Christo P C Dragnev, Zirui Fu, Sida Huang, Curtis J Perry, Adriana Kahn, Michaela A Dinan, Wei Shen Tan, Michael Leapman, David A Braun, Joshua Warrick, Himisha Beltran, Ping Mu
{"title":"The Great Escape: the systems biology of endocrine resistance and lineage plasticity.","authors":"Christo P C Dragnev, Zirui Fu, Sida Huang, Curtis J Perry, Adriana Kahn, Michaela A Dinan, Wei Shen Tan, Michael Leapman, David A Braun, Joshua Warrick, Himisha Beltran, Ping Mu","doi":"10.1530/ERC-26-0130","DOIUrl":null,"url":null,"abstract":"<p><p>Endocrine therapies initially enforce lineage identity in hormone-driven cancers, yet sustained hormonal suppression often triggers a coordinated systems-level rewiring through lineage plasticity. Previously, lineage plasticity has been characterized as a discrete bypass mechanism, which lacks fluidic identity changes. Here, we describe the process as a progressive, adaptive trajectory of endocrine escape. We describe how the disruption of hormone receptor-anchored identity programs creates permissive conditions for transitions into HR-indifferent or neuroendocrine- or basal-like states. Next, we summarize historical methodologies for investigating lineage plasticity, such as patient-derived organoids, genetically engineered mouse models (GEMMS), lineage tracing, and single-cell multi-omics, and discuss novel systems biology approaches to enable the early detection and modeling of these unstable intermediate states. Finally, we present promising and potential uses for artificial intelligence and machine learning for dissecting therapy-induced identity shifts and resistance mechanisms. Intercepting these trajectories before lineage commitment offers a critical window for precision oncology interventions.</p>","PeriodicalId":93989,"journal":{"name":"Endocrine-related cancer","volume":" ","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13545732/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Endocrine-related cancer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1530/ERC-26-0130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Endocrine therapies initially enforce lineage identity in hormone-driven cancers, yet sustained hormonal suppression often triggers a coordinated systems-level rewiring through lineage plasticity. Previously, lineage plasticity has been characterized as a discrete bypass mechanism, which lacks fluidic identity changes. Here, we describe the process as a progressive, adaptive trajectory of endocrine escape. We describe how the disruption of hormone receptor-anchored identity programs creates permissive conditions for transitions into HR-indifferent or neuroendocrine- or basal-like states. Next, we summarize historical methodologies for investigating lineage plasticity, such as patient-derived organoids, genetically engineered mouse models (GEMMS), lineage tracing, and single-cell multi-omics, and discuss novel systems biology approaches to enable the early detection and modeling of these unstable intermediate states. Finally, we present promising and potential uses for artificial intelligence and machine learning for dissecting therapy-induced identity shifts and resistance mechanisms. Intercepting these trajectories before lineage commitment offers a critical window for precision oncology interventions.