PD-1/PD-L1 Immune Checkpoint Inhibition with Radiation in Bladder Cancer: <i>In Situ</i> and Abscopal Effects
Bibliographic record
Abstract
Abstract The combination of radiation with immune checkpoint inhibitors was reported in some cancers to have synergic effects both locally and distally. Our aim was to assess this combined therapy on both radiated and nonradiated bladder tumors and to characterize the immune landscape within the tumor microenvironment. Murine bladder cancer cells (MB49) were injected subcutaneously in both flanks of C57BL/6 mice. Mice were randomly assigned to the following treatments: placebo, anti-PD-L1 (four intraperitoneal injections over 2 weeks), radiation to right flank (10 Gy in two fractions), or radiation+anti-PD-L1. Tumor digestion, flow cytometry, and qPCR were performed. Log-rank analysis was used for statistical significance. Radiation+anti-PD-L1 group demonstrated statistically significant slower tumor growth rate both in the radiated and nonirradiated tumors (P < 0.001). Survival curves demonstrated superior survival in the combination group compared with each treatment alone (P = 0.02). Flow cytometry showed increased infiltration of immunosuppressive cells as well as CTL in the radiation and combination groups (P = 0.04). Ratio of immunosuppressive cells to CTL shifted in favor of cytotoxic activity in the combination arm (P < 0.001). The qPCR analysis revealed downregulation of immunosuppressive genes (CCL22, IL22, and IL13), as well as upregulation of markers of CTL activation (CXCL9, GZMA, and GZMB) within both the radiated and distant tumors within the combination group. Combining radiation with immune checkpoint inhibitor provided better response in the radiated tumors and also the distant tumors along with a shift within the tumor microenvironment favoring cytotoxic activity. These findings demonstrate a possible abscopal effect in urothelial carcinoma with combination therapy.
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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) | 0.001 | 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".