Sasanlimab in combination with bacillus Calmette-Guérin (BCG) in BCG-naive in BCG-naive, high-risk non–muscle-invasive bladder cancer (NMIBC): Patient-reported outcomes (PROs) from CREST.
Bibliographic record
Abstract
4610 Background: Sasanlimab with BCG (induction [IND] + maintenance [MNT]) significantly improved investigator-assessed EFS vs BCG (IND + MNT) and had a manageable safety profile in patients (pts) with BCG-naive, high-risk NMIBC in the phase 3 CREST study primary analysis. We report PRO data not previously presented from CREST for Arms A and C assessing the impact of sasanlimab with BCG on QOL. Methods: Eligible pts were randomized 1:1:1 to receive sasanlimab with BCG (IND + MNT; Arm A), sasanlimab with BCG (IND; Arm B), or BCG (IND + MNT; Arm C). PROs were secondary endpoints and not included in the testing hierarchy. PROs were assessed prior to first dose (baseline [BL]), and at scheduled visits until an event or end of treatment (every 4 wk until Wk 28, every 12 wk until Wk 100) and at disease follow-up using the EORTC QLQ-C30 and QLQ-NMIBC24. Longitudinal mixed effect-model analyses were used to assess change from BL in the EORTC QLQ-C30 and NMIBC24 items. Results: At data cutoff (Dec 2, 2024), 695/703 pts randomized to Arms A (n = 348) and C (n = 347) had a BL score and ≥1 post-BL score. Completion rates were > 84% for all visits through the end-of-treatment visit (Cycle 25). QLQ-C30 Global Health Score QOL scores were numerically similar between arms (mean difference: −2.345; 95% CI: −4.058, −0.632; P = 0.007). No clinically meaningful differences (≥10-point change; Osoba et al. JCO 1998) were observed across urinary symptoms (NMIBC24; mean difference: 0.851; 95% CI: −1.030 , 2.731; P = 0.375), intravesical treatment issues (NMIBC24; mean difference: 1.271; 95% CI: −0.587, 3.130; P = 0.180), and EORTC QLQ-C30 items (Table) between arms, except in a small sample for female sexual problems (NMIBC24; mean difference: 18.502; 95% CI: 6.228, 30.775; P = 0.007). Results were statistically significant but not clinically meaningful. Conclusions: PROs from CREST showed QOL was maintained when combining sasanlimab with BCG vs BCG (both IND + MNT). These results can help inform the benefit-risk assessment of CREST. Clinical trial information: NCT04165317 . Arm AEstimated mean (95% CI)n=348 Arm CEstimated mean (95% CI)n=347 Estimated mean difference (95% CI) P value Physical functioning −2.485(−3.493, −1.477) −0.261(−1.272, 0.750) −2.224(−3.651, −0.796) 0.002 Role functioning −4.836(−6.187, −3.484) -0.910(−2.265, 0.445) −3.926(−5.840, −2.012) 0.000 Emotional functioning 0.074(−1.046, 1.194) 2.071(0.948, 3.194) −1.997(−3.583, −0.411) 0.014 Cognitive functioning −2.137(−3.174, −1.101) −0.832(−1.872, 0.208) −1.305(−2.774, 0.163) 0.081 Social functioning −3.863(−5.111, −2.615) −0.415(−1.666, 0.836) −3.448(−5.215, −1.681) 0.000 Fatigue 4.881(3.488, 6.274) 1.211(−0.185, 2.607) 3.670(1.698, 5.642) 0.000 Nausea and vomiting 0.908(0.473, 1.343) 0.545(0.108, 0.982) 0.363(−0.253, 0.980) 0.248 Pain 2.784(1.519, 4.048) −0.174(−1.443, 1.095) 2.958(1.166, 4.749) 0.001
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".