Antibody–Drug Conjugates in the Treatment of Genitourinary Cancers: An Updated Review of Data
Bibliographic record
Abstract
The treatment landscape of genitourinary cancers has significantly evolved over the past few years. Renal cell carcinoma, bladder cancer, and prostate cancer are the most common genitourinary malignancies. Recent advancements have produced new targeted therapies, particularly antibody-drug conjugates (ADCs), due to a better understanding of the underlying oncogenic factors and molecular mechanisms involved. ADCs function as a 'drug delivery into the tumor' system. They are composed of an antigen-directed antibody linked to a cytotoxic drug that releases cytotoxic components after binding to the tumor cell's surface antigen. ADCs have been proven to be extremely promising in the treatment of several cancer types. For GU cancers, this novel treatment has only benefited patients with metastatic urothelial cancer (mUC). The rest of the GU cancer paradigm does not have any FDA-approved ADC treatment options available yet. In this study, we have thoroughly completed a narrative review of the current literature and summarized preclinical studies and clinical trials that evaluated the utility, activity, and toxicity of ADCs in GU cancers, the prospects of ADC development, and the ongoing clinical trials. Prospective clinical trials, retrospective studies, case reports, and scoping reviews were included.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".