Prime time for immunotherapy in advanced urothelial cancer
Bibliographic record
Abstract
BACKGROUND: Metastatic urothelial cancer (UC) is a lethal disease. Until 2016, cytotoxic chemotherapy with substantial toxicity was the only therapeutic option. In the first-line metastatic setting, cisplatin-based combination chemotherapy remains the standard of care. For cisplatin-ineligible patients, carboplatin-based regimens that are less efficacious are often substituted. In platinum-refractory patients, taxanes and vinflunine are the most commonly used. Recently, treatment options have largely expanded with the development of the immune checkpoint inhibitors (ICIs). Here, we review the rationale, clinical trial data and recent advances in biomarker development in the field of immuno-oncology in this disease site. We will also explore future directions of the field with respect to sequencing and combination strategies. METHODS: A comprehensive literature review was performed using Pubmed, clinicaltrials.gov, and conference proceedings from the European Society of Medical Oncology (ESMO) and the American Society of Oncology (ASCO). RESULTS AND CONCLUSION: ICIs have disease activity in metastatic UC. Five ICIs gained FDA regulatory approval in metastatic UC in patients with platinum-refractory disease. Of these five agents, pembrolizumab has level I evidence based on the KEYNOTE-045 phase III trial showing an overall survival benefit of 3 months over standard chemotherapy. ICIs also play a role in the first-line setting for patients who are cisplatin ineligible. Currently, ICIs are administered to unselected patients as reliable predictive biomarkers are lacking; however, this is a very active area of research. The rapid expansion of ICIs has also led to many upcoming trials, testing ICIs earlier in the disease course and in various combination strategies. Studies on optimal sequencing of therapies are eagerly awaited.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".