Modulating T-cell Responses to Enhance the Effects of Radiotherapy
Bibliographic record
Abstract
Radiotherapy has been a key component of cancer treatment for over a hundred years, with the understanding that its action was driven only by direct and indirect toxic effects on the tumor cells. With the advent of immunotherapy in recent decades, interest in radiotherapy has expanded beyond just its ability to kill malignant cells directly, to include the potential for augmenting the antitumor immune response in combination with immunotherapy. However, radiotherapy has also been clearly demonstrated to exert immunosuppressive effects, reported in both preclinical and clinical settings, and this means that it has a double-edged immune effect. The cytotoxic effects of T cells are a critical element of the antitumor immune response, and it is cytotoxic T lymphocytes (CTL) that have been the primary target of clinically mature immunotherapies to date, notably antibodies blocking negative regulation of T cells. In this context, the question is how the combination of radiotherapy and immunotherapy can be optimized to leverage the immune-promoting effects of radiotherapy, while minimizing its immune deleterious consequences. In this review, we present the most recent understanding of this promising therapeutic combination, with a specific focus on modulating T-cell responses, also highlighting the need for more in-depth investigation of the responsible underlying mechanisms of action.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".