Do Cancer Genetics Impact Treatment Decision Making? Immunotherapy and Beyond in the Management of Advanced and Metastatic Urothelial Carcinoma
Bibliographic record
Abstract
Bladder cancer is one of the most commonly diagnosed genitourinary malignancies. For many years, the primary treatment for metastatic urothelial cancer (mUC) was predicated on the use of platinum-based chemotherapy. More recently, immune checkpoint inhibitors (ICIs) were approved by regulatory agencies such as the US FDA for use in both the first- and second-line settings. This review outlines the approved ICIs for mUC in the second-line setting and as an alternative to chemotherapy in the first-line setting, as well as the novel agents that have also been incorporated into the treatment of this malignancy. Single-agent ICIs are often used in second-line settings in mUC, and there are three drugs currently approved for those who progress after receiving platinum-based chemotherapy. In the first-line setting, the preferred treatment regimen remains cisplatin-based chemotherapy. However, single-agent ICI can be an alternative first-line treatment for those who are not candidates for cisplatin-based therapy. There are also clinical trials adding ICIs to chemotherapy as combination regimens. However, treatment for mUC has now expanded even beyond immunotherapy. Newer targeted agents such as erdafitinib, a fibroblast growth factor receptor inhibitor, and two antibody-drug conjugates, enfortumab vedotin and sacituzumab govitecan, have been recently approved. As new drug agents are discovered, it will be important to assess both the treatment outcomes as well as the effects on patients' quality of life. Furthermore, integrating genetic and molecular information can help guide treatment decisions as next-generation sequencing is more commonly acquired during the evaluation of newly diagnosed patients with advanced and metastatic cancer.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".