Tumors with mutations in chromatin regulators are associated with higher mutational burden and improved response to checkpoint immunotherapy

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We analyzed the distribution and characteristics of well-studied CRs across tens of thousands of tumor samples. Our analysis revealed that tumors with mutations in CRs are associated with high tumor mutational burden (TMB). The co-occurrence of mutations in multiple CRs was linked with a further increase in TMB. Given that TMB may predict the clinical response to immune checkpoint inhibitor (ICI) treatment, we investigated the relationship between mutations in CRs and ICI response. We found that patients harboring mutations in CRs exhibited improved responses to ICI treatment, comparable to those with deficiencies in canonical DNA repair pathways. Overall, this study uncovered significant relationships between mutations in chromatin regulators and critical features of cancer, underscoring the need for further functional and clinical studies. cancer epigenetics cancer genetics chromatin immunotherapy checkpoint inhibitors tumor mutational burden Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Biologically, cancer denotes a multifaceted group of diseases, typically characterized by cell regulation failure, which equips the cells with the potential to divide in an uncontrolled manner, evade the immune system, and over time, invade and spread into neighboring tissues and organs of the body (Hanahan, 2022 ). Traditionally, cancer has been thought to be driven by the accumulation of genetic defects such as mutations, deletions, amplifications, and translocations in so-called oncogenes and tumor-suppressor genes (Kontomanolis et al., 2020 ). Consequently, most of the endeavors undertaken in cancer biology research have historically focused on genetic aberrations in genes with prominent roles in DNA repair, cell proliferation, and cell signaling. However, in recent years, the widespread application of next-generation sequencing (NGS) technologies for comprehensive cancer profiling has revealed that many of the pervasive genetic defects during carcinogenesis occur in chromatin (epigenetic) regulators (CRs) (Rodrigues-Paredes & Esteller, 2011; Dawson & Kouzarides, 2012 ; Yu et al., 2024 ). Such mutations in chromatin writers, readers, erasers, remodelers, and even the histone proteins themselves, can rewire the chromatin landscape of cells, leading to altered epigenetic states and expression programs that contribute to the biological and clinical aspects of tumorigenesis (Baylin & Jones, 2016 ; Timp & Feinberg, 2013 ; Feinberg et al., 2016 ). Hence, a deeper understanding of the distribution, interrelationships, and features of chromatin regulators in cancer can significantly aid our understanding of cancer biology, and treatment. In this study, we sought to examine the features of a subset of mutated chromatin regulators across tens of thousands of available genetic maps from various cancer types. We uncovered a strong positive relationship between the presence of mutations in CRs and TMB, which in turn was associated with significantly improved survival following treatment with ICI. RESULTS Mutations in chromatin regulators are pervasive in cancer and associated with high tumor mutational burden We commenced by examining the distribution and characteristics of mutations in well-studied chromatin regulators (Timp & Feinberg, 2013 ; Feinberg et al., 2016 ) (Suppl. Data 1) across tens of thousands of tumor samples. This list is by no means exhaustive in capturing all chromatin regulators frequently mutated in cancer. However, it includes many well-studied CRs that are commonly captured by traditional molecular profiling panels and described in (Timp & Feinberg, 2013 ; Feinberg et al., 2016 ). We utilized data from large studies such as the TCGA, MSK-ICI, MSK-MET, MSK-CHORD, PCAWG, and the OrigiMed deposited in the cBioportal platform (TCGA Research Network, 2013; Zehir et al., 2017 : Samstein et al., 2019 ; Jee, et al., 2024; Consortium, 2020; Nguyen et al., 2022 ; Wu et al., 2022 ). Our analysis revealed that mutations in chromatin regulators were pervasive in cancer, especially enriched in particular cancer types such as endometrial, lung, esophagogastric, colorectal, melanoma, and bladder cancer (Suppl. Figures 1, and 2) . Given that many of these cancer types are known to be associated with a high mutational load (Suppl. Figure 2C) (Chalmers et al., 2017 ), we sought to ascertain the relationship between the presence of mutations in chromatin regulators and TMB. Tumors with high TMB will theoretically correlate with the probability of a somatic mutation in any part of the genome, including in chromatin regulators (CRs). To demonstrate that the prevalence and distribution of CR mutations are distinct and non-random in high-TMB tumors, we plotted the enrichment of 100 randomly selected non-CR genes alongside canonical tumor suppressors and oncogenes and indeed observed distinct patterns (Suppl. Figure 2A and B). Tumors with mutations in the selected CRs had a higher TMB compared to the general cohort or tumors without these CR mutations (Fig. 1 A and B, p < 0.0001 Kruskal-Wallis test ). Then we wanted to investigate the relationship between tumors harboring single CRs and their effect on TMB levels. We calculated the TMB in tumor samples harboring at least one mutation in a CR gene, in tumors harboring mutations in canonical oncogenes or tumor suppressors, or in tumors harboring mutations in canonical DNA repair pathways such as the mismatch repair (MMR) machinery or POLE/POLD1 as controls. The presence of mutations in the MMR or POLE/POLD1 machinery has been reported as a proxy marker for high TMB, and ICI-response (Le et al., 2015 ; Ma et al., 2022 ). Our results demonstrated significantly higher TMB levels in tumors harboring mutations in certain CRs (e.g SMARCD1, NSD2, DNMT1, BRD4, TET2, TET1, DNMT3B, EZH1/2 ) compared to MMR or POLE/D1 mutated tumors (p < 0.0001, Dunnett’s multiple comparisons test) (Fig. 1 C and D; Suppl. Figures 3 and 4; Suppl. Data 2–5 ). The types of CRs associated with high TMB were reproducible across datasets, indicating the non-random distribution of CRs in tumor samples ( Suppl. Figure 5 ). The co-occurrence of mutations in chromatin regulators leads to additional increases in TMB Next, we were curious to ascertain if the co-occurrence of mutations in two or more CRs would have an enhancing effect on the levels of TMB as displayed in the red box in (Fig. 2 A). Indeed, by analyzing the MSK-ICI data, we observed that the simultaneous presence of mutations in multiple CRs had a strong linear relationship with TMB levels (Pearson’s r = 0.87, R 2 = 0.76, p < 0.0001) (Fig. 2 B). A positive relationship was also observed when mutations were simultaneously present in multiple genes of the MMR machinery and/or POLE/D1 (Pearson’s r = 0.68), albeit with lower predictability (R 2 = 0.46) ( Suppl. Figure 6A ). This was not the case when TMB levels were assessed in association with co-occurring mutations in canonical oncogenes and tumor suppressors (Pearson’s r = 0.43, R 2 = 0.18) ( Suppl. Figure 6B ). Then we selected the top 10 high-TMB CRs from the MSK-ICI and TCGA datasets (Fig. 2 A; Suppl. Figure 1 ). By calculating the log2 odds ratio against all other CRs, and controls (MMR and POLE/D1) we discovered statistically significant associations of co-occurrence ( Suppl. Data 6 and 7 ), which again led to significant increases of TMB (p < 0.0001, Dunnett’s multiple comparisons test) (Fig. 2 C; Suppl. Figures 7, and 8 ). A similar cumulative effect leading to increased levels of TMB was observed when mutations in CRs and MMR or POLE/D1 were present at the same time, with CRs having a stronger effect on the TMB levels in MMR and POLE/D1 samples than vice versa ( Suppl. Figure 9A and B ). Similar observations were made when we selected the top 10 high TMB-associated CR and non-CR genes (out of 100 randomly selected non-CR genes), and showed that the top 10 CRs had a stronger effect on the TMB levels in top 10 non-CR samples than vice versa, again showcasing the effect of CRs on TMB (Suppl. Figure 9C and D) . All in all, these observations were consistently corroborated across multiple independent datasets, demonstrating that single or co-occurring mutations in chromatin regulators or other high TMB-associated genes are strongly linked to higher TMB. Mutations in chromatin regulators lead to improved response to checkpoint immunotherapy Next, we set out to examine the association between the presence of mutations in CRs, and overall survival after treatment with ICI. We used the MSK-ICI cohort (Samstein et al., 2019 ), which included 1661 patients whose tumors were profiled with the MSK-IMPACT assay, and who had received at least one dose of ICI as monotherapy or combination. As indicated in the original study, the overall survival was measured from the date of the first ICI treatment to the time of death or most recent follow-up (median 19 months, range 0–80). We corroborated the initial observation showing that higher TMB levels are associated with better outcomes ( Suppl. Figure 10 ). Further on, we defined subgroups based on the presence of mutations in selected CRs (e.g. top 10, high TMB CRs), MMR, or POLE/D1 as positive controls (likely responders) versus the average of the entire cohort. The analysis showed that the group of high TMB CRs was associated with improved survival compared to the entire cohort, and on par with MMR or POLE/D1 deficient tumors (Fig. 3 А ). Univariate Cox proportional-hazards regression showed the presence of CR mutations was significantly associated with better survival (all CRs, HR = 0.8520, p = 0.0075; TOP5 CRs, HR = 0.5758, p < 0.0007; TOP10 CRs, HR = 0.6845, p < 0.0001), on par with MMR (HR = 0.6162, p = 0.0025), and POLE/D1 (HR = 0.6576, p = 0.0074) deficient patients, and TMB ≥ 10 (HR = 0.6550, p = 0.0001). Moreover, we analyzed the distribution of mutated CRs in living and deceased patients by calculating the ratio, of a CR in living and deceased patients for each CR individually. We observed a significant enrichment of high TMB CRs in living vs deceased patients (HR ranging from 0.32 up to 1.38) (Fig. 3 B; Suppl. Data 8 ). To demonstrate that the beneficial effect is attributed to the ICI treatment, and not to a general benefit obtained from the mere presence of mutations in CRs, we compared the ratio of CRs in living and deceased patients taken from the MSK-ICI study (ICI-treated), to those from the MSK-MET, and TCGA studies (variously treated). We observed a low-to-no correlation between these datasets (MSK-ICI vs MSK-MET, r > 0.36 and MSK-ICI vs TCGA, r < -0.06) ( Suppl. Figure 11A and B ). In addition, there was a negligible or no association between high TMB CRs and improved survival in the MSK-MET and TCGA studies (TOP10 MSK-MET genes HR = 0.90, p < 0.0003; TOP10 MSK-ICI genes HR = 0.95, p = 0.05; TOP10 TCGA genes HR = 1.01, p = 0.78; TOP10 MSK-ICI genes, HR = 0.99, p = 0.87) ( Suppl. Figure 11C and D ). We validated our finding that tumors harboring mutations in high-TMB CRs were associated with improved survival compared to the overall cohort, and exhibited outcomes comparable to those of MMR- or POLE/D1-deficient tumors in the MSK-CHORD, an independent validation cohort (Jee at al., 2024) (Suppl. Figure 12). In summary, the above data show a strong association between the presence of mutations in selected CRs and improved survival due to ICI treatment. DISCUSSION The findings of our study highlight the importance of chromatin regulators in shaping the mutational landscape of cancers and their possible relevance to predicting immunotherapy outcomes. This is in agreement with previous studies associating CRs such as SMARCA4 and ARID1A with TMB and improved ICI response (Ravi et al., 2023 ; Wang et al., 2023 ). Our analysis, which encompassed tens of thousands of tumor samples from publicly available biorepositories, demonstrated that mutations in CRs are widespread across various cancer types and are significantly associated with elevated TMB, particularly pronounced in cancers known to have a high mutational load. This underlines the importance of epigenetic dysregulation in cancer formation, progression, and treatment, and at the same time alludes that some cancers might be more dependent on genetically induced epigenetic changes than others. The observation that tumors harboring CR mutations tend to exhibit higher TMB compared to those without such mutations underscores the importance of CRs in influencing the mutational landscape of cancers. Our association analyses indicate that single mutations in CRs are strongly linked with elevated TMB levels, and this effect is amplified when multiple CR mutations co-occur together or with another high TMB-associated gene. The strong linear relationship between the number of CR mutations and TMB, suggests that the detection of mutations CR and repair machinery mutations through the application of small, and cost-effective gene panels could be a reliable predictor of TMB. This approach might be particularly useful in laboratories where there is a need to estimate TMB, but the sequencing of large gene panels (> 1 Mb) is cost-prohibitive. However, prospective validation across multiple cohorts is needed to evaluate whether CRs are clinically meaningful predictive biomarkers. We also explored the clinical implications of CR mutations in the context of ICI treatment. Despite the heterogeneity, heavy pretreatment, and variable ICI-timing of the MSK-ICI cohort, and the MSK-CHORD validation cohort, our analysis revealed that patients with tumors harboring CR mutations had improved survival outcomes following ICI therapy. These outcomes were comparable to those with MMR- or POLE/D1 deficient tumors, which are traditionally associated with high TMB and favorable ICI responses. The association of CR mutations with increased levels of TMB, and improved ICI outcomes in such a heterogeneous cohort highlights the robustness of our observations. Future follow-up studies focusing on specific cancer types would offer more detailed and context-specific insights into these relationships. The mechanism by which mutations in CRs systematically dysregulate the epigenome, leading to elevated TMB and improved response can be delineated in two hypothetical ways. Firstly, the dysregulated epigenome might cause deficiencies in genome maintenance and repair, resulting in a hypermutable state that might yield a higher number of tumor neoantigens, and thus facilitate an improved immune response. Secondly, the dysregulated epigenome might enhance the expressivity of already established neoantigens, facilitating a more robust immune response and better outcomes. Future functional studies could provide deeper insights into the molecular mechanisms by which CRs contribute to cancerogenesis. Although the current study presents compelling evidence linking CR mutations to elevated TMB and improved responses to ICIs, it is not without limitations. First, the CR gene list used in this analysis is non-exhaustive and primarily includes well-characterized regulators. This approach may overlook novel or less frequently profiled, yet biologically relevant, CRs—potentially skewing the results and underestimating the full extent of CR involvement in cancer. In the same vein, the study does not differentiate between mutation types (e.g., loss-of-function vs. gain-of-function mutations, or copy number variations), limiting the granularity and interpretive precision of the findings. Future studies that incorporate a broader spectrum of CR genes and mutation types will be critical to elucidate the distinct contributions of various CR alterations to TMB and ICI response. Moreover, the manuscript lacks mechanistic and functional in vitro and in vivo validation to confirm the causal role of CR mutations in modulating TMB or enhancing ICI efficacy. Although CRs significantly impact TMB, they likely do not fully account for the presence of high TMB in tumors. The analysis also does not fully account for potential confounding variables, such as other co-occurring genetic alterations, tumor microenvironment characteristics, CR-mutation–specific changes in T-cell infiltration, IFN-γ signatures, antigen presentation or relevant clinical factors that would biologically connect CRs to ICI sensitivity. These unaddressed confounders may either exaggerate or obscure the true impact of CR mutations. Multivariate analyses or machine learning models that incorporate a wider range of genomic and clinical features could additionally provide deeper insights. Lastly, the study is based on retrospective and heterogeneous cohorts that include patients with diverse tumor types, treatment histories, and clinical backgrounds, which may limit the generalizability of the findings. External validation in prospective, well-characterized patient cohorts with matched clinical and molecular data will be essential to confirm the robustness and clinical applicability of these observations. In conclusion, our study identifies a strong association between mutations in selected chromatin regulators, elevated tumor mutational burden, and improved outcomes following immune checkpoint inhibitor treatment. While these findings provide a foundation for further exploration, to establish causality, further mechanistic studies and validation in controlled clinical settings are needed to determine whether CR mutations could serve as reliable biomarkers or therapeutic targets in cancer immunotherapy. MATERIALS AND METHODS Datasets used in the study All genomic datasets used in this study were downloaded from the cBioportal platform, http://www.cbioportal.org/ , and were statistically analyzed, and visualized using various tools including Microsoft Excel, Google Sheets, StatsDirect, Prism, and integrated tools within the cBioportal platform, such as Oncoprint (Cerami et al., 2012 ; Gao et al., 2013 ). Datasets in the study include the Tumor Cancer Genome Atlas (TCGA; 10967 samples), this is a landmark cancer genomics program, molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types; Memorial Sloan Kettering-Metastatis Events and Tropisms (MSK-MET; 25775 samples), this is a pan-cancer cohort of tumor genomic and clinical outcome data and it identifies associations between tumor genomic alterations and patterns of metastatic dissemination across 50 tumor types; Memorial Sloan-Kettering Immune Checkpoint Inhibitors (MSK-ICI; 1661 samples), this is a genomic and survival data from tumor-normal pairs from patients with various cancer types sequenced with the MSK-IMPACT assay; MSK-CHORD is large clinicogenomic, harmonized oncologic real-world dataset sequenced with the MSK-IMPACT assay (MSK-CHORD; 25040 samples, 3372 treated with ICI); The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes consortium (PCAWG; 2922 samples), this study encompasses whole-cancer genomes and their matched normal tissues across 38 tumor types; and the China PanCancer consortium (OrigiMed; 10194 samples), encompasses the landscape of genomic alterations in solid tumors from the Chinese population. All the datasets are deposited in the cBioportal platform (TCGA Research Network, 2013; Zehir et al., 2017 : Samstein et al., 2019 ; Jee, et al., 2024; Consortium, 2020; Nguyen et al., 2022 ; Wu et al., 2022 ). The number of samples per subgroup used in this study can be found in Suppl. Data 9 . The TCGA dataset was sequenced using whole exome sequencing (WES), whilst the PCAWG dataset was sequenced with whole genome sequencing (WGS). The remaining datasets were obtained via targeted panel sequencing. Specifically, the MSK-ICI and the MSK-MET datasets were generated using the FDA-approved MSK-IMPACT assay, which targets 341–468 cancer-related genes (Cheng, et al., 2015). The OrigiMed sequences were acquired using the CSYS assay, targeting 450 cancer-related genes (Cao, et al., 2019). Statistical analysis Statistical analyses were conducted using the Kruskal-Wallis non-parametric exact test for multiple-group comparisons to account for distribution differences among the samples. Dunnett’s multiple comparisons test was used to compare each group with a single control. Multiple pairwise comparisons were calculated post-hoc with the Dunn’s test. Survival data were analyzed and interpreted using the Cox proportional hazards model and Kaplan-Meier curves, with comparisons made using the log-rank test. Simple linear regression was employed to model the relationship between TMB and co-occurring mutations. To evaluate the strength of linear relationships between continuous variables, the Pearson correlation coefficient was calculated. Declarations Ethics approval and consent to participate Not applicable. All data are taken from publicly available datasets and cancer projects. Consent for publication Not applicable. All data are taken from publicly available datasets and already reported in the literature. Availability of data and material All data are publicly available at the cBioportal repository, http://www.cbioportal.org. Funding Privately funded by Fingerprint Diagnostics LLC and Zan Mitrev Clinic. Author contributions GK conceived and designed this study. GK, MG, SM, and DjB collected, sorted, curated, and analyzed the data. MR, IK and ZM provided valuable scientific insights. GK wrote the manuscript. All authors contributed to the improvement of the manuscript and read the final version of the manuscript. Acknowledgments We would like to acknowledge Aleksandar Trifunovski, Aleksandra Horvat, and Bobi Sofronijoski for their assistance in data sorting and integration. We are grateful to Maria Kitanoska and Tamara Cvetkovska for their help in data collection and curation. References Hanahan, D. Hallmarks of Cancer: New Dimensions. Cancer Discovery 12, 31-46 (2022). Kontomanolis, E.N. et al. Role of Oncogenes and Tumor-suppressor Genes in Carcinogenesis: A Review. Anticancer Res 40, 6009-6015 (2020). Rodríguez-Paredes, M. & Esteller, M. Cancer epigenetics reaches mainstream oncology. 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Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer. Nat Genet 55, 807-819 (2023). Wang, D. et al. Relationship among DDR gene mutations, TMB and PD-L1 in solid tumour genomes identified using clinically actionable biomarker assays. NPJ Precis Oncol 7, 103 (2023). Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2, 401-4 (2012). Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6, pl1 (2013). Cheng, D.T. et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology. J Mol Diagn 17, 251-64 (2015). Cao, J. et al. An Accurate and Comprehensive Clinical Sequencing Assay for Cancer Targeted and Immunotherapies. Oncologist 24, e1294-e1302 (2019) Additional Declarations No competing interests reported. Supplementary Files SupplementaryData.xlsx SupplementaryFigures.doc Cite Share Download PDF Status: Published Journal Publication published 31 Dec, 2025 Read the published version in Clinical Epigenetics → Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers invited by journal 30 Oct, 2025 Submission checks completed at journal 21 Oct, 2025 First submitted to journal 21 Oct, 2025 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. 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10:05:01","extension":"html","order_by":57,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72460,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6202098/v1/318f116239f26f29fdd421ba.html"},{"id":94985547,"identity":"7b25abeb-24ed-4a4b-9d6e-a80a8f65f9cc","added_by":"auto","created_at":"2025-11-03 06:58:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1230672,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of tumor mutational burden (TMB) in relevant groups of samples. A) Samples with mutations in chromatin regulators (CRs) n=5321, the cohort n=10443, and the cohort with CR genes removed n = 5122, taken from the TCGA dataset, B) Samples with mutations in chromatin regulators (CRs) n=935, the cohort n=1661, and the cohort with CR genes removed n=726, taken from the MSK-ICI dataset. The bars show the mean of the group, while the error bars represent the standard error of the mean (SEM). Statistical significance was calculated with the Kruskal-Wallis test. C) Samples with mutations in single chromatin regulators (CRs), samples with mutations in canonical oncogenes and tumor-suppressor genes, and samples with mutations in MMR-genes and \u003cem\u003ePOLE\u003c/em\u003e, and \u003cem\u003ePOLD1\u003c/em\u003e, taken from the TCGA dataset. The median of the analyzed samples is 312. D) Samples with mutations in single chromatin regulators (CRs), samples with mutations in canonical oncogenes and tumor-suppressor genes, and samples with mutations in MMR-genes and \u003cem\u003ePOLE \u003c/em\u003eand \u003cem\u003ePOLD1\u003c/em\u003e, taken from the MSK-ICI dataset. The median of the analyzed samples is 102. The graph represents the median with 95% confidence intervals (CI). The red boxes indicate the positive and negative controls. Supplementary Figures 1-5 are related to this figure. The statistical significance between samples can be found in Supplementary Data 2-5 calculated with the Dunn's multiple comparisons test. The number of samples per subgroup used in this study can be found in Suppl. Data 9\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6202098/v1/56b47929afe1363d5b2f2150.png"},{"id":94985593,"identity":"ff6e0284-68cb-413b-b65b-552abae96d4f","added_by":"auto","created_at":"2025-11-03 06:58:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10180963,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence of chromatin regulators leads to increased TMB. A) Oncoprint analysis of the distribution and prevalence of genomic alterations in CRs corresponding to overall survival status, and TMB. The red box visually displays the association of co-occurring mutations in CRs with increased levels of TMB, B) Simple linear regression model showing that TMB can be predicted based on the number of co-occurring mutations in multiple CRs. The datasets are taken from the MSK-ICI study. C) Statistically significant associations of co-occurrence between DNMT1 with other CRs lead to a further increase in TMB. The graph represents the median with 95% confidence intervals (CI). The datasets are taken from the TCGA study. Supplementary Figures 1, 6-9 are related to this figure. The statistical significance between samples can be found in Supplementary Data 6 and 7. The number of samples per subgroup used in this study can be found in Suppl. Data 9.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6202098/v1/5915d5e12a47e3cca0c90d7c.png"},{"id":94984708,"identity":"3aa512a4-31f0-49d0-b7e4-9f1fdc6bbafd","added_by":"auto","created_at":"2025-11-03 06:55:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1341529,"visible":true,"origin":"","legend":"\u003cp\u003eICI-survival analysis in selected groups of patients taken from the MSK-ICI dataset. A) Kaplan-Meier survival analysis between patients with mutations in the TOP 10 high TMB CRs, mutations in the MMR genes, and mutations in the \u003cem\u003ePOLE\u003c/em\u003e or \u003cem\u003ePOLD1\u003c/em\u003e genes compared with the cohort. The Cox proportional hazards model was used to calculate hazard ratios. B) The distribution of mutated CRs, mutated oncogenes or tumor-suppressors, mutated MMR-genes or \u003cem\u003ePOLE,\u003c/em\u003e and \u003cem\u003ePOLD1\u003c/em\u003ein living and deceased patients who underwent ICI treatment. The datasets are taken from the MSK-ICI study. Supplementary Figures 10, 11, and 12 are related to this figure. The Cox hazard ratios for each CR can be found in Suppl. Data 8. The number of samples per subgroup used in this study can be found in Suppl. Data 9.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6202098/v1/c506fcbf8b1ce685feae2ad5.png"},{"id":99545434,"identity":"c882b429-9ffd-4054-b8bc-13e9f681a615","added_by":"auto","created_at":"2026-01-05 16:07:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5727601,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6202098/v1/f23fa635-ade7-4945-ac7d-df357859b6f3.pdf"},{"id":94844643,"identity":"5cc6f251-dcf2-4a6e-9fc3-c701651c5634","added_by":"auto","created_at":"2025-10-31 10:05:00","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":74404,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6202098/v1/5daea44b6fdfd1fb73670450.xlsx"},{"id":94985292,"identity":"7130a6e4-85bf-4a6d-ba04-4922a8827acd","added_by":"auto","created_at":"2025-11-03 06:57:52","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3091743,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.doc","url":"https://assets-eu.researchsquare.com/files/rs-6202098/v1/e7a20d78ee1736a7c960b5b8.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tumors with mutations in chromatin regulators are associated with higher mutational burden and improved response to checkpoint immunotherapy","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBiologically, cancer denotes a multifaceted group of diseases, typically characterized by cell regulation failure, which equips the cells with the potential to divide in an uncontrolled manner, evade the immune system, and over time, invade and spread into neighboring tissues and organs of the body (Hanahan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Traditionally, cancer has been thought to be driven by the accumulation of genetic defects such as mutations, deletions, amplifications, and translocations in so-called oncogenes and tumor-suppressor genes (Kontomanolis et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, most of the endeavors undertaken in cancer biology research have historically focused on genetic aberrations in genes with prominent roles in DNA repair, cell proliferation, and cell signaling.\u003c/p\u003e\u003cp\u003eHowever, in recent years, the widespread application of next-generation sequencing (NGS) technologies for comprehensive cancer profiling has revealed that many of the pervasive genetic defects during carcinogenesis occur in chromatin (epigenetic) regulators (CRs) (Rodrigues-Paredes \u0026amp; Esteller, 2011; Dawson \u0026amp; Kouzarides, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such mutations in chromatin writers, readers, erasers, remodelers, and even the histone proteins themselves, can rewire the chromatin landscape of cells, leading to altered epigenetic states and expression programs that contribute to the biological and clinical aspects of tumorigenesis (Baylin \u0026amp; Jones, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Timp \u0026amp; Feinberg, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Feinberg et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Hence, a deeper understanding of the distribution, interrelationships, and features of chromatin regulators in cancer can significantly aid our understanding of cancer biology, and treatment.\u003c/p\u003e\u003cp\u003eIn this study, we sought to examine the features of a subset of mutated chromatin regulators across tens of thousands of available genetic maps from various cancer types. We uncovered a strong positive relationship between the presence of mutations in CRs and TMB, which in turn was associated with significantly improved survival following treatment with ICI.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eMutations in chromatin regulators are pervasive in cancer and associated with high tumor mutational burden\u003c/p\u003e\u003cp\u003eWe commenced by examining the distribution and characteristics of mutations in well-studied chromatin regulators (Timp \u0026amp; Feinberg, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Feinberg et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) \u003cb\u003e(Suppl. Data 1)\u003c/b\u003e across tens of thousands of tumor samples. This list is by no means exhaustive in capturing all chromatin regulators frequently mutated in cancer. However, it includes many well-studied CRs that are commonly captured by traditional molecular profiling panels and described in (Timp \u0026amp; Feinberg, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Feinberg et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We utilized data from large studies such as the TCGA, MSK-ICI, MSK-MET, MSK-CHORD, PCAWG, and the OrigiMed deposited in the cBioportal platform (TCGA Research Network, 2013; Zehir et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e: Samstein et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jee, et al., 2024; Consortium, 2020; Nguyen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our analysis revealed that mutations in chromatin regulators were pervasive in cancer, especially enriched in particular cancer types such as endometrial, lung, esophagogastric, colorectal, melanoma, and bladder cancer \u003cb\u003e(Suppl. Figures\u0026nbsp;1, and 2)\u003c/b\u003e. Given that many of these cancer types are known to be associated with a high mutational load \u003cb\u003e(Suppl. Figure\u0026nbsp;2C)\u003c/b\u003e (Chalmers et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), we sought to ascertain the relationship between the presence of mutations in chromatin regulators and TMB. Tumors with high TMB will theoretically correlate with the probability of a somatic mutation in any part of the genome, including in chromatin regulators (CRs). To demonstrate that the prevalence and distribution of CR mutations are distinct and non-random in high-TMB tumors, we plotted the enrichment of 100 randomly selected non-CR genes alongside canonical tumor suppressors and oncogenes and indeed observed distinct patterns \u003cb\u003e(Suppl. Figure\u0026nbsp;2A and B).\u003c/b\u003e Tumors with mutations in the selected CRs had a higher TMB compared to the general cohort or tumors without these CR mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u003cb\u003eand B, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 Kruskal-Wallis test\u003c/b\u003e). Then we wanted to investigate the relationship between tumors harboring single CRs and their effect on TMB levels. We calculated the TMB in tumor samples harboring at least one mutation in a CR gene, in tumors harboring mutations in canonical oncogenes or tumor suppressors, or in tumors harboring mutations in canonical DNA repair pathways such as the mismatch repair (MMR) machinery or POLE/POLD1 as controls. The presence of mutations in the MMR or POLE/POLD1 machinery has been reported as a proxy marker for high TMB, and ICI-response (Le et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our results demonstrated significantly higher TMB levels in tumors harboring mutations in certain CRs (e.g \u003cem\u003eSMARCD1, NSD2, DNMT1, BRD4, TET2, TET1, DNMT3B, EZH1/2\u003c/em\u003e) compared to MMR or POLE/D1 mutated tumors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Dunnett\u0026rsquo;s multiple comparisons test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC \u003cb\u003eand D; Suppl. Figures\u0026nbsp;3 and 4; Suppl. Data 2\u0026ndash;5\u003c/b\u003e). The types of CRs associated with high TMB were reproducible across datasets, indicating the non-random distribution of CRs in tumor samples (\u003cb\u003eSuppl. Figure\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe co-occurrence of mutations in chromatin regulators leads to additional increases in TMB\u003c/p\u003e\u003cp\u003eNext, we were curious to ascertain if the co-occurrence of mutations in two or more CRs would have an enhancing effect on the levels of TMB as displayed in the red box in (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Indeed, by analyzing the MSK-ICI data, we observed that the simultaneous presence of mutations in multiple CRs had a strong linear relationship with TMB levels (Pearson\u0026rsquo;s r\u0026thinsp;=\u0026thinsp;0.87, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A positive relationship was also observed when mutations were simultaneously present in multiple genes of the MMR machinery and/or POLE/D1 (Pearson\u0026rsquo;s r\u0026thinsp;=\u0026thinsp;0.68), albeit with lower predictability (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.46) (\u003cb\u003eSuppl. Figure\u0026nbsp;6A\u003c/b\u003e). This was not the case when TMB levels were assessed in association with co-occurring mutations in canonical oncogenes and tumor suppressors (Pearson\u0026rsquo;s r\u0026thinsp;=\u0026thinsp;0.43, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.18) (\u003cb\u003eSuppl. Figure\u0026nbsp;6B\u003c/b\u003e). Then we selected the top 10 high-TMB CRs from the MSK-ICI and TCGA datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; \u003cb\u003eSuppl. Figure\u0026nbsp;1\u003c/b\u003e). By calculating the log2 odds ratio against all other CRs, and controls (MMR and POLE/D1) we discovered statistically significant associations of co-occurrence (\u003cb\u003eSuppl. Data 6 and 7\u003c/b\u003e), which again led to significant increases of TMB (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Dunnett\u0026rsquo;s multiple comparisons test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; \u003cb\u003eSuppl. Figures\u0026nbsp;7, and 8\u003c/b\u003e). A similar cumulative effect leading to increased levels of TMB was observed when mutations in CRs and MMR or POLE/D1 were present at the same time, with CRs having a stronger effect on the TMB levels in MMR and POLE/D1 samples than vice versa (\u003cb\u003eSuppl. Figure\u0026nbsp;9A and B\u003c/b\u003e). Similar observations were made when we selected the top 10 high TMB-associated CR and non-CR genes (out of 100 randomly selected non-CR genes), and showed that the top 10 CRs had a stronger effect on the TMB levels in top 10 non-CR samples than vice versa, again showcasing the effect of CRs on TMB \u003cb\u003e(Suppl. Figure\u0026nbsp;9C and D)\u003c/b\u003e. All in all, these observations were consistently corroborated across multiple independent datasets, demonstrating that single or co-occurring mutations in chromatin regulators or other high TMB-associated genes are strongly linked to higher TMB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMutations in chromatin regulators lead to improved response to checkpoint immunotherapy\u003c/p\u003e\u003cp\u003eNext, we set out to examine the association between the presence of mutations in CRs, and overall survival after treatment with ICI. We used the MSK-ICI cohort (Samstein et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which included 1661 patients whose tumors were profiled with the MSK-IMPACT assay, and who had received at least one dose of ICI as monotherapy or combination. As indicated in the original study, the overall survival was measured from the date of the first ICI treatment to the time of death or most recent follow-up (median 19 months, range 0\u0026ndash;80). We corroborated the initial observation showing that higher TMB levels are associated with better outcomes (\u003cb\u003eSuppl. Figure\u0026nbsp;10\u003c/b\u003e). Further on, we defined subgroups based on the presence of mutations in selected CRs (e.g. top 10, high TMB CRs), MMR, or POLE/D1 as positive controls (likely responders) versus the average of the entire cohort. The analysis showed that the group of high TMB CRs was associated with improved survival compared to the entire cohort, and on par with MMR or POLE/D1 deficient tumors (Fig.\u0026nbsp;3\u003cb\u003eА\u003c/b\u003e). Univariate Cox proportional-hazards regression showed the presence of CR mutations was significantly associated with better survival (all CRs, HR\u0026thinsp;=\u0026thinsp;0.8520, p\u0026thinsp;=\u0026thinsp;0.0075; TOP5 CRs, HR\u0026thinsp;=\u0026thinsp;0.5758, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0007; TOP10 CRs, HR\u0026thinsp;=\u0026thinsp;0.6845, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), on par with MMR (HR\u0026thinsp;=\u0026thinsp;0.6162, p\u0026thinsp;=\u0026thinsp;0.0025), and POLE/D1 (HR\u0026thinsp;=\u0026thinsp;0.6576, p\u0026thinsp;=\u0026thinsp;0.0074) deficient patients, and TMB\u0026thinsp;\u0026ge;\u0026thinsp;10 (HR\u0026thinsp;=\u0026thinsp;0.6550, p\u0026thinsp;=\u0026thinsp;0.0001). Moreover, we analyzed the distribution of mutated CRs in living and deceased patients by calculating the ratio, of a CR in living and deceased patients for each CR individually. We observed a significant enrichment of high TMB CRs in living vs deceased patients (HR ranging from 0.32 up to 1.38) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; \u003cb\u003eSuppl. Data 8\u003c/b\u003e). To demonstrate that the beneficial effect is attributed to the ICI treatment, and not to a general benefit obtained from the mere presence of mutations in CRs, we compared the ratio of CRs in living and deceased patients taken from the MSK-ICI study (ICI-treated), to those from the MSK-MET, and TCGA studies (variously treated). We observed a low-to-no correlation between these datasets (MSK-ICI vs MSK-MET, r\u0026thinsp;\u0026gt;\u0026thinsp;0.36 and MSK-ICI vs TCGA, r \u0026lt; -0.06) (\u003cb\u003eSuppl. Figure\u0026nbsp;11A and B\u003c/b\u003e). In addition, there was a negligible or no association between high TMB CRs and improved survival in the MSK-MET and TCGA studies (TOP10 MSK-MET genes HR\u0026thinsp;=\u0026thinsp;0.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0003; TOP10 MSK-ICI genes HR\u0026thinsp;=\u0026thinsp;0.95, p\u0026thinsp;=\u0026thinsp;0.05; TOP10 TCGA genes HR\u0026thinsp;=\u0026thinsp;1.01, p\u0026thinsp;=\u0026thinsp;0.78; TOP10 MSK-ICI genes, HR\u0026thinsp;=\u0026thinsp;0.99, p\u0026thinsp;=\u0026thinsp;0.87) (\u003cb\u003eSuppl. Figure\u0026nbsp;11C and D\u003c/b\u003e). We validated our finding that tumors harboring mutations in high-TMB CRs were associated with improved survival compared to the overall cohort, and exhibited outcomes comparable to those of MMR- or POLE/D1-deficient tumors in the MSK-CHORD, an independent validation cohort (Jee at al., 2024) (Suppl. Figure\u0026nbsp;12). In summary, the above data show a strong association between the presence of mutations in selected CRs and improved survival due to ICI treatment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe findings of our study highlight the importance of chromatin regulators in shaping the mutational landscape of cancers and their possible relevance to predicting immunotherapy outcomes. This is in agreement with previous studies associating CRs such as \u003cem\u003eSMARCA4\u003c/em\u003e and \u003cem\u003eARID1A\u003c/em\u003e with TMB and improved ICI response (Ravi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our analysis, which encompassed tens of thousands of tumor samples from publicly available biorepositories, demonstrated that mutations in CRs are widespread across various cancer types and are significantly associated with elevated TMB, particularly pronounced in cancers known to have a high mutational load. This underlines the importance of epigenetic dysregulation in cancer formation, progression, and treatment, and at the same time alludes that some cancers might be more dependent on genetically induced epigenetic changes than others.\u003c/p\u003e\u003cp\u003eThe observation that tumors harboring CR mutations tend to exhibit higher TMB compared to those without such mutations underscores the importance of CRs in influencing the mutational landscape of cancers. Our association analyses indicate that single mutations in CRs are strongly linked with elevated TMB levels, and this effect is amplified when multiple CR mutations co-occur together or with another high TMB-associated gene. The strong linear relationship between the number of CR mutations and TMB, suggests that the detection of mutations CR and repair machinery mutations through the application of small, and cost-effective gene panels could be a reliable predictor of TMB. This approach might be particularly useful in laboratories where there is a need to estimate TMB, but the sequencing of large gene panels (\u0026gt;\u0026thinsp;1 Mb) is cost-prohibitive. However, prospective validation across multiple cohorts is needed to evaluate whether CRs are clinically meaningful predictive biomarkers.\u003c/p\u003e\u003cp\u003eWe also explored the clinical implications of CR mutations in the context of ICI treatment. Despite the heterogeneity, heavy pretreatment, and variable ICI-timing of the MSK-ICI cohort, and the MSK-CHORD validation cohort, our analysis revealed that patients with tumors harboring CR mutations had improved survival outcomes following ICI therapy. These outcomes were comparable to those with MMR- or POLE/D1 deficient tumors, which are traditionally associated with high TMB and favorable ICI responses. The association of CR mutations with increased levels of TMB, and improved ICI outcomes in such a heterogeneous cohort highlights the robustness of our observations. Future follow-up studies focusing on specific cancer types would offer more detailed and context-specific insights into these relationships.\u003c/p\u003e\u003cp\u003eThe mechanism by which mutations in CRs systematically dysregulate the epigenome, leading to elevated TMB and improved response can be delineated in two hypothetical ways. Firstly, the dysregulated epigenome might cause deficiencies in genome maintenance and repair, resulting in a hypermutable state that might yield a higher number of tumor neoantigens, and thus facilitate an improved immune response. Secondly, the dysregulated epigenome might enhance the expressivity of already established neoantigens, facilitating a more robust immune response and better outcomes. Future functional studies could provide deeper insights into the molecular mechanisms by which CRs contribute to cancerogenesis.\u003c/p\u003e\u003cp\u003eAlthough the current study presents compelling evidence linking CR mutations to elevated TMB and improved responses to ICIs, it is not without limitations. First, the CR gene list used in this analysis is non-exhaustive and primarily includes well-characterized regulators. This approach may overlook novel or less frequently profiled, yet biologically relevant, CRs\u0026mdash;potentially skewing the results and underestimating the full extent of CR involvement in cancer. In the same vein, the study does not differentiate between mutation types (e.g., loss-of-function vs. gain-of-function mutations, or copy number variations), limiting the granularity and interpretive precision of the findings. Future studies that incorporate a broader spectrum of CR genes and mutation types will be critical to elucidate the distinct contributions of various CR alterations to TMB and ICI response. Moreover, the manuscript lacks mechanistic and functional in vitro and in vivo validation to confirm the causal role of CR mutations in modulating TMB or enhancing ICI efficacy. Although CRs significantly impact TMB, they likely do not fully account for the presence of high TMB in tumors. The analysis also does not fully account for potential confounding variables, such as other co-occurring genetic alterations, tumor microenvironment characteristics, CR-mutation\u0026ndash;specific changes in T-cell infiltration, IFN-γ signatures, antigen presentation or relevant clinical factors that would biologically connect CRs to ICI sensitivity.\u003c/p\u003e\u003cp\u003eThese unaddressed confounders may either exaggerate or obscure the true impact of CR mutations. Multivariate analyses or machine learning models that incorporate a wider range of genomic and clinical features could additionally provide deeper insights. Lastly, the study is based on retrospective and heterogeneous cohorts that include patients with diverse tumor types, treatment histories, and clinical backgrounds, which may limit the generalizability of the findings. External validation in prospective, well-characterized patient cohorts with matched clinical and molecular data will be essential to confirm the robustness and clinical applicability of these observations.\u003c/p\u003e\u003cp\u003eIn conclusion, our study identifies a strong association between mutations in selected chromatin regulators, elevated tumor mutational burden, and improved outcomes following immune checkpoint inhibitor treatment. While these findings provide a foundation for further exploration, to establish causality, further mechanistic studies and validation in controlled clinical settings are needed to determine whether CR mutations could serve as reliable biomarkers or therapeutic targets in cancer immunotherapy.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eDatasets used in the study\u003c/p\u003e\u003cp\u003eAll genomic datasets used in this study were downloaded from the cBioportal platform, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, and were statistically analyzed, and visualized using various tools including Microsoft Excel, Google Sheets, StatsDirect, Prism, and integrated tools within the cBioportal platform, such as Oncoprint (Cerami et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Datasets in the study include the Tumor Cancer Genome Atlas (TCGA; 10967 samples), this is a landmark cancer genomics program, molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types; Memorial Sloan Kettering-Metastatis Events and Tropisms (MSK-MET; 25775 samples), this is a pan-cancer cohort of tumor genomic and clinical outcome data and it identifies associations between tumor genomic alterations and patterns of metastatic dissemination across 50 tumor types; Memorial Sloan-Kettering Immune Checkpoint Inhibitors (MSK-ICI; 1661 samples), this is a genomic and survival data from tumor-normal pairs from patients with various cancer types sequenced with the MSK-IMPACT assay; MSK-CHORD is large clinicogenomic, harmonized oncologic real-world dataset sequenced with the MSK-IMPACT assay (MSK-CHORD; 25040 samples, 3372 treated with ICI); The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes consortium (PCAWG; 2922 samples), this study encompasses whole-cancer genomes and their matched normal tissues across 38 tumor types; and the China PanCancer consortium (OrigiMed; 10194 samples), encompasses the landscape of genomic alterations in solid tumors from the Chinese population. All the datasets are deposited in the cBioportal platform (TCGA Research Network, 2013; Zehir et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e: Samstein et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jee, et al., 2024; Consortium, 2020; Nguyen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The number of samples per subgroup used in this study can be found in \u003cb\u003eSuppl. Data 9\u003c/b\u003e. The TCGA dataset was sequenced using whole exome sequencing (WES), whilst the PCAWG dataset was sequenced with whole genome sequencing (WGS). The remaining datasets were obtained via targeted panel sequencing. Specifically, the MSK-ICI and the MSK-MET datasets were generated using the FDA-approved MSK-IMPACT assay, which targets 341\u0026ndash;468 cancer-related genes (Cheng, et al., 2015). The OrigiMed sequences were acquired using the CSYS assay, targeting 450 cancer-related genes (Cao, et al., 2019).\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using the Kruskal-Wallis non-parametric exact test for multiple-group comparisons to account for distribution differences among the samples. Dunnett\u0026rsquo;s multiple comparisons test was used to compare each group with a single control. Multiple pairwise comparisons were calculated post-hoc with the Dunn\u0026rsquo;s test. Survival data were analyzed and interpreted using the Cox proportional hazards model and Kaplan-Meier curves, with comparisons made using the log-rank test. Simple linear regression was employed to model the relationship between TMB and co-occurring mutations. To evaluate the strength of linear relationships between continuous variables, the Pearson correlation coefficient was calculated.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable. All data are taken from publicly available datasets and cancer projects.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable. All data are taken from publicly available datasets and already reported in the literature.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eAll data are publicly available at the cBioportal repository, http://www.cbioportal.org.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003ePrivately funded by Fingerprint Diagnostics LLC and Zan Mitrev Clinic.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eGK conceived and designed this study. GK, MG, SM, and DjB collected, sorted, curated, and analyzed the data. MR, IK and ZM provided valuable scientific insights. GK wrote the manuscript. All authors contributed to the improvement of the manuscript and read the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe would like to acknowledge Aleksandar Trifunovski, Aleksandra Horvat, and Bobi Sofronijoski for their assistance in data sorting and integration. We are grateful to Maria Kitanoska and Tamara Cvetkovska for their help in data collection and curation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHanahan, D. Hallmarks of Cancer: New Dimensions. Cancer Discovery 12, 31-46 (2022).\u003c/li\u003e\n\u003cli\u003eKontomanolis, E.N. et al. Role of Oncogenes and Tumor-suppressor Genes in Carcinogenesis: A Review. Anticancer Res 40, 6009-6015 (2020).\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Paredes, M. \u0026amp; Esteller, M. Cancer epigenetics reaches mainstream oncology. Nat Med 17, 330-9 (2011).\u003c/li\u003e\n\u003cli\u003eDawson, M.A. \u0026amp; Kouzarides, T. 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Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med 23, 703-713 (2017).\u003c/li\u003e\n\u003cli\u003eSamstein, R.M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet 51, 202-206 (2019).\u003c/li\u003e\n\u003cli\u003eJee, J et al. Automated real-world data integration improves cancer outcome prediction. Nature 636, 728\u0026ndash;736 (2024) \u003c/li\u003e\n\u003cli\u003ePan-cancer analysis of whole genomes. Nature 578, 82-93 (2020).\u003c/li\u003e\n\u003cli\u003eNguyen, B. et al. Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell 185, 563-575.e11 (2022).\u003c/li\u003e\n\u003cli\u003eWu, L. et al. Landscape of somatic alterations in large-scale solid tumors from an Asian population. Nat Commun 13, 4264 (2022).\u003c/li\u003e\n\u003cli\u003eChalmers, Z.R. et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med 9, 34 (2017).\u003c/li\u003e\n\u003cli\u003eLe, D.T. et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N Engl J Med 372, 2509-20 (2015).\u003c/li\u003e\n\u003cli\u003eMa, X., Dong, L., Liu, X., Ou, K. \u0026amp; Yang, L. POLE/POLD1 mutation and tumor immunotherapy. J Exp Clin Cancer Res 41, 216 (2022).\u003c/li\u003e\n\u003cli\u003eRavi, A. et al. Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer. Nat Genet 55, 807-819 (2023).\u003c/li\u003e\n\u003cli\u003eWang, D. et al. Relationship among DDR gene mutations, TMB and PD-L1 in solid tumour genomes identified using clinically actionable biomarker assays. NPJ Precis Oncol 7, 103 (2023).\u003c/li\u003e\n\u003cli\u003eCerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2, 401-4 (2012).\u003c/li\u003e\n\u003cli\u003eGao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6, pl1 (2013).\u003c/li\u003e\n\u003cli\u003eCheng, D.T. et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology. J Mol Diagn 17, 251-64 (2015).\u003c/li\u003e\n\u003cli\u003eCao, J. et al. An Accurate and Comprehensive Clinical Sequencing Assay for Cancer Targeted and Immunotherapies. Oncologist 24, e1294-e1302 (2019) \u003c/li\u003e\n\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":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cancer epigenetics, cancer genetics, chromatin, immunotherapy, checkpoint inhibitors, tumor mutational burden","lastPublishedDoi":"10.21203/rs.3.rs-6202098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6202098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, it has been demonstrated that many of the pervasive genetic defects throughout cancerogenesis occur in genes encoding chromatin regulators (CRs). We analyzed the distribution and characteristics of well-studied CRs across tens of thousands of tumor samples. Our analysis revealed that tumors with mutations in CRs are associated with high tumor mutational burden (TMB). The co-occurrence of mutations in multiple CRs was linked with a further increase in TMB. Given that TMB may predict the clinical response to immune checkpoint inhibitor (ICI) treatment, we investigated the relationship between mutations in CRs and ICI response. We found that patients harboring mutations in CRs exhibited improved responses to ICI treatment, comparable to those with deficiencies in canonical DNA repair pathways. Overall, this study uncovered significant relationships between mutations in chromatin regulators and critical features of cancer, underscoring the need for further functional and clinical studies.\u003c/p\u003e","manuscriptTitle":"Tumors with mutations in chromatin regulators are associated with higher mutational burden and improved response to checkpoint immunotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 10:04:54","doi":"10.21203/rs.3.rs-6202098/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T08:24:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T10:53:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T18:13:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T02:46:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11235916069249902991422021968878945645","date":"2025-11-06T02:31:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109570550335515242020569228326734896580","date":"2025-11-04T14:09:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43413377397322851574430934221680394332","date":"2025-11-03T13:34:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91726634231338800737494524290067981819","date":"2025-11-02T12:38:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T08:13:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-21T22:48:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Epigenetics","date":"2025-10-21T09:35:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff8ce82f-bd7b-4212-b8c4-7836d72dbcb6","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:03:40+00:00","versionOfRecord":{"articleIdentity":"rs-6202098","link":"https://doi.org/10.1186/s13148-025-02038-0","journal":{"identity":"clinical-epigenetics","isVorOnly":false,"title":"Clinical Epigenetics"},"publishedOn":"2025-12-31 15:58:08","publishedOnDateReadable":"December 31st, 2025"},"versionCreatedAt":"2025-10-31 10:04:54","video":"","vorDoi":"10.1186/s13148-025-02038-0","vorDoiUrl":"https://doi.org/10.1186/s13148-025-02038-0","workflowStages":[]},"version":"v1","identity":"rs-6202098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6202098","identity":"rs-6202098","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}