Authors
Fernandez-Rebollo, I., Digilio, A., Oikonomou, A., Trastulla, L., Esteller, M., Iorio, F.
Abstract
Epigenetic clocks estimate biological age from DNA methylation patterns but perform poorly in cancer due to extensive epigenetic reprogramming, limiting the study of ageing in tumour biology.Here, we develop GepiClock, an epigenetic clock trained on DNA methylation data from 32 cancer types in The Cancer Genome Atlas. Based on 4,862 CpG sites, GepiClock accurately predicts age across both tumour and normal samples, indicating that core ageing-associatedmethylation programmes remain detectable despite malignant transformation.Applying GepiClock to molecularly profiled cancer cell lines with matched drug response and CRISPR screening data revealed age-associated vulnerabilities. Younger-predicted cell lines were more sensitive to mTOR, MEK1/2 and HSP90 inhibitors, whereas older lines showed increased sensitivity to AKT and PI3K inhibitors. Additional cancer-type-specific patterns and age-associated genetic dependencies were identified.These findings establish a framework to quantify biological age in cancer and link ageing-associated states to therapeutic vulnerabilities.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 27 Jun 2026.
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