Serum activinA and TGF-β as biomarkers of breast cancer bone metastasis behavior.
Bibliographic record
Abstract
10620 Background: Bisphosphonates (BP) are prescribed to pts with metastatic bone disease every 3-4 weeks regardless of individual risk for skeletal related events (SREs). In an era of personalized medicine this “one size fits all” approach is not appropriate and novel markers of SRE risk are required. TRIUMPH is an ongoing clinical trial evaluating 12 weekly IV BP therapy for 1 year in women with low risk bone metastases from breast cancer (BC) as defined by the bone resorption marker C-telopeptide (CTx,) levels <600 ng/L. This sub-study evaluated the utility of novel biomarkers in better predicting the risk of developing SREs. Methods: Serum obtained from pts at baseline and 6 weeks post-entry were analyzed for tumor growth factor-β (TGF-β) and activinA levels by ELISA (sensitivity ~15-30 pg/ml). Levels were correlated with pt parameters including time to development of bone metastasis, and number of previous SREs using linear regression analysis. Changes in levels of biomarkers from baseline to 6 weeks were used to calculate odds ratios using logistic regression analysis. Results: Baseline activinA correlated with baseline CTx and bone specific alkaline phosphatase (p=0.004 and p<0.0001 respectively). Baseline activinA also correlated with weight (p=0.02), BMI (p=0.007) and trended towards total number of prior SREs (p=0.07). Baseline TGF-β correlated with pt age (p=0.02), weight (0.006), BMI (p=0.0005) and duration of metastatic bone disease (p=0.004), but did not correlate with any other biomarker. Change in activinA (baseline to week 6) was the only biomarker that trended to predict coming off study early (p=0.053) as per protocol (i.e. CTx>600 ng/ml, SREs or pt/physician choice). Conclusions: Baseline levels of activinA trended to predict incidence of SREs in patients with bone metastases, and changes in levels from baseline to 6 weeks trended to predict coming off study early. These findings warrant future studies in BC pts assessing activinA as a predictor of risk associated with breast cancer bone metastases. This study was conducted with the support of the Ontario Institute for Cancer Research through funding provided by the Government of Ontario, and with funding from the Ontario Chapter of the Canadian Breast Cancer Foundation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".