Optimizing Sequence of PD-L1 Immune-Checkpoint Inhibitors and Radiation Therapy in Bladder Cancer
Bibliographic record
Abstract
BACKGROUND: New bladder preserving strategies are needed for muscle invasive bladder cancer (MIBC). Combined therapy of immune-checkpoint inhibitors and radiation was shown to have synergistic antitumoral effects in preclinical studies. OBJECTIVES: We aim to evaluate whether the sequence of administration of this combined therapy impacts antitumoral response. METHODS: We developed an in-vivo syngeneic MIBC mouse model where murine bladder cancer cells (MB49) were injected subcutaneously in the right flank of C57BL/6 mice. Mice were then randomized to the following treatments: control, anti-programmed cell death ligand 1 (PD-L1) alone, radiation alone (XRT) consisting of 6.25 Gy x2 fractions, concurrent anti-PD-L1 with XRT, neoadjuvant anti-PD-L1 followed by XRT, or XRT followed by adjuvant anti-PD-L1 therapy. Tumor growth, survival, and rate of response were analyzed. RESULTS: Total of 60 mice were randomized. One-way analysis of variance showed statistically significant difference in tumor growth rate across the treatment arms ( p = 0.029). Importantly, timing of immunotherapy (neoadjuvant, concurrent, or adjuvant) did not alter either tumor growth or survival ( p > 0.05). The rate of response was also similar in each combination arm ( p > 0.05). CONCLUSION: Combining anti-PD-L1 immunotherapy and radiation therapy offers optimal antitumoral responses. Timing of immunotherapy (neoadjuvant, concurrent, or adjuvant) does not appear to affect outcomes. Whether the toxicity profile differs across various sequential deliveries of combination therapy requires further evaluation.
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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) | 0.000 | 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) | 0.001 | 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".