乳腺癌是女性最常见的恶性肿瘤之一，也是全球妇科恶性肿瘤死亡的主要原因之一。乳腺癌的异质性很高，这使得制定有效的治疗策略具有挑战性。越来越多的证据凸显将乳腺癌患者分为临床意义明显的亚型在预后和治疗方面起到关键作用。结构深度聚类网络是一种基于图卷积网络的聚类算法，它整合了结构信息，在各种应用中取得了最先进的性能。在该研究中，我们采用结构深度聚类网络,将2526名乳腺癌患者的体细胞突变谱整合起来，将其分为两个临床可区分的亚型。研究发现，在聚类1中的乳腺癌患者的预后要好于聚类2中的乳腺癌患者，并且两者之间的差异具有统计学显著性。免疫遗传学研究结果进一步表明，聚类1与肿瘤浸润淋巴细胞的显著浸润相关。聚类亚型可用于评估乳腺癌患者在免疫治疗和化学治疗方面的治疗效益。此外，我们的方法有效地对来自八种不同癌症类型的患者进行了分类，证明了它的通用性。我们的研究代表了使用仅有体细胞突变数据和结构深度聚类网络方法对癌症患者进行分类的一种通用方法。采用结构深度聚类网络来识别乳腺癌亚型具有很大的发展前景，并可以为更准确、个性化的治疗方法的开发提供信息。 版权所有 © 2023 Elsevier B.V.。保留所有权利。

Breast cancer is among of the most malignant tumor that occurs in women and is one of the leading causes of death from gynecologic malignancy worldwide. The high degree of heterogeneity that characterizes breast cancer makes it challenging to devise effective therapeutic strategies. Accumulating evidence highlights the crucial role of stratifying breast cancer patients into clinically significant subtypes to achieve better prognoses and treatments. The structural deep clustering network is a graph convolutional network-based clustering algorithm that integrates structural information and has achieved state-of-the-art performance in various applications.In this study, we employed structural deep clustering network to integrate somatic mutation profiles for stratifying 2526 breast cancer patients from the Memorial Sloan Kettering Cancer Center into two clinically differentiable subtypes.Breast cancer patients in cluster 1 exhibited better prognosis than breast cancer patients in cluster 2, and the difference between them was statistically significant. The immunogenomic landscape further demonstrated that cluster 1 was associated with remarkable infiltration of the tumor infiltrating lymphocytes. The clustering subtype could be used to evaluate the therapeutic benefit of immunotherapy and chemotherapy in breast cancer patients. Furthermore, our approach effectively classified patients from eight different cancer types, demonstrating its generalizability.Our study represents a step towards a generic methodology for classifying cancer patients using only somatic mutation data and structural deep clustering network approaches. Employing structural deep clustering network to identify breast cancer subtypes is promising and can inform the development of more accurate and personalized therapies.Copyright © 2023 Elsevier B.V. All rights reserved.