CD103+ regulatory T cells underlie resistance to radio-immunotherapy and impair CD8+ T cell activation in glioblastoma
Bibliographic record
Abstract
Abstract Glioblastomas are aggressive primary brain tumors with an inherent resistance to T cell-centric immunotherapy due to their low mutational burden and immunosuppressive tumor microenvironment. Here we report that fractionated radiotherapy of preclinical glioblastoma models induce a tenfold increase in T cell content. Orthogonally, spatial imaging mass cytometry shows T cell enrichment in human recurrent tumors compared with matched primary glioblastoma. In glioblastoma-bearing mice, α-PD-1 treatment applied at the peak of T cell infiltration post-radiotherapy results in a modest survival benefit compared with concurrent α-PD-1 administration. Following α-PD-1 therapy, CD103 + regulatory T cells (Tregs) with upregulated lipid metabolism accumulate in the tumor microenvironment, and restrain immune checkpoint blockade response by repressing CD8 + T cell activation. Treg targeting elicits tertiary lymphoid structure formation, enhances CD4 + and CD8 + T cell frequency and function and unleashes radio-immunotherapeutic efficacy. These results support the rational design of therapeutic regimens limiting the induction of immunosuppressive feedback pathways in the context of T cell immunotherapy in glioblastoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".