ArticleScience advances2022
Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 20 citations in OpenAlex.
- Sex determines the natural killer cell-mediated immunity against pancreatic cancer.Oncoimmunology · 2026Article
- In situ APMV-4 vaccination primes systemic response to subtherapeutic dosing of aCTLA-4 in colorectal carcinoma.Journal for immunotherapy of cancer · 2026Article
- SpaLinker identifies phenotype-associated spatial tumor microenvironment features by integrating bulk and spatial sequencing data.Cell genomics · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Therapeutic Vaccination in Lung Cancer: Past Attempts, Current Approaches and Future Promises.Journal of respiratory biology and translational medicine · 2025Article
- CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma.Scientific reports · 2024Article
- Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond.Molecular cancer · 2024Review
- Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients.Clinical and experimental medicine · 2024Review
- Informing immunotherapy with multi-omics driven machine learning.NPJ digital medicine · 2024Review
- Engineering Heterogeneous Tumor Models for Biomedical Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Role and therapeutic potential of E3s in the tumor microenvironment of hepatocellular carcinoma.Frontiers in immunology · 2024Review
- Review
- Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.
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.