Abstract 14410: A Machine Learning Approach to Predicting Risk of Anthracycline Cardiotoxicity in Pediatric Cancer Survivors
Bibliographic record
Abstract
Background: Despite known clinical and genetic risk factors, predicting anthracycline cardiotoxicity remains challenging. Objective: To develop a risk prediction model for anthracycline cardiotoxicity in childhood cancer survivors. Methods: We performed exome sequencing in 289 childhood cancer survivors at least 3 years from anthracycline exposure. In a nested case-control design, 183 cases with LV ejection fraction (LVEF) ≤55% despite low-dose doxorubicin (DOX) (≤250 mg/m2), and 106 controls with LVEF >55% despite DOX >250 mg/m2 were selected as extreme phenotypes. Rare/low-frequency variants were collapsed to identify genes differentially enriched for variants between cases and controls. The top-ranked genes were functionally evaluated in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and variant enrichment was confirmed in a replication cohort. Using random forest, a risk prediction model that included genetic and clinical predictors was developed. Results: Thirty-one genes were differentially enriched for variants between cases and controls (p<0.001). Only 42.6% cases harbored a variant in these genes compared to 89.6% controls [Odds ratio (95% CI), 0.086 (0.043-0.171), p=3.98x10 -15 ] suggesting that absence of these variants may predispose to cardiotoxicity. A risk prediction model for cardiotoxicity that included clinical and genetic factors had a higher prediction accuracy and lower misclassification rate compared to the clinical model. In vitro inhibition of gene-associated pathways ( PI3KR2, ZNF827 ) provided protection from cardiotoxicity in CMs. Conclusions: Our study identified variants that protect against cardiotoxicity and informed the development of a prediction model for delayed anthracycline cardiotoxicity, and also provided new targets in autophagy genes for development of cardio-protective drugs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".