ArticleBritish journal of cancer2026
Abundant CD8
Article in British journal of cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
Abstract
backgroundThe effectiveness of immunotherapy against glioblastoma (GBM) remains limited, suggesting that antigen-specific tumor-infiltrating lymphocytes (TILs) are rare. This study aimed to identify tumor-specific T cells in GBM as the basis for antigen-specific immunotherapy.
methodsTumor specimens were collected from 56 patients newly diagnosed with IDH-wildtype GBM. In cases showing prominent intratumoral T-cell infiltration, CD3⁺CD8⁺CD4⁻ T cells were sorted for single-cell RNA and T-cell receptor (TCR) sequencing. Integrated analyses were performed using our single-cell dataset from the Aichi Cancer Center (ACC), publicly available GBM TILs datasets (PA), and lung cancer TILs and malignant pleural effusions datasets (LC). Candidate reactive TCRs were identified using an AI-based algorithm.
resultsUnsupervised analysis identified a dominant cluster characterized by the strong expression of exhaustion markers, most prominently LAG3, with high TCR clonality. Moreover, LAG3 and CXCL13 expression levels were significantly higher in the ACC TIL-rich subset than the PA dataset. Finally, a prediction algorithm suggested that our TIL-rich subset harbored a markedly higher proportion of potentially reactive T cells than the PA and LC datasets.
conclusionsTILs with marked clonal expansion in certain GBM cases may include potentially reactive T cells with distinct exhaustion features, providing a foundation for the development of antigen-specific and LAG-3-targeted immunotherapies.
Identifiers
42834163What 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.