ReviewEssays in biochemistry2023
T-cell response to checkpoint blockade immunotherapies: from fundamental mechanisms to treatment signatures.
Review in Essays in biochemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 2 citations in OpenAlex.
- Entering the Age of Specificity in T Cell Immunity.Immunological reviews · 2026Review
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
- Prognostic significance of continuing immunotherapy beyond progression in unresectable lung adenocarcinoma.Frontiers in immunology · 2026Article
- Microenvironment-based immunotherapy in oral cancer: a comprehensive review.Medical oncology (Northwood, London, England) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Immune checkpoint immunotherapies act to block inhibitory receptors on the surface of T cells and other cells of the immune system. This can increase activation of immune cells and promote tumour clearance. Whilst this is very effective in some types of cancer, significant proportions of patients do not respond to single-agent immunotherapy. To improve patient outcomes, we must first mechanistically understand what drives therapy resistance. Many studies have utilised genetic, transcriptional, and histological signatures to find correlates of effective responses to treatment. It is key that we understand pretreatment predictors of response, but also to understand how the immune system becomes treatment resistant during therapy. Here, we review our understanding of the T-cell signatures that are critical for response, how these immune signatures change during treatment, and how this information can be used to rationally design therapeutic strategies. We highlight how chronic antigen recognition drives heterogeneous T-cell exhaustion and the role of T-cell receptor (TCR) signal strength in exhausted T-cell differentiation and molecular response to therapy. We explore how dynamic changes in negative feedback pathways can promote resistance to single-agent therapy. We speculate that this resistance may be circumvented in the future through identifying the most effective combinations of immunotherapies to promote sustained and durable antitumour responses.
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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.