Combination therapies involving checkpoint-inhibitors for treatment of urothelial carcinoma: a narrative review
Bibliographic record
Abstract
The implementation of immune checkpoint-inhibitors (CPI) has significantly improved the prognosis of a subgroup of patients with urothelial bladder cancer (BC). Still, the majority of patients will progress or experience a recurrence on CPI monotherapy. The next generation of clinical trials is now testing combination therapy with CPI and other agents that target different oncogenic mechanisms in an effort to improve efficacy. The beneficial toxicity profile of CPI but also the approval of CPI combinations in other cancer sites justifies their investigation also in BC. Here we report on clinical trials in muscle-invasive, locally advanced and metastatic BC combining CPI with other therapies, with a focus on the latest results presented at ASCO GU 2020, ASCO 2020 and ESMO 2019 as well as Phase-III trials currently ongoing. Multiple phase I-III clinical trials are investigating the combination of a CPI with a second CPI, with chemotherapy, or with targeted therapies like fibroblast growth factor receptor (FGFR) inhibitors or Nectin-4 inhibitors in different disease states. The results of more than 10 phase-III trials in advanced BC are eagerly awaited. Preliminary data are contradictory, as some trials released promising interim results, while others reported failure to achieve the primary endpoints. Taken together, combining CPI with other therapies is a logical and potentially promising approach, but it is too early to draw conclusions on specific combinations. As combinatorial therapies markedly increase the level of complexity, bedside-to-bench studies are warranted to gain deeper insight of underlying biological mechanisms which can be used to optimize future 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".