ArticleNPJ precision oncology2025
Integration of multiple machine learning approaches develops a gene mutation-based classifier for accurate immunotherapy outcomes.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Targeting galactose metabolic reprogramming overcomes immunotherapy resistance in KRAS-mutant lung adenocarcinoma: Integrative multi-omics and machine learning approaches.Translational oncology · 2026Article
- LUADnet: a deep learning model for prediction of clinical outcomes in lung adenocarcinoma based on gene expression signatures.Translational lung cancer research · 2026Article
- Breast cancer immunotherapy: mechanisms of immune evasion, biomarkers, and emerging therapeutic strategies.Molecular cancer · 2026Review
- Integrative multi-omics reveals the POSTNFrontiers in immunology · 2026Article
- Identifying Distinct Molecular Subtypes and Establishing a Prognostic Framework for DLBCL Patients via Multiomics Analysis and Machine Learning Approaches.Human mutation · 2026Article
- Integrative Multiomics Analysis Identifies a Novel Gene Signature That Predicts Chemotherapy Resistance and Poor Survival in Osteosarcoma.Human mutation · 2026Article
- Evolving non-invasive biomarkers in NSCLC immunotherapy: integrating liquid biopsy and multi-omics profiling for precision oncology.Frontiers in immunology · 2026Review
- Targeting ARPC1BCell proliferation · 2025Article
- Review
- SAA restricts T cell mediated anti-tumor immunity by limiting antigen presentation in lung cancer.Frontiers in immunology · 2025Article
- Metabolic reprogramming in clear cell renal cell carcinoma: core pathways and targeted therapeutic strategies.Frontiers in genetics · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In addition to traditional biomarkers like PD-(L)1 expression and tumor mutation burden (TMB), more reliable methods for predicting immune checkpoint blockade (ICB) response in cancer patients are urgently needed. This study utilized multiple machine learning approaches on nonsynonymous mutations to identify key mutations that are most significantly correlated to ICB response. We proposed a classifier, Gene mutation-based Predictive Signature (GPS), to categorize patients based on their predicted response and clinical outcomes post-ICB therapy. GPS outperformed conventional predictors when validated in independent cohorts. Multi-omics analysis and multiplex immunohistochemistry (mIHC) revealed insights into tumor immunogenicity, immune responses, and the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) across different GPS groups. Finally, we validated distinct responses of different GPS samples to ICB in an ex-vivo tumor organoid-PBMC co-culture model. Overall, our findings highlight a simple, robust classifier for accurate ICB response prediction, which could reduce costs, shorten testing times, and facilitate clinical implementation.
Identifiers
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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.