Genomic alterations to refine prognostication of patients with metastatic renal cell carcinoma.
Bibliographic record
Abstract
626 Background: The IMDC risk score is a valid and simple tool to prognosticate patients (pts) with metastatic renal cell carcinoma (mRCC). Some non-VHL common genomic alterations may be associated with outcomes. We therefore assessed the prognostic value of most commonly mutated genes in mRCC beside VHL overall, and within IMDC risk groups. Methods: We identified patients treated at Dana-Farber Cancer Institute (n = 65) or part of TCGA (n = 33) who had genomic data available and were treated with first line vascular endothelial growth factor tyrosine kinase inhibitors. Information on genomic alterations (GA) focused on PBRM1, BAP1, SETD2, KDM5C and TP53 was extracted. Cox regression was performed to assess the association of each GA with overall survival (OS), adjusting for IMDC risk groups and age. Results: Overall, 98 pts were identified. 96/98 pts had clear-cell histology. Pts distribution by IMDC risk groups was: 7% good, 58% intermediate, 27% poor and 8% unknown. Mutation rates were 27% PBRM1, 17% BAP1, 29% SETD2, 9% KDM5C and 8% TP53. In multivariable models, there was an association between GA and worse OS for BAP1 and BAP1 or TP53 combined (Table). When stratified by IMDC risk groups, GA in BAP1 or TP53 was associated with shorter median OS in poor risk pts [12.1 mo (95%CI 8.3- 24.0) v. 27.6 mo (95%CI 18.9- 53.4), aHR 4.64 (95%CI 1.32-16.4), p = 0.017] and a trend toward worse median OS in intermediate risk pts [20.5 mo (95%CI 7.4-54.6) v. 36.3 mo (95%CI 21.1, NR), aHR 2.11 (95%CI 0.94-4.74)] compared to pts without GA in BAP1 or TP53. Too few death events were observed in good risk pts to assess the prognostic value of GA in BAP1 or TP53. Conclusions: GA in BAP1 or TP53 are prognostic in mRCC and further discriminate pts with distinct outcomes within IMDC risk groups. Validation in larger dataset is ongoing. [Table: see text]
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".