AS701 - Aging Effects of Cancer Treatment: The Role of the Epigenetic Clock

Investigator Names and Contact Information

Elizabeth Feliciano (elizabeth.m.cespedes@kp.org), Alexandra Binder (ambinder@hawaii.edu)

Introduction/Intent

By 2030, there will be 15 million cancer survivors in the US aged 60 or older.¹ While cancer treatments save lives, they also put survivors at premature risk for aging-related conditions.^2-5 Long-term follow-up studies of childhood cancer survivors provide compelling evidence that cancer accelerates aging-related declines in physical function and increases in chronic disease risk: by age 50, more than half of childhood survivors have one or more disabling or life-threatening conditions, twice the rate of sibling controls.6 To date, no study has had repeated measures of aging outcomes in long-term survivors of adult cancer. Thus, NCI is calling for research on the “effects of cancer and cancer treatment on aging trajectories,” “identification of cancer survivors at risk for an ‘accelerated aging’ phenotype” and “examination of the effects of specific cancer treatments on aging biology that may alter aging trajectories or aging outcomes.” To inform treatment decisions and survivorship care for a growing population of older survivors, we must understand how cancer treatments alter biologic and functional age, identify clinical biomarkers and elucidate underlying mechanisms. Epigenetic clocks, which predict chronologic or phenotypic age based on specific patterns of DNA methylation (DNAm), are well-validated markers of biologic aging. Epigenetic age acceleration (AgeAccel; DNAm-estimated age adjusting for chronologic age) has been consistently associated with morbidity and mortality across cohorts and with reduced physical performance and increased frailty in the elderly. Epigenetic age estimates are also responsive to diet, body size, and physical activity, suggesting AgeAccel can track changes over time. Emerging data suggest that cancer treatments alter rates of biologic aging: breast cancer treatment has been associated with marked increases in AgeAccel in small samples with short follow-up,⁷ but no prior study has linked longitudinal AgeAccel with aging outcomes in cancer survivors and age-matched controls. This study will examine how cancer and its treatments alter long-term trajectories of physical function and morbidity in older survivors, and if increased rates of epigenetic aging predict these phenotypic changes. Our long-term goal is to improve risk stratification and treatment selection for older patients and inform interventions (lifestyle or senolytic drugs) to mitigate the aging effects of cancer treatment. Our hypothesis is that the type and intensity of cancer treatment alters the rate of epigenetic AgeAccel in cancer survivors relative to women without a cancer history, which then manifests in an accelerated aging phenotype. Our efficient design combines three decades of clinical, self-reported, and physiological data from postmenopausal women in the Women’s Health Initiative (WHI). To examine how cancer treatment type and intensity alters long-term trajectories of functional aging, we will leverage annual assessments of functional status (RAND SF-36 and basic and instrumental activities of daily living [IADLs]) available in 16,138 cancer survivors matched by age to cancer-free controls. We will measure treatment by modality (surgery vs. chemoradiotherapy) and using MAX2,⁸ a validated toxicity index that allows comparisons across different chemotherapy regimens. To estimate the rate of biologic aging, we will assay AgeAccel at multiple time points in a subset of n=475 breast cancer survivors matched by age to cancer-free controls. Objective physical performance testing will also be evaluated for this subset. These unprecedented longitudinal data allow us to examine the following specific aims; in each, we will examine heterogeneity by cancer stage, treatment type and intensity, and time since diagnosis:

Aim 1: Characterize long-term trajectories of physical function (accelerated decline v. resilience) in cancer survivors and test whether they are modified by the intensity of cancer treatment. (n=16,138 cancer survivors plus age-matched controls). Hypothesis: Rates of decline in physical function and IADL scores for cancer survivors (vs. controls) will accelerate after cancer diagnosis; recipients of intense treatments (higher MAX2) will experience greater accelerations in functional decline.

Aim 2: Examine whether specific cancer treatments increase the rate of epigenetic aging from pre- to post-diagnosis. (n=475 breast cancer survivors plus matched controls with 1+ pre- and 1+ post-diagnosis samples) Hypothesis: Breast cancer survivors will have greater increases in the rate of AgeAccel from pre- to post-diagnosis (vs. controls), with the largest increases in those who received radiotherapy plus anthracyclines.

Aim 3: Test whether the rate of epigenetic aging continues to accelerate in the later post-diagnosis period and whether post-diagnosis increases in epigenetic aging are associated with accelerated declines in physical performance and function. (n=140 breast cancer survivors plus age-matched controls with 2+ post-diagnosis samples) Hypothesis: Increases in the rate of AgeAccel after diagnosis will be associated with faster declines in objectively measured physical performance and self-reported physical function compared to age-matched controls, with the fastest declines in those who received radiotherapy plus anthracyclines.

Cancer increases survivors’ risk of aging-related conditions, but factors affecting the severity of this aging, the extent of deficits, and underlying mechanisms remain unknown. Our study will examine how cancer treatments Feliciano & Binder: AS701 – Cancer treatment and trajectories of epigenetic and functional aging in long-term survivors alter long-term trajectories of functional aging and rates of biologic aging. Epigenetic clocks could be used to risk stratify aging cancer patients to prevent functional decline, but also provide a potential mechanism by which cancer treatment could result in an ‘accelerated aging’ phenotype. Results will guide treatment selection and inform interventions to maximize the health span of the rapidly growing population of older cancer patients.