board. R.B. is a member of Pangea Biomed’s scientific advisory board. (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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