Cancer genomes are characterized by a considerable amount of modifications on various multi-omics levels, often caused by genomic and epigenomic instability. This cancer hallmark represents, e.g., epigenetic alterations in DNA methylation, histone remodeling, and non-coding RNA regulation. Understanding these patterns can give insights into tumor evolution, heterogeneity, and therapeutic resistance. However, it requires both robust data infrastructure and mechanistic insight.
To address the computational challenge of handling large-scale cancer genomic data, we developed TCGADownloadHelper. This streamlined pipeline simplifies data retrieval from The Cancer Genome Atlas (TCGA) via the GDC portal. Thus, transparent preprocessing of multimodal, patient-linked datasets is possible. This tool formed the foundation for our downstream analyses of DNA methylation data across TCGA lung cancer cohorts.
Building on these resources, we investigated epigenetic age acceleration (EAA) across the lung cancer cohorts TCGA-LUAD and TCGA-LUSC. Illumina 450K DNA methylation data was obtained from the GDC portal via the TCGADownloadHelper. The deviation between biological (epigenetic) and chronological age was estimated from DNA methylation patterns using established epigenetic clocks (Horvath, Zhang2019,...). We demonstrate that approximately two thirds of tumors exhibited accelerated epigenetic aging, while one third showed a ""rejuvenating"" effect. Interestingly, patients with rejuvenating EAA showed significantly lower overall survival in Kaplan-Meier analyses. EAA varied by sex, tumor stage, and cancer type, highlighting the importance of patient-specific factors in the epigenetic age.
Together, these findings introduce epigenetic aging signatures as a promising stratification biomarker and motivate joined mechanistic studies on genomic and epigenomic instability to characterize actionable targets in precision oncology.

Co-authors: Michael Seifert, Zholdas Buribayev, Olaf Wolkenhauer, Markus Wolfien