McEwen Abbye E, Tejura Malvika, Fayer Shawn, Starita Lea M, Fowler Douglas M
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
Nat Rev Genet. 2025 Jul 21. doi: 10.1038/s41576-025-00870-x.
The rapid expansion of clinical genetic testing has markedly improved the detection of genetic variants. However, most variants lack the evidence needed to classify them as pathogenic or benign, resulting in the accumulation of variants of uncertain significance that cannot be used to diagnose or guide treatment of disease. Moreover, targeted therapy for cancer treatment increasingly depends on correctly identifying oncogenic driver mutations, but the oncogenicity of many variants identified in tumours remains unclear. To address these challenges, efforts to classify variants are increasingly using multiplexed assays of variant effect (MAVEs), which are massively scaled experiments that can generate functional data for thousands of variants simultaneously. The rise of MAVEs is accompanied by better guidance on the use of MAVE data for classifying germline variants to aid their clinical implementation. Here, we overview MAVE technologies from their inception to their increased use in the clinic, including their roles in uncovering mechanisms for variant pathogenicity and guiding targeted therapy and drug development.
临床基因检测的迅速发展显著提高了基因变异的检测能力。然而,大多数变异缺乏将其分类为致病性或良性所需的证据,导致意义未明的变异不断积累,无法用于疾病的诊断或治疗指导。此外,癌症治疗的靶向治疗越来越依赖于正确识别致癌驱动突变,但肿瘤中鉴定出的许多变异的致癌性仍不清楚。为应对这些挑战,变异分类工作越来越多地采用变异效应多重分析(MAVEs),这是一种大规模实验,能够同时为数千个变异生成功能数据。MAVEs的兴起伴随着关于如何利用MAVE数据对种系变异进行分类以促进其临床应用的更好指导。在此,我们概述了MAVE技术从诞生到在临床中更多应用的过程,包括它们在揭示变异致病性机制以及指导靶向治疗和药物开发方面的作用。