ArticleJournal for immunotherapy of cancer2020
Prediction of immune checkpoint inhibition with immune oncology-related gene expression in gastrointestinal cancer using a machine learning classifier.
Article in Journal for immunotherapy of cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Predicting Immunotherapy Efficacy with Machine Learning in Gastrointestinal Cancers: A Systematic Review and Meta-Analysis.International journal of molecular sciences · 2025Pooled it
- Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.NPJ precision oncology · 2026Article
- Global research trends on biomarkers for cancer immunotherapy: Visualization and bibliometric analysis.Human vaccines & immunotherapeutics · 2025Article
- Regulatory roles of non-coding RNAs in programmed cell death pathways and drug resistance in gastrointestinal stromal tumors.Clinical and experimental medicine · 2025Review
- Machine learning-based prediction of pulmonary embolism to reduce unnecessary computed tomography scans in gastrointestinal cancer patients: a retrospective multicenter study.Scientific reports · 2024Article
- Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy.Signal transduction and targeted therapy · 2024Review
- Biomarkers associated with immune-related adverse events induced by immune checkpoint inhibitors.World journal of clinical oncology · 2024Review
- Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond.Molecular cancer · 2024Review
- Review
- Who Should Receive Immunotherapy for Advanced Gastroesophageal Cancer?Current treatment options in oncology · 2024Review
- Informing immunotherapy with multi-omics driven machine learning.NPJ digital medicine · 2024Review
- Transcriptional profile and immune infiltration in colorectal cancer reveal the significance of inducible T-cell costimulator as a crucial immune checkpoint molecule.Cancer medicine · 2024Article
- Cellular MSI-H score: a robust predictive biomarker for immunotherapy response and survival in gastrointestinal cancer.American journal of cancer research · 2024Article
- Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.Frontiers in immunology · 2024Review
- Proteomic profiling and biomarker discovery for predicting the response to PD-1 inhibitor immunotherapy in gastric cancer patients.Frontiers in pharmacology · 2024Article
- Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer.Communications medicine · 2023Article
- PTPRD/PTPRT mutation correlates to treatment outcomes of immunotherapy and immune landscape in pan-cancers.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2023Article
- Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy.Journal of clinical medicine · 2023Review
- Article
- Biomarkers for response to immune checkpoint inhibitors in gastrointestinal cancers.World journal of gastrointestinal oncology · 2022Review
Corrections and comments
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Authors and funding
20 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of gastrointestinal cancer. However, biomarkers correlated with the efficacy of ICIs in gastrointestinal cancer are still lacking. In this study, we performed 395-plex immune oncology (IO)-related gene target sequencing in tumor samples from 96 patients with metastatic gastrointestinal cancer patients treated with ICIs, and a linear support vector machine learning strategy was applied to construct a predictive model. ResultsAll 96 patients were randomly assigned into the discovery (n=72) and validation (n=24) cohorts. A 24-gene RNA signature (termed the IO-score) was constructed from 395 immune-related gene expression profiling using a machine learning strategy to identify patients who might benefit from ICIs. The durable clinical benefit rate was higher in patients with a high IO-score than in patients with a low IO-score (discovery cohort: 92.0% vs 4.3%, p<0.001; validation cohort: 85.7% vs 17.6%, p=0.004). The IO-score may exhibit a higher predictive value in the discovery (area under the receiver operating characteristic curve (AUC)=0.97)) and validation (AUC=0.74) cohorts compared with the programmed death ligand 1 positivity (AUC=0.52), tumor mutational burden (AUC=0.69) and microsatellite instability status (AUC=0.59) in the combined cohort. Moreover, patients with a high IO-score also exhibited a prolonged overall survival compared with patients with a low IO-score (discovery cohort: HR, 0.29; 95% CI 0.15 to 0.56; p=0.003; validation cohort: HR, 0.32; 95% CI 0.10 to 1.05; p=0.04). Taken together, our results indicated the potential of IO-score as a biomarker for immunotherapy in patients with gastrointestinal cancers.
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