Longitudinal Changes in Multiple Biomarkers Are Associated with Cardiotoxicity in Breast Cancer Patients Treated with Doxorubicin, Taxanes, and Trastuzumab
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".