Tissue-specific Mitotic Clocks Link Cumulative Stem Cell Divisions to Human Cancer Susceptibility

Tissue-specific Mitotic Clocks Link Cumulative Stem Cell Divisions to Human Cancer Susceptibility | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Tissue-specific Mitotic Clocks Link Cumulative Stem Cell Divisions to Human Cancer Susceptibility Fredrick Schumacher, Yanning Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9013656/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Why cancer risk varies so dramatically across human tissues remains a fundamental question in oncology. While this variation has been linked to differences in total stem cell divisions (TNSD), molecular tools for measuring tissue-specific proliferative history are lacking. Existing DNA methylation-based mitotic clocks conflate chronological aging with mitotic activity and apply pan-tissue assumptions that obscure tissue-level turnover dynamics. Here, we develop tissue-specific mitotic clocks for six major tissues by modeling global hypomethylation at solo-WCGW CpG sites within partially methylated domains (PMDs) — an experimentally validated, age-independent marker of cumulative cell division — using biologically normal tissue from the Genotype-Tissue Expression (GTEx) project. This design explicitly avoids confounding from chronological age and field cancerization. Our tissue-specific clocks demonstrate strong generalizability, replicate across independent cohorts, and outperform existing pan-tissue clocks in recapitulating Vogelstein and Tomasetti-derived (2017) TNSD estimates and SEER cancer incidence data. These results provide direct molecular evidence that cumulative cellular turnover underlies tissue cancer susceptibility, and establish a precision framework for quantifying proliferative burden in human malignancies — with implications for cancer risk stratification, prevention, and mechanistic dissection of replication-driven versus exposure-related carcinogenesis. Biological sciences/Cancer/Cancer epidemiology Biological sciences/Genetics/Epigenetics/DNA methylation Health sciences/Biomarkers/Predictive markers Figures Figure 1 Figure 2 Figure 3 Introduction Accumulating evidence indicates that the variation of lifetime cancer risk across human tissues is strongly associated with the total number of stem cell divisions (TNSD) that occur within each tissue over a lifetime 1 – 5 . Among the molecular alterations that arise during cellular proliferation, DNA methylation (DNAm) changes – resulting from imperfect maintenance during DNA replication – accumulate with successive stem cell divisions, capturing both the biological progression and proliferative history of a tissue, and may ultimately predispose certain cell populations to the potential of malignant transformation 6 – 9 . To quantify this process, several DNAm-based mitotic clocks have been developed to estimate TNSD using distinct modeling strategies. Some clocks are tailored to specific tissues or cancer types. For example, Duran-Ferrer et al. introduced epiCMIT, a mitotic clock specifically designed to reflect the proliferative history of B-cell tumors, using both hyper- and hypomethylated CpGs in silent chromatin regions 10 . Other clocks target broader application across tissues. The MiAge calculator (Wang et al. ) applied a statistical model based on methylation transmission theory to tumor and adjacent normal data using publicly-available data from The Cancer Genome Atlas (TCGA) program sponsored by the National Cancer Institute, identifying increasing-methylated probes that are informative of mitotic activity across tissues 11 . A research group led by Andrew Teschendorff has developed a series of mitotic clocks – EpiTOC1 12 , EpiTOC2 13 , and StemTOC 14 – primarily derived from whole blood DNAm data. EpiTOC1 focused on CpG sites located within the promoter regions of Polycomb group target (PCGT) genes that are unmethylated in fetal tissues but progressively gain methylation with chronological age. EpiTOC2 refined this model by introducing mathematical formulations to estimate TNSD based on CpGs from EpiTOC1. StemTOC further extended this framework by implementing a multi-step filtering procedure that integrated DNAm data from fetal tissues, in-vitro cell lines, and in-vivo whole blood samples. Most established DNAm-based mitotic clocks relied on a core assumption: chronological age serves as a direct surrogate for mitotic age. This assumption is applied in multiple ways – either explicitly, by regressing CpGs methylation levels against chronological age to identify mitotically informative CpGs during model development, or the implicit validation benchmark where a strong correlation with chronological age is taken as evidence of model credibility. However, this general assumption warrants closer scrutiny: biological aging (e.g. cumulative stem cell divisions 15 , 16 ) is unlikely follow a simple linear relationship with chronological age, as the rate of cellular turnover varies across life stages such as puberty and menopause, and is further influenced by many factors including inflammation 17 , oxidative stress 18 , and microenvironment 19 . In contrast, chronological age reflects merely the passage of time. Thus, these clocks may, in fact, better predict chronological aging but remain limited in capturing the complex biological processes that underpin mitotic activity. Advancing beyond chronological age, we propose that global partially methylated domains (PMDs) hypomethylation offer a more precise surrogate of TNSD. Laird et al. first characterized PMDs using whole-genome bisulfite sequencing of TCGA adjacent normal tissues, identifying widespread hypomethylation specifically at solo-WCGW CpGs – isolated CpG sites occurring in an A/T – C – G – A/T (WCGW) sequence context – within PMDs 20 . They observed that these sites are initially hypermethylated but progressively lose methylation as cells divide, suggesting that methylation loss at PMD solo-WCGWs reflects TNSD rather than the passage of time 20 . A subsequent study from the same group provided experimental evidence using primary human cell cultures, showing that loss of DNA methylation at PMD solo-WCGWs tracks cumulative population doublings 21 . Collectively, these findings demonstrated that global hypomethylation at PMD solo-WCGW sites (global PMD hypomethylation) represents a biologically grounded and experimentally validated proxy for TNSD, offering a more accurate measure of proliferative history compared to chronological age. However, global PMD hypomethylation identified by Laird et al. relied on a single adjacent normal sample per tissue type, which may not adequately capture tissue-specific methylation variation. In addition, several studies have reported tissue-specific differences in DNAm-based aging and emphasized the importance of developing tissue-specific biomarkers of aging 22 – 24 . Therefore, we aim to build and validate tissue-specific mitotic clocks to estimate TNSD for six common tissue types. Unlike many existing models based on TCGA adjacent “normal” tissues, which potentially are impacted by field effects 25 – 27 or pre-neoplastic epigenetic alterations 28 , we used Genotype-Tissue Expression (GTEx) normal tissue to minimize these potential biases and enhance biological validity. Results Descriptive statistics of GTEx data DNAm data were generated for 836 tissue samples obtained from 421 donors in the GTEx cohort, spanning six tissue types. The characteristics of GTEx donors for each tissue type are summarized in Table 1. Each donor contributed from 1 to 6 tissue samples. Donor ages ranged from 20 to 70 years, with a mean age of 54.4 ± 12.1 years; notably, approximately 90% of donors across all tissue types were aged over 40, indicating the samples used for developing tissue-specific mitotic clocks were biologically mature. For non-sex-specific tissues, more than 70% of samples were derived from male donors. The cohort was predominantly White (86.1%), with a mean BMI of 27.1 ± 4.0 kg/m 2 . Additionally, 70.1% and 77.2% of donors reported smoking and alcohol use, respectively. Missing data across demographic and lifestyle variables were minimal (< 1%). Tissue-specific methylation variation within PMDs Larid et al. identified 26,732 PMD solo-WCGW CpGs represented on the EPIC array, of which 23,328 (87.2%) passed quality control (QC) in GTEx data and were retained for downstream analysis. The distribution of probes that did not pass QC showed no chromosome-specific bias (Supplementary Table 2). Using methylation levels at these CpGs, we performed PCA across 836 tissue samples from 421 GTEx donors to assess tissue-level methylation structure. As shown in Fig. 1, the first principal component (PC1) clearly separates ovarian samples from other tissues, while the remaining five tissue types form well-defined clusters. This pattern indicates substantial tissue-specific methylation variation within PMDs at the molecular level. However, the second (PC2) and third (PC3) principial components are non-informative. To determine whether the observed tissue clustering was driven by inter-individual differences or chronological age-related effects, we repeated the PCA using matched multi-tissue samples from the same individuals. A total of 10 donors contributed matched breast, colon and ovary samples. As shown in Supplementary Figure S1, distinct tissue clusters were preserved. In contrast, comparison of global PMD hypomethylation across these 3 matched tissues showed no statistically significant differences (P-value = 0.089; Supplementary Figure S2). Together, these results demonstrate tissue-specific methylation variation at PMD solo-WCGW CpGs, likely reflecting distinct tissue-specific mitotic aging dynamics that are not detectable using global PMD methylation measures, thereby providing strong evidence for the necessity of developing tissue-specific mitotic clocks. Construction and validation of tissue-specific mitotic clocks We hypothesized that global PMD hypomethylation represents a more precise surrogate for TNSD than chronological age. Accordingly, we trained tissue-specific mitotic clocks for each tissue type using GTEx data (Methods). For each tissue type, a subset of informative CpGs and corresponding coefficient was identified (Supplementary Table 1). For a given sample, the tissue-specific mitotic clock generates a tissue-specific hypomethylation score, by summing the weighted contributions of tissue-specific CpGs using their corresponding model coefficients. Lower scores indicate a higher degree of global PMD hypomethylation and therefore correspond to a higher estimated TNSD. Next, we evaluated generalizability of the tissue-specific mitotic clocks using TCGA adjacent normal samples as an independent external validation cohort for the corresponding six tissue types (Table 2). Across tissues, we observed a consistent systematic bias in model estimates, ranging from − 0.193 in ovary to -0.359 in kidney. RMSE values ranged from 0.19 to 0.359, reflecting overall deviation of predictions from observed values. Given the 0–1 scale of tissue-specific mitotic clock estimates, an RMSE of ~ 0.3 indicates moderate deviations on average, suggesting that overall TNSD trends are well captured. Table 2 External validation of tissue-specific mitotic clocks on TCGA adjacent normal samples Tissue Number of Samples Array Performance metrics Bias RMSE Prostate 50 HM450k -0.276 0.28 Breast 97 HM450k -0.358 0.358 Lung 74 HM450k -0.223 0.224 Colon 38 HM450k -0.303 0.304 Kidney 205 HM450k -0.359 0.359 Ovary 12 HM27k -0.193 0.19 RMSE root mean square error; HM450k Illumina HumanMethylation450 BeadChip; HM27k Illumina HumanMethylation27 BeadChip To further assess model performance while mitigating platform-related bias, we performed additional external validation using CPTAC adjacent normal samples generated on the EPIC array, consistent with the array used for model training. Due to limited tissue availability, only kidney and lung samples were included. Validation results are summarized in Table 3. The results revealed minimal bias (− 0.009 for lung and − 0.028 for kidney) and very low RMSE values (0.021 for lung and 0.029 for kidney), indicating that the predicted tissue-specific hypomethylation score closely matched observed global PMD hypomethylation. Table 3 External validation of tissue-specific mitotic clocks on CPTAC adjacent normal samples Tissue Number of Samples Array Performance metrics Bias RMSE Lung 280 EPIC -0.009 0.021 Kidney 159 EPIC -0.028 0.029 RMSE root mean square error; EPIC Illumina Infinium MethylationEPIC BeadChip Tissue-specific mitotic clocks correlate with lifetime stem cell divisions of a given tissue and cancer risk Tomasetti and Vogelstein previously reported that lifetime cancer risk across tissues is strongly associated with TNSD occurring over a tissue’s lifetime (lscd) 1,2 . Lscd reflects both the total number of stem cells in a fully developed tissue, and TNSD for each stem cell undergoes over a lifetime. Because direct quantification of lscd in vivo is challenging, they employed a mathematical approach based on experimentally observed data to derive estimates, including tracing cell lineage with markers (like viral tags), and functional assays such as the mammosphere assay, which assess self-renewal rates in vitro, and linked observed cell numbers and division frequencies to total lifetime activity and cancer risk 1,2 . Reported lscd values for selected tissues were: breast (lscd = 3.03 × 10 12 ), ovary (lscd = 2.20 × 10 7 ), prostate (lscd = 5.08 × 10 10 ), lung (lscd = 9.27 × 10 9 ), and colon (lscd = 1.17 × 10 12 ). To determine whether our tissue-specific mitotic clock and other established mitotic clocks can capture this tissue-level mitotic burden, we applied each clock to multiple TCGA adjacent normal samples per tissue and averaged the estimates to derive tissue-level proxies of lscd. As shown in Fig. 2, the tissue-specific mitotic clocks exhibited a strong association with experimentally derived lscd (cor = -0.745, P-value = 0.15), whereas other existing mitotic clocks, including epiTOC1, epiTOC2, and stemTOC, showed weak correlations (cor = 0.14, 0.01, 0.2 respectively). These results indicate that the tissue-specific clocks can effectively reflect TNSD at the tissue level. Notably, the observed trends are biologically coherent and markedly stronger than those produced by existing comparator clocks. We further examined whether the estimated lscd was associated with cancer risk to test the hypothesis by Tomasetti and Vogelstein 1,2 . As shown in Fig. 3, the tissue-specific mitotic clock estimates demonstrated a moderate positive association with cancer incidence (cor = 0.32, P-value = 0.53), whereas other established mitotic clocks showed no meaningful correlations (cor = 0.09, -0.15, 0.06 respectively). Although this association did not reach statistical significance, the observed trend supports the hypothesis that cumulative mitotic activity contributes to tissue-specific cancer risk and further highlights the biological relevance of the tissue-specific mitotic clock. In contrast, the lack of association observed for the other mitotic clocks suggests that they may be less sensitive to tissue-specific variation in cumulative mitotic activity relevant to cancer risk. Discussion In this study, we developed and validated tissue-specific mitotic clocks to estimate TNSD for six human tissues. By leveraging global hypomethylation at PMD solo-WCGW sites as a biological surrogate of mitotic activity — rather than relying on chronological age — our clocks significantly outperformed established mitotic clocks in capturing the distinct proliferative histories of different organs. Specifically, our models exhibited minimal estimation bias when validated in an independent, array-consistent CPTAC cohort. Our tissue-specific mitotic clocks generated directionally consistent associations with reported lscd and tissue-specific cancer incidence, supported by strong effect sizes and rank-order concordance. Although these relationships were statistically underpowered due to the limited number of tissue types available for analysis. In this context, the biological coherence and effect magnitude, rather than statistical significance, are most informative, and both were markedly stronger for our tissue-specific mitotic clocks than for existing comparator clocks. Chronological age-trained clocks primarily capture systemic aging signals. In contrast, our approach targets replication-coupled epigenetic erosion and loss of epigenetic integrity. This biological distinction likely explains why existing mitotic clocks perform well for age prediction but poorly for correlating tissue-level cancer risk. The improved performance supports our central assumption. Unlike existing mitotic clocks, which are largely pan-tissue and assume a universal set of CpG loci behaves consistently across tissues, our study based on the premise that different tissues exhibit unique intrinsic aging rates and molecular signatures. As a result, a universal model insufficient to capture this biological heterogeneity. We empirically support this hypothesis through PCA of 836 tissue samples from 421 GTEx donors (Fig. 1 ). PMD methylation profiles robustly distinguished tissue types, suggesting that inter-tissue epigenetic differences reflect variation in biological aging dynamics rather than stochastic noise. To account for the potential confounding of chronological age, we further analyzed matched breast, colon, and ovary tissues from 10 donors (Figure S1 ). The persistence of clear tissue separation in this controlled, intra-individual setting confirms that these epigenetic differences are tissue-intrinsic rather than age-dependent. These observations align with prior findings by Pierce et al. 22,23 and Schaefer et al. 24 , which also highlighted significant tissue-specific heterogeneity in DNA methylation aging signatures. Collectively, our results demonstrate that accurate quantification of proliferative history requires tissue-specific mitotic clocks rather than a global, pan-tissue model. Notably, despite the clear tissue-specific clustering observed in the PCA of matched tissue samples (Figure S1 ), global PMD hypomethylation levels were not statistically significantly different across the same cohort (Figure S2). Although this lack of significance in global comparisons may partly reflect limited statistical power due to the small sample size (N = 10), it more fundamentally highlights a methodological limitation: averaging thousands of CpG measurements into a single global metric could dilute biological signal and reduce sensitivity to biologically meaningful differences. Conversely, multi-dimensional reduction techniques, such as PCA, retain the covariance structure across all CpG sites, allowing for the detection of subtle yet consistent patterns that are often masked in univariate analyses, particularly in studies with limited sample sizes. This interpretation is supported by prior studies. Farré et al. demonstrated that PCA can identify major sources of variation (like tissue type, cell type, age) across methylation profiles on the matched brain and blood samples from 17 individuals 29 . Furthermore, Eulalio et al. developed regionalpcs approach, which uses PCA to summarize methylation across CpG clusters and achieved a ~ 54% improvement in sensitivity over mean-based metrics 30 . Together, these findings suggest that reliance on global methylation averages may underestimate tissue-specific epigenetic heterogeneity, reinforcing the advantage of multi-dimensional approach for detecting biologically meaningful differences. During external validation of our tissue-specific mitotic clocks on TCGA adjacent normal data, we observed a consistent systematic bias across tissues. This bias might be attributed to two primary factors. First, the tissue-specific mitotic clocks capture tissue-level methylation variability that is not fully represented by the global PMD hypomethylation, leading to deviations from the global PMD estimate. Second, TCGA methylation data were primarily generated using the HM450k array, which has limited probe coverage compared to the EPIC array used for model training, thereby introducing systematic bias due to missing CpG measurements. To mitigate this technical effect and more accurately assess model performance, we further validated our models on additional CPTAC adjacent normal samples profiled on the EPIC array. In this platform-consistent setting, systematic bias was largely shrunk, demonstrating that array consistency substantially improves model prediction accuracy. These results confirm that our tissue-specific mitotic clocks are highly robust and generalizable on independent datasets generated with compatible platforms. Accordingly, the bias observed in TCGA validation is most plausibly attributed to limitations of the legacy HM450k design, which lacks the probe density required for precise quantification, although such datasets remain informative for capturing broad directional trends. Array technologies are converging asymptotically toward a stable measurement regime, with marginal performance gains across successive updates. As next-generation, higher-density arrays such as EPIC continue to be widely adopted, platform-related bias will become negligible, ensuring the robustness and broad applicability of our mitotic clock models. While whole-genome bisulfite sequencing provides a useful technical reference for methylation measurement, the degree to which array-based platforms approximate sequencing-level resolution remains an open area for future systematic benchmarking. Our study has several notable strengths. First, it represents the first systematic effort to construct tissue-specific DNA methylation-based mitotic clocks using biologically “normal” samples from the GTEx project. Unlike prior mitotic clocks derived from TCGA adjacent normal tissues, the precision of our training data minimizes potential confounding from field effects 25 – 27 or pre-neoplastic epigenetic alterations 28 . Field cancerization refers to the presence of molecularly altered but histologically normal cells surrounding tumors, arising from early clonal expansions during tumorigenesis 31 . These altered fields can exhibit elevated proliferative activity and replication-associated epigenetic and genetic changes 32 . As a result, adjacent “normal” tissues may carry inflated mitotic signals that do not reflect baseline tissue turnover. Training mitotic clocks on TCGA normal-adjacent samples may therefore bias TNSD estimates upward and distort tissue-specific proliferative histories. Second, by modeling global PMD hypomethylation at solo-WCGW CpGs, our approach captures a biologically validated and experimentally supported biomarker of TNSD, rather than relying on chronological age as a surrogate. Finally, our tissue-specific estimates exhibit stronger associations with reported lscd and cancer risk compared to existing clocks, providing empirical support for the Tomasetti and Vogelstein hypothesis linking cumulative stem cell divisions to cancer susceptibility. Nevertheless, several limitations should be acknowledged. Sample sizes for breast and kidney training sets were modest, which may constrain model precision. Additionally, while our colon model was trained exclusively on transverse colon samples, the TCGA validation cohort included a mixture of ascending, transverse, and descending colon tissues, which could contribute to the observed bias. Lastly, although correlation analyses revealed strong associations with reported lscd and cancer risk, the evaluation was limited to six tissue types, reducing the statistical power to fully characterize the relationship between TNSD and cancer incidence across the human body. Beyond methodological advances, tissue-specific mitotic clocks also have several potential applications. They may help identify tissues or individuals with unusually high proliferative burden, indicating elevated baseline cancer susceptibility and supporting risk stratification. In addition, these measurements may help inform cancer prevention strategies by highlighting organs in which reducing proliferative stress or enhancing genomic maintenance could yield the greatest benefit. In addition, our mitotic clocks provide a quantitative baseline of replication-driven risk, enabling separation of intrinsic proliferative contributions from external exposure effects. By establishing the expected cancer risk attributable to endogenous cell turnover, this framework supports more precise estimation of the incremental risk associated with environmental or behavioral factor. Together, these applications position tissue-specific mitotic clocks as a tool for both mechanistic insight and population-level risk assessment. In summary, we demonstrate that tissue-specific mitotic clocks provide a more accurate measure of proliferative history than pan-tissue models. This finding highlights the importance of accounting for tissue heterogeneity in modeling mitotic aging. Future work could expand this framework to larger cohorts and more diverse tissue panels will be critical for improving model generalizability and biological relevance, as well as for more precisely characterizing the relationship between TNSD and cancer risk. Methods GTEx data for model development GTEx project is a large-scale, publicly accessible biobank designed to investigate the association between genetic variation and gene expression across human tissues 33 . It includes samples from approximately 960 postmortem donors who predominantly died from acute, non-disease-related causes 33 . Each donor contributed multiple tissue types and only samples confirmed to be free of significant disease determined by pathological examination and medical record review were included to ensure the representation of true normal tissues 34 . The GTEx v8 release comprises 15,201 RNA-seq samples from 838 donors across 49 tissue types, with detailed demographic and technical metadata 34 . The enhancing GTEx (eGTEx) project expands this resource by adding epigenomic and proteomic profiling. Among these, DNA methylation was measured using the Illumina Infinium MethylationEPIC BeadChip (EPIC) 35 . We obtained eGTEx methylation data from the Gene Expression Omnibus (GEO; accession number GSE213478 36 ). Quality control and preprocessing were performed by Pierce et al. 22 The dataset contains 754,119 probes across 987 samples from 9 normal tissue types. For model construction, we selected six tissues representing common sites of cancer origin: breast mammary tissue (n = 52), colon transverse (n = 224), kidney cortex (n = 50), lung (n = 223), ovary (n = 164), and prostate (n = 123). All samples were derived from 421 donors in the GTEx cohort. TCGA and CPTAC data for external validation We downloaded DNA methylation data of adjacent normal tissue for six selected tissue types from TCGA via the Genomic Data Commons (GDC) portal 37 . Although TCGA provides preprocessed data, additional quality control was performed to exclude low-quality samples and probes with detection p -value > 0.05. After quality control, the external validation cohort included female breast (n = 97), colon (n = 38), kidney (n = 205), lung (n = 74), ovary (n = 12) and prostate (n = 50). All samples were profiled using the Illumina HumanMethylation450 BeadChip (HM450k), except for ovarian tissue, which was generated using the Illumina HumanMethylation27 (HM27k) platform. To ensure consistency with our Illumina Infinium EPIC array-based training model, we further obtained an additional external validation dataset from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) 38 . CPTAC extends TCGA by performing deep proteomic characterization of tumor and adjacent normal samples, complementing genomic and epigenomic data to enable integrated proteogenomic analyses of cancer 38 . Because CPTAC profiled a limited number of tissues, only kidney (n = 280) and lung (n = 159) adjacent normal samples were included. In both validation datasets, DNA methylation levels were represented as β-values. Calculation of global PMD hypomethylation Global DNA hypomethylation was quantified using PMD solo-WCGW CpG sites. Although PMD solo-WCGWs were initially identified using WGBS, Laird et al. demonstrated that methylation levels at these sites measured on microarray platforms (e.g. HM450k) strongly correlate with WGBS measurements and reliably capture PMD hypomethylation patterns despite limited probe coverage 20 . Building on this work, we used a curated set of 26,732 PMD solo-WCGW CpGs present on EPIC array. Specifically, only probes targeting solo-WCGW CpGs were retained, while those located within annotated CpG islands or exhibiting low methylation levels (β < 0.2) were excluded 20 . For each sample, the average β-value across the retained probes was computed to derive global PMD hypomethylation, reflecting large-scale methylation loss across PMDs. This measure was used as the outcome variable in our model development. Tissue-specific mitotic clock construction We constructed tissue-specific mitotic clocks using the following strategy: for each tissue type, samples were randomly partitioned into training (70%) and testing (30%) subsets. An elastic net regression model with alpha parameter = 0.5 (R package ‘glmnet’) was trained to predict the global PMD hypomethylation, derived from CpG β-values within each tissue. Sex was included as an unpenalized covariate to account for sex-related effects, except in inherently sex-specific tissues such as prostate and ovary. For breast tissue, only female samples were analyzed given the markedly lower incidence rate of breast cancer in males. Model robustness was evaluated using five-fold cross-validation. The final set of CpGs selected for each tissue type was used to construct tissue-specific mitotic clocks. Coefficients for each tissue-specific model are provided in Supplementary Table 1. Statistical analysis All statistical analyses were performed using R version 4.3.2. Tissue-specific methylation variation was evaluated using principal component analysis (PCA) on PMD solo-WCGWs beta values. Global PMD hypomethylation differences across matched tissues were assessed using repeated-measures analysis of variance (RMANOVA). Missing values in CPTAC methylation data were imputed with impute.knn (R Bioconductor package ‘impute’) 39 . For external validation, the global PMD hypomethylation served as the observed outcome, and predicted tissue-specific hypomethylation score as predicted values; model performance was quantified using estimation bias and root mean squared error (RMSE). Pearson correlation was used to evaluate associations between mitotic clock estimates and experimentally derived lifetime stem cell divisions (lscd), as well as cancer incidence rates. Age-adjusted cancer incidence rates were obtained from the Surveillance, Epidemiology, and End Results (SEER) database 40 . Existing mitotic clocks, including epiTOC1, epiTOC2, and stemTOC2, were computed using the EpiMitClocks R package 14 . Statistical significance was defined as P-value < 0.05. Declarations Competing interests The authors declare that they have no competing interests. Author contributions F.R.S. conceptualized the study. Y.W. developed method development and all statistical analyses. All authors contributed to the interpretation of the findings. Y.W. and F.R.S. both contributed to the writing, reviewing, and editing. Materials & Correspondence Correspondence and requests for materials should be addressed to Fredrick R. Schumacher References Tomasetti C, Vogelstein B (2015) Variation in cancer risk among tissues can be explained by the number of stem cell divisions. 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Nat Genet 55:112–122 Weinstein JN et al (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45:1113–1120 Rudnick PA et al (2016) A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline. J Proteome Res 15:1023–1032 Troyanskaya O et al (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17:520–525 Surveillance Epidemiology, and End Results Program. SEER https://seer.cancer.gov/index.html Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTable1.xlsx Supplementary Table 1 SupplementaryTable2.csv Supplementary Table 2 Supplementaryinformation.pdf Supplementary Figures Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9013656","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":600142464,"identity":"53e25ef3-5d8c-4121-91d3-f15b927d3916","order_by":0,"name":"Fredrick Schumacher","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBAC9gYGxgcfoBwJorTwHGBgNpxBqhY2aR7StEgffyBtU2Nnz8/AfPA2D2ENQC18OQbGOceSE2c2sCVbE6XFnoeHITm3gTnB4ACPmTRxtvCwPzhs2VBvb3+A/xuxWhgMmxkbDjNuYOBhI1YLjzFjz7HjiTMOsxlbziHSYc9//Kiptudvb3544w0xWhCAmTTlo2AUjIJRMArwAQC4YSebij8QvAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3073-7463","institution":"Case Western Reserve University","correspondingAuthor":true,"prefix":"","firstName":"Fredrick","middleName":"","lastName":"Schumacher","suffix":""},{"id":600142465,"identity":"508af6ea-a88f-43ed-bfea-f5e203246fc7","order_by":1,"name":"Yanning Wu","email":"","orcid":"","institution":"Case Western Reserve University","correspondingAuthor":false,"prefix":"","firstName":"Yanning","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-03-02 20:46:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9013656/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9013656/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104206754,"identity":"8346dee4-9e41-406f-bdc3-c9ed587f93d6","added_by":"auto","created_at":"2026-03-09 06:58:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":523870,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of PMD solo-WCGW CpGs across six tissues\u003c/p\u003e\n\u003cp\u003eNote: PC1 represents the first principal component, PC2 represents the second principal component, PC3 represents the third principal component\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9013656/v1/1045d16cd7e4a6b3e333074c.png"},{"id":104206755,"identity":"3968f343-945b-4e76-b2fe-8dcc8dc70c11","added_by":"auto","created_at":"2026-03-09 06:58:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":256909,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between mitotic clock estimates and experimentally derived lifetime stem cell divisions across five tissues\u003c/p\u003e\n\u003cp\u003eNote: lscd represents the total number of stem cell divisions occurring over a tissue’s lifetime\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9013656/v1/6c493ed64cdccf0cfc91c7d1.png"},{"id":104206758,"identity":"9954ea52-1629-4704-8e9b-7dbe5bfa29cc","added_by":"auto","created_at":"2026-03-09 06:58:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":346121,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between mitotic clock estimates and cancer incidence\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9013656/v1/c1bfb7677ee639be0ce465fe.png"},{"id":106039199,"identity":"f34f1b50-2500-40fd-a51d-f037a60a2694","added_by":"auto","created_at":"2026-04-02 17:06:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1853474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9013656/v1/664bf92c-327c-45e6-9741-8c9bef33c35b.pdf"},{"id":104206757,"identity":"cd4d6004-7a10-4306-b390-7e9b97e6bb6d","added_by":"auto","created_at":"2026-03-09 06:58:31","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44292,"visible":true,"origin":"","legend":"Supplementary Table 1","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9013656/v1/4c9b04448b9225935114cfa3.xlsx"},{"id":104206763,"identity":"a4dc343b-1e92-4869-9db5-852640adf4cf","added_by":"auto","created_at":"2026-03-09 06:58:33","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":702832,"visible":true,"origin":"","legend":"Supplementary Table 2","description":"","filename":"SupplementaryTable2.csv","url":"https://assets-eu.researchsquare.com/files/rs-9013656/v1/338a78ba69f44e3a9c2dd1fe.csv"},{"id":104206756,"identity":"4b5efa74-d5a7-4e2d-bef4-2f4aff964bce","added_by":"auto","created_at":"2026-03-09 06:58:30","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":166388,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"Supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9013656/v1/9dbcf050b730b0eb967da99c.pdf"},{"id":104206752,"identity":"7fc7dd67-459a-4096-8e8d-2869cdd34bd5","added_by":"auto","created_at":"2026-03-09 06:58:29","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20844,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9013656/v1/1878551047df55006767dffc.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Tissue-specific Mitotic Clocks Link Cumulative Stem Cell Divisions to Human Cancer Susceptibility","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccumulating evidence indicates that the variation of lifetime cancer risk across human tissues is strongly associated with the total number of stem cell divisions (TNSD) that occur within each tissue over a lifetime\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Among the molecular alterations that arise during cellular proliferation, DNA methylation (DNAm) changes \u0026ndash; resulting from imperfect maintenance during DNA replication \u0026ndash; accumulate with successive stem cell divisions, capturing both the biological progression and proliferative history of a tissue, and may ultimately predispose certain cell populations to the potential of malignant transformation\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo quantify this process, several DNAm-based mitotic clocks have been developed to estimate TNSD using distinct modeling strategies. Some clocks are tailored to specific tissues or cancer types. For example, Duran-Ferrer \u003cem\u003eet al.\u003c/em\u003e introduced epiCMIT, a mitotic clock specifically designed to reflect the proliferative history of B-cell tumors, using both hyper- and hypomethylated CpGs in silent chromatin regions\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Other clocks target broader application across tissues. The MiAge calculator (Wang \u003cem\u003eet al.\u003c/em\u003e) applied a statistical model based on methylation transmission theory to tumor and adjacent normal data using publicly-available data from The Cancer Genome Atlas (TCGA) program sponsored by the National Cancer Institute, identifying increasing-methylated probes that are informative of mitotic activity across tissues\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. A research group led by Andrew Teschendorff has developed a series of mitotic clocks \u0026ndash; EpiTOC1\u003csup\u003e12\u003c/sup\u003e, EpiTOC2\u003csup\u003e13\u003c/sup\u003e, and StemTOC\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e \u0026ndash; primarily derived from whole blood DNAm data. EpiTOC1 focused on CpG sites located within the promoter regions of Polycomb group target (PCGT) genes that are unmethylated in fetal tissues but progressively gain methylation with chronological age. EpiTOC2 refined this model by introducing mathematical formulations to estimate TNSD based on CpGs from EpiTOC1. StemTOC further extended this framework by implementing a multi-step filtering procedure that integrated DNAm data from fetal tissues, in-vitro cell lines, and in-vivo whole blood samples.\u003c/p\u003e \u003cp\u003eMost established DNAm-based mitotic clocks relied on a core assumption: chronological age serves as a direct surrogate for mitotic age. This assumption is applied in multiple ways \u0026ndash; either explicitly, by regressing CpGs methylation levels against chronological age to identify mitotically informative CpGs during model development, or the implicit validation benchmark where a strong correlation with chronological age is taken as evidence of model credibility. However, this general assumption warrants closer scrutiny: biological aging (e.g. cumulative stem cell divisions\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e) is unlikely follow a simple linear relationship with chronological age, as the rate of cellular turnover varies across life stages such as puberty and menopause, and is further influenced by many factors including inflammation\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, oxidative stress\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and microenvironment\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In contrast, chronological age reflects merely the passage of time. Thus, these clocks may, in fact, better predict chronological aging but remain limited in capturing the complex biological processes that underpin mitotic activity.\u003c/p\u003e \u003cp\u003eAdvancing beyond chronological age, we propose that global partially methylated domains (PMDs) hypomethylation offer a more precise surrogate of TNSD. Laird \u003cem\u003eet al.\u003c/em\u003e first characterized PMDs using whole-genome bisulfite sequencing of TCGA adjacent normal tissues, identifying widespread hypomethylation specifically at solo-WCGW CpGs \u0026ndash; isolated CpG sites occurring in an A/T \u0026ndash; C \u0026ndash; G \u0026ndash; A/T (WCGW) sequence context \u0026ndash; within PMDs\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. They observed that these sites are initially hypermethylated but progressively lose methylation as cells divide, suggesting that methylation loss at PMD solo-WCGWs reflects TNSD rather than the passage of time\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. A subsequent study from the same group provided experimental evidence using primary human cell cultures, showing that loss of DNA methylation at PMD solo-WCGWs tracks cumulative population doublings\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings demonstrated that global hypomethylation at PMD solo-WCGW sites (global PMD hypomethylation) represents a biologically grounded and experimentally validated proxy for TNSD, offering a more accurate measure of proliferative history compared to chronological age.\u003c/p\u003e \u003cp\u003eHowever, global PMD hypomethylation identified by Laird \u003cem\u003eet al.\u003c/em\u003e relied on a single adjacent normal sample per tissue type, which may not adequately capture tissue-specific methylation variation. In addition, several studies have reported tissue-specific differences in DNAm-based aging and emphasized the importance of developing tissue-specific biomarkers of aging\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therefore, we aim to build and validate tissue-specific mitotic clocks to estimate TNSD for six common tissue types. Unlike many existing models based on TCGA adjacent \u0026ldquo;normal\u0026rdquo; tissues, which potentially are impacted by field effects\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e or pre-neoplastic epigenetic alterations\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, we used Genotype-Tissue Expression (GTEx) normal tissue to minimize these potential biases and enhance biological validity.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eDescriptive statistics of GTEx data\u003c/h2\u003e\n \u003cp\u003eDNAm data were generated for 836 tissue samples obtained from 421 donors in the GTEx cohort, spanning six tissue types. The characteristics of GTEx donors for each tissue type are summarized in Table 1. Each donor contributed from 1 to 6 tissue samples. Donor ages ranged from 20 to 70 years, with a mean age of 54.4 ± 12.1 years; notably, approximately 90% of donors across all tissue types were aged over 40, indicating the samples used for developing tissue-specific mitotic clocks were biologically mature. For non-sex-specific tissues, more than 70% of samples were derived from male donors. The cohort was predominantly White (86.1%), with a mean BMI of 27.1 ± 4.0 kg/m\u003csup\u003e2\u003c/sup\u003e. Additionally, 70.1% and 77.2% of donors reported smoking and alcohol use, respectively. Missing data across demographic and lifestyle variables were minimal (\u0026lt; 1%).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eTissue-specific methylation variation within PMDs\u003c/h3\u003e\n\u003cp\u003eLarid \u003cem\u003eet al.\u003c/em\u003e identified 26,732 PMD solo-WCGW CpGs represented on the EPIC array, of which 23,328 (87.2%) passed quality control (QC) in GTEx data and were retained for downstream analysis. The distribution of probes that did not pass QC showed no chromosome-specific bias (Supplementary Table\u0026nbsp;2). Using methylation levels at these CpGs, we performed PCA across 836 tissue samples from 421 GTEx donors to assess tissue-level methylation structure. As shown in Fig.\u0026nbsp;1, the first principal component (PC1) clearly separates ovarian samples from other tissues, while the remaining five tissue types form well-defined clusters. This pattern indicates substantial tissue-specific methylation variation within PMDs at the molecular level. However, the second (PC2) and third (PC3) principial components are non-informative.\u003c/p\u003e\n\u003cp\u003eTo determine whether the observed tissue clustering was driven by inter-individual differences or chronological age-related effects, we repeated the PCA using matched multi-tissue samples from the same individuals. A total of 10 donors contributed matched breast, colon and ovary samples. As shown in Supplementary Figure S1, distinct tissue clusters were preserved. In contrast, comparison of global PMD hypomethylation across these 3 matched tissues showed no statistically significant differences (P-value = 0.089; Supplementary Figure S2). Together, these results demonstrate tissue-specific methylation variation at PMD solo-WCGW CpGs, likely reflecting distinct tissue-specific mitotic aging dynamics that are not detectable using global PMD methylation measures, thereby providing strong evidence for the necessity of developing tissue-specific mitotic clocks.\u003c/p\u003e\n\u003ch3\u003eConstruction and validation of tissue-specific mitotic clocks\u003c/h3\u003e\n\u003cp\u003eWe hypothesized that global PMD hypomethylation represents a more precise surrogate for TNSD than chronological age. Accordingly, we trained tissue-specific mitotic clocks for each tissue type using GTEx data (Methods). For each tissue type, a subset of informative CpGs and corresponding coefficient was identified (Supplementary Table\u0026nbsp;1). For a given sample, the tissue-specific mitotic clock generates a tissue-specific hypomethylation score, by summing the weighted contributions of tissue-specific CpGs using their corresponding model coefficients. Lower scores indicate a higher degree of global PMD hypomethylation and therefore correspond to a higher estimated TNSD.\u003c/p\u003e\n\u003cp\u003eNext, we evaluated generalizability of the tissue-specific mitotic clocks using TCGA adjacent normal samples as an independent external validation cohort for the corresponding six tissue types (Table\u0026nbsp;2). Across tissues, we observed a consistent systematic bias in model estimates, ranging from − 0.193 in ovary to -0.359 in kidney. RMSE values ranged from 0.19 to 0.359, reflecting overall deviation of predictions from observed values. Given the 0–1 scale of tissue-specific mitotic clock estimates, an RMSE of ~ 0.3 indicates moderate deviations on average, suggesting that overall TNSD trends are well captured.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eExternal validation of tissue-specific mitotic clocks on TCGA adjacent normal samples\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTissue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNumber of Samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArray\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePerformance metrics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBias\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHM450k\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHM450k\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHM450k\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHM450k\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHM450k\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOvary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHM27k\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eRMSE\u003c/em\u003e root mean square error; \u003cem\u003eHM450k\u003c/em\u003e Illumina HumanMethylation450 BeadChip; \u003cem\u003eHM27k\u003c/em\u003e Illumina HumanMethylation27 BeadChip\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo further assess model performance while mitigating platform-related bias, we performed additional external validation using CPTAC adjacent normal samples generated on the EPIC array, consistent with the array used for model training. Due to limited tissue availability, only kidney and lung samples were included. Validation results are summarized in Table\u0026nbsp;3. The results revealed minimal bias (− 0.009 for lung and − 0.028 for kidney) and very low RMSE values (0.021 for lung and 0.029 for kidney), indicating that the predicted tissue-specific hypomethylation score closely matched observed global PMD hypomethylation.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eExternal validation of tissue-specific mitotic clocks on CPTAC adjacent normal samples\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTissue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNumber of Samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArray\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePerformance metrics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBias\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEPIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEPIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eRMSE\u003c/em\u003e root mean square error; \u003cem\u003eEPIC\u003c/em\u003e Illumina Infinium MethylationEPIC BeadChip\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTissue-specific mitotic clocks correlate with lifetime stem cell divisions of a given tissue and cancer risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTomasetti and Vogelstein previously reported that lifetime cancer risk across tissues is strongly associated with TNSD occurring over a tissue’s lifetime (lscd)\u003csup\u003e1,2\u003c/sup\u003e. Lscd reflects both the total number of stem cells in a fully developed tissue, and TNSD for each stem cell undergoes over a lifetime. Because direct quantification of lscd in vivo is challenging, they employed a mathematical approach based on experimentally observed data to derive estimates, including tracing cell lineage with markers (like viral tags), and functional assays such as the mammosphere assay, which assess self-renewal rates in vitro, and linked observed cell numbers and division frequencies to total lifetime activity and cancer risk\u003csup\u003e1,2\u003c/sup\u003e. Reported lscd values for selected tissues were: breast (lscd = 3.03 × 10\u003csup\u003e12\u003c/sup\u003e), ovary (lscd = 2.20 × 10\u003csup\u003e7\u003c/sup\u003e), prostate (lscd = 5.08 × 10\u003csup\u003e10\u003c/sup\u003e), lung (lscd = 9.27 × 10\u003csup\u003e9\u003c/sup\u003e), and colon (lscd = 1.17 × 10\u003csup\u003e12\u003c/sup\u003e). To determine whether our tissue-specific mitotic clock and other established mitotic clocks can capture this tissue-level mitotic burden, we applied each clock to multiple TCGA adjacent normal samples per tissue and averaged the estimates to derive tissue-level proxies of lscd. As shown in Fig.\u0026nbsp;2, the tissue-specific mitotic clocks exhibited a strong association with experimentally derived lscd (cor = -0.745, P-value = 0.15), whereas other existing mitotic clocks, including epiTOC1, epiTOC2, and stemTOC, showed weak correlations (cor = 0.14, 0.01, 0.2 respectively). These results indicate that the tissue-specific clocks can effectively reflect TNSD at the tissue level. Notably, the observed trends are biologically coherent and markedly stronger than those produced by existing comparator clocks.\u003c/p\u003e\n\u003cp\u003eWe further examined whether the estimated lscd was associated with cancer risk to test the hypothesis by Tomasetti and Vogelstein\u003csup\u003e1,2\u003c/sup\u003e. As shown in Fig.\u0026nbsp;3, the tissue-specific mitotic clock estimates demonstrated a moderate positive association with cancer incidence (cor = 0.32, P-value = 0.53), whereas other established mitotic clocks showed no meaningful correlations (cor = 0.09, -0.15, 0.06 respectively). Although this association did not reach statistical significance, the observed trend supports the hypothesis that cumulative mitotic activity contributes to tissue-specific cancer risk and further highlights the biological relevance of the tissue-specific mitotic clock. In contrast, the lack of association observed for the other mitotic clocks suggests that they may be less sensitive to tissue-specific variation in cumulative mitotic activity relevant to cancer risk.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed and validated tissue-specific mitotic clocks to estimate TNSD for six human tissues. By leveraging global hypomethylation at PMD solo-WCGW sites as a biological surrogate of mitotic activity \u0026mdash; rather than relying on chronological age \u0026mdash; our clocks significantly outperformed established mitotic clocks in capturing the distinct proliferative histories of different organs. Specifically, our models exhibited minimal estimation bias when validated in an independent, array-consistent CPTAC cohort.\u003c/p\u003e \u003cp\u003eOur tissue-specific mitotic clocks generated directionally consistent associations with reported lscd and tissue-specific cancer incidence, supported by strong effect sizes and rank-order concordance. Although these relationships were statistically underpowered due to the limited number of tissue types available for analysis. In this context, the biological coherence and effect magnitude, rather than statistical significance, are most informative, and both were markedly stronger for our tissue-specific mitotic clocks than for existing comparator clocks. Chronological age-trained clocks primarily capture systemic aging signals. In contrast, our approach targets replication-coupled epigenetic erosion and loss of epigenetic integrity. This biological distinction likely explains why existing mitotic clocks perform well for age prediction but poorly for correlating tissue-level cancer risk.\u003c/p\u003e \u003cp\u003eThe improved performance supports our central assumption. Unlike existing mitotic clocks, which are largely pan-tissue and assume a universal set of CpG loci behaves consistently across tissues, our study based on the premise that different tissues exhibit unique intrinsic aging rates and molecular signatures. As a result, a universal model insufficient to capture this biological heterogeneity. We empirically support this hypothesis through PCA of 836 tissue samples from 421 GTEx donors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PMD methylation profiles robustly distinguished tissue types, suggesting that inter-tissue epigenetic differences reflect variation in biological aging dynamics rather than stochastic noise. To account for the potential confounding of chronological age, we further analyzed matched breast, colon, and ovary tissues from 10 donors (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The persistence of clear tissue separation in this controlled, intra-individual setting confirms that these epigenetic differences are tissue-intrinsic rather than age-dependent. These observations align with prior findings by Pierce et al.\u003csup\u003e22,23\u003c/sup\u003e and Schaefer et al.\u003csup\u003e24\u003c/sup\u003e, which also highlighted significant tissue-specific heterogeneity in DNA methylation aging signatures. Collectively, our results demonstrate that accurate quantification of proliferative history requires tissue-specific mitotic clocks rather than a global, pan-tissue model.\u003c/p\u003e \u003cp\u003eNotably, despite the clear tissue-specific clustering observed in the PCA of matched tissue samples (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), global PMD hypomethylation levels were not statistically significantly different across the same cohort (Figure S2). Although this lack of significance in global comparisons may partly reflect limited statistical power due to the small sample size (N\u0026thinsp;=\u0026thinsp;10), it more fundamentally highlights a methodological limitation: averaging thousands of CpG measurements into a single global metric could dilute biological signal and reduce sensitivity to biologically meaningful differences. Conversely, multi-dimensional reduction techniques, such as PCA, retain the covariance structure across all CpG sites, allowing for the detection of subtle yet consistent patterns that are often masked in univariate analyses, particularly in studies with limited sample sizes. This interpretation is supported by prior studies. Farr\u0026eacute; et al. demonstrated that PCA can identify major sources of variation (like tissue type, cell type, age) across methylation profiles on the matched brain and blood samples from 17 individuals\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Furthermore, Eulalio et al. developed \u003cem\u003eregionalpcs\u003c/em\u003e approach, which uses PCA to summarize methylation across CpG clusters and achieved a\u0026thinsp;~\u0026thinsp;54% improvement in sensitivity over mean-based metrics\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Together, these findings suggest that reliance on global methylation averages may underestimate tissue-specific epigenetic heterogeneity, reinforcing the advantage of multi-dimensional approach for detecting biologically meaningful differences.\u003c/p\u003e \u003cp\u003eDuring external validation of our tissue-specific mitotic clocks on TCGA adjacent normal data, we observed a consistent systematic bias across tissues. This bias might be attributed to two primary factors. First, the tissue-specific mitotic clocks capture tissue-level methylation variability that is not fully represented by the global PMD hypomethylation, leading to deviations from the global PMD estimate. Second, TCGA methylation data were primarily generated using the HM450k array, which has limited probe coverage compared to the EPIC array used for model training, thereby introducing systematic bias due to missing CpG measurements. To mitigate this technical effect and more accurately assess model performance, we further validated our models on additional CPTAC adjacent normal samples profiled on the EPIC array. In this platform-consistent setting, systematic bias was largely shrunk, demonstrating that array consistency substantially improves model prediction accuracy. These results confirm that our tissue-specific mitotic clocks are highly robust and generalizable on independent datasets generated with compatible platforms. Accordingly, the bias observed in TCGA validation is most plausibly attributed to limitations of the legacy HM450k design, which lacks the probe density required for precise quantification, although such datasets remain informative for capturing broad directional trends. Array technologies are converging asymptotically toward a stable measurement regime, with marginal performance gains across successive updates. As next-generation, higher-density arrays such as EPIC continue to be widely adopted, platform-related bias will become negligible, ensuring the robustness and broad applicability of our mitotic clock models. While whole-genome bisulfite sequencing provides a useful technical reference for methylation measurement, the degree to which array-based platforms approximate sequencing-level resolution remains an open area for future systematic benchmarking.\u003c/p\u003e \u003cp\u003eOur study has several notable strengths. First, it represents the first systematic effort to construct tissue-specific DNA methylation-based mitotic clocks using biologically \u0026ldquo;normal\u0026rdquo; samples from the GTEx project. Unlike prior mitotic clocks derived from TCGA adjacent normal tissues, the precision of our training data minimizes potential confounding from field effects\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e or pre-neoplastic epigenetic alterations\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Field cancerization refers to the presence of molecularly altered but histologically normal cells surrounding tumors, arising from early clonal expansions during tumorigenesis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These altered fields can exhibit elevated proliferative activity and replication-associated epigenetic and genetic changes\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. As a result, adjacent \u0026ldquo;normal\u0026rdquo; tissues may carry inflated mitotic signals that do not reflect baseline tissue turnover. Training mitotic clocks on TCGA normal-adjacent samples may therefore bias TNSD estimates upward and distort tissue-specific proliferative histories.\u003c/p\u003e \u003cp\u003eSecond, by modeling global PMD hypomethylation at solo-WCGW CpGs, our approach captures a biologically validated and experimentally supported biomarker of TNSD, rather than relying on chronological age as a surrogate. Finally, our tissue-specific estimates exhibit stronger associations with reported lscd and cancer risk compared to existing clocks, providing empirical support for the Tomasetti and Vogelstein hypothesis linking cumulative stem cell divisions to cancer susceptibility.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations should be acknowledged. Sample sizes for breast and kidney training sets were modest, which may constrain model precision. Additionally, while our colon model was trained exclusively on transverse colon samples, the TCGA validation cohort included a mixture of ascending, transverse, and descending colon tissues, which could contribute to the observed bias. Lastly, although correlation analyses revealed strong associations with reported lscd and cancer risk, the evaluation was limited to six tissue types, reducing the statistical power to fully characterize the relationship between TNSD and cancer incidence across the human body.\u003c/p\u003e \u003cp\u003eBeyond methodological advances, tissue-specific mitotic clocks also have several potential applications. They may help identify tissues or individuals with unusually high proliferative burden, indicating elevated baseline cancer susceptibility and supporting risk stratification. In addition, these measurements may help inform cancer prevention strategies by highlighting organs in which reducing proliferative stress or enhancing genomic maintenance could yield the greatest benefit. In addition, our mitotic clocks provide a quantitative baseline of replication-driven risk, enabling separation of intrinsic proliferative contributions from external exposure effects. By establishing the expected cancer risk attributable to endogenous cell turnover, this framework supports more precise estimation of the incremental risk associated with environmental or behavioral factor. Together, these applications position tissue-specific mitotic clocks as a tool for both mechanistic insight and population-level risk assessment.\u003c/p\u003e \u003cp\u003eIn summary, we demonstrate that tissue-specific mitotic clocks provide a more accurate measure of proliferative history than pan-tissue models. This finding highlights the importance of accounting for tissue heterogeneity in modeling mitotic aging. Future work could expand this framework to larger cohorts and more diverse tissue panels will be critical for improving model generalizability and biological relevance, as well as for more precisely characterizing the relationship between TNSD and cancer risk.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGTEx data for model development\u003c/h2\u003e \u003cp\u003eGTEx project is a large-scale, publicly accessible biobank designed to investigate the association between genetic variation and gene expression across human tissues\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. It includes samples from approximately 960 postmortem donors who predominantly died from acute, non-disease-related causes\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Each donor contributed multiple tissue types and only samples confirmed to be free of significant disease determined by pathological examination and medical record review were included to ensure the representation of true normal tissues\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The GTEx v8 release comprises 15,201 RNA-seq samples from 838 donors across 49 tissue types, with detailed demographic and technical metadata\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The enhancing GTEx (eGTEx) project expands this resource by adding epigenomic and proteomic profiling. Among these, DNA methylation was measured using the Illumina Infinium MethylationEPIC BeadChip (EPIC)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe obtained eGTEx methylation data from the Gene Expression Omnibus (GEO; accession number GSE213478\u003csup\u003e36\u003c/sup\u003e). Quality control and preprocessing were performed by Pierce \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e22\u003c/sup\u003e The dataset contains 754,119 probes across 987 samples from 9 normal tissue types. For model construction, we selected six tissues representing common sites of cancer origin: breast mammary tissue (n\u0026thinsp;=\u0026thinsp;52), colon transverse (n\u0026thinsp;=\u0026thinsp;224), kidney cortex (n\u0026thinsp;=\u0026thinsp;50), lung (n\u0026thinsp;=\u0026thinsp;223), ovary (n\u0026thinsp;=\u0026thinsp;164), and prostate (n\u0026thinsp;=\u0026thinsp;123). All samples were derived from 421 donors in the GTEx cohort.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTCGA and CPTAC data for external validation\u003c/h3\u003e\n\u003cp\u003eWe downloaded DNA methylation data of adjacent normal tissue for six selected tissue types from TCGA via the Genomic Data Commons (GDC) portal\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Although TCGA provides preprocessed data, additional quality control was performed to exclude low-quality samples and probes with detection \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05. After quality control, the external validation cohort included female breast (n\u0026thinsp;=\u0026thinsp;97), colon (n\u0026thinsp;=\u0026thinsp;38), kidney (n\u0026thinsp;=\u0026thinsp;205), lung (n\u0026thinsp;=\u0026thinsp;74), ovary (n\u0026thinsp;=\u0026thinsp;12) and prostate (n\u0026thinsp;=\u0026thinsp;50). All samples were profiled using the Illumina HumanMethylation450 BeadChip (HM450k), except for ovarian tissue, which was generated using the Illumina HumanMethylation27 (HM27k) platform.\u003c/p\u003e \u003cp\u003eTo ensure consistency with our Illumina Infinium EPIC array-based training model, we further obtained an additional external validation dataset from the Clinical Proteomic Tumor Analysis Consortium (CPTAC)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. CPTAC extends TCGA by performing deep proteomic characterization of tumor and adjacent normal samples, complementing genomic and epigenomic data to enable integrated proteogenomic analyses of cancer\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Because CPTAC profiled a limited number of tissues, only kidney (n\u0026thinsp;=\u0026thinsp;280) and lung (n\u0026thinsp;=\u0026thinsp;159) adjacent normal samples were included. In both validation datasets, DNA methylation levels were represented as β-values.\u003c/p\u003e\n\u003ch3\u003eCalculation of global PMD hypomethylation\u003c/h3\u003e\n\u003cp\u003eGlobal DNA hypomethylation was quantified using PMD solo-WCGW CpG sites. Although PMD solo-WCGWs were initially identified using WGBS, Laird \u003cem\u003eet al.\u003c/em\u003e demonstrated that methylation levels at these sites measured on microarray platforms (e.g. HM450k) strongly correlate with WGBS measurements and reliably capture PMD hypomethylation patterns despite limited probe coverage\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Building on this work, we used a curated set of 26,732 PMD solo-WCGW CpGs present on EPIC array. Specifically, only probes targeting solo-WCGW CpGs were retained, while those located within annotated CpG islands or exhibiting low methylation levels (β\u0026thinsp;\u0026lt;\u0026thinsp;0.2) were excluded\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For each sample, the average β-value across the retained probes was computed to derive global PMD hypomethylation, reflecting large-scale methylation loss across PMDs. This measure was used as the outcome variable in our model development.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTissue-specific mitotic clock construction\u003c/h2\u003e \u003cp\u003eWe constructed tissue-specific mitotic clocks using the following strategy: for each tissue type, samples were randomly partitioned into training (70%) and testing (30%) subsets. An elastic net regression model with alpha parameter\u0026thinsp;=\u0026thinsp;0.5 (R package \u0026lsquo;glmnet\u0026rsquo;) was trained to predict the global PMD hypomethylation, derived from CpG β-values within each tissue. Sex was included as an unpenalized covariate to account for sex-related effects, except in inherently sex-specific tissues such as prostate and ovary. For breast tissue, only female samples were analyzed given the markedly lower incidence rate of breast cancer in males. Model robustness was evaluated using five-fold cross-validation. The final set of CpGs selected for each tissue type was used to construct tissue-specific mitotic clocks. Coefficients for each tissue-specific model are provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R version 4.3.2. Tissue-specific methylation variation was evaluated using principal component analysis (PCA) on PMD solo-WCGWs beta values. Global PMD hypomethylation differences across matched tissues were assessed using repeated-measures analysis of variance (RMANOVA).\u003c/p\u003e \u003cp\u003eMissing values in CPTAC methylation data were imputed with impute.knn (R Bioconductor package \u0026lsquo;impute\u0026rsquo;)\u003csup\u003e39\u003c/sup\u003e. For external validation, the global PMD hypomethylation served as the observed outcome, and predicted tissue-specific hypomethylation score as predicted values; model performance was quantified using estimation bias and root mean squared error (RMSE). Pearson correlation was used to evaluate associations between mitotic clock estimates and experimentally derived lifetime stem cell divisions (lscd), as well as cancer incidence rates. Age-adjusted cancer incidence rates were obtained from the Surveillance, Epidemiology, and End Results (SEER) database\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Existing mitotic clocks, including epiTOC1, epiTOC2, and stemTOC2, were computed using the EpiMitClocks R package\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Statistical significance was defined as P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.R.S. conceptualized the study. Y.W. developed method development and all statistical analyses. All authors contributed to the interpretation of the findings. Y.W. and F.R.S. both contributed to the writing, reviewing, and editing. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eMaterials \u0026amp; Correspondence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to Fredrick R. Schumacher\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTomasetti C, Vogelstein B (2015) Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science 347:78\u0026ndash;81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomasetti C, Li L, Vogelstein B (2017) Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355:1330\u0026ndash;1334\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomasetti C et al (2017) Role of stem-cell divisions in cancer risk. 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Cell Stem Cell 29:1273\u0026ndash;1284e8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu C, Luo J, He L, Montell C, Perrimon N (2017) Oxidative stress induces stem cell proliferation via TRPA1/RyR-mediated Ca2\u0026thinsp;+\u0026thinsp;signaling in the Drosophila midgut. \u003cem\u003eeLife\u003c/em\u003e 6, e22441\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScodellaro C et al (2024) Unlocking the Potential of Stem Cell Microenvironments In Vitro. \u003cem\u003eBioengineering\u003c/em\u003e 11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou W et al (2018) DNA methylation loss in late-replicating domains is linked to mitotic cell division. Nat Genet 50:591\u0026ndash;602\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndicott JL, Nolte PA, Shen H, Laird PW (2022) Cell division drives DNA methylation loss in late-replicating domains in primary human cells. 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J Natl Cancer Inst 116:974\u0026ndash;982\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeschendorff AE et al (2016) DNA methylation outliers in normal breast tissue identify field defects that are enriched in cancer. Nat Commun 7:10478\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026oslash;ller M et al (2017) Heterogeneous patterns of DNA methylation-based field effects in histologically normal prostate tissue from cancer patients. Sci Rep 7:40636\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaavedra KP, Brebi PM, Roa JC (2012) Epigenetic alterations in preneoplastic and neoplastic lesions of the cervix. Clin Epigenetics 4:13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarr\u0026eacute; P et al (2015) Concordant and discordant DNA methylation signatures of aging in human blood and brain. Epigenetics Chromatin 8:19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEulalio T et al (2025) regionalpcs improve discovery of DNA methylation associations with complex traits. Nat Commun 16:368\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillenbrink TJ et al (2020) Field cancerization: Definition, epidemiology, risk factors, and outcomes. J Am Acad Dermatol 83:709\u0026ndash;717\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemaria S et al (2010) Cancer and inflammation: promise for biologic therapy. J Immunother 33:335\u0026ndash;351\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLonsdale J et al (2013) The Genotype-Tissue Expression (GTEx) project. Nat Genet 45:580\u0026ndash;585\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe GTEx Consortium atlas of genetic regulatory effects across human tissues (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStranger BE et al (2017) Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease. Nat Genet 49:1664\u0026ndash;1670\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliva M et al (2023) DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits. Nat Genet 55:112\u0026ndash;122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeinstein JN et al (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45:1113\u0026ndash;1120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRudnick PA et al (2016) A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline. J Proteome Res 15:1023\u0026ndash;1032\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTroyanskaya O et al (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17:520\u0026ndash;525\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSurveillance Epidemiology, and End Results Program. \u003cem\u003eSEER\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seer.cancer.gov/index.html\u003c/span\u003e\u003cspan address=\"https://seer.cancer.gov/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9013656/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9013656/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Why cancer risk varies so dramatically across human tissues remains a fundamental question in oncology. While this variation has been linked to differences in total stem cell divisions (TNSD), molecular tools for measuring tissue-specific proliferative history are lacking. Existing DNA methylation-based mitotic clocks conflate chronological aging with mitotic activity and apply pan-tissue assumptions that obscure tissue-level turnover dynamics. Here, we develop tissue-specific mitotic clocks for six major tissues by modeling global hypomethylation at solo-WCGW CpG sites within partially methylated domains (PMDs) — an experimentally validated, age-independent marker of cumulative cell division — using biologically normal tissue from the Genotype-Tissue Expression (GTEx) project. This design explicitly avoids confounding from chronological age and field cancerization. Our tissue-specific clocks demonstrate strong generalizability, replicate across independent cohorts, and outperform existing pan-tissue clocks in recapitulating Vogelstein and Tomasetti-derived (2017) TNSD estimates and SEER cancer incidence data. These results provide direct molecular evidence that cumulative cellular turnover underlies tissue cancer susceptibility, and establish a precision framework for quantifying proliferative burden in human malignancies — with implications for cancer risk stratification, prevention, and mechanistic dissection of replication-driven versus exposure-related carcinogenesis.","manuscriptTitle":"Tissue-specific Mitotic Clocks Link Cumulative Stem Cell Divisions to Human Cancer Susceptibility","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 06:58:18","doi":"10.21203/rs.3.rs-9013656/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6965447d-078b-44ae-95cf-7f04c32be95c","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63863696,"name":"Biological sciences/Cancer/Cancer epidemiology"},{"id":63863697,"name":"Biological sciences/Genetics/Epigenetics/DNA methylation"},{"id":63863698,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2026-04-02T17:06:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 06:58:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9013656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9013656","identity":"rs-9013656","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}