Evidence map›Paper›PMID 42298098›Full record

ArticleNPJ precision oncology2026

Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.

Changsu L Park, Gal Dinstag, Carlos D Holanda Lopes, Scott Strum, Jeffrey P Bruce, Omer Tirosh, Saugato Rahman Dhruba, Danh-Tai Hoang, Tuvik Beker, Eldad Shulman and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Changsu L Park *Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Gal Dinstag *Pangea Biomed, Tel Aviv, Israel.
Carlos D Holanda Lopes *Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Scott StrumDivision of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Jeffrey P BruceDivision of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Omer TiroshPangea Biomed, Tel Aviv, Israel.
Saugato Rahman DhrubaCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Danh-Tai HoangCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Tuvik BekerPangea Biomed, Tel Aviv, Israel.
Eldad ShulmanCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Anna SpreaficoDivision of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Philippe L BedardDivision of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Sofia GentaKingston Health Sciences Centre, Queen's University, Kingston, ON, Canada.
Albiruni R Abdul RazakDivision of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Eytan RuppinCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Ranit AharonovPangea Biomed, Tel Aviv, Israel.
Lillian L SiuDivision of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada. lillian.siu@uhn.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) algorithms such as ENLIGHT and DeepPT represent promising approaches to identify predictive biomarkers for immune checkpoint blockade (ICB). Evaluation of ICB response prediction throughout the span of ICB treatment provides a dynamic perspective of a biomarker's predictive value. A post-hoc analysis of two pan-cancer trials of patients receiving ICB was performed. Samples were available from the pre-ICB, on-ICB and post-progression timepoints. ENLIGHT matching score (EMS) was calculated using measured transcriptome from sequencing (EMS-NGS), and imputed transcriptome from H&E images using DeepPT (EMS-DP). The predictive value of EMS-NGS and EMS-DP for clinical benefit rate (CBR), progression free survival (PFS) and overall survival (OS) were assessed alongside PD-L1 IHC and tumor mutational burden (TMB). Trajectory of EMS across the ICB treatment timepoints was assessed. 154 H&E slides with matched RNA sequencing from 111 patients, representing 14 tumor types were analyzed. The ROC AUC for predicting CBR was calculated for each biomarker: EMS-DP (0.78), EMS-NGS (0.73), PD-L1 IHC (0.68) and TMB (0.62). Higher EMS-NGS was associated with improved PFS and OS whereas EMS-DP was associated with improved PFS but not OS. Both EMS-NGS and EMS-DP at the on-ICB timepoint remained higher for patients with ongoing response or acquired ICB resistance compared to primary ICB resistance. At the post-progression timepoint, EMS did not differ based on type of resistance. In this pan-cancer study, EMS-NGS and EMS-DP were promising biomarkers of ICB response. EMS values were concordant with response or resistance throughout the ICB treatment course. Further prospective validation of ENLIGHT is warranted.

Identifiers

PMID42298098
PMCPMC13669336

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