衰老
老化
细胞衰老
癌症
生物
细胞
癌症研究
生物信息学
细胞生物学
表型
基因
生物化学
遗传学
作者
Israel Duran,Joaquim Pombo,Bin Sun,Suchira Gallage,Hiromi Kudo,Domhnall McHugh,Laura Bousset,Jose Efren Barragan Avila,Roberta Forlano,Pinelopi Manousou,Mathias Heikenwälder,Dominic J. Withers,Santiago Vérnia,Robert Goldin,Jesús Gil
标识
DOI:10.1038/s41467-024-45421-w
摘要
Abstract Cellular senescence is a stress response with broad pathophysiological implications. Senotherapies can induce senescence to treat cancer or eliminate senescent cells to ameliorate ageing and age-related pathologies. However, the success of senotherapies is limited by the lack of reliable ways to identify senescence. Here, we use nuclear morphology features of senescent cells to devise machine-learning classifiers that accurately predict senescence induced by diverse stressors in different cell types and tissues. As a proof-of-principle, we use these senescence classifiers to characterise senolytics and to screen for drugs that selectively induce senescence in cancer cells but not normal cells. Moreover, a tissue senescence score served to assess the efficacy of senolytic drugs and identified senescence in mouse models of liver cancer initiation, ageing, and fibrosis, and in patients with fatty liver disease. Thus, senescence classifiers can help to detect pathophysiological senescence and to discover and validate potential senotherapies.
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