The impact of PBRM1 and BAP1 expression on outcomes of patients with metastatic renal cell carcinoma (mRCC) treated with VEGF-targeted therapy (TT).
Bibliographic record
Abstract
616 Background: Polybromo-1 (PBRM1) and BRCA1 associated protein-1 (BAP1) are genes commonly mutated in clear cell RCC (ccRCC) and have been associated with clinical outcome. This work aims to evaluate the impact of PBRM1 and BAP1 expression by IHC in mRCC patients treated with VEGF-TT. Methods: PBRM1 and BAP1 expression was evaluated by IHC in a TMA including 146 mRCC patients. PBRM1 and BAP1 IHC scores were dichotomized as a binary variable: negative (-) or positive (+) (weak positivity was excluded). The associations of PBRM1 or BAP1 expression with baseline clinico-pathological characteristics were evaluated, as well as with overall survival (OS) and time to treatment failure (TTF) usingCox proportional hazards models. Results: Out of 146 patients, 116 and 109 samples had available results for PBRM1 and BAP1 staining, respectively. Overall, 90% (n = 131) patients had ccRCC. 70/116 samples (60%) were PBRM1- and 26/109 patients (24%) were BAP1-. Only 12% (n = 13) of patients had simultaneous negative PBRM1 and BAP1. While there was no association between PBRM1 expression status and clinical factors, BAP1- samples were associated with poor IMDC prognostic risk score (p = 0.004) and higher Fuhrman grade (p = 0.012). PBRM1+ patients showed a trend towards an increased risk of death and a shorter TTF compared to PBRM1- patients (OS: HR = 1.38, 95%CI: 0.92-2.07, p = 0.1; TTF: HR = 1.39, 95%CI: 0.94-2.06, p = 0.1). BAP1 expression was not independently associated with OS or TTF. Conclusions: Loss of PBRM1, but not BAP1 expression showed a trend towards longer TTF and OS in mRCC patients treated with VEGF-TT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".