A phase III randomized study comparing perioperative nivolumab vs. observation in patients with localized renal cell carcinoma undergoing nephrectomy (PROSPER RCC).
Bibliographic record
Abstract
TPS710 Background: The anti-PD-1 antibody nivolumab (nivo) improves overall survival (OS) in metastatic treatment refractory RCC and is generally tolerable. In 2017, there is no standard adjuvant therapy proven to increase OS over surgery alone in non-metastatic (M0) disease. Mouse solid tumor models have revealed an OS benefit with a short course of neoadjuvant PD-1 blockade compared to adjuvant therapy. Two ongoing phase 2 studies of perioperative nivo in RCC patients (pts) are showing preliminary feasibility and safety with no surgical delays/complications. PROSPER RCC will examine if the addition of perioperative nivo to radical or partial nephrectomy can improve clinical outcomes in pts with locally advanced RCC. We are implementing a three-pronged, multidisciplinary approach of presurgical priming with nivo followed by resection and adjuvant PD-1 blockade with the goal of increasing cure and recurrence-free survival (RFS) rates in M0 RCC. Methods: Tumor biopsy prior to randomization is mandatory to ensure RCC diagnosis but will also permit unparalleled correlative science in this global, unblinded, phase 3 National Clinical Trials Network randomized study. 766 pts with clinical stage ≥T2 or any node positive M0 RCC of any histology will be enrolled. The study arm will receive nivo 240mg IV for 2 doses prior to surgery followed by adjuvant dosing for 9 mo (q2 wks x 3 mo followed by q4 wks x 6 mo). The control arm will undergo the current standard of care: surgical resection followed by observation. Pts are stratified by clinical T stage, node positivity, and histology. There is 84.2% power to detect a 14.4% absolute increase in the primary endpoint of RFS from the ASSURE historical control of 55.8% to 70.2% at 5 yrs (HR 0.70). The study is also powered to detect a significant OS benefit (HR 0.67). Safety, feasibility, and quality of life are key secondary endpoints. PROSPER RCC exemplifies team science and incorporates a host of correlative work to examine the significance of the baseline immune milieu and changes induced by neoadjuvant priming and to identify predictive gene expression patterns. New collaborations welcomed. Clinical trial information: NCT03055013.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".