ArticleJournal for immunotherapy of cancer2025
Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC.
Article in Journal for immunotherapy of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Structure-Based Design, Synthesis, and Evaluation of Novel Ponatinib Derivatives With a Significantly Altered Selectivity Profile.ChemMedChem · 2026Article
- Target receptor expression dictates the selective intra-tumoral targeting of CD8Frontiers in immunology · 2026Article
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Drug resistance in cancer: molecular mechanisms and emerging treatment strategies.Molecular biomedicine · 2025Review
- Overcoming resistance to anti-PD-L1 immunotherapy: mechanisms, combination strategies, and future directions.Molecular cancer · 2025Review
- Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks.BMC cancer · 2025Article
- Editorial: Community series in biomarkers in the era of cancer immunotherapy: zooming in from periphery to tumor microenvironment, volume II.Frontiers in immunology · 2025Article
- Pharmacological strategies to overcome immune checkpoint inhibitor resistance in non-small cell lung cancer.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundImmune checkpoint inhibitors (CPIs) have revolutionized cancer therapy for several tumor indications. However, a substantial fraction of patients treated with CPIs derive no benefit or have short-lived responses to CPI therapy. Identifying patients who are most likely to benefit from CPIs and deciphering resistance mechanisms is therefore essential for developing adjunct treatments that can abrogate tumor resistance. PATIENTS AND
methodsIn this study, we used a machine learning approach that used the US-based nationwide de-identified Flatiron Health and Foundation Medicine non-small cell lung carcinoma (NSCLC) clinico-genomic database to identify genomic markers that predict clinical responses to CPI therapy. In total, we analyzed data from 4,433 patients with NSCLC.
resultsAnalysis of pretreatment genomic data from 1,511 patients with NSCLC identified. Of the 36 genomic signatures identified, 33 exhibited strong predictive capacity for CPI response (n=1150) compared with chemotherapy response (n=361), while three signatures were prognostic. These 36 genetic signatures had in common a core set of four genes (
conclusionsIn summary, we found several genomic markers and pathways that provide insight into biological mechanisms affecting response to CPI therapy. The analyses identified novel targets and biomarkers that have the potential to provide candidates for combination therapies or patient enrichment strategies, which could increase response rates to CPI therapy in patients with NSCLC.
Indexed as
Identifiers
What OpenQuestion holds
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.