Abstract 1079: Pre-treatment Circulating Vascular Biomarker Signatures Predict Cancer-therapy Related Cardiac Dysfunction During Breast Cancer Treatment
Bibliographic record
Abstract
Aim: Breast cancer treatment with anthracycline and trastuzumab can result in cancer therapy-related cardiac dysfunction (CTRCD). It is not currently possible to accurately identify patients at risk of CTRCD from clinical and demographic information alone. We sought to identify circulating biomarkers that can be used to identify individuals at risk of CTRCD before or early during cancer treatment. Methods: Multi-omic analysis of circulating biomarkers of cardiac damage, inflammation, and endothelial activation, together with profiling of plasma microRNAs was performed before and early during cancer treatment in 136 women ≥ 18 years of age with HER2+ breast cancer (Stages I-III) undergoing treatment with sequential anthracycline and trastuzumab therapy. Results: We identified three endothelial-centric biomarkers (i.e., Angiopoeitin-2, Endothelin-1 and Endoglin) that were elevated prior to and during cancer treatment, and one marker (i.e., E-Selectin) that was elevated during treatment in patients that went on to develop CTRCD. Additionally, there were significant elevations in inflammatory biomarkers (including Myeloperoxidase, Interferon gamma-induced protein-10 and Interferon-α2) before treatment in patients that developed CTRCD. No appreciable differences were observed in cardiac damage biomarkers (Troponin I, BNP, GDF-15) between groups. Assessment of plasma microRNAs prior to treatment revealed distinct microRNA signatures and predicted disease-relevant pathways in patients who went on to develop CTRCD. A Random Forest machine learning approach using all available baseline clinical, cardiac imaging and biomarker data revealed that pre-treatment Angiopoietin-2, Myeloperoxidase and Endoglin levels were the best predictors of CTRCD risk, and the model was further validated in an independent cohort. Conclusion: A combination of endothelial-centric and inflammatory biomarkers measured before treatment can accurately predict CTRCD during breast cancer therapy. Our findings suggest that sub-clinical vascular activation may predispose to cardiac dysfunction during cancer treatment, and that the vasculature may serve as a potential therapeutic target.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".