ICOS is upregulated on T cells following radiation and agonism combined with radiation results in enhanced tumor control
Bibliographic record
Abstract
Multiple preclinical studies have shown improved outcomes when radiation therapy is combined with immune modulating antibodies. However, to date, many of these promising results have failed to translate to successful clinical studies. This led us to explore additional checkpoint and co-stimulatory pathways that may be regulated by radiation therapy. Here, we demonstrate that radiation increases the expression of inducible T cell co-stimulator (ICOS) on both CD4 and CD8 T cells in the blood following treatment. Moreover, when we combined a novel ICOS agonist antibody with radiation we observed durable cures across multiple tumor models and mouse strains. Depletion studies revealed that CD8 T cells were ultimately required for treatment efficacy, but CD4 T cells and NK cells also partially contributed to tumor control. Phenotypic analysis showed that the combination therapy diminished the increased infiltration of regulatory T cells into the tumor that typically occurs following radiation alone. Finally, we demonstrate in a poorly immunogenic pancreatic tumor model which is resistant to combined radiation and anti-PD1 checkpoint blockade that the addition of this novel ICOS agonist antibody to the treatment regimen results in tumor control. These findings identify ICOS as part of a T cell pathway that is modulated by radiation and targeting this pathway with a novel ICOS antibody results in durable tumor control in preclinical models.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".