Abstract IA22: Realizing the potential of targeted therapy in non-muscle invasive bladder cancer
Bibliographic record
Abstract
Abstract Targeted therapies have revolutionized the management of most cancers, but the era of targeted therapy has mostly passed by bladder cancer. Ramicurimab (targeting VEGR) and erdafitinib and infigratinib (both targeting FGFR) have recently provided some reason for optimism that targeted therapies with highly potent inhibitors may yet find a role in advanced bladder cancer. Targeting genomic alterations in FGFR3 holds the most promise, but this molecular event is probably even more relevant in non-muscle invasive bladder cancer (NMIBC), and trials in this space are essential. Similarly, KDMT6a appears to be particularly important in NMIBC, and drugs targeting chromatin modification pathways are likely to be relevant in NMIBC. Novel therapies are also under development that are not specific to an individual cancer's genomic makeup, but instead target specific cancer-related pathways. Such agents include oportuzumab monatox and BC-819. Most clinical trial activity has focused on BCG-unresponsive high-risk NMIBC patients, but this is partly due to the pathway for drug registration, and we are likely to see some of the successful drugs in this disease state move forward to earlier disease states, especially to intermediate-risk patients. These therapies will need to be positioned in the treatment landscape along with other nontargeted intravesical agents such as nadofaragene firadenovec (rAd-IFN) and systemic immune checkpoint blockade. Citation Format: Peter C. Black. Realizing the potential of targeted therapy in non-muscle invasive bladder cancer [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr IA22.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".