ArticleNPJ precision oncology2024
Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers, 1 of them a synthesis that pooled it.
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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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- First-line PD-1/PD-L1 inhibitors plus chemotherapy vs. chemotherapy alone in stage IIIB-IV non-squamous NSCLC: an updated meta-analysis of phase 3 RCTs.BMC cancer · 2025Pooled it
- Advancing the understanding of the immune escape in lung cancer immunotherapy: Global trends, collaborations, and future directions.Human vaccines & immunotherapeutics · 2026Article
- Article
- ΔAverage of long and short axis diameter on non-contrast CT is associated with major pathological response to neoadjuvant immunotherapy in resectable non-small cell lung cancer.European radiology · 2026Article
- Toward comprehensive cellular characterization of H&E slides.Journal of pathology informatics · 2026Article
- Intratumor immune heterogeneity across simulated core biopsy sizes in non-small cell lung cancer: an exploratory study.Translational lung cancer research · 2026Article
- Statescope: an integrative deconvolution framework for discovering cell states in tumors.Nature communications · 2026Article
- Spatial Immunology in Translation: Linking Immune Organisation to Therapeutic Outcome.Medical sciences (Basel, Switzerland) · 2026Review
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Artificial Intelligence for Spatial Immunometabolic Analysis of the Tumor Microenvironment: Current Evidence and Future Directions.Current issues in molecular biology · 2026Review
- Review
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- The role of the tumor microenvironment in drug resistance acquisition in lung squamous cell carcinoma.Journal of experimental & clinical cancer research : CR · 2026Review
- Graph convolutional network-derived pathomics and clinical integration for predicting chemoimmunotherapy response in advanced lung squamous cell carcinoma: a multicenter study.Journal of translational medicine · 2026Article
- Spatial resolution of the metastatic osteosarcoma tumor microenvironment using immunolabeling across murine, canine and human lung.Journal of translational medicine · 2026Article
- Mohs Surgery vs. Wide Local Excision for Non-Melanoma Skin Cancer: Comparing Recurrence Rates, Economic Value, and Aesthetic Outcomes.Journal of cancer science and clinical therapeutics · 2026Article
- LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.PloS one · 2026Article
- Circulating and tumor-infiltrating immune cell profiles in cervical cancer checkpoint blockade: multi-omics biomarkers of response and resistance.Frontiers in immunology · 2026Review
- The Heterogeneity and Function of Stromal Cells in the Tumor Microenvironment.Research (Washington, D.C.) · 2026Review
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
Corrections and comments
- Erratum issued
- Update of
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach to infer the tumor microenvironment (TME) composition directly from histopathology images of NSCLC patients. We show that HistoTME accurately predicts the expression of 30 distinct cell type-specific molecular signatures directly from whole slide images, achieving an average Pearson correlation of 0.5 with the ground truth on independent tumor cohorts. Furthermore, we find that HistoTME-predicted microenvironment signatures and their underlying interactions improve prognostication of lung cancer patients receiving immunotherapy, achieving an AUROC of 0.75 [95% CI: 0.61-0.88] for predicting treatment responses following first-line ICI treatment, utilizing an external clinical cohort of 652 patients. Collectively, HistoTME presents an effective approach for interrogating the TME and predicting ICI response, complementing PD-L1 expression, and bringing us closer to personalized immuno-oncology.
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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.