ArticleOncogenesis2023
Deep learning identifies a T-cell exhaustion-dependent transcriptional signature for predicting clinical outcomes and response to immune checkpoint blockade.
Article in Oncogenesis, 2023. 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
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
15 citing papers in PubMed, 30 citations in OpenAlex.
- Inhibitory receptor profiles for joint prediction of benefit and toxicity in immune checkpoint blockade.iScience · 2026Review
- Identification of key genes associated with T cell exhaustion in HIV infection based on transcriptomic data.Virology journal · 2026Article
- Molecular features of response and resistance to glofitamab, a T-cell engager for treatment of large B-cell lymphoma.Blood advances · 2026Article
- Context defines precision: rethinking KRAS inhibition in oncology.NPJ precision oncology · 2026Review
- Novel Prognostic and Predictive miRNA Biomarkers Shape the Landscape of T Cell Dysfunction in Cancer.Journal of cellular and molecular medicine · 2026Article
- Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances, challenges, and future directions.Fundamental research · 2026Review
- Mapping Current Research Status and Emerging Frontiers of T-Cell Exhaustion: A Comprehensive Data-Mining-Based Study.Annals of surgical oncology · 2026Article
- Exploring the prognostic value of T cell exhaustion and mitochondrial dysfunction related genes in breast cancer through bioinformatics analysis and RT-qPCR validation.Clinical and experimental medicine · 2025Article
- iDICss robustly predicts melanoma immunotherapy response by synergizing genomic and transcriptomic knowledge via independent component analysis.Clinical and translational medicine · 2025Article
- Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer.Frontiers in endocrinology · 2025Article
- Metabolic reprogramming in the post-metastatic tumor microenvironment: multi-omics insights into determinants of immunotherapy response.Frontiers in immunology · 2025Review
- Automated early detection of acute retinal necrosis from ultra-widefield color fundus photography using deep learning.Eye and vision (London, England) · 2024Article
- Common immunological and prognostic features of lung and bladder cancer via smoking-related genes: PRR11 gene as potential immunotherapeutic target.Journal of cellular and molecular medicine · 2024Article
- Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.Scientific reports · 2024Article
- Genetic variants ofFrontiers in immunology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint blockade (ICB) therapies have brought unprecedented advances in cancer treatment, but responses are limited to a fraction of patients. Therefore, sustained and substantial efforts are required to advance clinical and translational investigation on managing patients receiving ICB. In this study, we investigated the dynamic changes in molecular profiles of T-cell exhaustion (TEX) during ICB treatment using single-cell and bulk transcriptome analysis, and demonstrated distinct exhaustion molecular profiles associated with ICB response. By applying an ensemble deep-learning computational framework, we identified an ICB-associated transcriptional signature consisting of 16 TEX-related genes, termed ITGs. Incorporating 16 ITGs into a machine-learning model called MLTIP achieved reliable predictive power for clinical ICB response with an average AUC of 0.778, and overall survival (pooled HR = 0.093, 95% CI, 0.031-0.28, P < 0.001) across multiple ICB-treated cohorts. Furthermore, the MLTIP consistently demonstrated superior predictive performance compared to other well-established markers and signatures, with an average increase in AUC of 21.5%. In summary, our results highlight the potential of this TEX-dependent transcriptional signature as a tool for precise patient stratification and personalized immunotherapy, with clinical translation in precision medicine.
Identifiers
What Socratic 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 Socratic graph.