Novel role of neutrophil extracellular traps and their implication in radiation resistance of muscle invasive bladder cancer
Bibliographic record
Abstract
Bladder cancer is the 5th most common cancer in Canada and 25% of cases are muscle-invasive at presentation. Radical cystectomy has been the standard treatment for muscle-invasive bladder cancer (MIBC); however, it is associated with significant physical and psychological implications and has a 5-year survival of 50%. Bladder sparing treatment modalities such as radiation therapy (RT) can significantly improve quality of life but, 30% of patients will have radioresistant tumors. In addition to the intrinsic mechanisms of radioresistance, extrinsic influence of the tumor immune microenvironment (TIME) also contributes to therapy resistance. Radiotherapy induces inflammation in the TIME which plays a pivotal role in modulating radiation response of tumors. Polymorphonuclear neutrophils (PMNs) make up a large portion of the inflammatory cell infiltrate and act as the immune system’s first line of defense. Emerging evidence suggests that PMNs contribute to tumor progression and one mechanism is through the formation of neutrophil extracellular traps (NETs). NETs are webs of DNA extruded by PMNs that can facilitate metastasis and tumor progression. Here, we demonstrate a novel role for NETs as contributors of radioresistance in MIBC. Using a syngeneic invasive bladder cancer model, we show that radiation induces NET deposition in the TIME and therapeutically inhibiting/degrading NETs significantly improves radiation response. Mechanistically, we show that this is driven by the protein HMGB1 as in vitro, HMGB1 promotes NETs in a TLR4-dependent manner. This was also confirmed in vivo as we noted that inhibiting HMGB1 and targeting NETs significantly delays tumor growth. Once NETs are formed in the TIME, we demonstrate that they inhibit infiltration of cytotoxic CD8 T-cells to the tumor site, which can contribute to radiation resistance. We confirm our preclinical findings using a retrospective cohort of MIBC patients treated with radiation. Increased NET deposition is observed in bladder tumors of patients who had persistent disease post-RT defined as non-responders, and these events are associated with worse overall survival. Further, we noted that non-responders exhibit a high tumoral PMN-to-CD8 ratio which also correlates to worse overall survival. Together, these findings offer a better understanding into the extrinsic influence of the TIME, notably the role of PMNs and NETs as players in radioresistance. We identify NETs as a potential therapeutic target to increase radiation efficacy and to better stratify patients that could benefit from radiation-based 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) | 0.000 | 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 | 0.000 |
| 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".