Sunitinib-associated hypertension and neutropenia as efficacy biomarkers in metastatic renal cell carcinoma patients
Bibliographic record
Abstract
BACKGROUND: Metastatic renal cell carcinoma (mRCC) prognostic models may be improved by incorporating treatment-induced toxicities. METHODS: In sunitinib-treated mRCC patients (N=770), baseline prognostic factors and treatment-induced toxicities (hypertension (systolic blood pressure ⩾140 mm Hg), neutropenia (grade ⩾2), thrombocytopenia (grade ⩾2), hand-foot syndrome (grade >0), and asthenia/fatigue (grade >0)) were analysed in multivariate analyses of progression-free survival (PFS) and overall survival (OS) end points. RESULTS: On-treatment neutropenia and hypertension were associated with longer PFS (P=0.0276 and P<0.0001, respectively) and OS (P=0.0014 and P<0.0001, respectively), independent of baseline prognostic factors, including International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria. By 12-week landmark analysis, neutropenia was significantly associated with longer PFS and OS (P=0.013 and P=0.0122, respectively) and hypertension or hand-foot syndrome with longer OS (P=0.0036 and P=0.0218, respectively). The concordance index was 0.65 (95% CI: 0.63-0.67) for IMDC classification alone and 0.72 (95% CI: 0.70-0.74) when combined with hypertension and neutropenia. Considering hypertension and neutropenia (developing both vs neither) changed IMDC-predicted median OS in each IMDC risk group (favourable: 45.3 vs 19.5 months; intermediate: 32.5 vs 8.0 months; poor: 21.1 vs 4.8 months). CONCLUSIONS: On-treatment neutropenia and hypertension are independent biomarkers of sunitinib efficacy and may add prognostic accuracy to the IMDC model.
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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) | 0.000 | 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".