Multidimensional hydrogel models reveal endothelial network angiocrine signals increase glioblastoma cell number, invasion, and temozolomide resistance.

IF 1.5 4区 生物学 Q4 CELL BIOLOGY Integrative Biology Pub Date : 2020-06-19 DOI:10.1093/intbio/zyaa010
Mai T Ngo, Elijah Karvelis, Brendan A C Harley
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引用次数: 12

Abstract

Glioblastoma (GBM) is the most common primary malignant brain tumor. The tissue microenvironment adjacent to vasculature, termed the perivascular niche, has been implicated in promoting biological processes involved in glioblastoma progression such as invasion, proliferation, and therapeutic resistance. However, the exact nature of the cues that support tumor cell aggression in this niche is largely unknown. Soluble angiocrine factors secreted by tumor-associated vasculature have been shown to support such behaviors in other cancer types. Here, we exploit macroscopic and microfluidic gelatin hydrogel platforms to profile angiocrine factors secreted by self-assembled endothelial networks and evaluate their relevance to glioblastoma biology. Aggregate angiocrine factors support increases in U87-MG cell number, migration, and therapeutic resistance to temozolomide. We also identify a novel role for TIMP1 in facilitating glioblastoma tumor cell migration. Overall, this work highlights the use of multidimensional hydrogel models to evaluate the role of angiocrine signals in glioblastoma progression.

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多维水凝胶模型显示内皮网络血管分泌信号增加胶质母细胞瘤细胞数量、侵袭和替莫唑胺耐药性。
胶质母细胞瘤是最常见的原发性恶性脑肿瘤。血管周围的组织微环境被称为血管周围生态位,与胶质母细胞瘤的侵袭、增殖和治疗抵抗等生物过程有关。然而,在这个生态位中支持肿瘤细胞攻击的线索的确切性质在很大程度上是未知的。由肿瘤相关脉管系统分泌的可溶性血管分泌因子已被证明在其他类型的癌症中支持这种行为。在这里,我们利用宏观和微流体明胶水凝胶平台来分析自组装内皮网络分泌的血管分泌因子,并评估它们与胶质母细胞瘤生物学的相关性。聚合血管分泌因子支持U87-MG细胞数量、迁移和替莫唑胺治疗耐药性的增加。我们还发现TIMP1在促进胶质母细胞瘤肿瘤细胞迁移中的新作用。总的来说,这项工作强调了使用多维水凝胶模型来评估血管分泌信号在胶质母细胞瘤进展中的作用。
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来源期刊
Integrative Biology
Integrative Biology 生物-细胞生物学
CiteScore
4.90
自引率
0.00%
发文量
15
审稿时长
1 months
期刊介绍: Integrative Biology publishes original biological research based on innovative experimental and theoretical methodologies that answer biological questions. The journal is multi- and inter-disciplinary, calling upon expertise and technologies from the physical sciences, engineering, computation, imaging, and mathematics to address critical questions in biological systems. Research using experimental or computational quantitative technologies to characterise biological systems at the molecular, cellular, tissue and population levels is welcomed. Of particular interest are submissions contributing to quantitative understanding of how component properties at one level in the dimensional scale (nano to micro) determine system behaviour at a higher level of complexity. Studies of synthetic systems, whether used to elucidate fundamental principles of biological function or as the basis for novel applications are also of interest.
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