Recommendations for genetic testing to reduce the incidence of anthracycline‐induced cardiotoxicity
Bibliographic record
Abstract
AIMS: Anthracycline-induced cardiotoxicity (ACT) occurs in 57% of treated patients and remains an important limitation of anthracycline-based chemotherapy. In various genetic association studies, potential genetic risk markers for ACT have been identified. Therefore, we developed evidence-based clinical practice recommendations for pharmacogenomic testing to further individualize therapy based on ACT risk. METHODS: We followed a standard guideline development process, including a systematic literature search, evidence synthesis and critical appraisal, and the development of clinical practice recommendations with an international expert group. RESULTS: RARG rs2229774, SLC28A3 rs7853758 and UGT1A6 rs17863783 variants currently have the strongest and the most consistent evidence for association with ACT. Genetic variants in ABCC1, ABCC2, ABCC5, ABCB1, ABCB4, CBR3, RAC2, NCF4, CYBA, GSTP1, CAT, SULT2B1, POR, HAS3, SLC22A7, SCL22A17, HFE and NOS3 have also been associated with ACT, but require additional validation. We recommend pharmacogenomic testing for the RARG rs2229774 (S427L), SLC28A3 rs7853758 (L461L) and UGT1A6*4 rs17863783 (V209V) variants in childhood cancer patients with an indication for doxorubicin or daunorubicin therapy (Level B - moderate). Based on an overall risk stratification, taking into account genetic and clinical risk factors, we recommend a number of management options including increased frequency of echocardiogram monitoring, follow-up, as well as therapeutic options within the current standard of clinical practice. CONCLUSIONS: Existing evidence demonstrates that genetic factors have the potential to improve the discrimination between individuals at higher and lower risk of ACT. Genetic testing may therefore support both patient care decisions and evidence development for an improved prevention of ACT.
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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 | 0.027 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".