Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53^mut-AURKA^amp and endocrine therapy resistance, germline BRCA1^mut-MYC^amp and improved sensitivity to PARP inhibitors, and TP53^mut-MYB^amp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

基因组改变的共现和相互排斥可能反映了遗传相互作用的存在，可能塑造不同的生物表型并影响乳腺癌的治疗反应。然而，我们对它们的了解仍然有限。在此，我们研究了一个大规模多组学队列（n = 873）和一个真实世界的临床测序队列（n = 4,405），包括多项具有详细治疗结果的临床试验，并在患者来源的类器官、肿瘤碎片中进行功能验证和体内模型。通过这种综合方法，我们构建了一个由共同改变和相互排斥事件组成的网络，并表征了它们的治疗潜力和潜在的生物学基础。值得注意的是，我们确定了TP53 ^mut -AURKA ^amp与内分泌治疗耐药性、种系BRCA1 ^mut -MYC ^amp与对 PARP 抑制剂敏感性提高以及TP53 ^mut -MYB ^amp与免疫治疗耐药性之间的关联。此外，我们发现，通过共同改变实现的精准治疗策略有望改善患者的治疗结果。我们的研究强调了遗传相互作用在指导基因组知情治疗决策方面的重要性，而不仅仅是单一驱动因素的改变。