Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Med (New York, N.Y. : Online), 2023-01, Vol.4 (1), p.15-30.e8

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. 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. 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. ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority. [Display omitted] •ENLIGHT enables clinical response prediction from the tumor transcriptome•ENLIGHT can effectively predict response across multiple therapies and cancer types•ENLIGHT does not require training on previous treatment response data•ENLIGHT successfully competes with known biomarkers and supervised predictors Improving cancer treatments requires both developing better treatments and finding new ways to best match them to individual patients. This precision oncology approach has been advancing into oncological practice, demonstrating its benefits but also calling for better ways to find the best tumor-drug match. Here, researchers at Pangea Biomed and the National Cancer Institute present ENLIGHT, a computational tool based on cancer gene expression profiles that aims at predicting the most appropriate drug and their dose for each patient, as well as improving clinical trial design by predicting patients unlikely to respond to the treatment. Their comprehensive results show that ENLIGHT can markedly enhance the ability to predict therapeutic response across multiple cancer types, laying a solid basis for testing it in clinical trials. Dinstag et al. describe ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions from the tumor transcriptome. ENLIGHT can predict treatment response across multiple therapies and cancer types better than published biomarkers, and it can potentially enhance clinical trial success by effectively excluding non-responders.