Editorial: Therapies and influences in urothelial carcinoma
Bibliographic record
Abstract
Editorial on the Research TopicTherapies and influences in urothelial carcinoma Urothelial carcinoma (UC) is one of the most common cancers worldwide, with more than 570,000 new cases per year.Despite recent therapeutic advances, long-term survival rates for metastatic UC remain poor.There is an urgent need not just for better therapies but also for clinical indicators, biomarkers, and treatment approaches for UC.This Research Topic encompasses a selection of research papers in UC, including molecular research, new therapeutic approaches, treatment guidance, and prediction of treatment response that may influence future directions.Nelson et al. set the stage with a thorough review of the many different approaches and molecular targets currently being exploited or explored for anti-UC therapeutics.There are already at least 4 different classes of therapies (cytotoxic chemotherapy, immunotherapy, small molecule inhibitors, antibody-drug conjugates) that are FDA approved for the treatment of advanced UC.We are reminded of the recent and rapid explosion of our understanding of UC as a disease that can be sub-classified according to differential RNA expression and further stratified through genomic and biomarker analyses such that targeted molecular therapies will play a larger role along with established therapies.Although immunotherapies have opened a new era in the treatment of bladder cancer, only a limited number of patients respond to treatment.Currently available markers such as tissue PD-L1 staining fails as predictive biomarkers of response, and a clinically useful marker is desperately needed.Using mass and flow cytometry techniques, Lavoie et al. evaluated the circulating immune compartment of patients exposed to PD-1 inhibitors.They discovered marked differences and dynamic changes in the frequency of specific immune cells after treatment.The main finding was that higher frequencies of naïve CD4+ T cells, lower frequencies of CD161+ Th17 cells, and CCR4+ Th2 cells were found in responders.Overall, a less naive and more activated T-cell phenotype was found in non-responder patients.They also showed a clonal expansion of g/d-T cells in a responder, which might serve as a new marker.While this is a small proofof-concept study, if validated, this approach could allow for non-invasive, serial sampling Frontiers in Oncology frontiersin.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".