Li Zheqi, Chen Fangyuan, Chen Li, Liu Jiebin, Tseng Danielle, Hadi Fazal, Omarjee Soleilmane, Kishore Kamal, Kent Joshua, Kirkpatrick Joanna, D'Santos Clive, Lawson Mandy, Gertz Jason, Sikora Matthew J, McDonnell Donald P, Carroll Jason S, Polyak Kornelia, Oesterreich Steffi, Lee Adrian V
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
Department of Medicine, Harvard Medical School, Boston, MA, USA.
NPJ Breast Cancer. 2024 Dec 19;10(1):106. doi: 10.1038/s41523-024-00709-4.
Endocrine therapies targeting the estrogen receptor (ER/ESR1) are the cornerstone to treat ER-positive breast cancers patients, but resistance often limits their effectiveness. Notable progress has been made although the fragmented way data is reported has reduced their potential impact. Here, we introduce EstroGene2.0, an expanded database of its precursor 1.0 version. EstroGene2.0 focusses on response and resistance to endocrine therapies in breast cancer models. Incorporating multi-omic profiling of 361 experiments from 212 studies across 28 cell lines, a user-friendly browser offers comprehensive data visualization and metadata mining capabilities ( https://estrogeneii.web.app/ ). Taking advantage of the harmonized data collection, our follow-up meta-analysis revealed transcriptomic landscape and substantial diversity in response to different classes of ER modulators. Endocrine-resistant models exhibit a spectrum of transcriptomic alterations including a contra-directional shift in ER and interferon signalings, which is recapitulated clinically. Dissecting multiple ESR1-mutant cell models revealed the different clinical relevance of cell model engineering and identified high-confidence mutant-ER targets, such as NPY1R. These examples demonstrate how EstroGene2.0 helps investigate breast cancer's response to endocrine therapies and explore resistance mechanisms.
针对雌激素受体（ER/ESR1）的内分泌疗法是治疗ER阳性乳腺癌患者的基石，但耐药性常常限制了它们的疗效。尽管数据报告方式零散降低了其潜在影响，但仍取得了显著进展。在此，我们推出EstroGene2.0，它是其前身1.0版本的扩展数据库。EstroGene2.0专注于乳腺癌模型中对内分泌疗法的反应和耐药性。该数据库整合了来自28种细胞系的212项研究中的361个实验的多组学分析结果，通过一个用户友好的浏览器提供全面的数据可视化和元数据挖掘功能（https://estrogeneii.web.app/）。利用统一的数据收集，我们的后续荟萃分析揭示了转录组格局以及对不同类别的ER调节剂反应的显著差异。内分泌耐药模型表现出一系列转录组改变，包括ER和干扰素信号的反向转变，这在临床上也有体现。对多个ESR1突变细胞模型的剖析揭示了细胞模型构建的不同临床相关性，并确定了高可信度的突变型ER靶点，如NPY1R。这些例子展示了EstroGene2.0如何有助于研究乳腺癌对内分泌疗法的反应并探索耐药机制。
