Cancer: The aging epigenome
Age is a prominent risk factor for most types of cancer, including breast, lung and colon cancers, which each have a large impact on public health (de Magalhães, 2013). Cancer risk increases with age, in part, because genetic mutations that arise from DNA replication errors and exposure to environmental carcinogens accumulate as we get older (Tomasetti et al., 2017).
Aging also alters the epigenome, the chemical marks spread across DNA that help switch genes on or off by altering how the genome is packaged. For instance, the addition of a methyl group to DNA can play a role in compressing the nearby DNA sequence so it can no longer be accessed by the cell’s machinery. Epigenetic modifications, including DNA methylation, have also been shown to contribute to the development of cancer (Flavahan et al., 2017; Saghafinia et al., 2018). However, the potential impact of age-related epigenetic changes on cancer development has not been fully characterized.
Previous studies have identified specific DNA methylation sites that are associated with age (Horvath and Raj, 2018). Researchers have developed algorithms, called ‘epigenetic clocks’, that use data from tens to hundreds of these methylation sites to estimate an individual’s ‘epigenetic age’. This includes the Horvath clock which predicts age using DNA methylation data from any tissue type (Horvath, 2013), and the Hannum clock which was designed to use data from blood cells (Hannum et al., 2013).
It has been hypothesized that people whose epigenetic age is greater than their age in years – a phenomenon known as accelerated aging – may be at higher risk of age-related diseases, including cancer (Yu et al., 2020). However, previous studies linking accelerated epigenetic aging and cancer have produced mixed results. Now, in eLife, Fernanda Morales Berstein from the University of Bristol and co-workers (who are based at various institutes in the United Kingdom, the United States, Greece and Australia) report how they tackled this question using a different approach to most prior studies called Mendelian randomization (Morales Berstein et al., 2022). Instead of associating a person’s risk of cancer with epigenetic clock estimates, they correlated it against genetic variations that are known to influence these algorithms.
First, the team examined results from a previously conducted genome-wide association study which had analyzed the DNA of over 34,000 individuals to identify genetic variations that influence epigenetic clocks (McCartney et al., 2021). They used these results to select specific variants that predict the epigenetic age values measured by four common clocks (Horvath, Hannum, PhenoAge and GrimAge).
Next, Morales Berstein et al. used the Mendelian randomization method to find out if the variants that predict accelerated aging also affect the risk of several different types of cancer (breast, prostate, ovarian, colorectal and lung cancer). To do this the team obtained data from several large genome-wide association studies that had searched the genome of individuals for differences that predict cancer status; genetic variations related to the aging clocks were then extracted to see if they were also associated with an increased risk of cancer.
The results of Morales Berstein et al. did not show many clear relationships between the epigenetic aging clocks and risk for the various types of cancer studied. The most promising finding was an association between the GrimAge clock and colorectal cancer. The GrimAge clock was not designed to predict age alone, but also reflects the effects of smoking and other mortality-related epigenetic features (Lu et al., 2019). Thus, the interpretation of this association is not straightforward, as this clock may capture the effects of environmental or lifestyle factors on the epigenome.
While the Morales Berstein et al. study did not show pervasive effects of epigenetic aging on cancer risk, their work is a critical contribution to cancer susceptibility research, as they have addressed an important question using rigorous methods, including Mendelian randomization. The primary strength of studies that use this approach is that they are less prone to certain types of biases that can affect observational research, such as confounding and reverse causation. Furthermore, it is important to acknowledge that epigenetic clocks have largely been developed based on how aging affects DNA methylation in blood cells. Much less is known regarding aging and epigenetics in other tissue types, including those prone to cancer, such as the ones examined by Morales Berstein et al.
Future studies will likely use Mendelian randomization to address similar hypotheses for additional cancer types and a wider variety of epigenetic aging algorithms. As the size of genome-wide association studies increase and more clock-related genetic variants are discovered, this approach will gain more power to detect the effects of epigenetic aging on cancer and other age-related diseases.
References
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How ageing processes influence cancerNature Reviews Cancer 13:357–365.https://doi.org/10.1038/nrc3497
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DNA methylation-based biomarkers and the epigenetic clock theory of ageingNature Reviews Genetics 19:371–384.https://doi.org/10.1038/s41576-018-0004-3
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- Version of Record published: April 28, 2022 (version 1)
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Background:
Many genes associated with asthma explain only a fraction of its heritability. Most genome-wide association studies (GWASs) used a broad definition of 'doctor-diagnosed asthma', thereby diluting genetic signals by not considering asthma heterogeneity. The objective of our study was to identify genetic associates of childhood wheezing phenotypes.
Methods:
We conducted a novel multivariate GWAS meta-analysis of wheezing phenotypes jointly derived using unbiased analysis of data collected from birth to 18 years in 9,568 individuals from five UK birth-cohorts.
Results:
44 independent SNPs were associated with early-onset persistent, 25 with preschool remitting, 33 with mid-childhood remitting and 32 with late-onset wheeze. We identified a novel locus on chr9q21.13 (close to annexin 1 (ANXA1), p<6.7×10-9), associated exclusively with early-onset persistent wheeze. We identified rs75260654 as the most likely causative single nucleotide polymorphism (SNP) using Promoter Capture Hi-C loops, and then showed that the risk allele (T) confers a reduction in ANXA1 expression. Finally, in a murine model of house dust mite (HDM)-induced allergic airway disease, we demonstrated that anxa1 protein expression increased and anxa1 mRNA was significantly induced in lung tissue following HDM exposure. Using anxa1-/- deficient mice, we showed that loss of anxa1 results in heightened airway hyperreactivity and Th2 inflammation upon allergen challenge.
Conclusions:
Targeting this pathway in persistent disease may represent an exciting therapeutic prospect.
Funding:
UK Medical Research Council Programme Grant MR/S025340/1 and the Wellcome Trust Strategic Award (108,818/15/Z) provided most of the funding for this study.