Single-nucleotide polymorphisms associated with outcome in metastatic renal cell carcinoma treated with sunitinib
Bibliographic record
Abstract
BACKGROUND: There are no validated markers that predict response in metastatic renal cell cancer (RCC) patients treated with sunitinib. We aim to study the impact of single-nucleotide polymorphisms (SNPs) that have recently been proposed as predictors of outcome to anti-VEGF-targeted therapy in metastatic RCC in an independent cohort of patients. METHODS: We genotyped 16 key SNPs in 10 genes involved in sunitinib pharmacokinetics, pharmacodynamics and VEGF-independent angiogenesis in patients with metastatic clear-cell RCC treated with sunitinib as the first-line targeted therapy. Association between SNPs, progression-free survival (PFS) and overall survival (OS) were studied by multivariate Cox regression using relevant clinical factors associated with PFS and OS as covariates. RESULTS: In a series of 88 patients, both PFS and OS were associated significantly with SNP rs1128503 in ABCB1 (P=0.027 and P=0.025), rs4073054 in NR1/3 (P=0.025 and P=0.035) and rs307821 in VEGFR3 (P=0.032 and P=0.011). Progression-free survival alone was associated with rs2981582 in FGFR2 (P=0.031) and rs2276707 in NR1/2 (P=0.047), whereas OS alone was associated with rs2307424 in NR1/3 (P=0.048) and rs307826 in VEGFR3 (P=0.013). CONCLUSION: Our results confirm former communications regarding the association between SNPs in ABCB1, NR1/2, NR1/3 and VEGFR3 and sunitinib outcome in clear-cell RCC. Prospective validation of these SNPs is now required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".