同源重组缺陷（HRD）是确定妇科癌症患者对铂类化疗和PARP抑制剂治疗的临床益处的重要生物标志物。准确预测HRD表型仍然具有挑战性。在这里，我们提出了一种名为MODeepHRD的新型多组学整合深度学习框架，用于检测HRD阳性表型。MODeepHRD利用卷积注意自编码器，有效地利用组学特异性和交叉组学互补知识学习。我们使用转录组、DNA甲基化和突变数据在351例卵巢癌（OV）患者上训练了MODeepHRD，并在22个数据集的2133例OV样本中进行了验证。预测的HRD阳性肿瘤与生存改善（HR=0.68；95% CI，0.60-0.77；对于元队列，log-rank p<0.001；HR=0.5；95% CI，0.29-0.86；对于ICGC-OV队列，log-rank p=0.01）和对铂类化疗的反应较高相关。MODeepHRD的翻译潜力在多中心乳腺和子宫内膜癌队列中得到了进一步验证。此外，MODeepHRD优于传统机器学习方法和其他类似任务方法。总之，我们的研究证明了深度学习作为HRD测试临床解决方案的有希望价值。MODeepHRD具有潜在的临床适用性，可以指导患者风险分层和治疗决策，并为精准肿瘤学和个体化治疗策略提供有价值的洞察。

Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of HRD phenotype remains challenging. Here, we proposed a novel Multi-Omics integrative Deep-learning framework named MODeepHRD for detecting HRD-positive phenotype. MODeepHRD utilizes a convolutional attention autoencoder that effectively leverages omics-specific and cross-omics complementary knowledge learning. We trained MODeepHRD on 351 ovarian cancer (OV) patients using transcriptomic, DNA methylation and mutation data, and validated it in 2133 OV samples of 22 datasets. The predicted HRD-positive tumors were significantly associated with improved survival (HR = 0.68; 95% CI, 0.60-0.77; log-rank p < 0.001 for meta-cohort; HR = 0.5; 95% CI, 0.29-0.86; log-rank p = 0.01 for ICGC-OV cohort) and higher response to platinum-based chemotherapy compared to predicted HRD-negative tumors. The translational potential of MODeepHRDs was further validated in multicenter breast and endometrial cancer cohorts. Furthermore, MODeepHRD outperforms conventional machine-learning methods and other similar task approaches. In conclusion, our study demonstrates the promising value of deep learning as a solution for HRD testing in the clinical setting. MODeepHRD holds potential clinical applicability in guiding patient risk stratification and therapeutic decisions, providing valuable insights for precision oncology and personalized treatment strategies.