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Results reveal diverse transcriptomic landscapes across ER modulator classes, including ER and interferon signaling shifts that mirror clinical patterns. Studies of multiple ESR1-mutant cell models identify cell-model engineering effects and high-confidence mutant-ER targets such as NPY1R.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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is EstroGene2.0 designed to help researchers do?","Question",{"text":63,"@type":64},"EstroGene2.0 supports investigation of breast cancer response and resistance to endocrine therapies by providing integrated, searchable multi-omic data, visualization, and metadata mining tools.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What dataset is included in EstroGene2.0?",{"text":68,"@type":64},"It incorporates multi-omic profiling from 361 experiments spanning 212 studies across 28 cell lines.",{"name":70,"@type":61,"acceptedAnswer":71},"What did the follow-up meta-analysis reveal?",{"text":72,"@type":64},"The meta-analysis identified a transcriptomic landscape and substantial diversity in response to different ER modulator classes, including transcriptomic alterations in endocrine-resistant models that reflect clinical 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\n[https://doi.org/10.1038/s41523-024-00709-4](https://doi.org/10.1038/s41523-024-00709-4)  \n\n| The EstroGene2.0 database for endocrine therapy response and resistance in breast cancer\u003Cbr> Check for updates |  |\n| --- | --- |\n| Zheqi Li 1,2,14, Fangyuan Chen 3,4,14, Li Chen5,14, Jiebin Liu4,6, Danielle Tseng4, Fazal Hadi7, Soleilmane Omarjee8, Kamal Kishore8, Joshua Kent8, Joanna Kirkpatrick8, Clive D’Santos8, Mandy Lawson7, Jason Gertz9, Matthew J. Sikora 10, Donald P. McDonnell11, Jason S. Carroll 8\u003Cbr>,\u003Cbr>Kornelia Polyak  1,2, Steffi Oesterreich 4,12 & Adrian V. Lee 4,12,13  |  |\n| Endocrine therapies targeting the estrogen receptor (ER/ESR1) are the cornerstone to treat ERpositive 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/](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 contradirectional 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. |  |\n| Breast cancer remains the most common cause of cancer-associated death amongwomen and is responsible for nearly15%ofall new cancer cases each year in the United States1. This disease presents as a heterogeneous entity, with various subtypes distinguished by unique molecular profiles, guiding different treatment strategies. Among these subtypes, estrogen receptorpositive (ER+) breast cancer, characterized by the presence of estrogen receptor (ER/ESR1), accounts for approximately 70% of cases2,3. Not surprisingly, targeted therapies which directly inhibit the transcriptional activity of ER, or which block the synthesis of estrogens have and will | continue to be the cornerstone of interventions used to treat this disease at all stages4–6.\u003Cbr>The Selective Estrogen Receptor Modulators (SERMs), like tamoxifen, function as competitive inhibitors ofestrogenbinding toERand also disrupt the integrity of the protein-protein interaction surfaces needed for coactivator recruitment7. Tamoxifen is used in combination with GnRH analogues (to disrupt estrogen biosynthesis) in premenopausal women with ERpositivebreast cancer5. In postmenopausal women with this disease subtype, aromatase inhibitors (AIs), such as letrozole and anastrozole, which inhibit |\n\n1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. 2Department of Medicine, Harvard Medical School, Boston, MA, USA. 3School of Medicine, Tsinghua University, Beijing, China. 4Women’s Cancer Research Center, UPMC Hillman Cancer Center, Pittsburgh, PA, USA. 5Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA. 6Medical Scientist Training Program, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. 7AstraZeneca, The Discovery Centre, Biomedical Campus, Cambridge, UK. 8Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK. 9De","cbCainqCAF9mB9cb","https://ap.wps.com/l/cbCainqCAF9mB9cb","pdf",3075200,17,"English","# Introduction\n## EstroGene2.0 overview and data integration\n## Meta-analysis findings on response diversity\n## Transcriptomic shifts in endocrine-resistant models\n## ESR1-mutant model insights and target identification","[{\"question\":\"What is EstroGene2.0 designed to help researchers do?\",\"answer\":\"EstroGene2.0 supports investigation of breast cancer response and resistance to endocrine therapies by providing integrated, searchable multi-omic data, visualization, and metadata mining tools.\"},{\"question\":\"What dataset is included in EstroGene2.0?\",\"answer\":\"It incorporates multi-omic profiling from 361 experiments spanning 212 studies across 28 cell lines.\"},{\"question\":\"What did the follow-up meta-analysis reveal?\",\"answer\":\"The meta-analysis identified a transcriptomic landscape and substantial diversity in response to different ER modulator classes, including transcriptomic alterations in endocrine-resistant models that reflect clinical patterns.\"}]","The EstroGene2.0 database for endocrine therapy response and resistance in breast cancer | PDF",1789985020,43]