Staphylococcus aureus (MRSA) could transform outbreak investigations, but its clinical introduction is hampered by the lack of automated data analysis tools to rapidly and accurately define transmission based on sequence relatedness. We aimed to evaluate a fully automated bioinformatics system for MRSA genome analysis versus a bespoke researcher-led manual informatics pipeline. We analyzed 781 MRSA genomes from 777 consecutive patients identified over a 9-month period in a clinical microbiology laboratory in the United Kingdom. Outputs were bacterial species identification, detection of mec genes, assignment to sequence types (STs), identification of pairwise relatedness using a definition of ≤25 single nucleotide polymorphisms (SNPs) apart, and use of genetic relatedness to identify clusters. There was full concordance between the two analysis methods for species identification, detection of mec genes, and ST assignment. A total of 3,311 isolate pairs ≤25 SNPs apart were identified by at least one method. These had a median (range) SNP difference between the two methods of 1.2 SNPs (0 to 22 SNPs), with most isolate pairs (87%) varying by ≤2 SNPs. This similarity increased when the research pipeline was modified to use a clonal-complex-specific reference . Both pipelines clustered 338 isolates/334 patients into 66 unique clusters based on genetic relatedness. We conclude that the automated transmission detection tool worked at least as well as a researcher-led manual analysis and indicates how such tools could support the rapid use of MRSA genomic epidemiology in infection control practice.Genomic epidemiology of methicillin-resistant IMPORTANCE It has been clearly established that genome sequencing of MRSA improves the accuracy of health care-associated outbreak investigations, including the confirmation and exclusion of outbreaks and identification of patients involved. This could lead to more targeted infection control actions but its use in clinical practice is prevented by several barriers, one of which is the availability of genome analysis tools that do not depend on specialist knowledge to analyze or interpret the results. We evaluated a prototype of a fully automated bioinformatics system for MRSA genome analysis versus a bespoke researcher-led manual informatics pipeline, using genomes from 777 patients over a period of 9 months. The performance was at least equivalent to the researcher-led manual genomic analysis. This indicates the feasibility of automated analysis and represents one more step toward the routine use of pathogen sequencing in infection prevention and control practice. Staphylococcus aureus (MRSA) is a globally leading cause of death due to antimicrobial resistance  practice. Methicillin-resistant sistance  and reprsistance , 3. Suchsistance , 3. Thersistance , 5. Howemec gene, and highly accurate  bioinformatics system . On compaccurate .An automated analysis tool has the advantage that results are generated without the need for an operator with any bioinformatic training. It would also provide greater standardization of analysis and less interoperator variability compared with research pipelines . Here, wOur aim was to validate the accuracy of a fully automated MRSA genome analysis pipeline. Acknowledging the absence of a “gold standard reference” against which new genome analysis tools can be assessed, we compared its performance against a manual bioinformatics approach (“pipeline”) developed in our research laboratory for bespoke analysis.n = 526), the remainder being from clinical specimens (n = 255). Metrics used in the comparison were species identification, detection of mec genes, assignment to STs, and genetic relatedness based on pairwise SNPs. Outputs were generated independently for both methods, with blinding of the results of the other method prior to comparison of findings. We defined putative patient clusters based on genomic findings.A total of 789 isolates were sequenced over 43 sequence runs. Thirty isolates failed the manual pipeline quality control (QC) metrics, 21 of which also failed the automated tool QC metrics. On further inspection, the nine discrepancies (fail/pass) had >10% contamination with another species or high numbers of heterozygous sites (not detected by the automated pipeline QC) . Twenty-10.1128/msphere.00283-22.2TABLE S2n = 30) or automated tool QC metrics (n = 21/30). Download Table S2, XLSX file, 0.01 MB.Details of sequencing metrics for 30 MRSA isolates for which genomes failed manual , the detection of a mec gene , and ST assignment , ST45  and ST59  proving the most common.The two analysis methods produced fully concordant results for species identification . An epidemiological link was made between each pair with genetically related isolates when either a case-pair was admitted to the same ward with overlapping dates of admission or within 7 days of each other in the past 6 months, or if cases shared a postcode or GP practice. Both the manual and automated method identified the same 66 clusters involving 338 isolates/334 patients. Moreover, both methods defined the same cluster sample composition with respect to samples present in each cluster.While no additional clusters were identified by the manual analysis alone a further four genetic clusters involving nine patients were identified by the automated tool alone. The SNP distance between the isolate pairs was close to the cutoff (range 21 to 25 SNPs). The timing between the positive samples was often protracted. None of the four clusters had relevant epidemiological links. Two further clusters were not clear-cut in their interpretation and were considered “indeterminate.” One cluster of 25 isolates/patients identified by the manual pipeline contained a further 2 isolates identified by the automated tool alone. These two isolates only just reached the cutoff threshold (23 to 25 SNPs) and were 26 to 29 SNPs from the cluster using the research pipeline. The timing between the positive samples in the cluster spanned more than 250 days, and the patients shared a community epidemiological link. The second indeterminate cluster involved nine isolates/patients called by the manual pipeline, which was split into two clusters separated by 29 SNPs (above the cutoff) and a singleton by the automated tool. This cluster spanned more than 100 days and there were no epidemiological links.n = 655, 83%). A total of 3,289 pairs were ≤25 SNPs apart based on CC-specific mapping or the automated tool, with a median (range) of 0 SNPs (0 to 22 SNPs) different between the two methods -specific references for any ST with more than 10 isolates ( methods . SNP resn = 21) were investigated further. There were 66 SNP locations identified by the automated tool but not mapped by the manual pipeline, which called an “N” (uncertain/absent base) at variable positions due to no reads mapping (these occurred over an ~450bp region with a transposase fragment (n = 19 SNP locations) and an ~8.5 kb region, including an LPXTG surface protein and a bone sialoprotein binding protein (n = 3 SNP locations)); short segments mapping in regions of the genome without any other mapping (n = 30 SNP locations); and low (2×) depth of coverage (n = 2 SNP locations). The basis for the other mismatches was unclear, although some appeared to have a mixture of base calls present in the bam file (n = 3 SNP locations).Isolate pairs >10 SNPs different between the automated pipeline and CC-specific manual pipeline (n = 24), and SNP locations relating to a mix of bases called in the bam file that were identified as a single base by the manual pipeline during variant calling (n = 6).There were 38 SNP locations identified by the manual pipeline but not by the automated tool. Seven mismatches appeared to be genuine SNP locations that were not detected by the automated tool. This was due to an error in the code such that when both isolates had a different SNP compared to the reference at the same location, or multiple alternative alleles were identified for a position, the SNP was not identified. This has been rectified in an updated version. Manual analysis of genome data identified several explanations for the remainder, including likely misalignments near to indel sites  Preventing unnecessary infection control escalation. Five patients in a neonatal intensive care unit were found to be MRSA positive over 4 months, four of which were within a 5-week period and were considered putatively linked based on time and place. Plans were developed for an urgent outbreak response, including an incident management meeting involving senior staff, staff MRSA screening, and additional hand hygiene and cleaning audits. However, sequencing and the rapid analysis enabled by the automated tool showed that the five isolates were unrelated (>495 SNPs different from each other). The planned outbreak response, including any related costs, resources and disruption, was deescalated.(ii) Supporting targeted infection control interventions. The IPC team investigated 5 MRSA-positive patients flagged over a 3-week period on a single ward. Four cases were in a four-bed bay, and the fifth case was elsewhere on the ward. The near real-time availability of strain relatedness information allowed IPC to recognize that all patients in the 4-bed bay carried the same MRSA strain, while the isolate from the 5th case was unrelated. This supported a targeted intervention in which the single bay was closed and cleaned; exclusion of case 5 prevented full ward closure and cleaning.mec genes compared to the research pipeline. There was a high degree of concordance between SNP calls, and between the designation of isolate pairs based on a cutoff ≤25 SNPs. All 66 clusters identified by the manual pipeline were identified by the automated system, which found additional clusters with relatedness at the 25 SNP border defined for the study.Here, we describe the findings of an evaluation of an automated bioinformatics system that undertakes real-time analysis of MRSA genome data to determine pathogen relatedness to provide information to confirm or exclude an outbreak. Outputs were compared with a research-led manual bespoke analysis approach that was time-consuming and required an experienced bioinformatician. The automated tool was 100% accurate in assigning species, ST and The most common reason for additional SNPs called by the automated tool was short mapping segments in otherwise unmapped regions which the manual pipeline excluded from variant calling. The most common reason for additional SNPs called by the manual pipeline was misalignment around indels. As a result, there are isolates that will be called as related by one tool and unrelated by the other. However, this largely affects designation of related or unrelated when the isolate pair is close to the cutoff, and which may have high sensitivity for clustering patients as potentially associated but low specificity for having a true epidemiological link. The cutoff used was designed to be inclusive rather than exclusive, and thus is likely to include epidemiologically unrelated isolates close to the cutoff.Genome relatedness data identified 66 patient clusters (putative outbreaks) by both methods, the majority of which had not been confirmed through existing IPC methods alone. This corroborates previous findings that genome data provides higher resolution for outbreak detection than existing methods. If this tool was in clinical use, it would rule out the need for investigation of MRSA patients who were clustered by chance in time and space and would provide a priority flag for clusters that required further IPC investigation. The data produced in this study was accessed on several occasions by the IPC team, which resulted in implementation of targeted measures both to reduce spread and to rapidly step-down unrequired actions in cases where suspected outbreaks could be refuted. It was not the aim of the study to implement the automated tool in clinical practice or systematically record the impact of the data provided. This is the focus of a follow-on study that will span an entire year and will track how findings would alter IPC practice and provide an analysis of the consequent economic benefit to the NHS Trust.Klebsiella pneumoniae and vancomycin-resistant Enterococcus faecium, which were analyzed for antimicrobial resistance, sequence type, and outbreak clusters. The reported outbreak clusters revealed discrepancies between centers even when almost identical bioinformatic workflows were used, and some centers failed to detect extended-spectrum beta-lactamase genes and MLST loci. Furthermore, applying a standardized method to determine outbreak clusters on the reported de novo assemblies did not result in uniformity of outbreak-cluster composition . A single 2 to 3 mm colony was picked from clinical culture plates using a 1 μL loop. Where colonies were smaller than 2 mm, several colonies were picked. Where bacterial growth was confluent, a 1 μL loopful was taken. If there were several positive plates for one clinical sample, the plate with the least visible background contamination was selected. Isolates were either extracted directly from the clinical plate or subcultured overnight on CBA before processing further. A 10 μL loopful was stored at −80°C in Microbank vials. The first available MRSA isolate from each patient was selected for sequencing, together with all isolates associated with bloodstream infection if this occurred. After de-duplication we identified 789 MRSA isolates cultured from 784 patients.The study had ethical approval from the National Research Ethics Service (ref: 11/EE/0499) and the Cambridge University Hospitals NHS Foundation Trust Research and Development Department (ref: A092428). The study was conducted at the Clinical Microbiology and Public Health Laboratory at the Cambridge University Hospitals NHS Foundation Trust (CUH) in the United Kingdom (UK). Consecutive patients with MRSA-positive samples submitted by three hospitals and 65 GP surgeries during a 9-month period between 24 January and 1 November 2018 were identified using the hospital IT system . Laboratory data were collected on date and place of sampling, and sample type . During the first 3 months, MRSA isolates were retrieved from the freezer archive of the routine laboratory. Isolates were plated onto Columbia Blood Agar (CBA) using a 1 μL loop and incubated overnight at 37°C in air. A single colony was selected for sequencing, and a 10 μL loopful was stored at −80°C in Microbank vials (Pro-lab Diagnostics). During the remaining 6 months, putative or confirmed MRSA-positive culture plates were prospectively retrieved from the laboratory and confirmed as E. coli NCTC12241)). The study sequences are available in the European Nucleotide Archive (https://www.ebi.ac.uk/ena) and accession numbers are provided in Isolates were renumbered with an anonymous study code. DNA was extracted using the Qiagen DNA mini extraction kit and library preparation was performed using the Illumina Nextera DNA flex kit, as described previously . LibrariFor the researcher-led manual bioinformatics pipeline, controls and clinical MRSA isolate sequences were required to pass quality control (QC) metrics prior to further analysis, as previously described , 11. Forhttps://ccb.jhu.edu/software/kraken/) and the miniKraken database (https://ccb.jhu.edu/software/kraken/dl/minikraken_20171019_8GB.tgz). Multilocus sequence types (ST) were identified for MRSA using ARIBA version 2.12.1, as described at https://github.com/sanger-pathogens/ariba/wiki/MLST-calling-with-ARIBA. MRSA were screened for the presence of mecA , mecB  or mecC  using ARIBA, with a minimum percentage identity of 70% required based on Ito et al. 2012 . Mapping was performed using SMALT  with mapping and base calling performed as described in Klemm et al. 2018  were identified using the script available at https://github.com/sanger-pathogens/snp-sites. The depth and percentage coverage of the mapping reference were determined using the script available at https://github.com/sanger-pathogens/vr-codebase/blob/master/lib/VertRes/Pipelines/Mapping.pm. The number of heterozygous sites >50bp apart was determined using the script available at Github . This script also provided depth of coverage and standard deviation of coverage for the mec gene, which could be used to calculate whether the mec gene had sufficient depth to pass QC (see above).Isolates were mapped to CC22 HO5096 0412 , CC5 N315 (accession number BA000018), CC8 USA300 (accession number CP000255), CC30 MRSA252 (accession number BX571856), CC45 CA347 (accession number CP006044), and CC59 M013 (accession number CP003166). Mobile genetic element files used for these references are available at http://figshare.com/authors/Francesc_Coll/5727779.CC-specific mapping was performed as described above with the following references: CC1 MW2 (accession number The fully automated tool used here has been updated since our previous report on 17 genomes . The clo


Childhood cancer survivors (CCS) exhibit significantly increased chronic diseases and premature death. Abnormalities in DNA methylation are associated with development of chronic diseases and reduced life expectancy. We investigated the hypothesis that anti-cancer treatments are associated with long-term DNA methylation changes that could be key drivers of adverse late health effects.n = 284).Genome-wide DNA methylation was assessed using MethylationEPIC arrays in paired samples (before/after therapy) from 32 childhood cancer patients. Separately, methylation was determined in 32 samples from different adult CCS (mean 22-years post-diagnosis) and compared with cancer-free controls , which were consistently altered in the 32 post-treatment samples. Analysis of adult CCS identified matching methylation changes at 107/146 of the DMRs, suggesting potential long-term retention of post-therapy changes. Adult survivors also exhibited epigenetic age acceleration, independent of DMR methylation. Furthermore, altered methylation at the These novel methylation changes could serve as biomarkers for assessing normal cell toxicity in ongoing treatments and predicting long-term health outcomes in CCS. Treatment of childhood cancer has improved dramatically in recent years and overall survival rates for childhood cancers exceed 80% in high-income countries . Thus, tHOXA4 locus [One potential mechanism that may contribute to long-term health effects in cancer survivors is epigenetic changes induced by cancer therapies. DNA methylation is a key epigenetic modification that has important roles in control of gene expression and suppression of repetitive elements . FurtherA4 locus . We hypoTo examine this hypothesis, we first identified genome-wide changes in DNA methylation in childhood cancer patients following anti-cancer treatments (study-1). Subsequently, we examined the presence of the same DNA methylation changes in a different group of adult survivors of childhood cancer many years post-treatment, and their capacity to predict survival in CCS (study-2). The findings thus have potential to advance understanding of mechanisms underlying late effects in CCS and inform strategies to prevent or reduce life threatening or life changing late effects.n = 25; acute lymphoblastic leukaemia , acute myeloid leukaemia , lymphoma n = 1), or solid tumours   were selected from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, an ongoing multi-centre prospective cohort study with participating centres in ten European countries  (lsewhere ). Cancern = 8) were treated before the use of chemotherapy became generally accepted for this disease . The 32 available cases included: Hodgkin lymphoma (n = 23), Non-Hodgkin lymphoma (n = 6), and testicular cancer (n = 3) diagnosed between 1958–1986 (Table n = 13), Germany (n = 12), Italy (n = 2), Norway (n = 2), and Spain (n = 3). Study participants had provided informed consent and the EPIC study was approved by the relevant ethical review boards of each centre and from the International Agency for Research on Cancer.Specific treatment information was not available, so the analysis was restricted to tumour types in which all survivors would have been treated with chemotherapy and/or radiotherapy. Due to sample availability, we concentrated on malignant lymphoma and testicular cancer survivors Table . Althoughttp://pbbregistry.emory.edu/) [n = 658) [n = 288; adult CCS, n = 32)  research registry (ry.edu/)  and compn = 658)  with <1 n = 658) , 20. Conn = 4 adult control samples). The single-sample (ssNoob) method was used for normalisation [p-value >0.01 and cross-reactive probes  [n = 820,134 of these passed quality control in the adult samples). Methylation values were transformed to β values, which range from 0 (0% methylation) to 1 (100% methylation), representing methylation intensity [Bisulfite conversion was performed using the Zymo EZ-96 DNA methylation kit (Zymo Research). Genome-wide DNA methylation was quantified in all samples using the Infinium MethylationEPIC BeadChip microarray, which evaluates genome-wide CpG methylation at over 850,000 sites and carried out at the Wellcome Trust Clinical Research Facility, University of Edinburgh . Raw methylation data from MethylationEPIC arrays for all test and control samples were processed using the minfi Bioconductor package version 1.28.4 in R studio version 3.5.3. Sample quality was confirmed (examining array control probes and gender discordance) for all samples. Samples which failed quality control were excluded (lisation . Cell colisation . Probes mosomes)  were remntensity .For Study 1, differentially methylated regions (DMRs) were identified using the DMRcate R package with the default settings . Differeχ²) tests.CpG sites and DMRs were annotated with their genomic position, nearest gene and relation to gene/CpG island. Differentially methylated CpG loci were annotated using the HumanMethylationEPIC array and UCSC annotations for genes, genomic regions , exon) and island positions . Regional enrichment for identified loci compared to overall distribution on the EPIC array was assessed using chi squared  was done as above, and distributions of the original DMRs and the retained DMRs were compared using chi squared (χ²) tests.For study 2, we examined methylation differences between adult CCS and controls samples, focusing only on the DMRs and DMPs (with >5% absolute change in methylation) identified in study 1. The differences in DNA methylation between adult CCS cases and controls at individual CpG sites and DMRs were determined. Statistical differences were examined using p-values were adjusted for multiple testing using FDR correction. Stratified linear regression analyses were similarly carried out in lymphoma cases to distinguish any notable differences in DMR methylation between cases diagnosed before or after the introduction of chemotherapy as a standard treatment for lymphoma  [Linear regression was used to determine if there were differences in methylation at individual DMRs (dependent variable) between cases and controls (independent variable) adjusted for age, sex, and white blood cell proportions. er 1970) .https://dnamage.genetics.ucla.edu) [n = 19) [t-test, and by cancer site using two sample t-test (study 1) or ANOVA (study 2).Epigenetic age using the skin-blood clock (hereinafter referred to as epigenetic age) was estimated for all samples, and surrogate DNA methylation biomarkers (study 2) using Horvath’s online estimator (available at cla.edu) , 28. Alt[n = 19) . EpigeneIn study 2, to further understand markers of late effects in CCS we examined surrogate DNA methylation biomarkers known to be associated with chronic diseases, such as PhenoAge which is associated with health span , or Grimχ² tests for categorical variables and two-sample t-tests for continuous variables. Associations between DMR methylation or surrogate DNA methylation biomarkers and vital status/second cancer were examined using linear regression adjusted for age and sex.Long-term follow up data for the adult CCS was collected as part of the EPIC study, including survival and development of second primary cancers (average follow-up 16.8 years). Baseline characteristics and DNA surrogate markers were compared between alive vs. deceased, and between those who had second cancers vs. those who had not, using To investigate the possibility that anti-cancer therapy is associated with altered DNA methylation in normal tissues, we assessed genome-wide methylation patterns in paired blood/bone marrow samples in a cohort of 32 childhood cancer patients undergoing multi-agent chemotherapy. Average time between paired samples was 43.5 weeks Table . Cell typ = 0.11), although sample size was small. Alterations in methylation were slightly higher in solid tumour patients  exhibiting methylation changes in the same direction (Supplementary Tables /146 exhin = 18). DMRs were enriched for sites within TSS200 (200 bp upstream of the TSS) and the 1st exon compared to sites on the EPIC array as a whole , with six of these mapping to the PRDM16 gene) and chromosome 6  mapping to the HLA region).Genomic locations of DMRs relative to gene locations and CpG islands are shown in Fig. p = 0.01) and so all subsequent analysis was adjusted for differences in cell type distribution (in addition to age and sex). The majority of DMRs were replicated in this sample set (73% (107/146), with adult CCS exhibiting statistically significant differences in methylation versus the non-CCS controls  to determine if the differences in methylation identified post-therapy were replicated in adult survivors. A DMR was regarded as replicated if the identical region exhibited a significant difference between the adult survivors and the controls, had the same direction of change and an absolute difference of at least 50% of the size seen in study 1. By focusing on the 146 DMRs, the power of the analysis is enhanced, and identification of false positives reduced . The average duration between the cancer diagnosis and DNA sampling in the adult survivor cohort was 22.3 years (Table  to detern = 8) would almost certainly have been predominantly treated with radiotherapy (as they were diagnosed pre-1970 before the use of chemotherapy became accepted practice in this disease). They exhibited similar altered methylation across the DMRs. The extent of altered methylation changes was generally lower in these individuals, although this difference was not statistically significant , did noDUSP6) as significantly associated with mortality, even after correction for multiple testing , specifically for the GrimAge clock, in deceased versus surviving members of the CCS group  and one-fifth were deceased  at a relatively young age . CCS who experienced a second malignancy or had died were not demographically different from other survivors  did not show evidence of age acceleration in late compared to early samples, however, age acceleration was clearly evident in the adult CCS population (study-2). As the DMR methylation only weakly correlated with the accelerated epigenetic ageing, this suggests the possibility that CCS exhibit two different epigenetic abnormalities; a large set of differentially methylated genes that are acutely associated with therapy and then retained long-term , and alterations in DNA methylation that lead to accelerated epigenetic ageing that arise in survivors post- treatment. Our current study provides initial evidence that these methylation changes may correlate to subsequent patient outcomes, but larger, ideally longitudinal, studies of adult CCS will be required to more clearly determine the extent to which these epigenetic changes can be used to predict survival or chronic disease development in the CCS population.HOXA4 locus in remission samples from both adult and childhood ALL [HOXA4, in the leukaemia cells [A potential explanation for the consistent alterations in DNA methylation following exposure to different therapies is that these are reflective of selection for subsets of normal cells that are more able to survive exposure to cytotoxic treatments/cellular stress. Some of the genes associated with the DMRs we identified have previously been described as having altered methylation following exposure to chemotherapy. For example, we have previously determined that exposure to chemotherapy leads to increased levels of DNA methylation at the hood ALL , a findiia cells . Restoraia cells . SimilarWe identified a set of DMRs exhibiting consistent changes post treatment, although the absolute magnitude of methylation changes varied between patients. This study was not powered to identify the underlying cause of this variation, which could reflect inter-individual differences, different therapies or different tumour types. Larger differences in altered methylation were observed in solid tumours vs. haematological cancers. However, the solid tumours also differed according to the timing of the first sample taken . Furthermore, when the analysis was restricted, post hoc, to just ALL samples, a correlation was identified between the timing of the initial sample and the overall level of methylation change (data not shown) suggesting that the timing of the initial sample may be an important determinant of the absolute size of methylation changes at the DMRs. However, changes across the same set of DMRs were seen regardless of the timing of the initial sample.WNT6, TP73, and CD37) [MEIS1, HOXA4, and HOXA5) [TP73) [UCN, PRDM16, TCAP, ALOX5) [We identified differential methylation of genes that have been related to cancers such as lymphoma nd CD37) , 34, leud HOXA5) , 36 or n , 34, le, ALOX5) –41. Thisp < 1 × 10−50 (Fisher exact test), with all differences at matching CpG sites altered in the same direction), further suggesting that these methylation changes are consistent consequences of exposure to highly toxic anti-cancer therapies.The hypothesis that methylation changes induced by cancer treatments could impact long-term health was first proposed by Lyon et al. . HoweverPRDM16 gene. As well as being linked to cardiovascular disease, PRDM16 deficiency has also been associated with the development of fibrosis, which is linked to multiple age-associated chronic diseases [PRDM16 methylation has been linked to obesity [Of particular interest was the cluster of DMRs identified within the diseases  and PRDM obesity . In addi obesity . Thus, aDUSP6 locus) was statistically significant, even after correction for multiple tests. Larger studies will be required to properly assess the link between altered DNA methylation and specific chronic illnesses that are increased in CCS.Although the study was not powered to assess the association between the DNA methylation changes and clinical outcome, the results provide some initial evidence that the extent of altered methylation may act as a marker for key clinical endpoints. Average DMR methylation was associated with longevity, and methylation at one specific DMR  and the relatively small sample size. This study did not aim to identify methylation changes induced in a drug specific fashion, however these changes may be differentiated in future studies with greater statistical power. As childhood cancer patients are typically treated with complex multi-agent approaches, we are now investigating the use of mouse models to more directly assess drug-specific methylation changes. This will also help determine whether any methylation changes could be induced by the disease itself, independent of treatment. Similarly, there was a lack of overlap between the cancer types included in study 2 with study 1, and the specific anti-cancer therapies used to treat the original cancers in study 2 participants were not known. While the overlapping methylation differences across the two diverse studies emphasises the general applicability of the loci that were replicated, it remains possible that some of the unreplicated DMRs may be more specific for the cancer therapies in study 1. Future research in larger series would be valuable to identify methylation changes associated with specific treatment protocols and specific cancer types. It is possible that the results of this study could also have been impacted by other environmental factors which can influence the epigenome . HoweverThe findings of this study may have potential clinical utility. A key finding is the similarity in methylation changes seen between childhood cancer patients/survivors regardless of cancer type, what treatment they received or when they were treated. This suggests there may be a common set of methylation changes that are present across many childhood cancer patients following exposure to cytotoxic agents. These would therefore be widely applicable for assessing normal cell toxicity and potential long-term health effects across childhood cancer patients. Given that all specific types of childhood cancer are rare, this potential applicability to multiple cancer types would be a significant advantage of these methylation changes as prospective clinical markers. For instance, if the identified methylation changes are shown to be predictive of specific late health effects, these could be utilised in precision medicine approaches to identify high-risk individuals and to direct follow up care. In addition, since increased methylation at DMRs can be measured during treatment, this could potentially be used to evaluate ongoing trials of childhood cancer patients to assess whether new treatment protocols or dose de-escalation studies result in lower level of epigenetic damage.Overall, these findings suggest that anti-cancer treatment in childhood cancer patients may induce a consistent set of DNA methylation changes, that occur largely independent of tumour type or specific treatment, which are retained in adult CCS and may be associated with adverse health outcomes. These findings offer significant scope for utilising multiple methylation markers  in the development of risk prediction tools for CCS.Supplementary filesReproducibility checklist