‘Epigenetic age’ may help predict lung cancer survival, influence treatment
Predicting survival among early-stage non-small cell lung cancer (NSCLC) may be improved by considering an individual’s “epigenetic age acceleration” (EAA)—whether their estimated biological age is older than their actual age—alongside traditional risk factors such as smoking history, according to a new study from Harvard T.H. Chan School of Public Health.
The study was published Oct. 16 in iScience. David Christiani, Elkan Blout Professor of Environmental Studies, was co-corresponding author. Other Harvard Chan co-authors included Mulong Du, department associate in the Department of Environmental Health, and Li Su, research specialist and director of the Christiani Lab.
Lung cancer, particularly NSCLC, is the leading cause of cancer-related deaths in the U.S. Survival rates for early-stage NSCLC are significantly lower than those of other common cancers, such as breast and prostate cancer. The new study looked at whether epigenetic age—a biomarker that reflects cumulative molecular changes influenced by genetic, environmental, and lifestyle factors—could be a useful tool in helping improve prognosis prediction in early-stage NSCLC and lead to more informed treatment decisions. Epigenetic age is estimated through data on DNA methylation, a type of DNA modification that changes how a gene behaves without changing its underlying sequence.
For the study, researchers analyzed blood DNA methylation from 75 participants with early-stage NSCLC enrolled in the Christiani Lab’s Boston Lung Cancer Study. The team estimated EAA among the participants using several different types of “epigenetic clocks”—tools that have been developed over the past decade or so to track DNA methylation. During the follow-up period, 63 of the patients died. Researchers found that, across all the clocks, EAA significantly predicted survival in early-stage NSCLC, with one clock—called GrimAge2—predicting with the highest level of accuracy. The co-authors noted that the clocks were more accurate at predicting survival than models based solely on factors such as participants’ smoking history or chronological age.
According to the co-authors, “these findings highlight the need for personalized prognostic models in NSCLC that account for both smoking history and epigenetic aging dynamics.”
