ArticleeLife2019
Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 205 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
205 citing papers in PubMed, 2 syntheses or guidelines pooled it, 328 citations in OpenAlex.
- Pooled it
- Efficacy of Immune Checkpoint Inhibitors in Rare Tumours: A Systematic Review.Frontiers in immunology · 2021Pooled it
- Trial
- Integrative molecular characterization of sarcomatoid and rhabdoid renal cell carcinoma.Nature communications · 2021Trial
- Revisiting tumor immunogenicity through the lens of mutant p53: Implications for cancer immunotherapy.Apoptosis : an international journal on programmed cell death · 2026Review
- Genomic Determinants of Lethality and Therapeutic Vulnerability in Castration-Resistant Prostate Cancer.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporal dynamics of adoptively transferred stem-like CD8bioRxiv : the preprint server for biology · 2026Article
- Messing with Signal 1: How Perturbed MHC Class I Antigen Presentation Contributes to Cancer.Cells · 2026Review
- Molecular classification and construction of prognostic risk model via machine learning based on metabolic-related genes in hepatocellular carcinoma.Translational cancer research · 2026Article
- CRESCENT: a deep learning framework with multi-scale attention for detecting recurrent copy number alterations.Briefings in bioinformatics · 2026Article
- Hypoxic stress granules trigger immunogenic dormancy in lung cancer.bioRxiv : the preprint server for biology · 2026Article
- HRD-Driven Reprogramming of Macrophage Function and Spatial Architecture in High-Grade Serous Ovarian Cancer.Genetics research · 2026Article
- Immunogenicity remodeling in hepatocellular carcinoma: mechanisms and translational strategies.Therapeutic advances in medical oncology · 2026Review
- Antigen processing machinery deficits in head and neck cancers: mechanisms of immune evasion and strategies for therapeutic restoration.Frontiers in oncology · 2026Review
- Multi-omics and machine learning refine HCC molecular subtypes and prognosis based on liquid-liquid phase separation related genes.Scientific reports · 2025Article
- Ferroptosis meets cancer immunotherapy: Overcoming the crosstalk challenges through advanced drug delivery strategies.Acta pharmaceutica Sinica. B · 2025Review
- Multi-omics analysis of epigenetic dysregulation reveals clinical heterogeneity and evaluates the immunotherapeutic potential of lung adenocarcinoma.Genes & diseases · 2025Article
- Identification and validation of synergistic drug strategies targeting macrophage polarization in triple-negative breast cancer via single-cell transcriptomics and deep learning.Translational oncology · 2025Article
- Metabolic genes interaction perturbation network identified and validated CD24 as a novel prognostic gene in hepatocellular carcinoma.Discover oncology · 2025Article
- Article
145 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Immunotherapy, represented by immune checkpoint inhibitors (ICI), is transforming the treatment of cancer. However, only a small percentage of patients show response to ICI, and there is an unmet need for biomarkers that will identify patients who are more likely to respond to immunotherapy. The fundamental basis for ICI response is the immunogenicity of a tumor, which is primarily determined by tumor antigenicity and antigen presentation efficiency. Here, we propose a method to measure tumor immunogenicity score (TIGS), which combines tumor mutational burden (TMB) and an expression signature of the antigen processing and presenting machinery (APM). In both correlation with pan-cancer ICI objective response rates (ORR) and ICI clinical response prediction for individual patients, TIGS consistently showed improved performance compared to TMB and other known prediction biomarkers for ICI response. This study suggests that TIGS is an effective tumor-inherent biomarker for ICI-response prediction.
Indexed as
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.