Journal Article

DKFZ-2024-00274

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Detection of senescence using machine learning algorithms based on nuclear features.

Duran, I. ; Pombo, J. ; Sun, B. ; Gallage, S. U.DKFZ* ; Kudo, H. ; McHugh, D. ; Bousset, L. ; Barragan Avila, J. E.DKFZ* ; Forlano, R. ; Manousou, P. ; Heikenwalder, M.DKFZ* ; Withers, D. J. ; Vernia, S. ; Goldin, R. D. ; Gil, J.

2024
Nature Publishing Group UK [London]

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Please use a persistent id in citations: doi:10.1038/s41467-024-45421-w

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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Contributing Institute(s):

1. Chronische Entzündung und Krebs (F180)
2. Chronische Entzündung und Krebs (D440)

Research Program(s):

1. 314 - Immunologie und Krebs (POF4-314) (POF4-314)

Appears in the scientific report 2024

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