蒽环类抗癌药物（人）引起心脏毒性（CDA）影响到了癌症患者，然而我们无法预测谁会出现这种并发症。CDA是一个含有多基因成分的复杂性状，其中大部分还未被鉴定。我们推测与心肌组织损伤相关的中间分子表型（IMPs）水平可能解释CDA易感性，并且编码这些IMPs的基因变异可能可鉴别出容易出现该并发症的患者。因此，通过回交产生的基因遗传异质小鼠群（n = 165）被用于多柔比星和阿霉素联合治疗。我们使用Ariol幻灯片扫描仪测定了心脏纤维化程度，并且使用多重细胞珠阵列和QPCR测量了心肌内IMPs的水平。我们通过连锁分析确定了与IMPs相关的定量性状位点（ipQTLs）和CDA位点（cdaQTLs）。在三个癌症患者队列中，使用超声心动图或心脏磁共振来定量CDA。CDA在小鼠队列中表现为一种复杂性状。心肌中的IMP水平与CDA相关。将ipQTLs与cdaQTLs集成到基因模型中，对CDA表型变异的解释超过仅使用cdaQTLs的解释。在小鼠中与CDA相关的编码IMPs的基因等位形式，包括AKT1，MAPK14，MAPK8，STAT3，CAS3和TP53，也是患者CDA的遗传决定因素。我们使用机器学习最小绝对收缩和选择操作（LASSO）回归生成了针对儿童患者（n = 71）和乳腺癌女性患者（n = 420）的两个遗传风险评分。因此，与心脏损伤相关的IMPs标识了CDA风险的遗传标记，从而可以实现更个体化的患者管理。

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.