转录组学彻底改变了生物医学研究并改进了乳腺癌亚型和诊断。然而，由于多种原因，其在临床实践中的更广泛使用受到阻碍，包括将转录组特征作为单样本预测因子的应用。在这里，我们提出了一种名为 EMBER 的嵌入方法，它创建了 11,000 个乳腺癌转录组的统一空间，并在单个样本的基础上预测转录组谱的表型。 EMBER 准确捕获五种分子亚型。雌激素受体信号传导、细胞增殖、DNA 修复和上皮间质转化等关键生物途径决定了样品在空间中的位置。我们在四个独立的患者队列中验证了 EMBER，并通过 POETIC 窗口试验的样本证明，它捕获了内分泌治疗的临床反应，并确定了雄激素受体信号传导增加和 TGFβ 信号传导减少作为内在治疗抵抗的潜在机制。具有直接临床重要性的是，我们表明基于 EMBER 的雌激素受体 (ER) 信号评分优于当前临床实践中用于选择内分泌治疗患者的基于免疫组织化学 (IHC) 的 ER 指数。因此，EMBER 提供了一种校准和参考工具，为使用 RNA-seq 作为 ER 乳腺癌的标准诊断和预测工具铺平了道路。© 2024。作者。

Transcriptomics has revolutionized biomedical research and refined breast cancer subtyping and diagnostics. However, wider use in clinical practice is hampered for a number of reasons including the application of transcriptomic signatures as single sample predictors. Here, we present an embedding approach called EMBER that creates a unified space of 11,000 breast cancer transcriptomes and predicts phenotypes of transcriptomic profiles on a single sample basis. EMBER accurately captures the five molecular subtypes. Key biological pathways, such as estrogen receptor signaling, cell proliferation, DNA repair, and epithelial-mesenchymal transition determine sample position in the space. We validate EMBER in four independent patient cohorts and show with samples from the window trial, POETIC, that it captures clinical responses to endocrine therapy and identifies increased androgen receptor signaling and decreased TGFβ signaling as potential mechanisms underlying intrinsic therapy resistance. Of direct clinical importance, we show that the EMBER-based estrogen receptor (ER) signaling score is superior to the immunohistochemistry (IHC) based ER index used in current clinical practice to select patients for endocrine therapy. As such, EMBER provides a calibration and reference tool that paves the way for using RNA-seq as a standard diagnostic and predictive tool for ER+ breast cancer.© 2024. The Author(s).