Inhibiting Immune Checkpoints for the Treatment of Bladder Cancer
Bibliographic record
Abstract
BACKGROUND: Increasing evidence supporting the role of immune checkpoint blockade in cancer management has been bolstered by recent reports demonstrating significant and durable clinical responses across multiple tumour types, including metastatic urothelial carcinoma (mUC). The majority of these results are achieved via blockade of the programmed death (PD) axis, which like CTLA-4 blockade permits T-cell activation and immune-mediated anti-tumour activity- essentially harnessing the patient's own immune system to mount an anti-neoplastic response. However, while clinical responses can be striking, our understanding of the biology of immune checkpoint blockade is only beginning to shed light on how to maximize and even improve patient outcomes with immune checkpoint blockade, especially in UC. METHODS: We performed a literature review for immune checkpoint blockade with a focus on rationale for checkpoint therapy and outcomes in UC. We also highlight the advances made in other tumour types, with a focus on the recent 2015 meeting of the American Society for Clinical Oncology. RESULTS: In heavily pre-treated UC, trials are suggesting objective response rates above 30% . These impressive results are seen across multiple different tumour types, especially those with high burden of DNA level mutations. Identification of prognostic biomarkers is currently under investigation, in order to improve patient selection. Interestingly, response to PD-1 directed therapy is seen even in patients with no evidence of PD-1 positivity on immunohistochemistry. This has led to the development of enhanced biomarkers including assessing DNA mutation rates and immune gene signatures, to improve patient selection. CONCLUSIONS: Immune checkpoint blockade is an exciting cancer treatment modality which is demonstrating impressive clinical results across multiple tumour types. For UC, anti-PD directed therapy represents a much needed treatment in the metastatic, post chemotherapy context. Potential for these agents to have clinical utility in non-metastatic UC is still to be assessed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".