ArticleJournal for immunotherapy of cancer2024
Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types.
Article in Journal for immunotherapy of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 43 citations in OpenAlex.
- Trial
- Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare tumors.Journal for immunotherapy of cancer · 2026Trial
- Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor.Journal for immunotherapy of cancer · 2025Trial
- Artificial Intelligence-Powered Human Epidermal Growth Factor Receptor 2 Quantification and Clinical Outcomes in Human Epidermal Growth Factor Receptor 2-Positive Biliary Tract Cancer Treated With Trastuzumab Plus Folinic Acid, Fluorouracil, and Oxaliplatin.JCO precision oncology · 2025Trial
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- Arginine metabolism, polyamine homeostasis and ferroptosis in cancer: Molecular links and therapeutic opportunities (Review).Molecular medicine reports · 2026Review
- The FAP × CD3 bispecific antibody OMTX305 induces T cell-mediated antitumor effects in patient-derived ex vivo models of solid tumors.Science advances · 2026Article
- Cytokines and chemokines in plasma as markers reflecting immunophenotype of head and neck squamous cell carcinoma.Human cell · 2026Article
- Article
- Tumor microenvironment-specific nanomedicine: from biology-driven to multi-omics-guided precision engineering.Journal of hematology & oncology · 2026Review
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
- Early Recurrence of HCC Is Driven by Inflammation-Related HIF-1α Independent Angiogenesis Rather than Hypoxia-Induced Immune Escape.Biomolecules · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Dual role of IFNγ in reprogramming the undifferentiated pleomorphic sarcoma cell line JBT19 towards cytotoxic chemotherapy and antitumor immunity.Oncology letters · 2026Article
- Digital immune twins and ai-integrated multi-omic biomarkers: Redefining personalized immunotherapy in non-small cell lung cancer.Iranian journal of basic medical sciences · 2026Review
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- A low-dose immunotherapy targeting Fc gamma Receptors and heparan sulfate proteoglycan to impact myeloid cells and control cancers with diverse immunosuppressive profiles.Frontiers in immunology · 2026Article
- Differential implications of tumor endothelial cell and lymphocyte densities in advanced hepatocellular carcinoma patients treated with immunotherapy.NPJ precision oncology · 2025Article
- AI-powered immune profiling from histopathology slides for chemo-radiotherapy outcome prediction in rectal cancer: a study using clinical trial and real-world cohorts.EBioMedicine · 2025Article
- Artificial Intelligence-Detected Tumor-Infiltrating Lymphocytes and Outcomes in Anti-PD-1-Based Treated Melanoma.JAMA oncology · 2025Article
Corrections and comments
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Authors and funding
30 authors at 13 institutions in 3 countries.
Funding
Abstract
backgroundThe inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types.
methodsLunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions.
resultsWe observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup.
conclusionThe AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.
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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.