One of the main risk factors for breast cancers is aging. But how exactly aging contributes to breast cancer is not fully understood. The mammary gland is the part of the breast that can produce milk, and is composed of two major cell types: luminal epithelial cells and myoepithelial cells. Most age-related breast cancers are thought to originate from luminal epithelial cells, but some age-related changes in myoepithelial cells may also cause cancer. These changes in breast epithelial cells vary across individuals, which may explain why some individuals are at a higher risk for breast cancer than others.

Studying age-related changes in luminal and myoepithelial cells may help scientists pinpoint what causes age-related breast cancers. It may also help scientists to identify those at high risk for breast cancer, or develop alternative treatments. It could also help prevent breast cancers from occuring, getting worse or coming back. Studying age-related changes in gene expression in these two types of breast epithelial cells is the first step.

Sayaman et al. showed that aging leads to widespread changes in the genes expressed by luminal and myoepithelial cells. The researchers compared luminal and myoepithelial cells from breast tissue samples taken from postmenopausal women older than 55 years and premenopausal women younger than 30 years. Their experiments and analyses revealed that age-related gene expression changes reduced the cells' ability to maintain their identity and function over time. Many of the age-related expression changes occurred in cancer-linked genes.

Sayaman et al. found luminal and myoepithelial cells had increasingly varied gene expression among older women compared to younger women. Specifically, they saw changes in genes that helped these cells communicate, essentially changing the message relayed from myoepithelial cells to the neighboring luminal cells. The experiments further revealed large increases and decreases in gene expression with age in luminal cells but not in myoepithelial cells. Most of these age-related changes in luminal cells were linked to genes that play a role in cancer development.

These findings suggest that age-related changes make luminal cells more prone to becoming cancerous. Sayaman et al. also developed machine learning algorithms to identify age-related gene expression patterns that may distinguish tumor cells from normal breast tissue. More research in larger populations will help confirm the results. But if these efforts are successful, they may one day help clinicians identify women at risk of age-related breast cancers, detect cancers early, or create personalized prevention or treatment approaches.

Organisms, organs, and tissues exhibit stereotyped aging phenotypes that represent the integration of accumulated, stochastically incurred damages to individual cells that result in commonly understood hallmarks of aging (López-Otín et al., 2013; Todhunter et al., 2018). Stereotypical changes appear directional to the observer and are apparent at different physiologic scales, for example phenotypically though wrinkling, graying hairs, and increasing frailty; cellularly through increasing organ dysfunction, and loss of bone density, muscle mass and fat pads; and molecularly through decreasing levels of androgens and estrogens, and the upregulation or downregulation of gene or protein levels. Age-associated directional changes in transcriptomes of whole tissues are well documented (de Magalhães et al., 2009; Glass et al., 2013; Peters et al., 2015; Volkova et al., 2005). These directional molecular changes explain, at least in part, the noticeable phenotypic changes that accompany aging. However, although increased susceptibility to a plethora of diseases, including cancers, is a prominent consequence of aging, the emergence and onset of diseases vary among same-aged individuals. Indeed, variance in gene expression between individuals arises in the contexts of tumors, diet, and aging (Bashkeel et al., 2019; Brinkmeyer-Langford et al., 2016; de Jong et al., 2019; Sharma et al., 2018; Viñuela et al., 2018). We propose that this variability among individuals may itself be an important molecular phenotype of aging, and individuals with outlier expression profiles provide an avenue for understanding biological processes that explain the differences in cancer susceptibility between individuals as they age.

The breast is an excellent model system for examining aging at the cellular and molecular levels because normal tissue from individuals spanning the adult lifespan is available from common cosmetic and prophylactic surgeries. Cultured pre-stasis human mammary epithelial cells (HMEC) support growth of breast epithelial cell lineages from women across the lifespan (Garbe et al., 2009; Labarge et al., 2013) and enable detailed, reproducible molecular studies of cancer progression (Stampfer et al., 2013). Moreover, there are well-established lineage-specific markers and cell-sorting protocols that facilitate experimentation at lineage-specific resolution. Furthermore, breast tissue provides an ideal model for studying aging-associated cancer susceptibility as 82% of new breast cancers are diagnosed in women ≥ 50 y (DeSantis et al., 2019). Directional changes in gene expression with age have been reported in whole breast tissue, including changes associated with biological processes involved in breast cancer (Lee and Lee, 2017; Yau et al., 2007). However, aging is also associated with significant shifts in proportions of breast cell lineages, including epithelial and stromal populations (Benz, 2008; Garbe et al., 2012). Thus, it is unclear how tissue-level molecular changes during normal aging reflect changes in cell-intrinsic and microenvironment states. Lineage-specific analyses are needed to unravel such mechanisms.

The mammary epithelium, the origin of breast carcinomas, is a bilayer of two major phenotypically and functionally distinct cell lineages—myoepithelial cells (MEPs) and luminal epithelial cells (LEPs). MEPs are basally located and contractile and have tumor suppressive properties (Pandey et al., 2010). Luminal epithelial cells (LEPs) are apically located and include subpopulations of estrogen receptor negative (ER-) secretory cells, which represent 80–95% of luminal cells in breast tissue, and ER positive cells (ER+) (Booth and Smith, 2006). We previously demonstrated loss of lineage fidelity in breast epithelia as an aging phenomenon—in which the faithfulness of expression of established lineage-specific markers diminishes with age without loss of the lineage-specificity of other canonical markers nor of the gross phenotypic and histological differences between LEPs and MEPs (Miyano et al., 2017). Although our earlier work focused on a few select LEP-specific and MEP-specific markers, we hypothesized that the aging mechanisms we observed could impinge upon genome-wide maintenance of lineage fidelity and could thus be potential drivers of susceptibility to cancer initiation in breast tissue.

Here, we demonstrate how age-dependent directional and variant transcriptional responses integrate in breast epithelia and explain how these changes could lead to increased susceptibility to cancer initiation. Directional responses reflect stereotyped changes associated with upregulation or downregulation of gene expression between younger and older cohorts; variant responses reflect increases in gene expression variance within a cohort associated with the heterogeneity of individuals within a group. Through transcriptomic profiling of primary LEPs and MEPs, we found that loss of lineage fidelity in gene expression with age was a genome-wide phenomenon. We identified two models mediating loss of lineage fidelity in breast epithelia with age: (i) via directional changes identified through differential expression (DE) analysis; and (ii) via an increase in variances identified through differential variability (DV) analysis. Age-dependent DE explained part of the observed loss of lineage fidelity, while our model of the overall increase in variances with age also accounted for a comparable fraction of this loss. Directional changes in expression with age strikingly occurred almost exclusively in luminal cells, whereas changes in variance were found in both lineages. Genome-wide directional changes in gene expression in LEPs involved dysregulation of chromatin and genome organizers such as SATB1 with age. We also detected this dysregulation in bulk tissue that consist of all lineages of the stroma and epithelia. Loss of lineage fidelity led to enrichment of genes and biological processes commonly dysregulated in cancers, and altered the LEP-MEP interactome that was significantly modulated by apical cell-cell junction proteins, such as GJB6. Modulating GJB6 expression via shRNA in MEPs was sufficient to reduce the rate of molecular aging of adjacent LEPs as determined with a breast-specific biological clock. Using machine learning, we showed that genes that had age-dependent directional and variable changes in normal LEPs had predictive value in distinguishing normal breast tissue from breast cancers and in classifying breast cancer PAM50 subtypes. Age-dependent changes in LEPs reflected dysregulation of biological processes that are convergent with breast cancer. The degree and variability of age-dependent changes across individuals may explain the differential susceptibility between individuals to breast cancer initiation, and to the development of specific breast cancer subtypes.

We have shown that aging phenotypes in the mammary gland result from the integration of directional and variant responses that reshape the transcriptomic landscape of the two main epithelial lineages of the breast, the LEPs and MEPs. Transcriptomic changes lead to a loss in lineage fidelity with age where faithfulness of lineage-specific expression is diminished. This is seen via the genome-wide loss of tuned windows of expression of lineage-specific genes and decrease in the magnitude of differential expression between the genes that define LEPs and MEPs. Our approach delineated the contribution of each epithelial lineage and identified two models mediating loss of lineage fidelity in breast epithelia with age—either via directional changes, as measured by differential gene expression, or via an increase in variance, as measured by differential variability analysis. Aging-dependent expression variances occurred in both lineages, whereas the overwhelming majority of directional expression changes with age occurred in LEPs. This is a striking finding when one considers the two lineages arise from common progenitors, and that their juxtaposition in tissue allows for direct cell-cell communication.

We hypothesize that LEPs are the nexus of integration for variant responses that lead to stereotyped directional age-dependent outcomes in mammary gland. This is likely the result of a dynamic process of iterative feedback between LEPs and MEPs, as well as other cell types in the breast. LEPs from older women still maintained canonical LEP-specific features but exhibited genome-wide loss of lineage fidelity that implicated dysregulation of genes with known roles in breast cancer. This suggests that susceptibility to cancer entails loss of proper specification of the luminal lineage, and that age-dependent molecular changes in LEPs contributes to this loss. Age-dependent directional changes in LEPs were detectable in bulk tissue and implicated downregulation of chromatin and genome organizers, such as SATB1, suggesting means by which loss of lineage fidelity may be perpetuated genome-wide. The pathways affected by transcriptomic changes during aging were commonly linked with breast cancer, and the age-dependent changes in LEPs reflected relevant biology that distinguished normal tissue from breast cancers and predicted PAM50 breast cancer subtypes. Together our findings illustrate how age-dependent changes in LEPs contribute to the aging biology of breast tissue, and we propose that this biology reflects dysregulation convergent with processes associated with breast cancers.

Studies of gene expression during aging in human tissues have been largely restricted to analysis of bulk tissue lacking in cell-type-specific resolution and have been focused on directional changes with age using DE analysis. Bulk analyses make it impossible to separate effects of aging that are driven by the intrinsic changes that occur molecularly within each lineage vs. compositional changes that reflect shifts in cell type proportions. Lineage-specific analyses provide intermediate resolution between bulk RNA-seq and single-cell RNA-seq and allow for cost effective analysis of cell population-level responses and interactions. As such, lineage-resolution analyses also provide an avenue to validate computational deconvolution methods that have emerged to extract cell-type-specific contributions in bulk tissue (Shen-Orr and Gaujoux, 2013; Titus et al., 2017). We provided evidence that breast tissue-level changes with age are driven not just by changing compositions of the breast (Benz, 2008; Garbe et al., 2012), but by intrinsic molecular changes in the underlying cell populations. Indeed, while bulk tissue expression reflects cellular heterogeneity, we were able to identify age-dependent changes in bulk tissue that mirror the DE in LEPs with age, suggesting that LEPs contribute, if not drive, certain emergent properties of breast tissue.

These findings were further validated in our analysis of existing scRNA-seq studies of non-tumorigenic primary breast tissues (Murrow et al., 2022; Nee et al., 2023; Pal et al., 2021). We showed that our LEP-derived aging signature developed from lineage-specific RNA-sequencing captured aging biology of epithelial cell types at the single-cell level, with genes upregulated in older LEPs enriched in Luminal1 cells from the menopausal cohort and genes upregulated in younger LEPs enriched in Luminal1 cells from the premenopausal cohort (Pal et al., 2021). We further showed that our LEP-derived aging signature was enriched in women who had received chemotherapy and those with increased risk of developing breast cancer, lending further support to earlier findings of accelerated aging phenotypes in these groups (Shalabi et al., 2021; Siddique et al., 2021). Thus, the age-related decline in lineage fidelity within the luminal compartment was a histologically and molecularly observable phenomenon at the population level, which signified alterations in cell subpopulations. These changes appear to encompass shifts in subpopulation proportions and modifications in the gene expression of specific cell types and across defined cell states.

We found significant downregulation of the genome organizer SATB1 in LEPs and breast tissue with age (adj. p<0.05), as well as concordant dysregulation of known SATB1-target genes (Han et al., 2008). Of note, SATB1 was also downregulated in PAM50 LumA, LumB, and Her2 breast cancer subtypes relative to their matched normal tissue and had the lowest expression in luminal subtype cancers—the subtype most associated with aging. SATB1 has genome organizing functions in tumor progression (Kohwi-Shigematsu et al., 2013), and has been described as a key regulator of EMT in cancers (Naik and Galande, 2019). Although our results suggest that loss of SATB1-mediated genome organization with age is associated with the observed age-dependent dysregulation in the luminal lineage, SATB1’s role in normal breast epithelia and in the context of aging remain to be fully elucidated. Moreover, we postulate that mechanisms of SATB1-mediated genome organization in breast cancers are likely more complex than in aging normal epithelia due to the highly dysregulated state of cancers; this biology is also likely to be highly subtype-specific given the differential expression of SATB1 across breast cancer subtypes. We also identified an additional 60 genes that showed concordant age-dependent directional changes in LEPs (adj. p<0.05) and in primary tissue (unadj. p<0.05) despite the difference in platforms (RNA-seq vs. microarray). Network and community analyses showed enrichment of genes involved in chromosomal rearrangement, including BCL11A—a subunit of the BAF (SWI/SNF) chromatin remodeling complex (Kadoch et al., 2013), MLLT3—a chromatin reader component of the SEC (Moustakim et al., 2018), ZNF521—a transcription co-factor with established roles in stem cells of diverse organs and tissues (Scicchitano et al., 2019), and PHC1 and PCGF3—components of the PcG multiprotein PRC-1-like complex required for developmental maintenance of repressed but transcriptionally poised chromatin configuration through alteration of chromatin accessibility, folding, and global architecture of nuclear organization (Illingworth, 2019). BCL11A, MLLT3, ZNF521, PHC1, and PCGF3 also showed subtype-specific dysregulation in breast cancers; BCL11A, ZNF521, and PHC1 were specifically downregulated in luminal subtype cancers relative to their matched normal tissue. We speculate that chromatin and genome organization play a key role in maintaining the luminal lineage and that their dysregulation may mediate loss of lineage fidelity observed genome-wide with age.

The striking phenotypic changes in LEPs are starkly juxtaposed to MEPs, which have so far revealed few obvious signs of changes with age. Nevertheless, our experiments using heterochronous bilayers of MEPs and LEPs demonstrated that the chronological age of MEPs controls the biological age of LEPs, thus illustrating that MEPs do change with age and revealing the existence of a non-cell autonomous mechanism that integrates aging-imposed damage across the tissue (Miyano et al., 2017). Here, we showed that changes in MEPs largely involved changes in gene expression variances with age. Moreover, we showed that aging-associated increases in variances in both lineages drove a large fraction of the observed loss of lineage fidelity in epithelia of older women, comparable to the contribution of age-dependent directional changes. In our opinion, changes in variance are an underappreciated component of aging analyses. DE analysis is a ubiquitous statistical tool used in expression profiling studies, whereas only more recently have changes in variance been systematically analyzed (Bashkeel et al., 2019; Brinkmeyer-Langford et al., 2016; de Jong et al., 2019; Sharma et al., 2018; Slieker et al., 2016; Viñuela et al., 2018; Xie et al., 2011) and differential variance analytical tools developed (Phipson et al., 2016; Phipson and Oshlack, 2014; Ran and Daye, 2017).

For changes to be detected as significant in DE analysis, the following assumptions must be met: (i) the biological phenomenon causes dysregulation that is directional (e.g., genes are either upregulated or downregulated), and (ii) dysregulation occurs at the same time in the same genes, and in the same pattern across the majority of individuals in the group of interest (i.e., it is stereotypic). Although some aging processes may be deterministic, like telomere shortening, other processes may be stochastic, born from accumulated random physicochemical insults that manifest as an increase in noise in the system (Todhunter et al., 2018). In the latter case, the signal itself is the noise in the system. Another way to view this type of dysregulation is by observing the deviation from a set range. A change in the dynamic range of expression, for instance, of regulatory genes such as TFs that have very tuned or narrow windows of expression, can lead to dysregulation as expression deviates from the set range. This noise can lead to decoupling of tightly regulated networks.

Although we observed both increases and decreases in dynamic range with age, we specifically focused on increases in variance and their effect on loss of lineage fidelity with age, hypothesizing that genes that have very tightly tuned windows of expression in younger healthy individuals and that see large increases in variance in older individuals, are good candidates for susceptibility factors that could be predictive of breast cancer risk.

Moreover, in our view, this age-dependent differential variability between individuals is linked to the described increase in cellular heterogeneity in older individuals in single-cell studies, where aged cells were shown to have increased transcriptional variability and loss of transcriptional coordination compared to younger cells of the same tissue (Enge et al., 2017; Kowalczyk et al., 2015; Levy et al., 2020; Martinez-Jimenez et al., 2017). We had previously proposed that this within-sample transcriptional variability which leads to the identification of cell states and cell sub-populations in scRNAseq data, in turn, could lead to the observed shifts in transcriptomes at the cell population-level and bulk tissue-level in RNAseq data between individuals (Todhunter et al., 2018). As this gene expression shift in the cell population-level and bulk tissue-level varies per individual and is dependent on the distribution of specific cell states and identified cell sub-populations in any specific individual, transcriptomic profiles of samples from some older individuals can deviate from the mean expression observed in the studied cohort. Thus, we suggest that the increase in cellular heterogeneity with age underlies the cell population-level increases in variances between the individuals. Indeed, our scRNA-seq analysis revealed how variations in cellular characteristics within specific cell lineages, as identified by defined cell states, were strongly linked to particular samples—underscoring the significance of the differential variability between individuals. This further supported the utility of DV analysis in RNA-seq studies that could identify genes for which increasing variances among individuals reflect the unique cell states and increasing cellular heterogeneity at the single-cell level. As such, the molecular signals of aging cells may not be fully captured as stereotyped directional changes. Instead, a large fraction of age-associated changes will be reflected as increases in measured variance in the molecular signal across an aged cohort.

We identified potential ligand-receptor pairs and cell-cell junction proteins, including tight junctions, desmosomes, and gap junction components, that potentially mediate dysregulated cell-cell and cell-microenvironment signaling within the epithelium. We provided experimental validation for the role of the gap junction protein GJB6 (Connexin-30) in mediating the ability of MEPs to impose an aging phenotype on LEPs (Miyano et al., 2017). It is unclear whether this occurs chemically through passage of ions or small molecules through gap junction channels, indirectly via gap junction-mediated structural proximity of LEPs and MEPs, or via signaling complexes with connexin-interacting proteins including cytoskeletal elements, tight and adherens junctions, and enzymes like kinases and phosphatases (Dbouk et al., 2009). How this occurs will require further exploration.

GSEA further identified age-dependent enrichment of gene sets in LEPs and MEPs that were commonly dysregulated in breast cancers, including gene sets related to inflammation and immunosenescence, processes synonymous with aging and cancer progression (Fulop et al., 2017); cell-cycle related targets of E2F transcription factors, which are thought to play a role in regulating cellular senescence (Lanigan et al., 2011); and targets of the oncogene MYC. Aging associated changes in the immune response were further implicated through our ligand-receptor pair analysis where we identified known immune-associated ligands and receptors that exhibited loss of lineage-specific expression in breast epithelia. We had previously shown that in situ innate and adaptive immune cell infiltration of the breast epithelia and interstitial stroma change with age consistent with a decline in immune surveillance and increased immunosuppression (Zirbes et al., 2021). How age-dependent changes in other cell types in the breast including stromal, vascular, and immune cell populations are linked to the dysregulation of epithelial signaling with age remains to be fully elucidated. Comprehensive transcriptomic profiling of FACS isolated primary immune, stromal, and vascular cell types from normal breast tissue had identified lineage-specific expression of ligands and receptors that could contribute to dynamic and reciprocal signaling between cell types in the breast interactome (Del Toro et al., 2024; Thi et al., 2024). Thus, reciprocal changes in these other cell types may contribute to the differential variability observed in epithelial cell types via cell-cell and cell-ECM interactions.

Although our age-specific analyses did not identify oncogenes that were DE between younger and older epithelia, gene set enrichment in LEPs and MEPs revealed a putative example of priming, a process in which certain pathway components undergo molecular changes that set the stage for potential dysregulation as aging progresses, a phenomenon we previously described in Sayaman et al., 2021. We did not detect changes in MYC expression with age, but MYC targets were among the gene sets that were significantly enriched in DE or DV analysis. MYC is amplified or overexpressed in ~35% of breast cancers and exerts pleiotropic effects across the genome (Xu et al., 2010). In the context of cancer progression, MYC can induce telomerase activity, which enables bypass of the replicative senescence barrier in mammary epithelial cells (Garbe et al., 2014). Although we do not have evidence for the direct involvement of MYC in this context, we speculate that secondary events such as demethylation at MYC binding sites at target genes could explain the enrichment of MYC relevant signatures. In another candidate example of priming, we observed significant downregulation of known EMT regulatory genes GRHL2 and SGSM2 in older LEPs (Lin et al., 2019; Xiang et al., 2012), but no loss of E-cadherin (CDH1) expression in LEPs with age. Given E-cadherin’s pivotal role in maintaining cell-cell adhesion and cell polarity within epithelial structures, we did not expect dysregulation of E-cadherin in the context of aging in normal cells. As above, we speculate that further secondary events downstream of GRHL2 and SGSM2 dysregulation could lead to the perturbations in E-cadherin expression necessary to drive EMT and oncogenic transformation.

That age-dependent changes are evident in epithelial lineages isolated from primary HMEC cultures suggest a degree of epigenetic stability that allow aged epithelial cells to occupy metastable states. Indeed, our examination of DNA methylation in matched LEP and MEP samples showed age-dependent differential methylation (DM) at promoter proximal regions of DE genes and negative correlation between DNA methylation and gene expression levels in these samples (Miyano et al., 2017; Sayaman et al., 2021). DM genes in older LEPs likewise showed enrichment for signaling pathways that have been shown to play a role in cancer progression (Sayaman et al., 2021). Moreover, LEPs of older women also exhibited loss of epigenetic suppression of retrotransposons that affect regulation of genes with oncogenic potential, specifically genes associated with luminal breast cancers (Senapati et al., 2023). Thus, age-dependent activation of gene expression networks in older breast epithelia that are stabilized through concomitant age-dependent changes in the epigenetic landscape could prime aged epithelia for oncogenic gene activation.

We proposed two distinct pathways through which we could explain how aging mechanisms contribute to aging-associated cancer susceptibility. (i) General dysregulatory mechanisms, which were reflected in directional age-dependent changes that share common features with known cancer mechanisms. These changes contribute to an increased overall susceptibility to cancer as individuals age. (ii) Individual-specific dysregulatory mechanisms, which were identified by variations in aged populations. These variations may help explain why certain individuals are more vulnerable to cancer initiation (i.e., not all aged individuals develop cancer), and why those who have cancer develop specific subtypes.

Using machine learning, we built a predictive elastic net model using the expression of age-dependent DE and DV genes identified in LEPs in breast tissue samples. This model could classify normal breast tissue from breast tumors and predict breast cancer subtypes in publicly available normal and cancer tissue transcriptomes from more >3000 women. Although association of mammary lineage-derived signatures with breast cancer subtype is well-established in the literature, our study notably demonstrates that changes in aging-associated genes specifically in the luminal lineage were robust predictors of breast cancer subtypes. These findings suggest that what is typically attributed to oncogenesis might actually be age-related changes within the luminal lineage. Moreover, based on our analysis of the relative contribution of the identified LEP-derived aging-associated DE and DV genes to the predictive model of breast cancer subtypes, we found DV genes to be strong predictors of PAM50 LumA, LumB and Her2 subtypes but held no strong predictive value for PAM50 Basal subtypes. Luminal subtypes are by far the most associated with aging, and we hypothesize that increased variances may underly the transcriptomic architecture of luminal cancer subtypes. Therefore, we speculate that inclusion of age-dependent DV genes derived from the luminal lineage could lead to better subtype predictors.

Collectively, our findings illustrate how tissue-level predictive biomarkers of breast cancer that have subtype-specific expression relative to matched normal samples are dysregulated with age at the cell population-level, specifically in the luminal lineage. The contribution of non-epithelial cell types to the age-dependent expression of these genes in bulk tissue remains the subject of future studies. Given that the mammary epithelium is the origin of breast carcinomas and age is the most significant risk factor for breast cancers, age-dependent changes in the transcriptomic landscape of luminal cells may be key contributing factors to the tissue-level dysregulation of cell-cell and cell-microenvironment signaling in breast cancers and may reflect relevant biology convergent with the development of frank tumors. Indeed, the variance in expression of these genes across aged individuals may reflect the differential susceptibility of certain individuals to specific breast cancer subtypes.

The datasets generated and analyzed during the current study include RNA-sequencing count data publicly available as part of GSE182338 (Miyano et al., 2021; Sayaman et al., 2021; Sayaman et al., 2022; Shalabi et al., 2021; Todhunter et al., 2021). The gene expression data that support the findings of this study are available from GSE102088 (Song et al., 2017); GSE81540 (Brueffer et al., 2020; Brueffer et al., 2018; Dahlgren et al., 2021); The Cancer Genome Atlas (TCGA) Research Network: https://www.cancer.gov/tcga; and The Genotype-Tissue Expression (GTEx) Project: https://gtexportal.org/. Single-cell RNAseq data sets used for validation are available from GSE161529 (Pal et al., 2021); GSE174588 (Nee et al., 2023); and GSE198732 (Murrow et al., 2022). Analysis was conducted using standard R/Bioconductor packages and statistical tests implemented in R. All package versions, model design, and parameters are described in detail in Methods. Summary statistics at defined significance levels are provided in source data; full summary statistics are provided via FigShare (https://figshare.com/s/2a7ceffccfe3f35f3ce8).

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Author details

1. Rosalyn W Sayaman

   1. City of Hope, Department of Population Sciences, Beckman Research Institute, Duarte, United States
   2. City of Hope, Center for Cancer and Aging, Beckman Research Institute, Duarte, United States
   3. City of Hope, Cancer Metabolism Training Program, Beckman Research Institute, Duarte, United States
   4. Lawrence Berkeley National Lab, Biological Sciences and Engineering, Berkeley, United States

   Present address

   University of California, San Francisco, Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, San Francisco, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Masaru Miyano

   For correspondence

   rwsayaman@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1343-0619

2. Masaru Miyano

   1. City of Hope, Department of Population Sciences, Beckman Research Institute, Duarte, United States
   2. City of Hope, Center for Cancer and Aging, Beckman Research Institute, Duarte, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Rosalyn W Sayaman

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1490-4743

3. Eric G Carlson

   1. City of Hope, Department of Population Sciences, Beckman Research Institute, Duarte, United States
   2. City of Hope, Irell and Manella Graduate School of Biological Sciences, Duarte, United States

   Contribution

   Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2784-1729

4. Parijat Senapati

   City of Hope, Department of Diabetes Complications and Metabolism, Beckman Research Institute, Duarte, United States

   Present address

   Rajiv Gandhi Centre for Biotechnology, Thycaud, India

   Contribution

   Validation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Arrianna Zirbes

   1. City of Hope, Department of Population Sciences, Beckman Research Institute, Duarte, United States
   2. City of Hope, Irell and Manella Graduate School of Biological Sciences, Duarte, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3849-2616

6. Sundus F Shalabi

   1. City of Hope, Department of Population Sciences, Beckman Research Institute, Duarte, United States
   2. City of Hope, Irell and Manella Graduate School of Biological Sciences, Duarte, United States

   Present address

   Arab American University, Department of Biology and Biochemistry, Faculty of Medicine, Ramallah, Palestine

   Contribution

   Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8440-2474

7. Michael E Todhunter

   1. City of Hope, Department of Population Sciences, Beckman Research Institute, Duarte, United States
   2. City of Hope, Center for Cancer and Aging, Beckman Research Institute, Duarte, United States

   Contribution

   Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Victoria E Seewaldt

   City of Hope, Department of Population Sciences, Beckman Research Institute, Duarte, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

9. Susan L Neuhausen

   City of Hope, Department of Population Sciences, Beckman Research Institute, Duarte, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

10. Martha R Stampfer

    Lawrence Berkeley National Lab, Biological Sciences and Engineering, Berkeley, United States

    Contribution

    Resources, Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3801-5086

11. Dustin E Schones

    City of Hope, Department of Diabetes Complications and Metabolism, Beckman Research Institute, Duarte, United States

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7692-8583

12. Mark A LaBarge

    1. City of Hope, Department of Population Sciences, Beckman Research Institute, Duarte, United States
    2. City of Hope, Center for Cancer and Aging, Beckman Research Institute, Duarte, United States
    3. Center for Cancer Biomarkers Research, University of Bergen, Bergen, Norway

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

    For correspondence

    mlabarge@coh.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2405-4719

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