The Great Escape: the systems biology of endocrine resistance and lineage plasticity.

IF 4.6
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
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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.

大逃亡:内分泌抵抗和谱系可塑性的系统生物学。
内分泌疗法最初在激素驱动的癌症中强制谱系认同,但持续的激素抑制通常会通过谱系可塑性触发协调的系统水平的重新布线。以前,谱系可塑性被描述为一种离散的旁路机制,缺乏流体同一性的改变。在这里,我们将这一过程描述为一个渐进的、适应性的内分泌逃逸轨迹。我们描述了激素受体锚定身份程序的破坏如何为过渡到hr无关或神经内分泌或基底样状态创造了许可条件。接下来,我们总结了研究谱系可塑性的历史方法,如患者来源的类器官、基因工程小鼠模型(GEMMS)、谱系追踪和单细胞多组学,并讨论了新的系统生物学方法,以实现这些不稳定中间状态的早期检测和建模。最后,我们提出了人工智能和机器学习在解剖治疗诱导的身份转移和抵抗机制方面的前景和潜在用途。在谱系承诺之前拦截这些轨迹为精确的肿瘤干预提供了一个关键窗口。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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