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与机器学习相结合的微滴微流控技术揭示了脂肪来源干细胞如何调节雌激素受体阳性乳腺癌中的内分泌反应和肿瘤异质性。

Droplet microfluidics integrated with machine learning reveals how adipose-derived stem cells modulate endocrine response and tumor heterogeneity in ER breast cancer.

作者信息

Ortega Quesada Braulio Andrés, Chauvin Calley, Martin Elizabeth, Melvin Adam

机构信息

Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC 29634, USA.

Department of Biological and Agricultural Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.

出版信息

Lab Chip. 2025 Jun 19. doi: 10.1039/d5lc00320b.

DOI:10.1039/d5lc00320b
PMID:40534514
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12177710/
Abstract

Approximately 70% of breast cancer (BC) diagnoses are estrogen receptor positive (ER) with ∼40% of ER BC patients presenting resistance to endocrine therapy (ET). Recent studies identify the tumor microenvironment (TME) as having a key role in endocrine resistance in which adipose-derived stem cells (ASCs) play an essential role in cancer progression. Prior studies have indicated that ASC characteristics such as age and BMI may play a role in cancer progression. Unfortunately, most studies on ASC-BC cross talk have relied on established two-dimensional (2D) culture systems or the use of conditioned media that cannot replicate the complexity of the three-dimensional (3D) environment. This study used a microfluidic droplet trapping array and thiol-acrylate (TA) hydrogel scaffold to co-culture ER BC cells and ASCs as individual 3D spheroids (single culture) or organoids (co-culture) in a single device. Endocrine response was interrogated in both spheroids and organoids through the evaluation of proliferation following treatment with the selective estrogen receptor degrader (SERD) fulvestrant (ICI 182 780) followed by 17β-estradiol (E2). Terminal immunostaining for the proliferation marker (Ki67) was performed to evaluate how the presence of ASCs from different donor backgrounds (age and BMI) can modulate endocrine response. Results demonstrated that organoids containing two model ER cell lines (MCF7 and ZR-75) exhibited enhanced Ki67 expression even in the presence of ICI, suggesting a role for ASCs in cancer progression and endocrine resistance. Data clustering and classification algorithms were employed to categorize cellular behavior based on Ki67 expression and spheroid area to identify distinct clusters with high (H), intermediate (I), and low (L) Ki67 expression. Machine learning further stratified the data and revealed the direct effects of ASCs on Ki67 expression as well as how donor-specific features influenced ASC-driven changes in the TME. Notably, ASCs from an aged donor (>50) with lower BMI (<30) were able to enhance Ki67 expression even in the presence of endocrine therapy, while younger (<40) donors substantially enhanced Ki67 expression in the absence of both ICI and E2. Together, this study demonstrates the utility/development of a biomimetic culture system that recreates heterogenic 3D ER tumors through the co-culture of cancer cells with ASCs. This system provided insight into cell-extrinsic factors that govern ER breast cancer heterogeneity and response to endocrine therapy can be gained.

摘要

大约70%的乳腺癌(BC)诊断为雌激素受体阳性(ER),约40%的ER BC患者对内分泌治疗(ET)存在耐药性。最近的研究表明肿瘤微环境(TME)在内分泌耐药中起关键作用,其中脂肪来源的干细胞(ASC)在癌症进展中起重要作用。先前的研究表明,ASC的特征如年龄和体重指数(BMI)可能在癌症进展中起作用。不幸的是,大多数关于ASC与BC相互作用的研究依赖于已建立的二维(2D)培养系统或使用条件培养基,而这些无法复制三维(3D)环境的复杂性。本研究使用微流控液滴捕获阵列和硫醇 - 丙烯酸酯(TA)水凝胶支架,在单个装置中将ER BC细胞和ASC作为单个3D球体(单培养)或类器官(共培养)进行共培养。通过评估用选择性雌激素受体降解剂(SERD)氟维司群(ICI 182 780)处理后再用17β - 雌二醇(E2)处理后的增殖情况,在球体和类器官中研究内分泌反应。对增殖标志物(Ki67)进行终末免疫染色,以评估来自不同供体背景(年龄和BMI)的ASC的存在如何调节内分泌反应。结果表明,含有两种模型ER细胞系(MCF7和ZR - 75)的类器官即使在存在ICI的情况下也表现出增强的Ki67表达,表明ASC在癌症进展和内分泌耐药中起作用。采用数据聚类和分类算法,根据Ki67表达和球体面积对细胞行为进行分类,以识别具有高(H)、中(I)和低(L)Ki67表达的不同簇。机器学习进一步对数据进行分层,揭示了ASC对Ki67表达的直接影响以及供体特异性特征如何影响ASC驱动的TME变化。值得注意的是,来自年龄较大(>50岁)且BMI较低(<30)的供体的ASC即使在内分泌治疗存在的情况下也能增强Ki67表达,而较年轻(<40岁)的供体在不存在ICI和E2的情况下显著增强Ki67表达。总之,本研究证明了一种仿生培养系统的实用性/开发,该系统通过癌细胞与ASC的共培养重建异质性3D ER肿瘤。该系统为控制ER乳腺癌异质性和内分泌治疗反应的细胞外因素提供了深入了解。

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