Patient age at diagnosis influences the mutational landscape and clinical prognosis of tumors.
Major Finding: Patient age at diagnosis influences the mutational landscape and clinical prognosis of tumors.
Concept: Oncogenic processes differ in activity in age-stratified patients, underlying clinical outcomes.
Impact: This study reveals genetic alterations with prognostic value in specific contexts of patient age.
Cancer is commonly described as a disease of aging, as the risk of cancer increases with age in the majority of tumor types. However, processes underlying the complex relationship between cancer and aging are not well understood. To explore the mechanisms by which age influences the molecular and clinical features of cancer, Li and colleagues performed a comprehensive pan-cancer analysis of age-associated molecular differences in the somatic mutational landscape of over 20,000 tumors from The Cancer Genome Atlas, the International Cancer Genome Consortium/TCGA Pan-Cancer Analysis of Whole Genomes, and the AACR Project Genomics Evidence Neoplasia Information Exchange databases. Through univariate analysis followed by multivariate modeling, associations were identified between genetic properties of tumors and the recorded age of patients at diagnosis, considering associations present in pan-cancer and tumor type–specific analyses. Age was positively associated with measures of mutation accumulation, such as single-nucleotide variant (SNV) density and genomic instability, with mutations accumulating at an estimated 0.077 mutations per megabase per year. Analysis of mutational signatures to evaluate the relative activity of various oncogenic processes associated with age in specific tumor types suggested that different mutational processes were preferentially active in age-stratified patients, as exemplified in melanoma, in which younger versus older patients frequently involved UV damage or DNA replication errors, respectively. Age-associated copy-number aberrations (CNA) in cancer driver genes were associated not only with changes in mRNA abundance but also with patient survival, as evidenced by the prognostic value of the loss of the tumor suppressor gene SUFU in younger but not older patients with glioblastoma. In addition to age-associated CNAs, specific SNVs associated with age influenced mRNA abundance and altered survival outcome, as observed with SNVs in the tumor suppressor gene ATRX, which was associated with improved survival in older patients but reduced survival in younger patients with low-grade glioma. In summary, this work characterizes the impact of age on the mutational landscape of cancer, highlighting molecular bases of age-related cancer health disparities. ■
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