Genome-wide association studies identify novel genetic loci for epigenetic age acceleration among survivors of childhood cancer

Map of epigenetic age acceleration: A worldwide analysis

We present a systematic analysis of epigenetic age acceleration based on by far the largest collection of publicly available DNA methylation data for healthy samples (93 datasets, 23 K samples), focusing on the geographic (25 countries) and ethnic (31 ethnicities) aspects around the world. We employed the most popular epigenetic tools for assessing age acceleration and examined their quality metrics and ability to extrapolate to epigenetic data from different tissue types and age ranges different from the training data of these models. In most cases, the models proved to be inconsistent with each other and showed different signs of age acceleration, with the PhenoAge model tending to systematically underestimate and different versions of the GrimAge model tending to systematically overestimate the age prediction of healthy subjects. Referring to data availability and consistency, most countries and populations are still not represented in GEO, moreover, different datasets use different criteria for determining healthy controls. Because of this, it is difficult to fully isolate the contribution of "geography/environment", "ethnicity" and "healthiness" to epigenetic age acceleration. Among the explored metrics, only the DunedinPACE, which measures aging rate, appears to adequately reflect the standard of living and socioeconomic indicators in countries, although it has a limited application to blood methylation data only. Invariably, by epigenetic age acceleration, males age faster than females in most of the studied countries and populations.

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

Race and Ethnicity, Socioeconomic Factors, and Epigenetic Age Acceleration in Survivors of Childhood Cancer

Importance

Current research in epigenetic age acceleration (EAA) is limited to non-Hispanic White individuals. It is imperative to improve inclusivity by considering racial and ethnic minorities in EAA research.

Objective

To compare non-Hispanic Black with non-Hispanic White survivors of childhood cancer by examining the associations of EAA with cancer treatment exposures, potential racial and ethnic disparity in EAA, and mediating roles of social determinants of health (SDOH).

Design, Setting, and Participants

In this cross-sectional study, participants were from the St Jude Lifetime Cohort, which was initiated in 2007 with ongoing follow-up. Eligible participants included non-Hispanic Black and non-Hispanic White survivors of childhood cancer treated at St Jude Children's Research Hospital between 1962 and 2012 who had DNA methylation data. Data analysis was conducted from February 2023 to May 2024.

Exposure

Three treatment exposures for childhood cancer (chest radiotherapy, alkylating agents, and epipodophyllotoxin).

Main Outcomes and Measures

DNA methylation was generated from peripheral blood mononuclear cell-derived DNA. EAA was calculated as residuals from regressing Levine or Horvath epigenetic age on chronological age. SDOH included educational attainment, annual personal income, and the socioeconomic area deprivation index (ADI). General linear models evaluated cross-sectional associations of EAA with race and ethnicity (non-Hispanic Black and non-Hispanic White) and/or SDOH, adjusting for sex, body mass index, smoking, and cancer treatments. Adjusted least square means (ALSM) of EAA were calculated for group comparisons. Mediation analysis treated SDOH as mediators with average causal mediation effect (ACME) calculated for the association of EAA with race and ethnicity.

Results

Among a total of 1706 survivors including 230 non-Hispanic Black survivors (median [IQR] age at diagnosis, 9.5 [4.3-14.3] years; 103 male [44.8%] and 127 female [55.2%]) and 1476 non-Hispanic White survivors (median [IQR] age at diagnosis, 9.3 [3.9-14.6] years; 766 male [51.9%] and 710 female [48.1%]), EAA was significantly greater among non-Hispanic Black survivors (ALSM = 1.41; 95% CI, 0.66 to 2.16) than non-Hispanic White survivors (ALSM = 0.47; 95% CI, 0.12 to 0.81). Among non-Hispanic Black survivors, EAA was significantly increased among those exposed to chest radiotherapy (ALSM = 2.82; 95% CI, 1.37 to 4.26) vs those unexposed (ALSM = 0.46; 95% CI, -0.60 to 1.51), among those exposed to alkylating agents (ALSM = 2.33; 95% CI, 1.21 to 3.45) vs those unexposed (ALSM = 0.95; 95% CI, -0.38 to 2.27), and among those exposed to epipodophyllotoxins (ALSM = 2.83; 95% CI, 1.27 to 4.40) vs those unexposed (ALSM = 0.44; 95% CI, -0.52 to 1.40). The association of EAA with epipodophyllotoxins differed by race and ethnicity (β for non-Hispanic Black survivors, 2.39 years; 95% CI, 0.74 to 4.04 years; β for non-Hispanic White survivors, 0.68; 95% CI, 0.05 to 1.31 years) and the difference was significant (1.77 years; 95% CI, 0.01 to 3.53 years; P for interaction = .049). Racial and ethnic disparities in EAA were mediated by educational attainment (

Conclusions and Relevance

In this cross-sectional study of childhood cancer survivors, race and ethnicity moderated the association of EAA with epipodophyllotoxin exposure and racial and ethnic differences in EAA were partially mediated by educational attainment and ADI, indicating differential treatment toxic effects by race and ethnicity. These findings suggest that improving social support systems may mitigate socioeconomic disadvantages associated with even greater accelerated aging and reduce health disparities among childhood cancer survivors.

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Key Words Collapse

MESH Headings

• Humans
• Female
• Male
• Cross-Sectional Studies
• Cancer Survivors/statistics & numerical data
• Child
• Socioeconomic Factors
• Epigenesis, Genetic
• Neoplasms/genetics
• Neoplasms/ethnology
• Adolescent
• White People/statistics & numerical data
• White People/genetics
• Black or African American/statistics & numerical data
• Black or African American/genetics
• DNA Methylation
• Adult
• Ethnicity/statistics & numerical data
• Social Determinants of Health/statistics & numerical data

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Grants

• P30 CA021765 NCI NIH HHS
• U01 CA195547 NCI NIH HHS
• U24 CA055727 NCI NIH HHS

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Affiliation(s)

Cheng Chen The Fourth Affiliated Hospital of Soochow University, SuZhou, Jiangsu, China State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Noel-Marie Plonski Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Qian Dong Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Nan Song College of Pharmacy, Chungbuk National University, Cheongju, Korea
Xijun Zhang Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Hemang M. Parikh Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa
Emily R. Finch Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
John Easton Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, Tennessee
Heather L. Mulder Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, Tennessee
Emily Walker Hartwell Center, St Jude Children’s Research Hospital, Memphis, Tennessee
Geoffrey Neale Hartwell Center, St Jude Children’s Research Hospital, Memphis, Tennessee
Yue Pan Department of Biostatistics, St Jude Children’s Research Hospital, Memphis, Tennessee
Qian Li Department of Biostatistics, St Jude Children’s Research Hospital, Memphis, Tennessee
Jinghui Zhang Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, Tennessee
Kevin Krull Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee Department of Psychology, St Jude Children’s Research Hospital, Memphis, Tennessee
Leslie L. Robison Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Gregory T. Armstrong Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Yutaka Yasui Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Kirsten K. Ness Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Melissa M. Hudson Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee Department of Oncology, St Jude Children’s Research Hospital, Memphis, Tennessee
Hui Wang State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
I-Chan Huang Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Zhaoming Wang Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, Tennessee

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Health-related quality of life and DNA methylation-based aging biomarkers among survivors of childhood cancer

BACKGROUND

Childhood cancer survivors are at high risk for morbidity and mortality and poor patient-reported outcomes, typically health-related quality of life (HRQOL). However, associations between DNA methylation-based aging biomarkers and HRQOL have not been evaluated.

METHODS

DNA methylation was generated with Infinium EPIC BeadChip on blood-derived DNA (median for age at blood draw = 34.5 years, range = 18.5-66.6 years), and HRQOL was assessed with age at survey (mean = 32.3 years, range = 18.4-64.5 years) from 2206 survivors in the St Jude Lifetime Cohort. DNA methylation-based aging biomarkers, including epigenetic age using multiple clocks (eg, GrimAge) and others (eg, DNAmB2M: beta-2-microglobulin; DNAmADM: adrenomedullin), were derived from the DNAm Age Calculator (https://dnamage.genetics.ucla.edu). HRQOL was assessed using the Medical Outcomes Study 36-Item Short-Form Health Survey to capture 8 domains and physical and mental component summaries. General linear models evaluated associations between HRQOL and epigenetic age acceleration (EAA; eg, EAA_GrimAge) or other age-adjusted DNA methylation-based biomarkers (eg, ageadj_DNAmB2M) after adjusting for age at blood draw, sex, cancer treatments, and DNA methylation-based surrogate for smoking pack-years. All P values were 2-sided.

RESULTS

Worse HRQOL was associated with greater EAA_GrimAge (physical component summaries: β = -0.18 years, 95% confidence interval [CI] = -0.251 to -0.11 years; P = 1.85 × 10-5; and 4 individual HRQOL domains), followed by ageadj_DNAmB2M (physical component summaries: β = -0.08 years, 95% CI = -0.124 to -0.037 years; P = .003; and 3 individual HRQOL domains) and ageadj_DNAmADM (physical component summaries: β = -0.082 years, 95% CI = -0.125 to -0.039 years; P = .002; and 2 HRQOL domains). EAA_Hannum (Hannum clock) was not associated with any HRQOL.

CONCLUSIONS

Overall and domain-specific measures of HRQOL are associated with DNA methylation measures of biological aging. Future longitudinal studies should test biological aging as a potential mechanism underlying the association between poor HRQOL and increased risk of clinically assessed adverse health outcomes.

Conserved epigenetic hallmarks of T cell aging during immunity and malignancy

Chronological aging correlates with epigenetic modifications at specific loci, calibrated to species lifespan. Such 'epigenetic clocks' appear conserved among mammals, but whether they are cell autonomous and restricted by maximal organismal lifespan remains unknown. We used a multilifetime murine model of repeat vaccination and memory T cell transplantation to test whether epigenetic aging tracks with cellular replication and if such clocks continue 'counting' beyond species lifespan. Here we found that memory T cell epigenetic clocks tick independently of host age and continue through four lifetimes. Instead of recording chronological time, T cells recorded proliferative experience through modification of cell cycle regulatory genes. Applying this epigenetic profile across a range of human T cell contexts, we found that naive T cells appeared 'young' regardless of organism age, while in pediatric patients, T cell acute lymphoblastic leukemia appeared to have epigenetically aged for up to 200 years. Thus, T cell epigenetic clocks measure replicative history and can continue to accumulate well-beyond organismal lifespan.

CheekAge: a next-generation buccal epigenetic aging clock associated with lifestyle and health

Epigenetic aging clocks are computational models that predict age using DNA methylation information. Initially, first-generation clocks were developed to make predictions using CpGs that change with age. Over time, next-generation clocks were created using CpGs that relate to both age and health. Since existing next-generation clocks were constructed in blood, we sought to develop a next-generation clock optimized for prediction in cheek swabs, which are non-invasive and easy to collect. To do this, we collected MethylationEPIC data as well as lifestyle and health information from 8045 diverse adults. Using a novel simulated annealing approach that allowed us to incorporate lifestyle and health factors into training as well as a combination of CpG filtering, CpG clustering, and clock ensembling, we constructed CheekAge, an epigenetic aging clock that has a strong correlation with age, displays high test-retest reproducibility across replicates, and significantly associates with a plethora of lifestyle and health factors, such as BMI, smoking status, and alcohol intake. We validated CheekAge in an internal dataset and multiple publicly available datasets, including samples from patients with progeria or meningioma. In addition to exploring the underlying biology of the data and clock, we provide a free online tool that allows users to mine our methylomic data and predict epigenetic age.

Deciphering the role of immune cell composition in epigenetic age acceleration: Insights from cell-type deconvolution applied to human blood epigenetic clocks

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health-related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell-type deconvolution with EAA predicted by six widely-used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging-related diseases, precision medicine, and therapeutic interventions.

Mortality After Major Cardiovascular Events in Survivors of Childhood Cancer

BACKGROUND

Adult survivors of childhood cancer are at risk for cardiovascular events.

OBJECTIVES

In this study, we sought to determine the risk for mortality after a major cardiovascular event among childhood cancer survivors compared with noncancer populations.

METHODS

All-cause and cardiovascular cause-specific mortality risks after heart failure (HF), coronary artery disease (CAD), or stroke were compared among survivors and siblings in the Childhood Cancer Survivor Study (CCSS) and participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Cox proportional hazard regression models were used to estimate HRs and 95% CIs between groups, adjusted for demographic and clinical factors.

RESULTS

Among 25,658 childhood cancer survivors (median age at diagnosis 7 years, median age at follow-up or death 38 years) and 5,051 siblings, 1,780 survivors and 91 siblings had a cardiovascular event. After HF, CAD, and stroke, 10-year all-cause mortalities were 30% (95% CI: 26%-33%), 36% (95% CI: 31%-40%), and 29% (95% CI: 24%-33%), respectively, among survivors vs 14% (95% CI: 0%-25%), 14% (95% CI: 2%-25%), and 4% (95% CI: 0%-11%) among siblings. All-cause mortality risks among childhood cancer survivors were increased after HF (HR: 7.32; 95% CI: 2.56-20.89), CAD (HR: 5.54; 95% CI: 2.37-12.93), and stroke (HR: 3.57; 95% CI: 1.12-11.37). CAD-specific mortality risk was increased (HR: 3.70; 95% CI: 1.05-13.02). Among 5,114 CARDIA participants, 345 had a major event. Although CARDIA participants were on average decades older at events (median age 57 years vs 31 years), mortality risks were similar, except that all-cause mortality after CAD was significantly increased among childhood cancer survivors (HR: 1.85; 95% CI: 1.16-2.95).

CONCLUSIONS

Survivors of childhood cancer represent a population at high risk for mortality after major cardiovascular events.

Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors: A Report From COG-ALTE03N1, BMTSS, and CCSS

Background

Anthracyclines are highly effective in treating cancer, albeit with increased cardiomyopathy risk. Although risk is attributed to associations with single nucleotide polymorphisms (SNPs), multiple SNPs on a gene and their interactions remain unexamined.

Objectives

This study examined gene-level associations with cardiomyopathy among cancer survivors using whole-exome sequencing data.

Methods

For discovery, 278 childhood cancer survivors (129 cases; 149 matched control subjects) from the COG (Children's Oncology Group) study ALTE03N1 were included. Logic regression (machine learning) was used to identify gene-level SNP combinations for 7,212 genes and ordinal logistic regression to estimate gene-level associations with cardiomyopathy. Models were adjusted for primary cancer, age at cancer diagnosis, sex, race/ethnicity, cumulative anthracycline dose, chest radiation, cardiovascular risk factors, and 3 principal components. Statistical significance threshold of 6.93 × 10-6 accounted for multiple testing. Three independent cancer survivor populations (COG study, BMTSS [Blood or Marrow Transplant Survivor Study] and CCSS [Childhood Cancer Survivor Study]) were used to replicate gene-level associations and examine SNP-level associations from discovery genes using ordinal logistic, conditional logistic, and Cox regression models, respectively.

Results

Median age at cancer diagnosis for discovery cases and control subjects was 6 years and 8 years, respectively. Gene-level association for P2RX7 (OR: 0.10; 95% CI: 0.04-0.27; P = 2.19 × 10-6) was successfully replicated (HR: 0.65; 95% CI: 0.47-0.90; P = 0.009) in the CCSS cohort. Additional signals were identified on TNIK, LRRK2, MEFV, NOBOX, and FBN3. Individual SNPs across all discovery genes, except FBN3, were replicated.

Conclusions

In our study, SNP sets having 1 or no copies of P2RX7 variant alleles were associated with reduced risk of cardiomyopathy, presenting a potential therapeutic target to mitigate cardiac outcomes in cancer survivors.

Epigenetic modifiers as game changers for healthy ageing

Epigenetic alterations during ageing are manifested with altered gene expression linking it to lifespan regulation, genetic instability, and diseases. Diet and epigenetic modifiers exert a profound effect on the lifespan of an organism by modulating the epigenetic marks. However, our understanding of the multifactorial nature of the epigenetic process during ageing and the onset of disease conditions as well as its reversal by epidrugs, diet, or environmental factors is still mystifying. This review covers the key findings in epigenetics related to ageing and age-related diseases. Further, it holds a discussion about the epigenetic clocks and their implications in various age-related disease conditions including cancer. Although, epigenetics is a reversible process how fast the epigenetic alterations can revert to normal is an intriguing question. Therefore, this paper touches on the possibility of utilizing nutrition and MSCs secretome to accelerate the epigenetic reversal and emphasizes the identification of new therapeutic epigenetic modifiers to counter epigenetic alteration during ageing.

Epigenetic Age in Peripheral Blood Among Children, Adolescent, and Adult Survivors of Childhood Cancer

Importance

Certain cancer therapies are risk factors for epigenetic age acceleration (EAA) among survivors of childhood cancer, and EAA is associated with chronic health conditions (CHCs). However, small numbers of younger survivors (aged <20 years) previously evaluated have limited the ability to calculate EAA among this age group.

Objective

To evaluate the change rate of epigenetic age (EA) and EAA in younger compared with older survivors and the possible association of EAA with early-onset obesity (aged <20 years), severity/burden of CHCs, and late mortality (>5 years from cancer diagnosis).

Design, Setting, and Participants

Study participants were from the St Jude Lifetime Cohort, initiated in 2007 with ongoing follow-up. The present study was conducted from April 17, 2022, to March 23, 2023. Survivors in this cohort of European ancestry with DNA methylation data were included. Cross-sectional annual changes in EA and EAA were compared across 5 different chronologic age groups: age 0 to 9 (children), 10 to 19 (adolescents), 20 to 34 (younger adults), 35 to 49 (middle-aged adults), and greater than or equal to 50 (older adults) years. Logistic regression evaluated the association between EAA and early-onset obesity or severity/burden of CHCs. Cox proportional hazards regression assessed the association between EAA and late mortality.

Main Outcomes and Measures

Early-onset obesity, severity/burden of CHCs (graded using the Common Terminology Criteria for Adverse Events (grade 1, mild; 2, moderate; 3, severe/disabling; 4, life-threatening) and were combined into high vs low severity/burden based on frequency and grade), and late mortality were the outcomes based on follow-up until April 2020. Expanded DNA methylation profiling increased the number of survivors younger than 20 years (n = 690). Epigenetic age was calculated primarily using the Levine clock, and EAA was derived from least squares regression of EA against chronologic age and was standardized to a z score (Levine EEA).

Results

Among 2846 participants (median age, 30.3 [IQR, 9.3-41.5] years; 53% males), the cross-sectional annual change in EA_Levine was higher in children (1.63 years) and adolescents (1.14 years), and the adjusted least-squares mean of Levine EEA was lower in children (-0.22 years) and older adults (-1.70 years). Each 1-SD increase in Levine EEA was associated with increased risk of developing early-onset obesity (odds ratio [OR], 1.46; 95% CI, 1.19-1.78), high severity/burden of CHCs (OR, 1.13; 95% CI, 1.03-1.24), and late mortality (hazard ratio, 1.75; 95% CI, 1.35-2.26).

Conclusions and Relevance

The findings of this study suggest that EAA measured in children and adolescent survivors of childhood cancer is associated with early-onset obesity, severity/burden of all CHCs, and late mortality. Evaluating EAA may help identify survivors of childhood cancer at increased risk for early-onset obesity, morbidity in general, and mortality.

Distinct DNA methylation signatures associated with blood lipids as exposures or outcomes among survivors of childhood cancer: a report from the St. Jude lifetime cohort

BACKGROUND

DNA methylation (DNAm) plays an important role in lipid metabolism, however, no epigenome-wide association study (EWAS) of lipid levels has been conducted among childhood cancer survivors. Here, we performed EWAS analysis with longitudinally collected blood lipid data from survivors in the St. Jude lifetime cohort study.

METHODS

Among 2052 childhood cancer survivors of European ancestry (EA) and 370 survivors of African ancestry (AA), four types of blood lipids, including high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), and triglycerides (TG), were measured during follow-up beyond 5-years from childhood cancer diagnosis. For the exposure EWAS (i.e., lipids measured before blood draw for DNAm), the DNAm level was an outcome variable and each of the blood lipid level was an exposure variable; vice versa for the outcome EWAS (i.e., lipids measured after blood draw for DNAm).

RESULTS

Among EA survivors, we identified 43 lipid-associated CpGs in the HDL (n = 7), TC (n = 3), and TG (n = 33) exposure EWAS, and 106 lipid-associated CpGs in the HDL (n = 5), LDL (n = 3), TC (n = 4), and TG (n = 94) outcome EWAS. Among AA survivors, we identified 15 lipid-associated CpGs in TG exposure (n = 6), HDL (n = 1), LDL (n = 1), TG (n = 5) and TC (n = 2) outcome EWAS with epigenome-wide significance (P < 9 × 10^-8). There were no overlapping lipids-associated CpGs between exposure and outcome EWAS among EA and AA survivors, suggesting that the DNAm changes of different CpGs could be the cause or consequence of blood lipid levels. In the meta-EWAS, 12 additional CpGs reached epigenome-wide significance. Notably, 32 out of 74 lipid-associated CpGs showed substantial heterogeneity (P_het < 0.1 or I² > 70%) between EA and AA survivors, highlighting differences in DNAm markers of blood lipids between populations with diverse genetic ancestry. Ten lipid-associated CpGs were cis-expression quantitative trait methylation with their DNAm levels associated with the expression of corresponding genes, out of which seven were negatively associated.

CONCLUSIONS

We identified distinct signatures of DNAm for blood lipids as exposures or outcomes and between EA and AA survivors, revealing additional genes involved in lipid metabolism and potential novel targets for controlling blood lipids in childhood cancer survivors.

Inflammatory biomarkers after an exercise intervention in childhood acute lymphoblastic leukemia survivors

Cancer survivors show increased risk for non-communicable diseases and chronic low-grade inflammation characterizes the development of such diseases. We investigated inflammatory plasma protein profiles of survivors of childhood acute lymphoblastic leukemia (ALL) in comparison to healthy controls and after an intervention with a home-based exercise program. Survivors of childhood ALL aged 16-30 years (n = 21) with a median age at diagnosis 4.9 (1.6-12.9) years and a median time of 15.9 years from diagnosis, and sex- and age-matched healthy controls (n = 21) were studied. Stored plasma samples were analyzed with Olink's 92-protein-wide Inflammation panel in 21 ALL long-term survivors at baseline, after a previous 16-week home-based exercise intervention (n = 17) and in 21 age- and sex-matched controls at baseline. Protein expression levels were compared between the groups. Inflammatory protein levels did not differ between the survivors and controls at baseline. Significantly reduced levels after the intervention were found in 11 proteins related to either vascular inflammation, insulin resistance, or both: tumor necrosis factor superfamily member 14 (TNFSF14), oncostatin M (OSM), monocyte chemoattractant protein 1 (MCP-1), MCP-2, fibroblast growth factor 21 (FGF-21), chemokine (C-C motif) ligand 4 (CCL4), transforming growth factor alpha (TGF-α), tumor necrosis factor-related apoptosis-inducing ligand 10 (TRAIL), adenosine deaminase (ADA), chemokine (C-X-C motif) ligand 6 (CXCL6), and latency-associated peptide transforming growth factor beta 1 (LAP TGF-β1). The ALL survivors were not significantly more affected by inflammation than controls at baseline. The survivors' 16-week exercise intervention led to significant reduction in inflammatory protein levels. Physical exercise should be promoted for survivors of childhood cancer.

Genome-wide association studies identify novel genetic loci for epigenetic age acceleration among survivors of childhood cancer

Background

Increased epigenetic age acceleration (EAA) in survivors of childhood cancer is associated with specific treatment exposures, unfavorable health behaviors, and presence of certain chronic health conditions. To better understand inter-individual variability, we investigated the genetic basis underlying EAA.

Methods

Genome-wide association studies of EAA based on multiple epigenetic clocks (Hannum, Horvath, PhenoAge, and GrimAge) were performed. MethylationEPIC BeadChip array and whole-genome sequencing data were generated with blood-derived DNA from participants in the St. Jude Lifetime Cohort Study (discovery: 2138 pre-existing and 502 newly generated data, all survivors; exploratory: 282 community controls). Linear regression models were fit for each epigenetic age against the allelic dose of each genetic variant, adjusting for age at sampling, sex, and cancer treatment exposures. Fixed-effects meta-analysis was used to combine summary statistics from two discovery data sets. LD (Linkage disequilibrium) score regression was used to estimate single-nucleotide polymorphism (SNP)-based heritability.

Results

For EAA-Horvath, a genome-wide significant association was mapped to the SELP gene with the strongest SNP rs732314 (meta-GWAS: β=0.57, P=3.30×10^-11). Moreover, the stratified analysis of the association between rs732314 and EAA-Horvath showed a substantial heterogeneity between children and adults (meta-GWAS: β=0.97 vs. 0.51, I²=73.1%) as well as between survivors with and without chest/abdominal/pelvic-RT exposure (β=0.64 vs. 0.31, I²=66.3%). For EAA-Hannum, an association was mapped to the HLA locus with the strongest SNP rs28366133 (meta-GWAS: β=0.78, P=3.78×10^-11). There was no genome-wide significant hit for EAA-PhenoAge or EAA-GrimAge. Interestingly, among community controls, rs732314 was associated with EAA-Horvath (β=1.09, P=5.43×10^-5), whereas rs28366133 was not associated with EAA-Hannum (β=0.21, P=0.49). The estimated heritability was 0.33 (SE=0.20) for EAA-Horvath and 0.17 (SE=0.23) for EAA-Hannum, but close to zero for EAA-PhenoAge and EAA-GrimAge.

Conclusions

We identified novel genetic variants in the SELP gene and HLA region associated with EAA-Horvath and EAA-Hannum, respectively, among survivors of childhood cancer. The new genetic variants in combination with other replicated known variants can facilitate the identification of survivors at higher risk in developing accelerated aging and potentially inform drug targets for future intervention strategies among vulnerable survivors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01038-6.