Lin Shijuan, Nguyen Lily L, McMellen Alexandra, Leibowitz Michael S, Davidson Natalie, Spinosa Daniel, Bitler Benjamin G
Divisions of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Colorado Denver, Anschutz Medical Campus, 12700 East 19th Avenue, MS 8613, Aurora, CO, 80045, USA.
Center for Cancer and Blood Disorders, Children's Hospital Colorado, Aurora, CO, USA.
Mol Diagn Ther. 2025 Mar;29(2):145-151. doi: 10.1007/s40291-024-00757-3. Epub 2024 Nov 18.
To better understand ovarian cancer lethality and treatment resistance, sophisticated computational approaches are required that address the complexity of the tumor microenvironment, genomic heterogeneity, and tumor evolution. The ovarian cancer tumor ecosystem consists of multiple tumors and cell types that support disease growth and progression. Over the last two decades, there has been a revolution in -omic methodologies to broadly define components and essential processes within the tumor microenvironment, including transcriptomics, metabolomics, proteomics, genome sequencing, and single-cell analyses. While most of these technologies comprehensively characterize a single biological process, there is a need to understand the biological and clinical impact of integrating multiple -omics platforms. Overall, multi-omics is an intriguing analytic framework that can better approximate biological complexity; however, data aggregation and integration pipelines are not yet sufficient to reliably glean insights that affect clinical outcomes.
为了更好地理解卵巢癌的致死性和治疗抗性，需要复杂的计算方法来应对肿瘤微环境的复杂性、基因组异质性和肿瘤进化。卵巢癌肿瘤生态系统由多种支持疾病生长和进展的肿瘤及细胞类型组成。在过去二十年中， -组学方法发生了一场革命，以广泛定义肿瘤微环境中的成分和基本过程，包括转录组学、代谢组学、蛋白质组学、基因组测序和单细胞分析。虽然这些技术大多全面表征单个生物学过程，但仍需要了解整合多个 -组学平台的生物学和临床影响。总体而言，多组学是一个引人入胜的分析框架，能够更好地逼近生物学复杂性；然而，数据汇总和整合流程尚不足以可靠地获取影响临床结果的见解。
