Abstract IA21: Intravesical gene therapy for NMIBC
Bibliographic record
Abstract
Abstract BCG is our most effective therapy for treating NMIBC, but over time, most patients will eventually recur. Alternative therapies to avoid cystectomy are needed as to date, only valrubicin, with a CR approaching 10% at 12 months in BCG-refractory CIS, has been FDA approved. Significant unmet need thus remains for an effective second-line therapy for patients facing cystectomy. The elucidation of a pathway for registration of new agents for BCG-unresponsive NMIBC has drawn the attention of pharma, and a variety of new approaches are under evaluation. Gene therapy is a promising approach for the management of BCG-unresponsive NMIBC. Effective gene transfer across the urothelium has been accomplished, and several agents are being evaluated in ongoing clinical trials. Coldgenesys reported a 47% CR at 6 months for BCG-unresponsive NMIBC using a replication-competent oncolytic adenovirus. The SUO CTC reported a 35% RFS at 12 months for patients treated with rAd-IFNα2b/Syn3 gene therapy in a phase II trial. The RFS for patients with papillary disease was 50% and the CR for patients with CIS was 30%. The SUO-CTC have recently completed recruitment for the phase III trial and further preclinical work has progressed to elucidate rAd-IFN/Syn3 treatment efficacy via predictive efficacy biomarkers for patient selection, vectors that might improve transduction efficiency and the design of novel therapeutic combination strategies to take advantage of rAd-IFN's immunologic activity. Citation Format: Sharada Mokkapati, Jon Duplisea, Michael Metcalfe, Amy Lim, Vikram Narayan, Devin Plote, Debashish Sundi, James E. Furguson III, Nigel R. Parker, Seppo Yla-Herttuala, David McConkey, Kimberly S. Shluns, Colin P.N. Dinney. Intravesical gene therapy for NMIBC [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 IA21.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".