ArticleNPJ precision oncology2024
Integrative analysis of pan-cancer single-cell data reveals a tumor ecosystem subtype predicting immunotherapy response.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Cross-Cancer Circulating Proteins for the Prognosis of Primary and Metastatic Liver Cancer Treated with Local Ablation: Post Hoc Exploratory Analysis of the ESTIMATE Trial.Radiology. Imaging cancer · 2026Article
- Fibroblast TGF-β signaling defines spatial tumor ecosystems linked to immune checkpoint blockade resistance.Communications biology · 2025Article
- Single-cell and bulk transcriptome analysis identifies B-cell subpopulations and associated cancer subtypes with distinct clinical and molecular characteristics.Cellular oncology (Dordrecht, Netherlands) · 2025Article
- Single-cell RNA sequencing: enhancing the predictive accuracy of tumor immunotherapy efficacy.Essays in biochemistry · 2025Review
- Inflammatory reprogramming of the solid tumor microenvironment by infiltrating clonal hematopoiesis is associated with adverse outcomes.Cell reports. Medicine · 2025Article
- Tumor Immunotherapy Targeting B7-H3: From Mechanisms to Clinical Applications.ImmunoTargets and therapy · 2025Review
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.
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.