ArticleJournal of translational medicine2024
A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 11 papers.
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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.
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.
An Open-Label, Randomized, Phase 3 Trial of Nivolumab Versus Investigator's Choice Chemotherapy as First-Line Therapy for Stage IV or Recurrent PD-L1+ Non-Small Cell Lung Cancer
An Open-Label, Randomized Phase 3 Trial of Nivolumab, or Nivolumab Plus Ipilimumab, or Nivolumab Plus Platinum Doublet Chemotherapy Versus Platinum Doublet Chemotherapy in Subjects With Chemotherapy-Naïve Stage IV or Recurrent Non-Small Cell Lung Cancer (NSCLC)
Who cites it
11 citing papers in PubMed, 9 citations in OpenAlex.
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- Tumor DNA methylation subtypes predict immunotherapy outcomes in pleural mesothelioma patients in the NIBIT-EPI-MESO study.Nature genetics · 2026Article
- Pairwise ratio transformation of gene expression data leads to improved checkpoint response prediction in lung cancer patients.BMC bioinformatics · 2025Article
- Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data.Journal of proteome research · 2025Article
- Advances in the understanding and therapeutic manipulation of cancer immune responsiveness: a Society for Immunotherapy of Cancer (SITC) review.Journal for immunotherapy of cancer · 2025Review
- A comprehensive prognostic and immunological implications of Gremlin 1 in lung adenocarcinoma.Frontiers in immunology · 2025Article
- Optimizing T cell inflamed signature through a combination biomarker approach for predicting immunotherapy response in NSCLC.Scientific reports · 2024Article
- Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanoma.Science advances · 2024Article
- Tumour mutational burden: clinical utility, challenges and emerging improvements.Nature reviews. Clinical oncology · 2024Review
- An integrated tumor, immune and microbiome atlas of colon cancer.Nature medicine · 2023Article
- OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
45 authors at 17 institutions in 8 countries.
Funding
Abstract
backgroundPredictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.
methodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.
resultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.
conclusionsThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy.
trial registrationCheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.