The molecular landscape in breast cancer is characterized by large biological heterogeneity and variable clinical outcomes. Here, we performed an integrative multi-omics analysis of patients diagnosed with breast cancer. Using transcriptomic analysis, we identified three subtypes (cluster A, cluster B and cluster C) of breast cancer with distinct prognosis, clinical features, and genomic alterations: Cluster A was associated with higher genomic instability, immune suppression and worst prognosis outcome; cluster B was associated with high activation of immune-pathway, increased mutations and middle prognosis outcome; cluster C was linked to Luminal A subtype patients, moderate immune cell infiltration and best prognosis outcome. Combination of the three newly identified clusters with PAM50 subtypes, we proposed potential new precision strategies for 15 subtypes using L1000 database. Then, we developed a robust gene pair (RGP) score for prognosis outcome prediction of patients with breast cancer. The RGP score is based on a novel gene-pairing approach to eliminate batch effects caused by differences in heterogeneous patient cohorts and transcriptomic data distributions, and it was validated in ten cohorts of patients with breast cancer. Finally, we developed a user-friendly web-tool (https://sujiezhulab.shinyapps.io/BRCA/) to predict subtype, treatment strategies and prognosis states for patients with breast cancer.

乳腺癌分子景观的特点是生物异质性大，临床结果多变。在这里，我们对诊断为乳腺癌的患者进行了综合多组学分析。使用转录组学分析，我们确定了三种具有不同预后、临床特征和基因组改变的乳腺癌亚型（A 组、B 组和 C 组）：A 组与较高的基因组不稳定性、免疫抑制和最差的预后结果相关；簇 B 与免疫通路的高度激活、突变增加和中等预后结果相关；簇 C 与 Luminal A 亚型患者、中度免疫细胞浸润和最佳预后结果有关。三个新鉴定的聚类与 PAM50 亚型的组合，我们使用 L1000 数据库为 15 种亚型提出了潜在的新精确策略。然后，我们开发了一种用于预测乳腺癌患者预后结果的稳健基因对 (RGP) 评分。RGP 评分基于一种新的基因配对方法，以消除由异质患者队列和转录组数据分布差异引起的批次效应，并在十个乳腺癌患者队列中得到验证。最后，我们开发了一个用户友好的网络工具 (https://sujiezhulab.shinyapps.io/BRCA/) 来预测乳腺癌患者的亚型、治疗策略和预后状态。RGP 评分基于一种新的基因配对方法，以消除由异质患者队列和转录组数据分布差异引起的批次效应，并在十个乳腺癌患者队列中得到验证。最后，我们开发了一个用户友好的网络工具 (https://sujiezhulab.shinyapps.io/BRCA/) 来预测乳腺癌患者的亚型、治疗策略和预后状态。RGP 评分基于一种新的基因配对方法，以消除由异质患者队列和转录组数据分布差异引起的批次效应，并在十个乳腺癌患者队列中得到验证。最后，我们开发了一个用户友好的网络工具 (https://sujiezhulab.shinyapps.io/BRCA/) 来预测乳腺癌患者的亚型、治疗策略和预后状态。