Tumor irradiation combined with vascular-targeted photodynamic therapy enhances anti-tumor effects in preclinical prostate cancer
Bibliographic record
Abstract
Abstract Rationale There is an important clinical need to improve the treatment of high risk localized and locally advanced prostate cancer (PCa), and to reduce the side effects of these treatments. We hypothesized that multi-modality therapy combining radiotherapy and vascular-targeted photodynamic therapy (VTP) could PCa tumour control compared against monotherapy with each of these treatments alone. This could provide proof-of-concept to take to the clinic. VTP is a focal therapy for localized PCa, which rapidly destroys targeted tumors through vascular disruption. Tumor vasculature is characterized by vessel immaturity, increased permeability, aberrant branching and inefficient flow. Fractionated radiotherapy (FRT) alters the tumor microenvironment and promotes transient vascular normalization. Objective We investigated whether sequential delivery of FRT followed by VTP 7 days later improves PCa tumor control compared to monotherapy with FRT or VTP alone. Findings FRT induced vascular normalization changes in PCa flank tumor allografts, improving vascular function as demonstrated using dynamic contrast enhanced magnetic resonance imaging. FRT followed by VTP significantly delayed tumor growth in flank PCa allograft pre-clinical models, compared with monotherapy with FRT or VTP alone, and improved overall survival. Conclusion Taken together, these results suggest that combining FRT and VTP could become a promising multimodal clinical strategy in PCa therapy. This provides proof-of-concept for this multi-modality therapy approach to take forward to early phase clinical trials.
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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".