The effects of sorafenib and sunitinib on bone turnover markers in patients with bone metastases from renal cell carcinoma
Bibliographic record
Abstract
e16145 Background: Bone metastases (BM) from renal cell carcinoma (RCC) are common and associated with poor outcomes. While the multi-tyrosine kinase inhibitors (TKI's) sunitinib and sorafenib have advanced the treatment of metastatic RCC, their efficacy on BM is unknown. Urinary N-telopeptide (uNTX) is a marker of bone turnover measured in nmol/mmol creatinine. Elevated uNTX levels correlate with an increased risk of skeletal related events and mortality in patients receiving bisphosphonates for BM from a range of primaries. In this pilot biomarker study we sought to prospectively evaluate the effects on BM of these multi-TKI's in RCC patients. Methods: Eligible patients had advanced RCC, at least one BM evident on imaging and no bisphosphonate exposure within 4 weeks. UNTX levels (OsteoMark) were measured at; baseline and weeks-1, 4, 8 and 12 after commencing either sunitinib or sorafenib. The primary endpoint was the percentage change (Ch) in uNTX levels from baseline. Serum samples were also collected for KIT and VEGFR-2 (Quantikine). Patients also completed pain (including bone pain) and quality of life questionnaires. Results: The uNTX results on the first 9 patients are presented in the table below (7 received sunitinib and 2 sorafenib). In this group, sVEGFR-2 and sKIT levels fell by week-1 and 4 respectively and at week-12 the mean % changes (95% CI) were -34% (-0.53,-0.14) and -38% (-0.58,-0.18). Conclusions: In patients with BM from RCC and at least moderately elevated uNTX levels at baseline, these multi-TKI's show a significant trend to decrease uNTX levels, but perhaps not as effectively as bone-specific therapies (e.g. bisphosphonates) do in other malignancies. SVEGFR-2 and sKIT levels also fell across the patient group over the same period. This pilot data raises questions about the activity of the multi-TKI's in BM from RCC and further research is needed. [Table: see text] [Table: see text]
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".