Defective X Chromosome Inactivation and Cancer Risk in Women

Defective X Chromosome Inactivation and Cancer Risk in Women | 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 Defective X Chromosome Inactivation and Cancer Risk in Women alejandro caceres, Luis Perez-Jurado, Albert Alegret-Garcia, Varun Dwaraka, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4412470/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Feb, 2025 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Abstract X-chromosome inactivation (XCI) is a fundamental mechanism in placental mammals that compensates for gene dosage differences between sexes. Using methylation levels of genes under XCI, we establish defective levels of XCI as a new source of interindividual variation among cancer types in females, characterized by strong XIST downregulation and upregulation and enrichment of genes under XCI. We show that defective XCI is an additive factor to the cancer risk of XCI escape deregulation in women. Defective XCI of more than 10% has an attributable risk of 40% among 12 different cancers from The Cancer Genome Atlas. Validations between independent studies of breast cancer samples show that defective XCI increases triple-negative subtype frequency, decreases survival rates, and is reduced by chemotherapy treatment. Mechanistically, it is associated with somatic mutations at TP53 and top MCY gains. In independent studies, defective XCI is detectable in blood and increases with aging, menopause, and cancer diagnosis. Health sciences/Oncology/Cancer/Cancer genetics/Cancer epigenetics Health sciences/Biomarkers/Diagnostic markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background During early female development, one of the two X chromosomes in each embryonic cell is randomly inactivated (Xi), leading to the formation of Barr bodies, which are packed corpuscles in the cell nuclei that are visible under a microscope 1 . Chromosome X inactivation (XCI) thus achieves gene dosage compensation with males, who carry only one X chromosome per cell 2 , and is a mechanism to avoid lethal consequences of X chromosome aneuploidies. However, not all the genes in Xi undergo inactivation. Under normal conditions, some genes always escape XCI, whereas others appear to escape depending on the tissue 3,4 . It is estimated that up to 23% of X-linked genes can naturally escape inactivation 5,6 . In contrast, in some aggressive breast tumor cells, there is complete erosion of XCI, and consequently, the entire chromosome escapes inactivation, as evidenced by the loss of Barr bodies 7 . Loss of XCI has been observed in other cancer cells; however, the extent to which complete or partial XCI loss is a feature of female cancer remains unknown 8 . Given that XCI levels vary among genes, cells 9 and tissues 5 , it is likely that global XCI maintenance also varies among women. However, the lack of a quantitative measure that can assess the level of defective XCI maintenance in a bulk biological sample remains an obstacle for assessing the health risks associated with the loss of XCI maintenance in women. XCI occurs early in development and is considered to be stably maintained throughout the cells and lifespan of females 10 . Facultative heterochromatin is regulated in Xi by XIST , which has important consequences for development and disease 11 . Histone modifications and DNA methylation are the main epigenetic modifications that lead to silencing of most genes on Xi 12 . In this study, we propose that a consequence of defective XCI is the loss of methylation of consistently silenced genes 5,6 . As such, we computed the percentage of 2-allele demethylation of CpGs in XCI regions as a metric of defective XCI frequency in a sample and called it X-chromosome reactivation (X-Ra) level. Using data from The Cancer Genome Atlas (TCGA), we demonstrated the global transcriptomic consequences of X-Ra and determined its overarching role in female cancer. In breast cancer, we determined the molecular mechanisms driving the interindividual variations of X-Ra and validated its association with lower survival, tumor heterogeneity in triple-negative breast cancer (TNBC) and chemotherapy treatment, in independent studies. Finally, we used a large methylome study and independent studies to show that X-Ra was detectable in the blood and increased with age and cancer diagnosis. Our study thus proposes the first personal biomarker of defective XCI in women, and demonstrates its translation potential in the management and treatment strategies of female cancer. Results Study workflow The study design and workflow are shown in Fig. 1 . Briefly, the strong control of XCI over epigenetic modifications in Xi implies widespread methylation of CpGs over the inactivated region of Xi 13,14 . Therefore, we hypothesized that 2-allele demethylation in a CpG site expected to be under XCI would provide evidence for its defective control or maintenance. Consequently, we reasoned that in a bulk biological sample, the global frequency of defective XCI could be measured by the percentage of infrequent 2-allele demethylation events in CpGs under XCI. After showing robust evidence that this is the case, we then aimed to show that the percentage of defective XCI in bulk data could be robustly detected in different tissues and correlated with relevant clinical cancer outcomes, using multiple lines of evidence from independent studies ( Tables S1-S2 ). X-Ra definition We first selected CpGs that were mapped to X-linked genes with strong previously published evidence of consistent inactivation 5,6 . We then estimated the frequency of defective XCI in a female biological sample by surveying the number of CpGs that were observed with 2-allele demethylation, i.e. methylation beta values < 0.2. We defined X-Ra (X-Reactivation) as the percentage of these events, to measure the quantitative level of partial activation of Xi supported by methylation removal. Therefore, under a strong XCI, X-Ra is expected to be low, and when XCI is weakened, X-Ra should increase. A list of 4,862 CpGs was compiled from harmonized CpGs mapped in TCGA workflow to genes reportedly under XCI 5,6 . For each study, we selected CpGs in the reference panel and extracted the beta methylation levels. Finally, for each individual, X-Ra was the percentage of CpGs under XCI with beta values lower than 0.2 (See Methods ). X-Ra as a measure of a patient’s defective maintenance of XCI Using methylation data of 446 constitutional biological samples from 12 different tissues from TCGA ( Table S1 ), we established X-Ra as a measure of defective XCI frequency in a biological sample. We first observed that CpGs under XCI with 2-allele demethylation were infrequent and varied among women. Across all 12 healthy tissues, we found that 96% of the 2-allele demethylation calls were in 20% of women and 64% in only 5% of women (Fig. 2 A). We then annotated XCI CpGs with 15-chromatin states in blood and DNase hypersensitivity regions in six constitutional tissues 15 released from the ROADMAP Epigenomics Mapping Consortium (ChromHMM v1.10) 16 . We observed that while XCI CpGs with 2-allele demethylation spread through the chromosome ( Fig. S1 ), they were significantly enriched in transcription start sites (TSS) and DNase hypersensitivity regions, in relation to all the CpGs under XCI. They were also significantly depleted in heterochromatin, ZNF genes, and repeats, weakly repressed PolyComb, and strong transcription states (Fig. 2 B). These results were in line with previous work showing that the CpGs in the island-containing promoters of genes under XCI are consistently methylated in Xi 3,13 , and with the notion that the more accessible the CpGs are, the more likely they are to undergo 2-allele demethylation. We subsequently confirmed typically low percentages of X-Ra in females, i.e. low frequency of 2-allele demethylation in XCI CpGs (Fig. 2 C), suggesting high XCI maintenance in women, but not in men ( Fig. S2 ). We validated that X-Ra was a measure of XCI status, as random permutations on XCI labels of the CpGs in X robustly showed higher mean values of X-Ra than correct labeling in 12 different tissues (P < 0.001 in all cases, Fig. 2 D). We also observed that 67% (P = 7.12×10 − 28 ) of the X-Ra variance was explained by X-Ra computed with only the 2-allele methylation calls that were present in less than 5% of the women, demonstrating that the infrequent demethylation of CpGs in Xi is the main contributor of the measure (Fig. 2 E). Overall, our analyses showed that X-Ra can be regarded as a suitable and robust quantitative measure of global defective maintenance of XCI in women. X-Ra as a marker of female cancer We examined the association of X-Ra with the tumor status of samples from the 12 previously studied tissues ( Table S3 ). We found that tumors featured a substantial increase in the percentage of XCI CpGs with 2-allele demethylation (Fig. 3 A). To ascertain the molecular mechanisms associated with X-Ra in cancer, we conducted a differential expression analysis for each tumor ( Fig. S3 ). We found several significant genes at the genome-wide level (P < 3.79×10 − 6 ). Importantly, we observed a strong downregulation of XIST with X-Ra (Log2FC=-0.067, P = 1.32×10 − 11 , Fig. 3 B, Table S2 ), further demonstrating X-Ra as a measure of defective XCI maintenance. Meta-analyses of 13,192 genes from cancer studies identified 537 genome-wide significant genes ( Table S4 ). Thirty-two of these genes have been reported to be under XCI (enrichment OR = 2.87, P = 6.5×10 − 7 ), underlying the expected consequence of partial activation of Xi by downregulation of XIST expression. The top genome-wide significant genes included LRCH1 (Leucine Rich Repeats and Calponin Homology Domain Containing 1) and PHF11 (Plant Homeodomain Finger Protein 11), which play important roles in CD8 + T cell response against tumors and pathogens 17 , as well as T-cell activation in autoimmune disease 18 . Enrichment analyses of these significant genes revealed several pathways relevant to cancer, such as signal transduction, cell proliferation, apoptosis, and viral response ( Fig. S4 ). Overall, these results demonstrated that X-Ra was strongly correlated with global transcriptomic levels, which was consistent with the loss of XCI control in cancer, also suggesting associations with the immune response. We observed that the cancer status of the biological samples was associated with X-Ra levels, adjusted for age (OR = 1.19, P = 6.05×10 − 5 , Fig. S5 ). Heterogeneity suggests tumor stratification according to X-Ra. While demethylation across the entire X chromosome is a general feature of cancer ( Fig. S6 ), the risk estimates of X-Ra across tumors could still be explained substantially by the reduction of X-Ra as a measure of XCI status in cancer (Pearson’s R = 0.66, P = 0.018). Furthermore, the percentage of 2-allele demethylated CpGs of escapees, i.e. not under XCI (grey area under the blue curve Fig. 3 A), was not significantly associated with cancer status (OR = 0.97, P = 0.46) ( Fig. S7 ), suggesting a limited role of overall demethylation of the X chromosome in the association between X-Ra and cancer. Importantly, the percentage of CpGs in escapees with methylation values in the 1-allele methylation zone (0.2 < beta < 0.6, blue area under the blue curve Fig. 3 A) increased in tumors, suggesting defective escape of these genes in cancer. The percentages of 2-allele demethylation in XCI GpGs (X-Ra) and 1-allele methylation in escapees were strong additive factors for cancer risk adjusted for age (XCI CpGs OR = 1.45, P = 4.09×10 − 6 , CpG in escapees OR = 1.90, P = 3.22×10 − 9 ) (Fig. 3 C). Furthermore, X-Ra was significantly associated with a lower probability of LOF somatic mutations in genes under XCI (OR = 0.98, P = 9.71×10 − 5 , Fig. 3 D), adjusting for cancer type. In contrast, the percentage of 1-allele methylation in escapees was associated with a higher probability of LOF somatic mutations (OR = 1.04, P = 1.82×10 − 4 , Fig. 3 D), suggesting that the risk of cancer increases when both methylation and mutations affect both alleles. The strong negative correlation between the percentages (Pearson’s R=-0.39, P = 9.72×10 − 138 , Fig. S8 ) indicated underlying tumor stratification. To assess the attributable risk of defective XCI maintenance in any type of cancer given by high X-Ra, we performed a ROC analysis and obtained the optimal Youden value at X-Ra = 10%, which had 85.6% specificity. The attributable risk of dichotomized X-Ra for cancer status was 40% (95%CI = 23–56%). The proportion of high X-Ra (> 10%) was 43% across cancer types, with the highest rates in the thyroid (75%), liver (56%), and breast (50%), and the lowest in lung adenocarcinoma (13%) and pancreas (17%), in line with the association with cancer status (Fig. 3 C). X-Ra as a marker of breast cancer We investigated the association between X-Ra and breast cancer (BRCA) subtypes, survival, chemotherapy treatment and somatic mutations. A 1%-point increase in X-Ra in tumor biopsy increased the hazard ratio of death from BRCA by 1.7%, adjusting for age (HR = 1.017, P = 0.025) (Fig. 4 A). We validated this finding in an independent study (GSE78754) of 70 patients with TNBC (HR = 1.033, P = 0.0081), adjusting for age. Consistent with these findings, we found in GSE141441 that 95 TNBC patients who did not undergo surgery had a higher probability of recurrence due to a high frequency of X-Ra, after adjusting for chemotherapy treatment (OR = 1.08, P = 0.04) ( Fig. S9 ). With respect to breast cancer subtypes, we found that X-Ra significantly decreased the probability of having no estrogen receptor type (ER-) (OR = 0.982, P = 0.004), no progesterone receptor type (PR-) (OR = 0.984, P = P = 0.008), and no human epidermal growth factor receptor 2 (HER2-) status, although not significantly (OR = 0.988, P = 0.15). Consistent with these results, X-Ra was significantly associated with TNBC (OR = 1.029, P = 3.1×10 − 4 ). This finding was validated in 224 patients (GSE225845), where X-Ra was associated with TNBC versus HE2R+/HER + status (OR = 1.07, 95%CI = 0.0015) (Fig. 4 B). In summary, we observed that X-Ra stratified patients with breast cancer, particularly TNBC, and was associated with important clinical outcomes. We analyzed longitudinal studies to determine whether X-Ra could be used as a monitoring biomarker of neoadjuvant chemotherapy for breast cancer. In 290 patients with three visits during chemotherapy treatment (GSE207460), we found a significant reduction of 1.78% in X-Ra for every 12 weeks of treatment (β=-1.78, P = 1.78×10 − 13 ) (Fig. 4 C), adjusting for whether patients were additionally treated with bevacizumab. Interestingly, we noted that patients treated with bevacizumab further reduced X-Ra at each 12-week visit (interaction β=-1.30, P = 0.013). We validated the reduction of X-Ra with neoadjuvant chemotherapy in 22 TNBC patients (GSE184159) with four cycles of chemotherapy during 12 weeks (β=-3.56, P = 0.014), suggesting a stronger modifiable nature of X-Ra by chemotherapy for this subtype. X-Ra associations with somatic mutations in breast cancer We then investigated the potential molecular mechanisms underlying X-Ra activity in breast cancer tumors. We considered chromosomal copy number gains in 0.5MB windows. We discovered 492 windows with significant increments in log(X-Ra) associated with the frequency of gains (Fig. 4 C, Table S5 ). Remarkably, most of the top associations on each chromosome included notable oncogenes ( Table S6 ). Notably, we observed gains in MYC locus 19 , which is known to induce pluripotent stem cell reprogramming with reactivated X-chromosomes in females 20 . The most significant association was found in a window that included TNFRSF6B , which prevents apoptosis 21 and RTEL1 which encodes a regulator of telomere length 22 . X-Ra in BRCA was significantly associated with longer telomere length (β = 6.6, P = 0.003) 23 . These data underscore the potential mechanism of X-Ra in BRCA, consistent with the association between telomere length and RTEL1 gains (TL difference = 1.72, P = 6.47×10 − 5 ). Additionally, 22 significantly deleted regions were associated with X-Ra, all located in the short arm of chromosome 8, with the highest peak at CSMD1 (Fig. 4 C), which encodes a complement inhibitor known to act as a tumor suppressor in breast cancer 24 . Finally, we did not find any association with somatic copy number loss, gain, or copy neutral loss of heterozygosity in any region or in the entire X chromosome, suggesting that X-Ra increments are not due to the loss of X or copy neutral variants of X (LOX/GOX/UPD) or parts of it. The association of X-Ra with 18 frequent somatic mutations (> 5%) revealed an increase of X-Ra by 3.42 percentage points for each TP53 mutation (P = 5.42×10 − 5 ), and decreased by 5.42 for each CDH1 mutation (P = 4.93×10 − 6 ). Consequently, X-Ra is likely associated with the basal-like 1 subtype of TNBC 25 . X-Ra in blood is associated with cancer and aging We analyzed methylation data from three different studies to test the association of X-Ra with age > 65 years (median age of cancer diagnosis), adjusting for immune cell count in the blood. We excluded patients with a cancer diagnosis in TruDiagnostic; cancer diagnosis was an exclusion criterion in the other two studies. We observed significant increases in X-Ra with age in all studies (β = 0.44, P = 3.3×10 − 3 , Fig. 5 A-B), with significant heterogeneity given by GENOA, a pedigree study on hypertension. Associations with continuous age were significant in TruDiagnostic (β = 0.014, P = 0.0038) and MESA (β = 0.029, P = 3.81×10 − 5 ), but not in GENOA (β = 0.0018, P = 0.65). In the TruDiagnostic data, the association between X-Ra and continuous age increased 3-fold in menopausal women (β = 0.044, P = 6.64×10 − 5 ). In addition, X-Ra was associated with shorter telomere length (β=-1.008, P = 1.19×10 − 5 , Fig. 5 C) and with Horvath’s (β = 0.029, P = 4.85×10 − 6 ) and Levine’s methylation ages (β = 0.016, P = 0.006), which do not include CpGs in the X chromosome. These results show that X-Ra in the blood is an indicator of female aging. Differential expression analysis of X-Ra in monocytes from the MESA study revealed significant genes relevant to breast cancer and histone modifications ( Fig. S10 ), such as upregulation of ABTB2 26 , downregulation of SOGA1 27,28 , and upregulation of RBBP4 and RSPO2 29 . We also observed significant enrichment of inactivated genes in the differential expression analysis of X-Ra (OR = 3.00, P = 0.002, Table S7 ) and nominal but expected downregulation of XIST (log2FC=-0.024, P = 0.02). Therefore, high X-Ra values in the blood were associated with breast cancer-related genes and with the expected transcriptomic signatures of XCI maintenance. We found a significant association between X-Ra in the blood and cancer diagnosis in the TruDiagnostic cohort (167 were diagnosed with any type of cancer) and observed an increase of 7.9% in cancer diagnosis for a 1%-point increase in X-Ra (OR = 1.077, P = 0.034, Fig. 6 A). We did not find significant associations between cancer and methylation ages of Horvath and Levine genes. The association between X-Ra in the blood and cancer in GSE237036, adjusted for age and immune cell abundance, was not significant (OR = 1.54, P = 0.25). However, the combination of both studies showed a significant association (OR = 1.08, P = 0.02), with no apparent heterogeneity (P = 0.34). The association between X-Ra in the blood and cancer was independently validated in a longitudinal study (GSE142536) of 17 elderly women, where cancer diagnosis increased the frequency of X-Ra by 2.2% (β = 2.2, P = 0.006) (Fig. 6 B). No association was observed between Levine’s methylation age and cancer ( Fig. S11 ). Taken as a test-retest study, X-Ra showed high statistical reliability (ICC = 0.95, 95%CI = 0.91 0.98). Discussion Our study provides evidence that defective XCI frequency in a bulk sample is a novel source of inter-individual variation among women. We introduced X-Ra, the first personal measure of defective XCI. The measure is based on the frequency of demethylation marks arising from the likely weakening of XCI maintenance. Thus, poor XCI maintenance appears to be a hallmark of female cancer in general 30 . We noted that both X-Ra and hemimethylation of escapees, the former of which is associated with a higher frequency of LOF mutations, are additive risk factors for cancer in women. As mosaic loss of the Y chromosome (mLOY) is a risk factor in males that is associated with LOF of escapees with homologous copies in Y 31 , our results offer an integrated picture of gonosome dysregulation in cancer for both sexes. Therefore, defective XCI maintenance in silenced genes and disruption of escapees should be targeted in further studies to assess their potential as diagnostic or prognostic biomarkers in female cancer 32 . Our blood analyses supported the notion that a possible mechanism of cancer risk from defective XCI is by altering the immune response 33 , in line with the immunosurveillance hypothesis of mLOY in males 31,34,35 . We observed that X-Ra was a powerful insightful marker for breast cancer. X-Ra was associated with the downregulation of XIST 36 , 20q ( RTEL1 ), and 8q ( MYC ) copy number gains 19 ; 8p ( CSMD1 ) deletions 24 ; lower breast cancer survival 37 and basal type 1 TNBC 25 . In addition, X-Ra was responsive to chemotherapy. These results show that defective XCI maintenance is an important mechanism underlying tumor heterogeneity, particularly in TNBC. TNBC displays heightened aggressiveness, proliferation, and propensity for early metastasis compared with hormonal subtypes 38 . Treatment is complicated by the absence of defined molecular targets owing to its heterogeneous nature. Previous stratification of TNBC usually relies on substantial data reduction, population dependence, and needs mechanistic interpretation 39,40 . We showed that X-Ra reliably defines a new type of breast cancer that is associated with TNBC by a clear mechanism, opening the door to the design of novel interventions strategies based on XCI regulation. As a developmental process, it is important to note the role of MYC in cancer and the regulation of the pluripotency in embryonic stem cells[ 41 ], which are cells with important differences in global regulation of XCI that lead to different XCI states[ 42 ]. However, given that those states appear to vary between pluripotent cell subcultures and populations[ 43 ], it has remained unknown the extent to which global regulation of XCI at an organismal level quantifiable in adult complex biological samples. Here, we demonstrate that defective XCI maintenance is a characteristic of individuals, as we analyze bulk cell populations with random XCI. From our study in blood, it is interesting to remark the association between the interindividual variation of defective XCI and aging. Therefore, it is important to further investigate modifiable and genetic factors that may contribute to X-Ra variation in aging, cancer and also development. Overall, the range of X-Ra values was large (measured in percentage units, it had mean value of 75% in thyroid and 50% in breast cancer). Strong significant associations were observed across the study to the order of 1% unit increases in the biomarker, demonstrating its high-resolution power. Using longitudinal data, we also showed the high statistical reliability of X-Ra in tumor biopsies and blood, increasing its clinical applicability and translation to the clinic. Our study has some limitations that need to be further addressed to improve the measurement X-Ra. For instance, X-Ra's definition does not consider the individual likelihood of each XCI CpG to present 2-allele demethylation. In addition, our analyses do not account for the origin of the inactive X-chromosome, and therefore, the effect of skew inactivation and its relation to X-Ra should be considered in future studies. Conclusions Our work provides new insights into the risks and mechanisms associated with the deregulation of XCI in female cancer. We discovered that the level of XCI control varies in strength among women and decreases in cancer biopsies. Defective XCI is strongly associated with triple-negative breast cancer and worse survival. Defective XCI was reduced by chemotherapy treatment, suggesting potential new targets of intervention. The variability of XCI is also present in blood with validated correlations with cancer diagnosis. In a similar manner as mLOY is an important risk factor for cancer, several aging-related diseases and overall mortality in males, we show here that defective XCI is an important female-specific gonosome biomarker with long-range health consequences that are yet to be fully determined. Our study highlights the importance of considering inter-individual variation in XCI when investigating cancer risk and aging in females. Materials and Methods X-Ra definition DNA methylation is an important epigenetic modification that contributes to gene silencing on Xi 41 . Previous work has shown that the CpGs in the island-containing promoters of genes under XCI are methylated in Xi. Consequently, the mean methylation levels of these CpGs correspond to either 1-allele methylation values (methylation in Xi and no methylation in Xa) or 2-allele methylation values (methylation in both Xi and Xa) 3,13 . Therefore, a strong regulation of XCI should imply that no CpG in inactivated genes can have 2-allele demethylation values (hypomethylation in both Xi and Xa). After a thorough validation of this observation using multiple sources of data, we, consequently, propose to measure the level of defective Xi inactivation in a female biological sample by examining the number of these CpGs that are observed with 2-allele demethylation, i.e. methylation beta values < 0.2. Our hypothesis is that a consequence of a potential weakening of XCI is to allow the infrequent removal of methylation from any of the CpGs in the inactivated region of Xi. To differentiate between women with different quantitative levels of XCI, we compute the percentage of XCI CpGs that have 2-allele demethylated and should not have been under strong XCI. This percentage is then a measure of a patient’s defective XCI and/or a woman’s level of chromosome X reactivation (X-Ra), meaning the quantitative level of partial activation of Xi. Conceptually, the measure is a state variable that describes the level of XCI at a particular point in time and is given by the estimation of a woman’s probability that a CpG under XCI selected at random is observed with 2-allele demethylation \(\:XRa=P(M=0\) ) where M is the methylation state of a randomly selected CpG in Xi (0: demethylated, 1: methylated). Therefore, under strong XCI, X-Ra is expected to be low, and when XCI is weaken, X-Ra should increase. We selected CpGs that map to X-linked genes with strong previously published evidence of consistent inactivation 5,6 . To allow wide applicability of X-Ra in different platforms, we used harmonized CpGs mapped in the TCGA workflow that included Human Methylation 27 (HM27), HumanMethylation 450 (HM450), and EPIC platforms. We thus created a final list of 4,862 CpGs expected to have beta values in females higher than 0.2 due to XCI. The hypomethylation threshold of 0.2 surely captured CpGs that are clearly hypomethylated in different platforms 42,43 . This list of 4,862 CpGs was our reference panel to compute X-Ra across studies with different methylation arrays. For each study, we then selected the CpGs that were in the reference panel and extracted the beta methylation levels. Then, for each individual, we defined the percentage of selected CpGs that had beta values lower than 0.2 as a measure of X-Ra. TCGA data The Cancer Genome Atlas (TCGA) consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450K BeadChip (Illumina 450K array). We obtained methylation data for 15 different cancer sites and normal tissue. We downloaded data from ExperimentHub version 2.0.1 using the R package curatedTCGAData version 1.16.0. The cancer sites included breast (BRCA), bladder (BLCA), colon (COAD), head and neck (HNSC), kidney (KIRC), liver (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreas (PAAD), rectum (READ), thyroid (THCA), uterus (UCEC), skin (SKCM) and ovary (OV). We discarded the last two because they did not have methylation data on more than 5 samples of undiseased tissue. We analyzed a total of 11,232 CpGs on chromosome X for 3,741 samples. We downloaded clinical, RNA-seq and CNV data for BRCA to perform associations with X-Ra. For clinical associations, we downloaded data using TCGAbiolinks version 2.22.4 and fitted proportional hazards regression models for survival data adjusting by age for each independent cancer site. We also tested the association between X-Ra and hormonal status (ER, PR, HER2) of breast cancer, adjusting by age. For RNA-seq data, we inferred surrogate variables with svaseq from the SVA R library for each individual cancer. We tested the association of transcription levels of 20,532 genes with X-Ra adjusting by age and surrogate variables, transforming the expression levels with voom and fitting regression models with limma . In each cancer site, genes with less than 15 counts in more than 75% of the samples were removed from the analysis. Finally, we downloaded data of somatic mutations in BRCA and tested the association of X-Ra with the presence of recurrent mutations in tumors across genes. TrueDiagnostic DNA biobank The TruDiagnostic DNA biobank is a USA population-based cohort aged between 13 and 97 years old. The biobank includes 3,590 individuals recruited between October 2020 and February 2022, who have chosen TruDiagnostic for DNA methylation analysis for biological aging. TruDiagnostic is an aging study in which a health questionnaire was administer at the time of the sample collection. Participants were screened over 120 clinical outcomes to determine their general health state. In particular, participants reported whether they had any cancer diagnosis, but no further details on treatment are available. Clinical data was collected, including anthropometric measures and diagnosis of common diseases. Telomere length data were available together with immune cell contamination. We analyzed methylation data from 1,414 females of the 3,259 individuals who passed quality control. For the DNA methylation analysis, DNA was extracted from peripheral whole blood. The Infinium HumanMethylationEPIC BeadChip was used for DNA methylation assessment following the manufacturer's protocol. Several quality controls and functional normalizations were performed using the meffil package, resulting in 745,150 probes, as described in detail elsewhere 44 . CpG sites were annotated to genes using the EPIC Illumina annotation ilm10b4.hg19. Blood cell types were estimated using the blood gse35069 reference panel from the meffil package. We analyzed a total of 4,471 CpG sites from chromosome X. We assessed the association of X-Ra with age adjusting for leukocyte content and slide. MESA study We downloaded data from the Gene Expression Omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/geo/ ) with the reference GSE56046, to test the association between X-Ra and gene expression in blood, and validate the association with aging. This is a large population study with gene expression (Illumina HumanHT-12 v4 Expression) and methylation (Illumina HumanMethylation450) data in purified human monocytes from blood. Exclusion criteria included any serious medical condition which would prevent long-term participation. The data included covariates such as ancestry, gender, study site, and sample contamination with B-cells, T-cells, natural killer cells, and neutrophils. As ancestry, gender, and study site were encoded in a categorical variable with 18 different categories, we inferred sex from the mean expression of chromosome Y divided by the mean expression from the whole genome (Ry). Individuals from categories with Ry values less than − 0.35 were classified as females. The classification was validated by the average methylation from inactive genes from chromosome X, where categories with values less than − 0.5 were classified as females, thus completely matching those classified by Ry. We then analyzed data for 605 females with a mean age of 60 years (SD = 9.47). The methylation data were obtained in M values and transformed into Beta values [ 26 ]. We analyzed a total of 11,648 CpG sites from chromosome X. We assessed the association between X-Ra and age adjusting by leukocyte content and well. For gene expression data, we analyzed 47,308 transcripts and detected surrogate variables with SVA, adjusting by age, cohort and contamination with B-cells, T-cells, natural killer cells, and neutrophils. Validation Studies We downloaded relevant methylation data from GEO to validate our findings in independent studies. We used methylation data of different cancer studies to validate X-Ra associations with cancer survival in TNBC (GSE78754, n = 70) and TNBC status (GSE225845, n = 224). We also analyzed studies of cancer recurrence for TNBC (GSE141441, n = 95) and a longitudinal study of chemotherapy intervention on 22 TNBC patients (GSE184159, n = 63). We downloaded data from with reference GSE210255. The Genetic Epidemiology Network of Arteriopathy (GENOA) study is a large community-based study of hypertensive siblings, including African Americans from Jackson, USA, with methylation data obtained with the Illumina Infinium HumanMethylationEPIC BeadChip. After quality control and filtering, a total of 1,394 samples were included in the analysis. We focused on female samples (n = 986) with a mean age of 57.2 years (SD = 10.3). We analyzed a total of 10,125 CpG sites on chromosome X and assessed the association between X-Ra and age, adjusting for plate and immune cell contamination and including a random effect on pedigree. Exclusion criteria included active malignancy. We downloaded data from GSE142536 to validate the association between X-Ra in blood and cancer diagnosis. In this study, we also computed the test-retest reliability of the biomarker. The study included 17 elderly women, assayed immediately before major surgery, in the morning of postoperative day and between 4 and 7 days after surgery. Methylation data in blood was collected at each visit. Patients had a mean age of 79 years and three of them had cancer. Statistical analyses Data were analyzed using R version 4.1.1 and Bioconductor version 3.13. The goal was to determine the biological correlates of chromosome X reactivation (X-Ra) in studies with different characteristics and to establish the biological plausibility of the biomarker as a measure of defective X inactivation. In the TCGA, tor each of the 12 cancer sites, we inferred the value of X-Ra in constitutional and tumor tissues. We tested the association with cancer status (with logistic regression models) and survival (with proportional hazard regression models) across cancer sites, adjusting by age. We meta-analyzed the results with fixed and random effects models, using the rmeta library. The prediction power of X-Ra on cancer status was tested with a ROC area under the curve statistic. The optimal X-Ra was defined as the Youden distance, which was used to compute the attributable fraction of X-Ra of affected tissue on cancer risk, using the AFglm function of the AF library. For transcriptome-wide associations of X-Ra in each cancer site, we used limma and adjusted for SVA and age. Log2FC with their standard error were then meta-analyzed across cancer sites, and the associations were adjusted for multiple comparisons for the number of transcripts tested, taking Bonferroni significant threshold at 3.79×10 − 6 . On these associations, enrichment analyses on biological processes were performed on genes with transciptome-wide significance, using the enrichGO from clusterProfiler . In the TCGA, we tested the association of X-Ra in tumor and healthy tissues with breast cancer risk and survival. For breast cancer risk, we fitted linear regression models of X-Ra on cancer status of the samples and adjusted for age. We also tested the association between X-Ra and ER, HER, and PER and triple-negative status, adjusting for age. Survival associations were performed with proportional hazards regression models adjusting for age. We also studied genome-wide associations of X-Ra with the frequency of copy number gains and deletions of breast cancer tissues in sliding windows of 0.5 Mb. To deal with windows with low frequency in copy number changes (1% at least), we fitted Bayesian regression models with bayesglm of arm , adjusting for age. Associations were adjusted for multiple comparisons using Bonferroni threshold of significance at P < 8.5×10 − 6 . To determine associations with loss of function mutations (LOF), we downloaded a detailed catalogue of somatic mutation data for TCGA from xenabrowser.net, and classified LOF mutations labeled as coding sequence variants, incomplete terminal codon variants, splice acceptor/donor variant, start lost, and stop gained or lost. We also inferred X-Ra in blood and tested its association with age and markers of biological aging such telomere length, and Horvarth's 45 and Levine’s methylation age 46 , as computed by the R package methylclock 47 . Horvarth’s methylation clock uses 353 CpGs to estimate biological aging from methylation data, while Levine's clock is based on 513 CpGs, none of which are in chromosome X. In the TruDiagnostic data, we tested the associations with age, markers of aging and with cancer diagnosis at any site. We fitted linear regression models, adjusting for immune cell abundance. In the MESA study, we assessed the association between X-Ra and differential expression in purified monocytes from peripheral blood and with the age. In this study, the associations of X-Ra with transcriptomic signatures specific to XCI were also tested. The enrichment in associations (P 65) was also tested using a linear regression model, adjusting for cohort and immune cell contamination. Associations of X-Ra in blood with age were performed for the GENOA study (GSE210255). In GENOA, a linear model was fitted adjusting for plate and immune cell contamination. The regression coefficients for age across the three studies (MESA, GENOA, TruDiagnostic) were meta-analyzed using fixed and random effects models with two-sided tests using metagen from meta . Dichotomization of age was taken as the median age of cancer at any site according to SEER 22 2015–2019. In the longitudinal study GSE142536 that included follow up of individuals with cancer and controls, we fitted mixed models on X-Ra with condition, visit and age as fixed effects and subject as a random effect. We used the lmer R library. We also tested similar mixed models for the Levine’s methylation age, to contrast the effect of the condition between X-Ra and global methylation age. We also tested the significance of the intraclass correlation, to determine the test-retest reliability of X-Ra. Declarations Data and code availability All public data, code, and materials used in the analysis are cited and referenced in the manuscript. The entire computer code with required data to replicate all reported findings can be found at https://github.com/isglobal-brge/Supplementary-Material/Caceres_2024. The data from the TruDiagnostic Biobank is available upon reasonable request due to ensure the privacy of the participants. Please e-mail [email protected] for data requests. The computer software, documentation and licensing to calculate X-Ra is available at https://xra.isglobal.org. Acknowledgments We thank participants of TrueDiagnostic data bank. We kindly acknowledge Xavier Escribà for creating the software’s chrXRA web site. We thank Dr. Mariona Bustamante for insightful comments. Funding Funded by the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019-2023 (CEX2018-000806-S) program, and support from the Generalitat de Catalunya through the CERCA Program [AC, JRG]. Funded by the Spanish Ministry of Science and Innovation MCIN/FIS-ISCIII, Ref PI21/00050, and Fondo Europeo de Desarrollo Regional (FEDER) and the Generalitat de Catalunya through the Consolidated Research Group (2017SGR01974) [LAPJ]. The Department of Medicine and Life Sciences-UPF acknowledges also support from the Spanish National Investigation Agency (AEI) (DOI: 10.13039/501100011033) through the “Unidad de Excelencia María de Maeztu” (CEX2018-000792-MDM). Funded by CaixaResearch Institute Innovation Hub [JRG]. Author Contributions Conceptualization: AC, LPJ Methodology: AC. Investigation: AC, JRG. Funding acquisition: JRG, BVD, RS. Project administration: JRG Supervision: LPJ, JRG Writing – original draft: AC, LPJ, JRG. Writing – review & editing: AC, LPJ, JRG, BVD, RS. Ethics declarations The data from the TruDiagnostic Biobank is available upon reasonable request due to ensure the privacy of the participants. Please e-mail [email protected] for a data use agreement (DUA) form to apply for access of the deidentified dataset. All DUA forms are needed to track data transfers and to maintain HIPAA compliance as according to the Human Health Services (https://www.hhs.gov/hipaa/for-professionals/special-topics/emergency-preparedness/data-use-agreement/index.html) Conflict of interests LPJ is a member of the scientific board of qGenomics. BVD and RS are employed by TruDiagnostics. All other authors declare that they have no competing interests. Acknowledgments We thank the participants of the TrueDiagnostic Data Bank. We acknowledge Xavier Escribà for creating the chrXRA software website. We thank Dr. Mariona Bustamante for insightful comment. References Barr, M.L., Bertram, E.G.: A Morphological Distinction between Neurones of the Male and Female, and the Behaviour of the Nucleolar Satellite during Accelerated Nucleoprotein Synthesis. Nat. 1949 1634148. 163 , 676–677 (1949) Lyon, M.F.: Gene action in the X-chromosome of the mouse (mus musculus L). 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Treat. 161 , (2017) Am Soc Clin InvestigBD Lehmann, JA Bauer, X Chen, ME Sanders, AB Chakravarthy, Y Shyr,JA PietenpolThe J. Clin. Investig. 2011•Am Soc Clin Investig Yin, L., Duan, J.J., Bian, X.W., Yu, S.C.: Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res. 22 , (2020) Sharp, A.J., et al.: DNA methylation profiles of human active and inactive X chromosomes. 10.1101/gr.112680.110 Wright, M.L., et al.: Establishing an analytic pipeline for genome-wide DNA methylation. (2016). 10.1186/s13148-016-0212-7 Li, S., et al.: Exploratory analysis of age and sex dependent DNA methylation patterns on the X-chromosome in whole blood samples. Genome Med. 12 , 1–13 (2020) Carreras-Gallo, N., et al.: The early-life exposome modulates the effect of polymorphic inversions on DNA methylation. Commun. Biol. 5, 1–13 (2022). (2022) Horvath, S.: DNA methylation age of human tissues and cell types. Genome Biol. 14 , (2013) Levine, M.E., et al.: An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 10 , (2018) Pelegi-Siso, D., De Prado, P., Ronkainen, J., Bustamante, M., Gonzalez, J.R., Methylclock: A Bioconductor package to estimate DNA methylation age methylclock: A Bioconductor package to estimate DNA methylation age. Bioinformatics 37 , (2021) Additional Declarations Yes there is potential Competing Interest. LPJ is a member of the scientific board of qGenomics. BVD and RS are employed by TruDiagnostics. All other authors declare that they have no competing interests. Supplementary Files SupplementaryFiguresS1S11TablesS1S2S6S7.docx Supplementary data Supplementary_Methods_FiguresS1-S11_TablesS1-S2,S6-S7.docx SupplementaryTablesS3-S5.xlsx SupplementaryTablesS3S5.xlsx RS.pdf Reporting Summary Cite Share Download PDF Status: Published Journal Publication published 22 Feb, 2025 Read the published version in Communications Biology → 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-4412470","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":340179303,"identity":"c14d6672-0484-45bb-8883-017aaf2eca1d","order_by":0,"name":"alejandro caceres","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8551-6695","institution":"Instituto de Salud Global de Barcelona (ISGlobal)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"alejandro","middleName":"","lastName":"caceres","suffix":""},{"id":340179304,"identity":"4958dec7-d623-4d77-a46b-e65600a432a8","order_by":1,"name":"Luis Perez-Jurado","email":"","orcid":"https://orcid.org/0000-0002-1988-3005","institution":"Pompeu Fabra University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Perez-Jurado","suffix":""},{"id":340179305,"identity":"25200218-69a3-4c80-a7cd-3a24fa1fa9ec","order_by":2,"name":"Albert Alegret-Garcia","email":"","orcid":"https://orcid.org/0009-0009-5420-1311","institution":"Instituto de Salud Global de Barcelona (ISGlobal)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Albert","middleName":"","lastName":"Alegret-Garcia","suffix":""},{"id":340179306,"identity":"f4dc9d32-1174-4cb0-a376-05b7ca1d2aeb","order_by":3,"name":"Varun Dwaraka","email":"","orcid":"","institution":"Trudiagnostic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Varun","middleName":"","lastName":"Dwaraka","suffix":""},{"id":340179307,"identity":"42c2ccdc-a2ed-4647-ac6d-58f940255208","order_by":4,"name":"Ryan Smith","email":"","orcid":"","institution":"TruDiagnostic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"","lastName":"Smith","suffix":""},{"id":340179308,"identity":"a2e428f8-a89e-4cbd-845a-46f43a76ecf5","order_by":5,"name":"Juan Conzalez","email":"","orcid":"https://orcid.org/0000-0003-3267-2146","institution":"Instituto de Salud Global de Barcelona (ISGlobal)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Conzalez","suffix":""}],"badges":[],"createdAt":"2024-05-13 10:06:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4412470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4412470/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42003-025-07691-y","type":"published","date":"2025-02-22T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64386531,"identity":"9a2785ff-95e6-48fc-9543-668f8badf878","added_by":"auto","created_at":"2024-09-12 12:37:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129279,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study. (A) We determined that XCI exerts a strong control on the methylation levels in inactivated genes in Xi. 2-allele demethylation in both copies of CpGs is not observed in genes under XCI under strong XCI maintenance. 2-allele demethylation (from Xi and Xa) may be observed for a defective XCI level. (B) We define X-Ra as the percentage of CpGs in genes under XCI that are observed with 2-allele demethylation. Here, seven XCI CPGs are illustrated on the red band of Xi. In the study, we consider a total of 4,862 CpGs. (C) As a marker of tumor stratification, we assess X-Ra correlations with transcriptomic data, DNA somatic mutations, cancer risk, survival, and recurrence. As a blood biomarker, we assess the correlations of X-Ra with aging and cancer. Data from different studies are indicated and described in the text.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/bef270e5ecad766099e2e780.png"},{"id":64386532,"identity":"65748176-d361-4ef1-9e6d-3c1a090cd81a","added_by":"auto","created_at":"2024-09-12 12:37:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":163898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eX-Ra as a measure of defective XCI in 12 undiseased tissues.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Low population frequencies of CpGs under XCI with beta methylation values \u0026lt;0.2 (2-allele demethylation) in 12 constitutional healthy tissues of the TCGA. (\u003cstrong\u003eB\u003c/strong\u003e) Enrichment of chromatin states and DNase hotspots by XCI CpGs with 2-allele demethylation, taking all XCI CpGs as reference. (\u003cstrong\u003eC\u003c/strong\u003e) The red line represents the distribution of methylation levels for CpGs under XCI. The blue line represents the distribution of methylation levels for CpGs of genes that escape XCI, 2-allele demethylation is a feature of escapees and not of inactivated genes (grey area under the blue curve). X-Ra is the area under the red curve (in percentage points). (\u003cstrong\u003eD\u003c/strong\u003e) The red horizontal line shows the mean X-Ra across women in healthy tissues of each TCGA study. In blue, the distribution of mean X-Ra for random permutations of the XCI levels of CpGs. (\u003cstrong\u003eE\u003c/strong\u003e) X-Ra computed for rare XCI CpG 2-allele methylation (frequency\u0026lt;0.05) Vs all X-Ra computes with all CpGs mapping to genes reportedly under XCI.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/3eca2a548e19d38c8e347a32.png"},{"id":64386536,"identity":"85b10237-8ca0-4535-87e0-6e7f178fd517","added_by":"auto","created_at":"2024-09-12 12:37:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eX-Ra as a marker of multiple cancer types. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Methylation beta levels distribution of CpGs in inactive genes and escapees. The area under the red curve is the percentage of CpGs under XCI with 2-allele demethylation (X-Ra). The blue area under the blue line is the percentage of CpGs in escapees in the 1-allele methylation zone. (\u003cstrong\u003eB\u003c/strong\u003e) Meta-analysis for the differential expression of \u003cem\u003eXIST\u003c/em\u003e by X-Ra across the 12 TCGA cancer types. Cancer sites included: breast (BRCA), bladder (BLCA), colon (COAD) head and neck (HNSC), kidney (KIRC), liver (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreas (PAAD), rectum (READ), thyroid (THCA) and uterus (UCEC). (\u003cstrong\u003eC\u003c/strong\u003e) Meta-analysis across 12 different cancer types from the TCGA for additive cancer risk effects of the percentage of CpGs under XCI with 2-allele demethylation (in % units) and the percentage of CpGs in escapees with 1-allele methylation (in % units). (\u003cstrong\u003eD\u003c/strong\u003e) Probability of loss of function somatic mutations (LOF) in genes under XCI as a function of X-Ra (left). Probability of LOF in escapees as a function of the percentage of 1-allele methylation in escapees (right).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/60dada96da7fe9a113fde0ad.png"},{"id":64386534,"identity":"9e4451e0-8a20-4a3b-9636-25b3397a4aef","added_by":"auto","created_at":"2024-09-12 12:37:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":448610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eX-Ra as a marker of breast cancer (BRCA).\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Association between X-Ra and the survival time from breast cancer diagnosis to death (months) in TCGA and validated in GSE78754 for TNBC. (\u003cstrong\u003eB\u003c/strong\u003e) Predicted probability of TNBC by X-Ra in TCGA and validated in GSE225845. (\u003cstrong\u003eC\u003c/strong\u003e) Reduction of X-Ra by chemotherapy treatment for breast cancer in GSE207460 and validated for TNBC in GSE184159. (\u003cstrong\u003eD\u003c/strong\u003e) Genome-wide association for the frequency of copy number gains and deletions in BRCA with X-Ra, in TCGA, using sliding windows of 0.05MB and duplication frequency \u0026gt;0.01. The highest peaks are observed in the \u003cem\u003eRTL1-TNFRSF6B\u003c/em\u003e and \u003cem\u003eMYC\u003c/em\u003e loci for gains, and in \u003cem\u003eCSMD1 \u003c/em\u003efor deletions.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/1a462931685b6a72e1c51bf5.png"},{"id":64388042,"identity":"822640aa-daec-430c-b325-4a77d6aca43d","added_by":"auto","created_at":"2024-09-12 12:45:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":517898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of X-Ra in blood with aging and cancer\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Meta-analysis for the association between X-Ra and age (\u0026gt;65 years) across three large methylation studies in adult women. X-Ra significantly increases in elder women in each of the studies. (\u003cstrong\u003eB\u003c/strong\u003e) X-Ra over 5-year age groups in the TruDiagnostic study. (\u003cstrong\u003eC\u003c/strong\u003e) Association of X-Ra with telomere length in the TruDiagnostic study.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/0d71550ba386390c73013a96.png"},{"id":64386537,"identity":"a9a18fde-1d5b-483f-a8ad-4a121576b886","added_by":"auto","created_at":"2024-09-12 12:37:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":81510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of X-Ra in blood with cancer. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Association of X-Ra in blood with a probability of having been diagnosed with cancer in the TruDiagnostic study.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Association of X-Ra in blood with cancer. A longitudinal study (GSE142536) of methylation changes in blood through major surgical intervention. Methylation was assessed one day before intervention (BL), one day post-operative intervention (PoD1), and 4-7 days post-operative intervention (PoD4/7). The overall effect of cancer is shown with 95% confidence bands from a mixed model with random effect on the subject and adjusted by age.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/b0c0c62f0099577e0b0a6482.png"},{"id":76951719,"identity":"f64d8c02-d11c-4195-8491-6f05eb7a3b7b","added_by":"auto","created_at":"2025-02-23 08:06:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2503500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/4868b32a-951b-444a-8183-5ee27ffde1c7.pdf"},{"id":64386539,"identity":"3e871569-cb8e-4bb6-ae40-a8a30e07b24b","added_by":"auto","created_at":"2024-09-12 12:37:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":987768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary_Methods_FiguresS1-S11_TablesS1-S2,S6-S7.docx\u003c/p\u003e\n\u003cp\u003eSupplementaryTablesS3-S5.xlsx\u003c/p\u003e","description":"","filename":"SupplementaryFiguresS1S11TablesS1S2S6S7.docx","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/57ea98148e8bdf60f395e284.docx"},{"id":64388043,"identity":"45ca460a-3cf9-4b30-9717-f013f71194ac","added_by":"auto","created_at":"2024-09-12 12:45:07","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":331686,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS3S5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/c44aa7b71e06834eb55664de.xlsx"},{"id":64386540,"identity":"6bf4695d-b44a-45d5-9b8c-aafd996fd78a","added_by":"auto","created_at":"2024-09-12 12:37:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1365055,"visible":true,"origin":"","legend":"\u003cp\u003eReporting Summary\u003c/p\u003e","description":"","filename":"RS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4412470/v1/acdc4e07ae65702215d51969.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nLPJ is a member of the scientific board of qGenomics. BVD and RS are employed by TruDiagnostics. All other authors declare that they have no competing interests.","formattedTitle":"Defective X Chromosome Inactivation and Cancer Risk in Women","fulltext":[{"header":"Background","content":"\u003cp\u003eDuring early female development, one of the two X chromosomes in each embryonic cell is randomly inactivated (Xi), leading to the formation of Barr bodies, which are packed corpuscles in the cell nuclei that are visible under a microscope\u003csup\u003e1\u003c/sup\u003e. Chromosome X inactivation (XCI) thus achieves gene dosage compensation with males, who carry only one X chromosome per cell\u003csup\u003e2\u003c/sup\u003e, and is a mechanism to avoid lethal consequences of X chromosome aneuploidies. However, not all the genes in Xi undergo inactivation. Under normal conditions, some genes always escape XCI, whereas others appear to escape depending on the tissue\u003csup\u003e3,4\u003c/sup\u003e. It is estimated that up to 23% of X-linked genes can naturally escape inactivation\u003csup\u003e5,6\u003c/sup\u003e. In contrast, in some aggressive breast tumor cells, there is complete erosion of XCI, and consequently, the entire chromosome escapes inactivation, as evidenced by the loss of Barr bodies\u003csup\u003e7\u003c/sup\u003e. Loss of XCI has been observed in other cancer cells; however, the extent to which complete or partial XCI loss is a feature of female cancer remains unknown\u003csup\u003e8\u003c/sup\u003e. Given that XCI levels vary among genes, cells\u003csup\u003e9\u003c/sup\u003e and tissues\u003csup\u003e5\u003c/sup\u003e, it is likely that global XCI maintenance also varies among women. However, the lack of a quantitative measure that can assess the level of defective XCI maintenance in a bulk biological sample remains an obstacle for assessing the health risks associated with the loss of XCI maintenance in women.\u003c/p\u003e \u003cp\u003eXCI occurs early in development and is considered to be stably maintained throughout the cells and lifespan of females\u003csup\u003e10\u003c/sup\u003e. Facultative heterochromatin is regulated in Xi by \u003cem\u003eXIST\u003c/em\u003e, which has important consequences for development and disease\u003csup\u003e11\u003c/sup\u003e. Histone modifications and DNA methylation are the main epigenetic modifications that lead to silencing of most genes on Xi\u003csup\u003e12\u003c/sup\u003e. In this study, we propose that a consequence of defective XCI is the loss of methylation of consistently silenced genes\u003csup\u003e5,6\u003c/sup\u003e. As such, we computed the percentage of 2-allele demethylation of CpGs in XCI regions as a metric of defective XCI frequency in a sample and called it X-chromosome reactivation (X-Ra) level. Using data from The Cancer Genome Atlas (TCGA), we demonstrated the global transcriptomic consequences of X-Ra and determined its overarching role in female cancer. In breast cancer, we determined the molecular mechanisms driving the interindividual variations of X-Ra and validated its association with lower survival, tumor heterogeneity in triple-negative breast cancer (TNBC) and chemotherapy treatment, in independent studies. Finally, we used a large methylome study and independent studies to show that X-Ra was detectable in the blood and increased with age and cancer diagnosis. Our study thus proposes the first personal biomarker of defective XCI in women, and demonstrates its translation potential in the management and treatment strategies of female cancer.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy workflow\u003c/h2\u003e \u003cp\u003eThe study design and workflow are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Briefly, the strong control of XCI over epigenetic modifications in Xi implies widespread methylation of CpGs over the inactivated region of Xi\u003csup\u003e13,14\u003c/sup\u003e. Therefore, we hypothesized that 2-allele demethylation in a CpG site expected to be under XCI would provide evidence for its defective control or maintenance. Consequently, we reasoned that in a bulk biological sample, the global frequency of defective XCI could be measured by the percentage of infrequent 2-allele demethylation events in CpGs under XCI. After showing robust evidence that this is the case, we then aimed to show that the percentage of defective XCI in bulk data could be robustly detected in different tissues and correlated with relevant clinical cancer outcomes, using multiple lines of evidence from independent studies (\u003cb\u003eTables S1-S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eX-Ra definition\u003c/h2\u003e \u003cp\u003eWe first selected CpGs that were mapped to X-linked genes with strong previously published evidence of consistent inactivation\u003csup\u003e5,6\u003c/sup\u003e. We then estimated the frequency of defective XCI in a female biological sample by surveying the number of CpGs that were observed with 2-allele demethylation, \u003cem\u003ei.e.\u003c/em\u003e methylation beta values\u0026thinsp;\u0026lt;\u0026thinsp;0.2. We defined X-Ra (X-Reactivation) as the percentage of these events, to measure the quantitative level of partial activation of Xi supported by methylation removal. Therefore, under a strong XCI, X-Ra is expected to be low, and when XCI is weakened, X-Ra should increase. A list of 4,862 CpGs was compiled from harmonized CpGs mapped in TCGA workflow to genes reportedly under XCI\u003csup\u003e5,6\u003c/sup\u003e. For each study, we selected CpGs in the reference panel and extracted the beta methylation levels. Finally, for each individual, X-Ra was the percentage of CpGs under XCI with beta values lower than 0.2 (See \u003cb\u003eMethods\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eX-Ra as a measure of a patient\u0026rsquo;s defective maintenance of XCI\u003c/h2\u003e \u003cp\u003eUsing methylation data of 446 constitutional biological samples from 12 different tissues from TCGA (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), we established X-Ra as a measure of defective XCI frequency in a biological sample. We first observed that CpGs under XCI with 2-allele demethylation were infrequent and varied among women. Across all 12 healthy tissues, we found that 96% of the 2-allele demethylation calls were in 20% of women and 64% in only 5% of women (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then annotated XCI CpGs with 15-chromatin states in blood and DNase hypersensitivity regions in six constitutional tissues\u003csup\u003e15\u003c/sup\u003e released from the ROADMAP Epigenomics Mapping Consortium (ChromHMM v1.10)\u003csup\u003e16\u003c/sup\u003e. We observed that while XCI CpGs with 2-allele demethylation spread through the chromosome (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), they were significantly enriched in transcription start sites (TSS) and DNase hypersensitivity regions, in relation to all the CpGs under XCI. They were also significantly depleted in heterochromatin, ZNF genes, and repeats, weakly repressed PolyComb, and strong transcription states (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These results were in line with previous work showing that the CpGs in the island-containing promoters of genes under XCI are consistently methylated in Xi\u003csup\u003e3,13\u003c/sup\u003e, and with the notion that the more accessible the CpGs are, the more likely they are to undergo 2-allele demethylation.\u003c/p\u003e \u003cp\u003eWe subsequently confirmed typically low percentages of X-Ra in females, \u003cem\u003ei.e.\u003c/em\u003e low frequency of 2-allele demethylation in XCI CpGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), suggesting high XCI maintenance in women, but not in men (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). We validated that X-Ra was a measure of XCI status, as random permutations on XCI labels of the CpGs in X robustly showed higher mean values of X-Ra than correct labeling in 12 different tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in all cases, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). We also observed that 67% (P\u0026thinsp;=\u0026thinsp;7.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;28\u003c/sup\u003e) of the X-Ra variance was explained by X-Ra computed with only the 2-allele methylation calls that were present in less than 5% of the women, demonstrating that the infrequent demethylation of CpGs in Xi is the main contributor of the measure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Overall, our analyses showed that X-Ra can be regarded as a suitable and robust quantitative measure of global defective maintenance of XCI in women.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eX-Ra as a marker of female cancer\u003c/h2\u003e \u003cp\u003eWe examined the association of X-Ra with the tumor status of samples from the 12 previously studied tissues (\u003cb\u003eTable S3\u003c/b\u003e). We found that tumors featured a substantial increase in the percentage of XCI CpGs with 2-allele demethylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). To ascertain the molecular mechanisms associated with X-Ra in cancer, we conducted a differential expression analysis for each tumor (\u003cb\u003eFig. S3\u003c/b\u003e). We found several significant genes at the genome-wide level (P\u0026thinsp;\u0026lt;\u0026thinsp;3.79\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). Importantly, we observed a strong downregulation of \u003cem\u003eXIST\u003c/em\u003e with X-Ra (Log2FC=-0.067, P\u0026thinsp;=\u0026thinsp;1.32\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e), further demonstrating X-Ra as a measure of defective XCI maintenance. Meta-analyses of 13,192 genes from cancer studies identified 537 genome-wide significant genes (\u003cb\u003eTable S4\u003c/b\u003e). Thirty-two of these genes have been reported to be under XCI (enrichment OR\u0026thinsp;=\u0026thinsp;2.87, P\u0026thinsp;=\u0026thinsp;6.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e), underlying the expected consequence of partial activation of Xi by downregulation of \u003cem\u003eXIST\u003c/em\u003e expression. The top genome-wide significant genes included \u003cem\u003eLRCH1\u003c/em\u003e (Leucine Rich Repeats and Calponin Homology Domain Containing 1) and \u003cem\u003ePHF11\u003c/em\u003e (Plant Homeodomain Finger Protein 11), which play important roles in CD8\u0026thinsp;+\u0026thinsp;T cell response against tumors and pathogens \u003csup\u003e17\u003c/sup\u003e, as well as T-cell activation in autoimmune disease\u003csup\u003e18\u003c/sup\u003e. Enrichment analyses of these significant genes revealed several pathways relevant to cancer, such as signal transduction, cell proliferation, apoptosis, and viral response (\u003cb\u003eFig. S4\u003c/b\u003e). Overall, these results demonstrated that X-Ra was strongly correlated with global transcriptomic levels, which was consistent with the loss of XCI control in cancer, also suggesting associations with the immune response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe observed that the cancer status of the biological samples was associated with X-Ra levels, adjusted for age (OR\u0026thinsp;=\u0026thinsp;1.19, P\u0026thinsp;=\u0026thinsp;6.05\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, \u003cb\u003eFig. S5\u003c/b\u003e). Heterogeneity suggests tumor stratification according to X-Ra. While demethylation across the entire X chromosome is a general feature of cancer (\u003cb\u003eFig. S6\u003c/b\u003e), the risk estimates of X-Ra across tumors could still be explained substantially by the reduction of X-Ra as a measure of XCI status in cancer (Pearson\u0026rsquo;s R\u0026thinsp;=\u0026thinsp;0.66, P\u0026thinsp;=\u0026thinsp;0.018). Furthermore, the percentage of 2-allele demethylated CpGs of escapees, \u003cem\u003ei.e.\u003c/em\u003e not under XCI (grey area under the blue curve Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), was not significantly associated with cancer status (OR\u0026thinsp;=\u0026thinsp;0.97, P\u0026thinsp;=\u0026thinsp;0.46) (\u003cb\u003eFig. S7\u003c/b\u003e), suggesting a limited role of overall demethylation of the X chromosome in the association between X-Ra and cancer.\u003c/p\u003e \u003cp\u003eImportantly, the percentage of CpGs in escapees with methylation values in the 1-allele methylation zone (0.2\u0026thinsp;\u0026lt;\u0026thinsp;beta\u0026thinsp;\u0026lt;\u0026thinsp;0.6, blue area under the blue curve Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) increased in tumors, suggesting defective escape of these genes in cancer. The percentages of 2-allele demethylation in XCI GpGs (X-Ra) and 1-allele methylation in escapees were strong additive factors for cancer risk adjusted for age (XCI CpGs OR\u0026thinsp;=\u0026thinsp;1.45, P\u0026thinsp;=\u0026thinsp;4.09\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, CpG in escapees OR\u0026thinsp;=\u0026thinsp;1.90, P\u0026thinsp;=\u0026thinsp;3.22\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Furthermore, X-Ra was significantly associated with a lower probability of LOF somatic mutations in genes under XCI (OR\u0026thinsp;=\u0026thinsp;0.98, P\u0026thinsp;=\u0026thinsp;9.71\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), adjusting for cancer type. In contrast, the percentage of 1-allele methylation in escapees was associated with a higher probability of LOF somatic mutations (OR\u0026thinsp;=\u0026thinsp;1.04, P\u0026thinsp;=\u0026thinsp;1.82\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), suggesting that the risk of cancer increases when both methylation and mutations affect both alleles. The strong negative correlation between the percentages (Pearson\u0026rsquo;s R=-0.39, P\u0026thinsp;=\u0026thinsp;9.72\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;138\u003c/sup\u003e, \u003cb\u003eFig. S8\u003c/b\u003e) indicated underlying tumor stratification.\u003c/p\u003e \u003cp\u003eTo assess the attributable risk of defective XCI maintenance in any type of cancer given by high X-Ra, we performed a ROC analysis and obtained the optimal Youden value at X-Ra\u0026thinsp;=\u0026thinsp;10%, which had 85.6% specificity. The attributable risk of dichotomized X-Ra for cancer status was 40% (95%CI\u0026thinsp;=\u0026thinsp;23\u0026ndash;56%). The proportion of high X-Ra (\u0026gt;\u0026thinsp;10%) was 43% across cancer types, with the highest rates in the thyroid (75%), liver (56%), and breast (50%), and the lowest in lung adenocarcinoma (13%) and pancreas (17%), in line with the association with cancer status (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eX-Ra as a marker of breast cancer\u003c/h2\u003e \u003cp\u003eWe investigated the association between X-Ra and breast cancer (BRCA) subtypes, survival, chemotherapy treatment and somatic mutations. A 1%-point increase in X-Ra in tumor biopsy increased the hazard ratio of death from BRCA by 1.7%, adjusting for age (HR\u0026thinsp;=\u0026thinsp;1.017, P\u0026thinsp;=\u0026thinsp;0.025) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We validated this finding in an independent study (GSE78754) of 70 patients with TNBC (HR\u0026thinsp;=\u0026thinsp;1.033, P\u0026thinsp;=\u0026thinsp;0.0081), adjusting for age. Consistent with these findings, we found in GSE141441 that 95 TNBC patients who did not undergo surgery had a higher probability of recurrence due to a high frequency of X-Ra, after adjusting for chemotherapy treatment (OR\u0026thinsp;=\u0026thinsp;1.08, P\u0026thinsp;=\u0026thinsp;0.04) (\u003cb\u003eFig. S9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith respect to breast cancer subtypes, we found that X-Ra significantly decreased the probability of having no estrogen receptor type (ER-) (OR\u0026thinsp;=\u0026thinsp;0.982, P\u0026thinsp;=\u0026thinsp;0.004), no progesterone receptor type (PR-) (OR\u0026thinsp;=\u0026thinsp;0.984, P\u0026thinsp;=\u0026thinsp;P\u0026thinsp;=\u0026thinsp;0.008), and no human epidermal growth factor receptor 2 (HER2-) status, although not significantly (OR\u0026thinsp;=\u0026thinsp;0.988, P\u0026thinsp;=\u0026thinsp;0.15). Consistent with these results, X-Ra was significantly associated with TNBC (OR\u0026thinsp;=\u0026thinsp;1.029, P\u0026thinsp;=\u0026thinsp;3.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). This finding was validated in 224 patients (GSE225845), where X-Ra was associated with TNBC versus HE2R+/HER\u0026thinsp;+\u0026thinsp;status (OR\u0026thinsp;=\u0026thinsp;1.07, 95%CI\u0026thinsp;=\u0026thinsp;0.0015) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In summary, we observed that X-Ra stratified patients with breast cancer, particularly TNBC, and was associated with important clinical outcomes.\u003c/p\u003e \u003cp\u003eWe analyzed longitudinal studies to determine whether X-Ra could be used as a monitoring biomarker of neoadjuvant chemotherapy for breast cancer. In 290 patients with three visits during chemotherapy treatment (GSE207460), we found a significant reduction of 1.78% in X-Ra for every 12 weeks of treatment (β=-1.78, P\u0026thinsp;=\u0026thinsp;1.78\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), adjusting for whether patients were additionally treated with bevacizumab. Interestingly, we noted that patients treated with bevacizumab further reduced X-Ra at each 12-week visit (interaction β=-1.30, P\u0026thinsp;=\u0026thinsp;0.013). We validated the reduction of X-Ra with neoadjuvant chemotherapy in 22 TNBC patients (GSE184159) with four cycles of chemotherapy during 12 weeks (β=-3.56, P\u0026thinsp;=\u0026thinsp;0.014), suggesting a stronger modifiable nature of X-Ra by chemotherapy for this subtype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eX-Ra associations with somatic mutations in breast cancer\u003c/h2\u003e \u003cp\u003eWe then investigated the potential molecular mechanisms underlying X-Ra activity in breast cancer tumors. We considered chromosomal copy number gains in 0.5MB windows. We discovered 492 windows with significant increments in log(X-Ra) associated with the frequency of gains (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cb\u003eTable S5\u003c/b\u003e). Remarkably, most of the top associations on each chromosome included notable oncogenes (\u003cb\u003eTable S6\u003c/b\u003e). Notably, we observed gains in \u003cem\u003eMYC\u003c/em\u003e locus\u003csup\u003e19\u003c/sup\u003e, which is known to induce pluripotent stem cell reprogramming with reactivated X-chromosomes in females\u003csup\u003e20\u003c/sup\u003e. The most significant association was found in a window that included \u003cem\u003eTNFRSF6B\u003c/em\u003e, which prevents apoptosis\u003csup\u003e21\u003c/sup\u003e and \u003cem\u003eRTEL1\u003c/em\u003e which encodes a regulator of telomere length\u003csup\u003e22\u003c/sup\u003e. X-Ra in BRCA was significantly associated with longer telomere length (β\u0026thinsp;=\u0026thinsp;6.6, P\u0026thinsp;=\u0026thinsp;0.003)\u003csup\u003e23\u003c/sup\u003e. These data underscore the potential mechanism of X-Ra in BRCA, consistent with the association between telomere length and \u003cem\u003eRTEL1\u003c/em\u003e gains (TL difference\u0026thinsp;=\u0026thinsp;1.72, P\u0026thinsp;=\u0026thinsp;6.47\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Additionally, 22 significantly deleted regions were associated with X-Ra, all located in the short arm of chromosome 8, with the highest peak at \u003cem\u003eCSMD1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), which encodes a complement inhibitor known to act as a tumor suppressor in breast cancer\u003csup\u003e24\u003c/sup\u003e. Finally, we did not find any association with somatic copy number loss, gain, or copy neutral loss of heterozygosity in any region or in the entire X chromosome, suggesting that X-Ra increments are not due to the loss of X or copy neutral variants of X (LOX/GOX/UPD) or parts of it.\u003c/p\u003e \u003cp\u003eThe association of X-Ra with 18 frequent somatic mutations (\u0026gt;\u0026thinsp;5%) revealed an increase of X-Ra by 3.42 percentage points for each\u003cem\u003eTP53\u003c/em\u003e mutation (P\u0026thinsp;=\u0026thinsp;5.42\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), and decreased by 5.42 for each \u003cem\u003eCDH1\u003c/em\u003e mutation (P\u0026thinsp;=\u0026thinsp;4.93\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). Consequently, X-Ra is likely associated with the basal-like 1 subtype of TNBC\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eX-Ra in blood is associated with cancer and aging\u003c/h2\u003e \u003cp\u003eWe analyzed methylation data from three different studies to test the association of X-Ra with age\u0026thinsp;\u0026gt;\u0026thinsp;65 years (median age of cancer diagnosis), adjusting for immune cell count in the blood. We excluded patients with a cancer diagnosis in TruDiagnostic; cancer diagnosis was an exclusion criterion in the other two studies. We observed significant increases in X-Ra with age in all studies (β\u0026thinsp;=\u0026thinsp;0.44, P\u0026thinsp;=\u0026thinsp;3.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B), with significant heterogeneity given by GENOA, a pedigree study on hypertension. Associations with continuous age were significant in TruDiagnostic (β\u0026thinsp;=\u0026thinsp;0.014, P\u0026thinsp;=\u0026thinsp;0.0038) and MESA (β\u0026thinsp;=\u0026thinsp;0.029, P\u0026thinsp;=\u0026thinsp;3.81\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), but not in GENOA (β\u0026thinsp;=\u0026thinsp;0.0018, P\u0026thinsp;=\u0026thinsp;0.65). In the TruDiagnostic data, the association between X-Ra and continuous age increased 3-fold in menopausal women (β\u0026thinsp;=\u0026thinsp;0.044, P\u0026thinsp;=\u0026thinsp;6.64\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). In addition, X-Ra was associated with shorter telomere length (β=-1.008, P\u0026thinsp;=\u0026thinsp;1.19\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and with Horvath\u0026rsquo;s (β\u0026thinsp;=\u0026thinsp;0.029, P\u0026thinsp;=\u0026thinsp;4.85\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) and Levine\u0026rsquo;s methylation ages (β\u0026thinsp;=\u0026thinsp;0.016, P\u0026thinsp;=\u0026thinsp;0.006), which do not include CpGs in the X chromosome. These results show that X-Ra in the blood is an indicator of female aging.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDifferential expression analysis of X-Ra in monocytes from the MESA study revealed significant genes relevant to breast cancer and histone modifications (\u003cb\u003eFig. S10\u003c/b\u003e), such as upregulation of \u003cem\u003eABTB2\u003c/em\u003e\u003csup\u003e26\u003c/sup\u003e, downregulation of \u003cem\u003eSOGA1\u003c/em\u003e\u003csup\u003e27,28\u003c/sup\u003e, and upregulation of \u003cem\u003eRBBP4\u003c/em\u003e and \u003cem\u003eRSPO2\u003c/em\u003e\u003csup\u003e29\u003c/sup\u003e. We also observed significant enrichment of inactivated genes in the differential expression analysis of X-Ra (OR\u0026thinsp;=\u0026thinsp;3.00, P\u0026thinsp;=\u0026thinsp;0.002, \u003cb\u003eTable S7\u003c/b\u003e) and nominal but expected downregulation of \u003cem\u003eXIST\u003c/em\u003e (log2FC=-0.024, P\u0026thinsp;=\u0026thinsp;0.02). Therefore, high X-Ra values in the blood were associated with breast cancer-related genes and with the expected transcriptomic signatures of XCI maintenance.\u003c/p\u003e \u003cp\u003eWe found a significant association between X-Ra in the blood and cancer diagnosis in the TruDiagnostic cohort (167 were diagnosed with any type of cancer) and observed an increase of 7.9% in cancer diagnosis for a 1%-point increase in X-Ra (OR\u0026thinsp;=\u0026thinsp;1.077, P\u0026thinsp;=\u0026thinsp;0.034, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). We did not find significant associations between cancer and methylation ages of Horvath and Levine genes. The association between X-Ra in the blood and cancer in GSE237036, adjusted for age and immune cell abundance, was not significant (OR\u0026thinsp;=\u0026thinsp;1.54, P\u0026thinsp;=\u0026thinsp;0.25). However, the combination of both studies showed a significant association (OR\u0026thinsp;=\u0026thinsp;1.08, P\u0026thinsp;=\u0026thinsp;0.02), with no apparent heterogeneity (P\u0026thinsp;=\u0026thinsp;0.34). The association between X-Ra in the blood and cancer was independently validated in a longitudinal study (GSE142536) of 17 elderly women, where cancer diagnosis increased the frequency of X-Ra by 2.2% (β\u0026thinsp;=\u0026thinsp;2.2, P\u0026thinsp;=\u0026thinsp;0.006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). No association was observed between Levine\u0026rsquo;s methylation age and cancer (\u003cb\u003eFig. S11\u003c/b\u003e). Taken as a test-retest study, X-Ra showed high statistical reliability (ICC\u0026thinsp;=\u0026thinsp;0.95, 95%CI\u0026thinsp;=\u0026thinsp;0.91 0.98).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides evidence that defective XCI frequency in a bulk sample is a novel source of inter-individual variation among women. We introduced X-Ra, the first personal measure of defective XCI. The measure is based on the frequency of demethylation marks arising from the likely weakening of XCI maintenance. Thus, poor XCI maintenance appears to be a hallmark of female cancer in general\u003csup\u003e30\u003c/sup\u003e. We noted that both X-Ra and hemimethylation of escapees, the former of which is associated with a higher frequency of LOF mutations, are additive risk factors for cancer in women. As mosaic loss of the Y chromosome (mLOY) is a risk factor in males that is associated with LOF of escapees with homologous copies in Y\u003csup\u003e31\u003c/sup\u003e, our results offer an integrated picture of gonosome dysregulation in cancer for both sexes. Therefore, defective XCI maintenance in silenced genes and disruption of escapees should be targeted in further studies to assess their potential as diagnostic or prognostic biomarkers in female cancer\u003csup\u003e32\u003c/sup\u003e. Our blood analyses supported the notion that a possible mechanism of cancer risk from defective XCI is by altering the immune response\u003csup\u003e33\u003c/sup\u003e, in line with the immunosurveillance hypothesis of mLOY in males\u003csup\u003e31,34,35\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe observed that X-Ra was a powerful insightful marker for breast cancer. X-Ra was associated with the downregulation of \u003cem\u003eXIST\u003c/em\u003e\u003csup\u003e36\u003c/sup\u003e, 20q (\u003cem\u003eRTEL1\u003c/em\u003e), and 8q (\u003cem\u003eMYC\u003c/em\u003e) copy number gains\u003csup\u003e19\u003c/sup\u003e; 8p (\u003cem\u003eCSMD1\u003c/em\u003e) deletions\u003csup\u003e24\u003c/sup\u003e; lower breast cancer survival\u003csup\u003e37\u003c/sup\u003e and basal type 1 TNBC\u003csup\u003e25\u003c/sup\u003e. In addition, X-Ra was responsive to chemotherapy. These results show that defective XCI maintenance is an important mechanism underlying tumor heterogeneity, particularly in TNBC. TNBC displays heightened aggressiveness, proliferation, and propensity for early metastasis compared with hormonal subtypes\u003csup\u003e38\u003c/sup\u003e. Treatment is complicated by the absence of defined molecular targets owing to its heterogeneous nature. Previous stratification of TNBC usually relies on substantial data reduction, population dependence, and needs mechanistic interpretation\u003csup\u003e39,40\u003c/sup\u003e. We showed that X-Ra reliably defines a new type of breast cancer that is associated with TNBC by a clear mechanism, opening the door to the design of novel interventions strategies based on XCI regulation.\u003c/p\u003e \u003cp\u003eAs a developmental process, it is important to note the role of \u003cem\u003eMYC\u003c/em\u003e in cancer and the regulation of the pluripotency in embryonic stem cells[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], which are cells with important differences in global regulation of XCI that lead to different XCI states[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, given that those states appear to vary between pluripotent cell subcultures and populations[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], it has remained unknown the extent to which global regulation of XCI at an organismal level quantifiable in adult complex biological samples. Here, we demonstrate that defective XCI maintenance is a characteristic of individuals, as we analyze bulk cell populations with random XCI. From our study in blood, it is interesting to remark the association between the interindividual variation of defective XCI and aging. Therefore, it is important to further investigate modifiable and genetic factors that may contribute to X-Ra variation in aging, cancer and also development.\u003c/p\u003e \u003cp\u003eOverall, the range of X-Ra values was large (measured in percentage units, it had mean value of 75% in thyroid and 50% in breast cancer). Strong significant associations were observed across the study to the order of 1% unit increases in the biomarker, demonstrating its high-resolution power. Using longitudinal data, we also showed the high statistical reliability of X-Ra in tumor biopsies and blood, increasing its clinical applicability and translation to the clinic.\u003c/p\u003e \u003cp\u003eOur study has some limitations that need to be further addressed to improve the measurement X-Ra. For instance, X-Ra's definition does not consider the individual likelihood of each XCI CpG to present 2-allele demethylation. In addition, our analyses do not account for the origin of the inactive X-chromosome, and therefore, the effect of skew inactivation and its relation to X-Ra should be considered in future studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur work provides new insights into the risks and mechanisms associated with the deregulation of XCI in female cancer. We discovered that the level of XCI control varies in strength among women and decreases in cancer biopsies. Defective XCI is strongly associated with triple-negative breast cancer and worse survival. Defective XCI was reduced by chemotherapy treatment, suggesting potential new targets of intervention. The variability of XCI is also present in blood with validated correlations with cancer diagnosis. In a similar manner as mLOY is an important risk factor for cancer, several aging-related diseases and overall mortality in males, we show here that defective XCI is an important female-specific gonosome biomarker with long-range health consequences that are yet to be fully determined. Our study highlights the importance of considering inter-individual variation in XCI when investigating cancer risk and aging in females.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eX-Ra definition\u003c/h2\u003e\n \u003cp\u003eDNA methylation is an important epigenetic modification that contributes to gene silencing on Xi\u003csup\u003e41\u003c/sup\u003e. Previous work has shown that the CpGs in the island-containing promoters of genes under XCI are methylated in Xi. Consequently, the mean methylation levels of these CpGs correspond to either 1-allele methylation values (methylation in Xi and no methylation in Xa) or 2-allele methylation values (methylation in both Xi and Xa)\u003csup\u003e3,13\u003c/sup\u003e. Therefore, a strong regulation of XCI should imply that no CpG in inactivated genes can have 2-allele demethylation values (hypomethylation in both Xi and Xa). After a thorough validation of this observation using multiple sources of data, we, consequently, propose to measure the level of defective Xi inactivation in a female biological sample by examining the number of these CpGs that are observed with 2-allele demethylation, \u003cem\u003ei.e.\u003c/em\u003e methylation beta values\u0026thinsp;\u0026lt;\u0026thinsp;0.2. Our hypothesis is that a consequence of a potential weakening of XCI is to allow the infrequent removal of methylation from any of the CpGs in the inactivated region of Xi. To differentiate between women with different quantitative levels of XCI, we compute the percentage of XCI CpGs that have 2-allele demethylated and should not have been under strong XCI. This percentage is then a measure of a patient\u0026rsquo;s defective XCI and/or a woman\u0026rsquo;s level of chromosome X reactivation (X-Ra), meaning the quantitative level of partial activation of Xi. Conceptually, the measure is a state variable that describes the level of XCI at a particular point in time and is given by the estimation of a woman\u0026rsquo;s probability that a CpG under XCI selected at random is observed with 2-allele demethylation\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:XRa=P(M=0\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003ewhere M is the methylation state of a randomly selected CpG in Xi (0: demethylated, 1: methylated). Therefore, under strong XCI, X-Ra is expected to be low, and when XCI is weaken, X-Ra should increase.\u003c/p\u003e\n \u003cp\u003eWe selected CpGs that map to X-linked genes with strong previously published evidence of consistent inactivation\u003csup\u003e5,6\u003c/sup\u003e. To allow wide applicability of X-Ra in different platforms, we used harmonized CpGs mapped in the TCGA workflow that included Human Methylation 27 (HM27), HumanMethylation 450 (HM450), and EPIC platforms. We thus created a final list of 4,862 CpGs expected to have beta values in females higher than 0.2 due to XCI. The hypomethylation threshold of 0.2 surely captured CpGs that are clearly hypomethylated in different platforms\u003csup\u003e42,43\u003c/sup\u003e. This list of 4,862 CpGs was our reference panel to compute X-Ra across studies with different methylation arrays. For each study, we then selected the CpGs that were in the reference panel and extracted the beta methylation levels. Then, for each individual, we defined the percentage of selected CpGs that had beta values lower than 0.2 as a measure of X-Ra.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eTCGA data\u003c/h2\u003e\n \u003cp\u003eThe Cancer Genome Atlas (TCGA) consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450K BeadChip (Illumina 450K array). We obtained methylation data for 15 different cancer sites and normal tissue. We downloaded data from ExperimentHub version 2.0.1 using the R package \u003cem\u003ecuratedTCGAData\u003c/em\u003e version 1.16.0. The cancer sites included breast (BRCA), bladder (BLCA), colon (COAD), head and neck (HNSC), kidney (KIRC), liver (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreas (PAAD), rectum (READ), thyroid (THCA), uterus (UCEC), skin (SKCM) and ovary (OV). We discarded the last two because they did not have methylation data on more than 5 samples of undiseased tissue. We analyzed a total of 11,232 CpGs on chromosome X for 3,741 samples.\u003c/p\u003e\n \u003cp\u003eWe downloaded clinical, RNA-seq and CNV data for BRCA to perform associations with X-Ra. For clinical associations, we downloaded data using \u003cem\u003eTCGAbiolinks\u003c/em\u003e version 2.22.4 and fitted proportional hazards regression models for survival data adjusting by age for each independent cancer site. We also tested the association between X-Ra and hormonal status (ER, PR, HER2) of breast cancer, adjusting by age. For RNA-seq data, we inferred surrogate variables with \u003cem\u003esvaseq\u003c/em\u003e from the \u003cem\u003eSVA\u003c/em\u003e R library for each individual cancer. We tested the association of transcription levels of 20,532 genes with X-Ra adjusting by age and surrogate variables, transforming the expression levels with \u003cem\u003evoom\u003c/em\u003e and fitting regression models with \u003cem\u003elimma\u003c/em\u003e. In each cancer site, genes with less than 15 counts in more than 75% of the samples were removed from the analysis. Finally, we downloaded data of somatic mutations in BRCA and tested the association of X-Ra with the presence of recurrent mutations in tumors across genes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eTrueDiagnostic DNA biobank\u003c/h2\u003e\n \u003cp\u003eThe TruDiagnostic DNA biobank is a USA population-based cohort aged between 13 and 97 years old. The biobank includes 3,590 individuals recruited between October 2020 and February 2022, who have chosen TruDiagnostic for DNA methylation analysis for biological aging. TruDiagnostic is an aging study in which a health questionnaire was administer at the time of the sample collection. Participants were screened over 120 clinical outcomes to determine their general health state. In particular, participants reported whether they had any cancer diagnosis, but no further details on treatment are available. Clinical data was collected, including anthropometric measures and diagnosis of common diseases. Telomere length data were available together with immune cell contamination. We analyzed methylation data from 1,414 females of the 3,259 individuals who passed quality control.\u003c/p\u003e\n \u003cp\u003eFor the DNA methylation analysis, DNA was extracted from peripheral whole blood. The Infinium HumanMethylationEPIC BeadChip was used for DNA methylation assessment following the manufacturer\u0026apos;s protocol. Several quality controls and functional normalizations were performed using the \u003cem\u003emeffil\u003c/em\u003e package, resulting in 745,150 probes, as described in detail elsewhere\u003csup\u003e44\u003c/sup\u003e. CpG sites were annotated to genes using the EPIC Illumina annotation ilm10b4.hg19. Blood cell types were estimated using the blood gse35069 reference panel from the \u003cem\u003emeffil\u003c/em\u003e package. We analyzed a total of 4,471 CpG sites from chromosome X. We assessed the association of X-Ra with age adjusting for leukocyte content and slide.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eMESA study\u003c/h2\u003e\n \u003cp\u003eWe downloaded data from the Gene Expression Omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) with the reference GSE56046, to test the association between X-Ra and gene expression in blood, and validate the association with aging. This is a large population study with gene expression (Illumina HumanHT-12 v4 Expression) and methylation (Illumina HumanMethylation450) data in purified human monocytes from blood. Exclusion criteria included any serious medical condition which would prevent long-term participation. The data included covariates such as ancestry, gender, study site, and sample contamination with B-cells, T-cells, natural killer cells, and neutrophils. As ancestry, gender, and study site were encoded in a categorical variable with 18 different categories, we inferred sex from the mean expression of chromosome Y divided by the mean expression from the whole genome (Ry). Individuals from categories with Ry values less than \u0026minus;\u0026thinsp;0.35 were classified as females. The classification was validated by the average methylation from inactive genes from chromosome X, where categories with values less than \u0026minus;\u0026thinsp;0.5 were classified as females, thus completely matching those classified by Ry. We then analyzed data for 605 females with a mean age of 60 years (SD\u0026thinsp;=\u0026thinsp;9.47). The methylation data were obtained in M values and transformed into Beta values [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. We analyzed a total of 11,648 CpG sites from chromosome X. We assessed the association between X-Ra and age adjusting by leukocyte content and well. For gene expression data, we analyzed 47,308 transcripts and detected surrogate variables with SVA, adjusting by age, cohort and contamination with B-cells, T-cells, natural killer cells, and neutrophils.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eValidation Studies\u003c/h2\u003e\n \u003cp\u003eWe downloaded relevant methylation data from GEO to validate our findings in independent studies. We used methylation data of different cancer studies to validate X-Ra associations with cancer survival in TNBC (GSE78754, n\u0026thinsp;=\u0026thinsp;70) and TNBC status (GSE225845, n\u0026thinsp;=\u0026thinsp;224). We also analyzed studies of cancer recurrence for TNBC (GSE141441, n\u0026thinsp;=\u0026thinsp;95) and a longitudinal study of chemotherapy intervention on 22 TNBC patients (GSE184159, n\u0026thinsp;=\u0026thinsp;63).\u003c/p\u003e\n \u003cp\u003eWe downloaded data from with reference GSE210255. The Genetic Epidemiology Network of Arteriopathy (GENOA) study is a large community-based study of hypertensive siblings, including African Americans from Jackson, USA, with methylation data obtained with the Illumina Infinium HumanMethylationEPIC BeadChip. After quality control and filtering, a total of 1,394 samples were included in the analysis. We focused on female samples (n\u0026thinsp;=\u0026thinsp;986) with a mean age of 57.2 years (SD\u0026thinsp;=\u0026thinsp;10.3). We analyzed a total of 10,125 CpG sites on chromosome X and assessed the association between X-Ra and age, adjusting for plate and immune cell contamination and including a random effect on pedigree. Exclusion criteria included active malignancy.\u003c/p\u003e\n \u003cp\u003eWe downloaded data from GSE142536 to validate the association between X-Ra in blood and cancer diagnosis. In this study, we also computed the test-retest reliability of the biomarker. The study included 17 elderly women, assayed immediately before major surgery, in the morning of postoperative day and between 4 and 7 days after surgery. Methylation data in blood was collected at each visit. Patients had a mean age of 79 years and three of them had cancer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analyses\u003c/h2\u003e\n \u003cp\u003eData were analyzed using R version 4.1.1 and Bioconductor version 3.13. The goal was to determine the biological correlates of chromosome X reactivation (X-Ra) in studies with different characteristics and to establish the biological plausibility of the biomarker as a measure of defective X inactivation.\u003c/p\u003e\n \u003cp\u003eIn the TCGA, tor each of the 12 cancer sites, we inferred the value of X-Ra in constitutional and tumor tissues. We tested the association with cancer status (with logistic regression models) and survival (with proportional hazard regression models) across cancer sites, adjusting by age. We meta-analyzed the results with fixed and random effects models, using the \u003cem\u003ermeta\u003c/em\u003e library. The prediction power of X-Ra on cancer status was tested with a ROC area under the curve statistic. The optimal X-Ra was defined as the Youden distance, which was used to compute the attributable fraction of X-Ra of affected tissue on cancer risk, using the \u003cem\u003eAFglm\u003c/em\u003e function of the \u003cem\u003eAF\u003c/em\u003e library. For transcriptome-wide associations of X-Ra in each cancer site, we used \u003cem\u003elimma\u003c/em\u003e and adjusted for \u003cem\u003eSVA\u003c/em\u003e and age. Log2FC with their standard error were then meta-analyzed across cancer sites, and the associations were adjusted for multiple comparisons for the number of transcripts tested, taking Bonferroni significant threshold at 3.79\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e. On these associations, enrichment analyses on biological processes were performed on genes with transciptome-wide significance, using the \u003cem\u003eenrichGO\u003c/em\u003e from \u003cem\u003eclusterProfiler\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eIn the TCGA, we tested the association of X-Ra in tumor and healthy tissues with breast cancer risk and survival. For breast cancer risk, we fitted linear regression models of X-Ra on cancer status of the samples and adjusted for age. We also tested the association between X-Ra and ER, HER, and PER and triple-negative status, adjusting for age. Survival associations were performed with proportional hazards regression models adjusting for age. We also studied genome-wide associations of X-Ra with the frequency of copy number gains and deletions of breast cancer tissues in sliding windows of 0.5 Mb. To deal with windows with low frequency in copy number changes (1% at least), we fitted Bayesian regression models with \u003cem\u003ebayesglm\u003c/em\u003e of \u003cem\u003earm\u003c/em\u003e, adjusting for age. Associations were adjusted for multiple comparisons using Bonferroni threshold of significance at P\u0026thinsp;\u0026lt;\u0026thinsp;8.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e. To determine associations with loss of function mutations (LOF), we downloaded a detailed catalogue of somatic mutation data for TCGA from xenabrowser.net, and classified LOF mutations labeled as coding sequence variants, incomplete terminal codon variants, splice acceptor/donor variant, start lost, and stop gained or lost.\u003c/p\u003e\n \u003cp\u003eWe also inferred X-Ra in blood and tested its association with age and markers of biological aging such telomere length, and Horvarth\u0026apos;s\u003csup\u003e45\u003c/sup\u003e and Levine\u0026rsquo;s methylation age\u003csup\u003e46\u003c/sup\u003e, as computed by the R package \u003cem\u003emethylclock\u003c/em\u003e\u003csup\u003e47\u003c/sup\u003e. Horvarth\u0026rsquo;s methylation clock uses 353 CpGs to estimate biological aging from methylation data, while Levine\u0026apos;s clock is based on 513 CpGs, none of which are in chromosome X. In the TruDiagnostic data, we tested the associations with age, markers of aging and with cancer diagnosis at any site. We fitted linear regression models, adjusting for immune cell abundance.\u003c/p\u003e\n \u003cp\u003eIn the MESA study, we assessed the association between X-Ra and differential expression in purified monocytes from peripheral blood and with the age. In this study, the associations of X-Ra with transcriptomic signatures specific to XCI were also tested. The enrichment in associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of inactivated genes was tested using a hypergeometric (Fisher exact) test. For this study, the association between X-Ra and age (\u0026gt;\u0026thinsp;65) was also tested using a linear regression model, adjusting for cohort and immune cell contamination.\u003c/p\u003e\n \u003cp\u003eAssociations of X-Ra in blood with age were performed for the GENOA study (GSE210255). In GENOA, a linear model was fitted adjusting for plate and immune cell contamination. The regression coefficients for age across the three studies (MESA, GENOA, TruDiagnostic) were meta-analyzed using fixed and random effects models with two-sided tests using \u003cem\u003emetagen\u003c/em\u003e from \u003cem\u003emeta\u003c/em\u003e. Dichotomization of age was taken as the median age of cancer at any site according to SEER 22 2015\u0026ndash;2019.\u003c/p\u003e\n \u003cp\u003eIn the longitudinal study GSE142536 that included follow up of individuals with cancer and controls, we fitted mixed models on X-Ra with condition, visit and age as fixed effects and subject as a random effect. We used the \u003cem\u003elmer\u003c/em\u003e R library. We also tested similar mixed models for the Levine\u0026rsquo;s methylation age, to contrast the effect of the condition between X-Ra and global methylation age. We also tested the significance of the intraclass correlation, to determine the test-retest reliability of X-Ra.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData and code availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll public data, code, and materials used in the analysis are cited and referenced in the manuscript. The entire computer code with required data to replicate all reported findings can be found at https://github.com/isglobal-brge/Supplementary-Material/Caceres_2024. The data from the TruDiagnostic Biobank is available upon reasonable request due to ensure the privacy of the participants. Please e-mail [email protected] for data requests. The computer software, documentation and licensing to calculate X-Ra is available at https://xra.isglobal.org. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank participants of TrueDiagnostic data bank. We kindly acknowledge Xavier Escribà for creating the software’s chrXRA web site. We thank Dr. Mariona Bustamante for insightful comments. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunded by the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019-2023 (CEX2018-000806-S) program, and support from the Generalitat de Catalunya through the CERCA Program [AC, JRG]. Funded by the Spanish Ministry of Science and Innovation MCIN/FIS-ISCIII, Ref PI21/00050, and Fondo Europeo de Desarrollo Regional (FEDER) and the Generalitat de Catalunya through the Consolidated Research Group (2017SGR01974) [LAPJ]. The Department of Medicine and Life Sciences-UPF acknowledges also support from the Spanish National Investigation Agency (AEI) (DOI: 10.13039/501100011033) through the “Unidad de Excelencia María de Maeztu” (CEX2018-000792-MDM). Funded by CaixaResearch Institute Innovation Hub [JRG].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: AC, LPJ Methodology: AC. Investigation: AC, JRG. Funding acquisition: JRG, BVD, RS. Project administration: JRG Supervision: LPJ, JRG Writing – original draft: AC, LPJ, JRG. Writing – review \u0026amp; editing: AC, LPJ, JRG, BVD, RS.\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003eThe data from the TruDiagnostic Biobank is available upon reasonable request due to ensure the privacy of the participants. Please e-mail [email protected] for a data use agreement (DUA) form to apply for access of the deidentified dataset. All DUA forms are needed to track data transfers and to maintain HIPAA compliance as according to the Human Health Services (https://www.hhs.gov/hipaa/for-professionals/special-topics/emergency-preparedness/data-use-agreement/index.html)\u003c/p\u003e\n\u003cp\u003eConflict of interests \u003c/p\u003e\n\u003cp\u003eLPJ is a member of the scientific board of qGenomics. BVD and RS are employed by TruDiagnostics. All other authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants of the TrueDiagnostic Data Bank. We acknowledge Xavier Escribà for creating the chrXRA software website. 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Bioinformatics \u003cb\u003e37\u003c/b\u003e, (2021)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4412470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4412470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eX-chromosome inactivation (XCI) is a fundamental mechanism in placental mammals that compensates for gene dosage differences between sexes. Using methylation levels of genes under XCI, we establish defective levels of XCI as a new source of interindividual variation among cancer types in females, characterized by strong \u003cem\u003eXIST\u003c/em\u003edownregulation and upregulation and enrichment of genes under XCI. We show that defective XCI is an additive factor to the cancer risk of XCI escape deregulation in women. Defective XCI of more than 10% has an attributable risk of 40% among 12 different cancers from The Cancer Genome Atlas. Validations between independent studies of breast cancer samples show that defective XCI increases triple-negative subtype frequency, decreases survival rates, and is reduced by chemotherapy treatment. Mechanistically, it is associated with somatic mutations at \u003cem\u003eTP53\u003c/em\u003e and top \u003cem\u003eMCY\u003c/em\u003e gains. In independent studies, defective XCI is detectable in blood and increases with aging, menopause, and cancer diagnosis.\u003c/p\u003e","manuscriptTitle":"Defective X Chromosome Inactivation and Cancer Risk in Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-12 12:37:02","doi":"10.21203/rs.3.rs-4412470/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6749c501-434d-4fc8-9107-46152b52e77e","owner":[],"postedDate":"September 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36038029,"name":"Health sciences/Oncology/Cancer/Cancer genetics/Cancer epigenetics"},{"id":36038030,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2025-02-23T08:06:28+00:00","versionOfRecord":{"articleIdentity":"rs-4412470","link":"https://doi.org/10.1038/s42003-025-07691-y","journal":{"identity":"communications-biology","isVorOnly":false,"title":"Communications Biology"},"publishedOn":"2025-02-22 05:00:00","publishedOnDateReadable":"February 22nd, 2025"},"versionCreatedAt":"2024-09-12 12:37:02","video":"","vorDoi":"10.1038/s42003-025-07691-y","vorDoiUrl":"https://doi.org/10.1038/s42003-025-07691-y","workflowStages":[]},"version":"v1","identity":"rs-4412470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4412470","identity":"rs-4412470","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}