基于约束的建模预测低高分级浆液性卵巢癌的代谢特征
Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer
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影响因子:3.5
分区:生物学2区 / 数学与计算生物学2区
发表日期:2024 Aug 24
作者:
Kate E Meeson, Jean-Marc Schwartz
DOI:
10.1038/s41540-024-00418-5
摘要
卵巢癌是一种侵袭性强、多样性高的疾病，常伴有晚期诊断和化疗耐药。通过研究其代谢机制及特定途径的调控如何与个体表型相关，可以解释卵巢癌的临床特征。鉴于卵巢癌的异质性，已识别出五个不同亚型，每个亚型可能具有独特的代谢特征。为揭示代谢差异，限制性模型（CBM）成为一种强大的技术，支持整合‘组学’数据（如转录组学）。然而，许多CBM方法未能优先考虑准确的生长速率预测，且关于卵巢癌的全基因组规模研究很少。在此，开发了一种新颖的CBM方法，利用基因组规模模型Human1和通量平衡分析（FBA），结合体外生长速率、转录组数据和培养基条件，预测细胞的代谢行为。通过低级和高级卵巢癌亚型的预测，支持了其代谢差异，这些发现得到了Cancer Cell Line Encyclopaedia公开的CRISPR-Cas9数据和广泛的文献综述的支持。研究提出了导致侵袭性表型的代谢驱动因素，以及在低级别细胞系中增加耐药性的途径。利用实验基因依赖性数据，验证了五碳糖磷酸途径在低级别细胞生长中的关键作用，突显了该亚型潜在的脆弱性。
Abstract
Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of 'omics' data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.