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Article Commentary

Adolescent and young adult cancer survivorship and aging: the next step to take?

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Article: 2234818 | Received 03 May 2023, Accepted 05 Jul 2023, Published online: 30 Aug 2023

Adolescents and young adults (AYA) with cancer form a distinct oncological population due to the unique spectrum of their cancer types, tumor biology, developmental life challenges, and clinical care needs that differ from children and older adults [Citation1]. Remarkably the causes and consequences of cancer at AYA age have long been understudied. Over 80% of AYA with cancer will survive >5 years. In the United States alone there are >633,000 survivors of AYA cancer [Citation2]. However, AYA cancer survivors are more than 10 times as likely to die compared to unaffected individuals of the same age, a risk that remains elevated at >20 years after diagnosis [Citation3]. This rapidly growing cohort will spend the remainder of their lives at risk for development of long-term and late effects. The challenge for this group is how to maintain this excellent initial survival rate and ensure the subsequent quality of their survival.

AYA are more likely than older patients to harbor a genetic vulnerability, bringing the emotional burden of living with a hereditary cancer syndrome and putting them at risk for the development of subsequent cancers [Citation4]. Nevertheless, the increasing incidence of cancer in AYA might predominantly be attributed to behavioral (diet, inactive lifestyle, HPV, smoking) and environmental risk factors (e.g. sun exposure, air pollution), and complex epigenetic interactions resulting from these factors. More recently social adversity was found to impact cancer incidence in AYA hypothesized to operate through various mechanisms including differential exposure to risk factors as well as differential vulnerability [Citation5]. Moreover, social adversity might also result in disparities in long-term and late consequences of cancer given the differences in support, access to care, co-morbidities, and adherence and response to treatment.

The majority of long-term and late effects observed in AYA cancer survivors are age-related health conditions (e.g. subsequent malignancies, cardiovascular disease, and infertility) that develop prematurely [Citation6], supporting the body of evidence that cancer and its treatment appear to accelerate the biological aging process. Interestingly, outside the context of cancer, it has been shown that increased levels of social adversity are also associated with accelerated aging across multiple outcomes [Citation7]. Given the developmental life phase of AYA cancer patients at the time of diagnosis and the changing stressful societal trends, it is extremely important to take social determinants of health (SDOH), including low socioeconomic status, minority status, adverse life events, adverse psychological states, and adverse lifestyle behaviors, into account when studying the impact of cancer and its treatment on aging processes. The interplay between cancer, its predisposition, treatment and SDOH probably accounts for a proportion of the tremendous variability in outcomes seen among certain subgroups of AYA cancer survivors, with those exposed to increased social adversity potentially being in double trouble as they might be particularly vulnerable to the harmful effects of cancer treatments.

The research field of Geroscience offers promising ways to study accelerated aging in cancer survivors, by trying to delay the whole ageing process to increase health span instead of treating each late effect (or age-related disease) separately [Citation8]. Remarkably, at present, Geroscience is focused on the older end of the lifespan, while ideally interventions should be applied while people are still young. Therefore, a paradigm shift is needed; however, the main obstacle is the absence of methods to quantify accelerated aging in young people. There are nine well-accepted hallmarks of biological aging, which comprise genomic instability and epigenetic alterations. Clonal hematopoiesis of indeterminate potential (CHIP) can be interpreted as marker of genomic instability, defined by the presence of a cancer-associated somatic mutation in the blood cells of people without a blood cancer or other known clonal disorder [Citation9]. A recent study from St Jude, in which deep sequencing of 39 clonal hematopoiesis (CH)-related genes was performed among 2860 long-term survivors of pediatric cancer (median follow-up time of 23.5 years), revealed mutations in 15% of the survivors versus 8.5% in community controls [Citation10]. Moreover, DNA methylation patterns change over an individual’s life and thereby form markers of epigenetic alterations, allowing the establishment of epigenetic clocks that estimate epigenetic age through regression of methylation ratios at predefined CpG sites on chronological age [Citation11]. In a small study, epigenetic age of young adult cancer survivors was found to be significantly older (measured epigenetic age > chronological age) and pace of aging significantly faster compared to similarly aged, noncancer comparators [Citation12]. Both CHIP and epigenetic age acceleration have been shown to correlate with various age-associated conditions and overall mortality and could be promising prognostic and predictive biomarkers for adverse health outcomes in AYA cancer survivors. We call for future research to investigate the extent to which cancer, its predisposition, cancer treatment as well as SDOH act as accelerants of biological ageing in AYA, thereby increasing the risk of long-term and late effects. These insights will promote the development of surveillance strategies and bespoke targeted personalized risk-directed interventions (e.g. pharmacological or behavioral) reversing, delaying or mitigating accelerated aging, thereby improving outcomes (e.g. cure, healthy lifespan and quality of life) and reducing health disparities and inequalities.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing not applicable – no new data generated

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