Potentiating Salvage Radiotherapy in Radiorecurrent Prostate Cancer Through Anti-CTLA4 Therapy: Implications from a Syngeneic Model
Bibliographic record
Abstract
High-risk prostate cancer (PCa) is a leading cause in cancer death and can elicit significant morbidity and mortality. Currently, the salvage of local disease recurrence after radiation therapy (RT) is a major clinical problem. Immune checkpoint inhibitors (ICIs), which enhance immune activation, have demonstrated clinical therapeutic promise in combination with ionizing radiation (IR) in certain advanced cancers. We generated the TRAMP-C2 HF radiorecurrent syngeneic mouse model to evaluate the therapeutic efficacy of ICIs in combination with RT. The administration of anti-PDL1 and/or anti-CTLA4 did not achieve a significant tumor growth delay compared to the control. The combination of IR and anti-PDL1 did not yield additional a growth delay compared to IR and the isotype control. Strikingly, a significant tumor growth delay and complete cure in one-third of the mice were seen with the combination of IR and anti-CTLA4. Immune cells in tumor-draining lymph nodes and tumor-infiltrating lymphocytes from mice treated with IR and anti-CTLA4 demonstrated an upregulation of genes in T-cell functions and enrichment in both CD4+ and CD8+ T-cell populations compared to mice given IR and the isotype control. Taken together, these results indicate enhancement of T-cell response in radiorecurrent PCa by IR and anti-CTLA4.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".