Longitudinal Changes in Multiple Biomarkers Are Associated with Cardiotoxicity in Breast Cancer Patients Treated with Doxorubicin, Taxanes, and Trastuzumab
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Bibliographic record
Abstract
BACKGROUND: Biomarkers may play an important role in identifying patients at risk for cancer therapy cardiotoxicity. Our objectives were to define the patterns of change in biomarkers with cancer therapy and their associations with cardiotoxicity. METHODS: In a multicenter cohort of 78 breast cancer patients undergoing doxorubicin and trastuzumab therapy, 8 biomarkers were evaluated at baseline and every 3 months over a maximum follow-up of 15 months. These biomarkers, hypothesized to be mechanistically relevant to cardiotoxicity, included high-sensitivity cardiac troponin I (hs-cTnI), high-sensitivity C-reactive protein (hsCRP), N-terminal pro-B-type natriuretic peptide (NT-proBNP), growth differentiation factor 15 (GDF-15), myeloperoxidase (MPO), placental growth factor (PlGF), soluble fms-like tyrosine kinase receptor-1 (sFlt-1), and galectin 3 (gal-3). We determined if biomarker increases were associated with cardiotoxicity at the same visit and the subsequent visit over the entire course of therapy. Cardiotoxicity was defined by the Cardiac Review and Evaluation Criteria; alternative definitions were also considered. RESULTS: Across the entire cohort, all biomarkers except NT-proBNP and gal-3 demonstrated increases by 3 months; these increases persisted for GDF-15, PlGF, and hs-cTnI at 15 months. Increases in MPO, PlGF, and GDF-15 were associated with cardiotoxicity at the same visit [MPO hazard ratio 1.38 (95% CI 1.10-1.71), P = 0.02; PlGF 3.78 (1.30-11.0), P = 0.047; GDF-15 1.71 (1.15-2.55), P = 0.01] and the subsequent visit. MPO was robust to alternative outcome definitions. CONCLUSIONS: Increases in MPO are associated with cardiotoxicity over the entire course of doxorubicin and trastuzumab therapy. Assessment with PlGF and GDF-15 may also be of value. These findings motivate validation studies in additional cohorts.
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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 it