ArticleCancer immunology research2024
Integrated Germline and Somatic Features Reveal Divergent Immune Pathways Driving Response to Immune Checkpoint Blockade.
Article in Cancer immunology research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed.
- Endogenous metabolite signaling orchestrates tumor immune evasion.Cell insight · 2026Review
- Pan-cancer analysis of single-cell profiles with polygenic signals reveals genetic influences on tumor immunity across cancers.Communications biology · 2026Article
- Review
- NeoPrecis: enhancing immunotherapy response prediction through integration of qualified immunogenicity and clonality-aware neoantigen landscapes.Nature communications · 2026Article
- Autoimmune HLA alleles and neoantigens predict myelodysplastic syndrome outcomes after allogeneic HSCT: A CIBMTR analysis.iScience · 2026Article
- Interferon Epsilon Loss Is Elusive 9p21 Link to Immune-Cold Tumors, Resistant to Immune Checkpoint Therapy, and Endogenous CXCL9/10 Induction.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2025Review
- Regulatory polymorphisms ofJournal for immunotherapy of cancer · 2025Article
- NeoPrecis: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes.bioRxiv : the preprint server for biology · 2025Article
- Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response.Cell reports · 2025Article
- Five latent factors underlie response to immunotherapy.Nature genetics · 2024Article
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9 authors.
Funding
Abstract
Immune checkpoint blockade (ICB) has revolutionized cancer treatment; however, the mechanisms determining patient response remain poorly understood. Here, we used machine learning to predict ICB response from germline and somatic biomarkers and interpreted the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher infiltration of T-follicular helper cells had responses even in the presence of defects in the MHC class-I (MHC-I). Further investigation uncovered different ICB responses in tumors when responses were reliant on MHC-I versus MHC-II neoantigens. Despite similar response rates, MHC II-reliant responses were associated with significantly longer durable clinical benefits (discovery: median overall survival of 63.6 vs. 34.5 months; P = 0.0074; validation: median overall survival of 37.5 vs. 33.1 months; P = 0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC II-reliant but not MHC I-reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses.
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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.