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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".