Chromatin accessibility maps of chronic lymphocytic leukaemia identify subtype-specific epigenome signatures and transcription regulatory networks
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Chronic lymphocytic leukaemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, here we present genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients measured by the ATAC-seq assay. We also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting data set, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. IGHV mutation status—which distinguishes the two major subtypes of CLL—was accurately predicted by the chromatin profiles and gene regulatory networks inferred for IGHV -mutated versus IGHV -unmutated samples identified characteristic differences between these two disease subtypes. In summary, we discovered widespread heterogeneity in the chromatin landscape of CLL, established a community resource for studying epigenome deregulation in leukaemia and demonstrated the feasibility of large-scale chromatin accessibility mapping in cancer cohorts and clinical research.
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Using fold changes from a study comparing CLL subtypes iCLL vs mCLL as effect size priors, the maximum overall detection power is 74%.
“Using fold changes from a study comparing CLL subtypes iCLL vs mCLL53 as effect size priors (sample size of 6, 84 DEGs with median absolute log fold change of 2.8) we find a maximum overall detection power of 74% (Fig. 3a).”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [53]
To get realistic estimates for effect sizes (fold changes), data sets from FACS sorted bulk RNA-seq studies were taken53,54.
“To get realistic estimates for effect sizes (fold changes), data sets from FACS sorted bulk RNA-seq studies were taken53,54.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [53]
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“1a), such as depicting enhancer landscapes in healthy mammalian tissue and cell types [23–25], studying accessibility changes between normal hematopoiesis and leukemia [26, 27], as well as the chromatin state within schizophrenia patients and the Cancer Genome Atlas (TCGA) pan-cancer cohort [28, 29].”
BackgroundIdentification of transcription factor binding sites using ATAC-seq
Li, Z., Schulz, M. H., Look, T., Begemann, M., Zenke, M., & Costa, I. G. (2019) · Genome biology · doi:10.1186/s13059-019-1642-2 · Cited by 436 · see citing works
“000 cells from [14] and combined single cell ATAC-seq data from H1ESC and K562 cells [33] and Omni-ATAC K562 cells from [6].”
BackgroundFate mapping of human glioblastoma reveals an invariant stem cell hierarchy
Lan, X., Jörg, D. J., Cavalli, F. M. G., Richards, L. M., Nguyen, L. V., Vanner, R. J., Guilhamon, P., Lee, L., Kushida, M. M., Pellacani, D., et al. (2017) · Nature · doi:10.1038/nature23666 · Cited by 390 · see citing works
Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia
Gaiti, F., Chaligne, R., Gu, H., Brand, R. M., Kothen-Hill, S., Schulman, R. C., Grigorev, K., Risso, D., Kim, K.-T., Pastore, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1198-z · Cited by 262 · see citing works
Chromatin accessibility profiling methods
Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206 · see citing works
Self-citationThe reference epigenome and regulatory chromatin landscape of chronic lymphocytic leukemia
Beekman, R., Chapaprieta, V., Russiñol, N., Vilarrasa-Blasi, R., Verdaguer-Dot, N., Martens, J., Duran-Ferrer, M., Kulis, M., Serra, F., Javierre, B. M., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0028-4 · Cited by 185 · see citing works
Piper, C. J. M., Rosser, E., Oļeiņika, K., Nistala, K., Krausgruber, T., Rendeiro, A. F., Banos, A., Drozdov, I., Villa, M., Thomson, S., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.018 · Cited by 168 · see citing works
Self-citationChromatin accessibility: biological functions, molecular mechanisms and therapeutic application
Chen, Y., Liang, R., Li, Y., Jiang, L., Ma, D., Luo, Q., & Song, G. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-02030-9 · Cited by 174 · see citing works
Enhancer Architecture and Essential Core Regulatory Circuitry of Chronic Lymphocytic Leukemia
Ott, C. J., Alexander Joel Federation, Schwartz, L. S., Kasar, S. N., Klitgaard, J. L., Lenci, R., Li, Q., Lawlor, M. A., Fernandes, S. M., Souza, A., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.11.001 · Cited by 148 · see citing works
“Another cohort of CLL samples has also recently been assessed by ATAC-seq, revealing subtype-specific open chromatin signatures of CLL (Rendeiro et al., 2016).”
BackgroundMethodResultEpigenetic mechanisms regulating T-cell responses
Schmidl, C., Delacher, M., Huehn, J., & Feuerer, M. (2018) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2018.07.014 · Cited by 144 · see citing works
Self-citationÖzer, H. G., El‐Gamal, D., Powell, B., Hing, Z. A., Blachly, J. S., Harrington, B. K., Mitchell, S. R., Grieselhuber, N. R., Williams, K., Lai, T.-H., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0902 · Cited by 134 · see citing works
“Recent DNA methylation and assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) studies in CLL revealed that methylation profiles and genome-wide chromatin accessibility can more precisely differentiate CLL subtypes and predict clinical course compared with gene expression profiles (24…”
BackgroundMethodBerest, I., Arnold, C., Reyes-Palomares, A., Palla, G., Rasmussen, K. D., Giles, H., Bruch, P.-M., Huber, W., Dietrich, S., Helin, K., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.106 · Cited by 119 · see citing works
Miraldi, E. R., Pokrovskii, M. V., Watters, A., Castro, D. M., Veaux, N. D., Hall, J. A., Lee, J., Ciofani, M., Madar, A., Carriero, N., et al. (2019) · Genome Research · doi:10.1101/gr.238253.118 · Cited by 104 · see citing works
“In the context of mammalian TRN inference, several studies build TRNsdirectlyfromchromatinaccessibili-ty without further refinement by multi-variate gene expression modeling (Neph et al. 2012; Rendeiro et al. 2016).”
MethodscPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · see citing works
“…eQTL and DE power were taken from published studies, accessible in the supplement of Chen et al. 2016 (https://doi.org/10.1016/j.cell.2016.10.026), Rendeiro et al. 2016 (https://doi.org/10.1038/ncomms11938), Moreno-Moral et al. 2018 (https://doi.org/10.1136/annrheumdis-2017-212454) and Arda et…”
MethodInfluentialPrecision medicine based on epigenomics: the paradigm of carcinoma of unknown primary
Morán, S., Martínez‐Cardús, A., Boussios, S., & Esteller, M. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.97 · Cited by 104 · see citing works
Law, P., Berndt, S. I., Speedy, H. E., Camp, N. J., Sava, G. P., Skibola, C. F., Holroyd, A., Vijai, J., Sunter, N. J., Nieters, A., et al. (2017) · Nature Communications · doi:10.1038/ncomms14175 · Cited by 99 · see citing works
Rendeiro, A. F., Krausgruber, T., Fortelny, N., Zhao, F., Penz, T., Farlik, M., Schuster, L. C., Nemc, A., Tasnády, S., Réti, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14081-6 · Cited by 96 · see citing works
Self-citationDetect accessible chromatin using ATAC-sequencing, from principle to applications
Sun, Y., Miao, N., & Sun, T. (2019) · Hereditas · doi:10.1186/s41065-019-0105-9 · Cited by 93 · see citing works
Precision cancer therapy is impacted by oncogene-dependent epigenome remodeling
Liu, F., Mischel, P. S., & Cavenee, W. K. (2017) · npj Precision Oncology · doi:10.1038/s41698-017-0005-2 · Cited by 78 · see citing works
Making Sense of the Epigenome Using Data Integration Approaches
Cazaly, E., Saad, J. J., Wang, W., Heckman, C. A., Ollikainen, M., & Tang, J. P. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00126 · Cited by 77 · see citing works
“One case study implemented a genome-wide chromatin accessibility profiling for chronic lymphocytic leukemia (CLL) patient samples using ChIPmentation and RNA-seq profiling (Rendeiro et al., 2016).”
MethodCao, F., Zhang, Y., Cai, Y., Animesh, S., Zhang, Y., Akıncılar, S. C., Loh, Y. P., Li, X., Chng, W. J., Tergaonkar, V., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02453-5 · Cited by 72 · see citing works
“Using this set of ATAC-seq data in CLL samples, it was reported that regions with higher open chromatin signals in uCLL samples showed strong enrichment of binding sites of CTCF, RAD21 and SMC3 [28], which could also contribute to the high variability of CTCF chromatin interactions.”
MethodInfluentialLi, H., Sun, Y., Hong, H., Huang, X., Tao, H., Huang, Q., Wang, L., Xu, K., Gan, J., Chen, H., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00469-5 · Cited by 66 · see citing works
“This approach proved to be effective in several studies 39,40 as open chromatin regions often contain active regulatory elements for TF binding and gene regulations.”
BackgroundMethodRbpj expression in regulatory T cells is critical for restraining TH2 responses
Delacher, M., Schmidl, C., Herzig, Y., Breloer, M., Hartmann, W., Brunk, F., Kägebein, D., Träger, U., Hofer, A.-C., Bittner, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09276-w · Cited by 63 · see citing works
Self-citationWeaver, I. C. G., Korgan, A. C., Lee, K., Wheeler, R. V., Hundert, A. S., & Goguen, D. (2017) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2017.00041 · Cited by 64 · see citing works
“Frontiers in Behavioral Neuroscience | www.frontiersin.org 5 March 2017 | Volume 11 | Article 41”
BackgroundAhamed, N. U., Kobsar, D., Benson, L. C., Clermont, C. A., Kohrs, R., Osis, S. T., & Ferber, R. (2018) · PLoS ONE · doi:10.1371/journal.pone.0203839 · Cited by 60 · see citing works
Yi, G., Wierenga, A. T. J., Petraglia, F., Narang, P., Janssen‐Megens, E. M., Mandoli, A., Merkel, A., Berentsen, K., Kim, B., Matarese, F., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2018.12.098 · Cited by 60 · see citing works
“To infer transcriptional regulatory networks, we conducted similar analyses based on these putative footprint sites as previous studies (Qu et al., 2015; Rendeiro et al., 2016).”
MethodSelf-citationGoronzy, J. J., Hu, B., Kim, C., Jadhav, R. R., & Weyand, C. M. (2018) · Journal of Leukocyte Biology · doi:10.1002/jlb.1ri0418-160r · Cited by 60 · see citing works
Bempt, M. V., Demeyer, S., Broux, M., Bie, J. D., Bornschein, S., Mentens, N., Vandepoel, R., Geerdens, E., Radaelli, E., Bornhauser, B. C., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.07.007 · Cited by 58 · see citing works
Self-citationLinking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks
Mallm, J., Iskar, M., Ishaque, N., Klett, L. C., Kugler, S. J., Muiño, J. M., Teif, V. B., Poos, A. M., Großmann, S., Erdel, F., et al. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188339 · Cited by 55 · see citing works
“The IGHV mutated vs. non-mutated CLL can be distinguished according to the ATAC-seq profiles (Rendeiro et al, 2016).”
BackgroundNon-coding genetic variation in cancer
Cuykendall, T. N., Rubin, M. A., & Khurana, E. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2016.12.017 · Cited by 55 · see citing works
The G protein-coupled receptor GPR34 – The past 20 years of a grownup
Schöneberg, T., Meister, J., Knierim, A. B., & Schulz, A. (2018) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2018.04.008 · Cited by 54 · see citing works
Identification of Transcription Factor Binding Sites using ATAC-seqPreprint
Li, Z., Schulz, M. H., Zenke, M., & Costa, I. G. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/362863 · Cited by 53 · see citing works
IChrom-Deep: An Attention-Based Deep Learning Model for Identifying Chromatin Interactions
Zhang, P., & Wu, H. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3292299 · Cited by 53 · see citing works
Inflammation-associated DNA methylation patterns in epithelium of ulcerative colitis
Barnicle, A., Seoighe, C., Greally, J. M., Golden, A., & Egan, L. J. (2017) · Epigenetics · doi:10.1080/15592294.2017.1334023 · Cited by 52 · see citing works
“is the inter-individual variation that exists in epigenetic signatures,(27) specifically in the presence of disease.(28)”
BackgroundNetwork Approaches for Dissecting the Immune System
Shi, H., Yan, K., Ding, L., Qian, C., Chi, H., & Yu, J. (2020) · iScience · doi:10.1016/j.isci.2020.101354 · Cited by 50 · see citing works
Rogerson, C., Britton, E., Withey, S., Hanley, N. A., Ang, Y. S., & Sharrocks, A. D. (2019) · Genome Research · doi:10.1101/gr.243345.118 · Cited by 51 · see citing works
“Changes to the epigenetic landscapemight be amajor contributing factor, and this scenario has been implicated in other cancers (Davie et al. 2015; Rendeiro et al. 2016).”
BackgroundPopova, A. A., & Levkin, P. A. (2020) · Advanced Therapeutics · doi:10.1002/adtp.201900100 · Cited by 50 · see citing works
Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data
Xu, J., Lu, C., Jin, S., Meng, Y., Fu, X., Zeng, X., Nussinov, R., & Cheng, F. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf138 · Cited by 48 · see citing works
“This approach has proven effective because open chromatin regions often contain active regulatory elements for TF binding and gene regulation [ 43 , 44 ].”
BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization
de Boer, C. G., & Regev, A. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2255-6 · Cited by 48 · see citing works
Liu, Q., Zaba, L. C., Satpathy, A. T., Longmire, M. R., Zhang, W., Li, K., Granja, J. M., Guo, C., Lin, J., Li, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19702-z · Cited by 47 · see citing works
Targeting epigenetic machinery: Emerging novel allosteric inhibitors
Ye, F., Huang, J. L., Wang, H., Luo, C., & Zhao, K. (2019) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2019.107406 · Cited by 45 · see citing works
Enabling Personalized Medicine in Europe by the European Commission’s Funding Activities
Nimmesgern, E., Norstedt, I., & Draghia, R. (2017) · Personalized Medicine · doi:10.2217/pme-2017-0003 · Cited by 44 · see citing works
Alvarez-Benayas, J., Trasanidis, N., Katsarou, A., Ponnusamy, K., Chaidos, A., May, P. C., Xiao, X., Bua, M., Atta, M. L. B. S., Roberts, I. A. G., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25704-2 · Cited by 48 · see citing works
The Open Chromatin Landscape of Non–Small Cell Lung Carcinoma
Wang, Z., Tu, K., Lin, X., Luo, K., Luo, W., Tang, J., Lu, K., Hu, X., He, Y., Qiao, W., et al. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-3663 · Cited by 42 · see citing works
Combined chemosensitivity and chromatin profiling prioritizes drug combinations in CLL
Schmidl, C., Vladimer, G. I., Rendeiro, A. F., Schnabl, S., Krausgruber, T., Taubert, C., Krall, N., Pemovska, T., Araghi, M., Snijder, B., et al. (2019) · Nature Chemical Biology · doi:10.1038/s41589-018-0205-2 · Cited by 39 · see citing works
Self-citationDechassa, M., Tryndyak, V., de Conti, A., Xiao, W., Beland, F. A., & Pogribny, I. (2018) · Molecular Carcinogenesis · doi:10.1002/mc.22818 · Cited by 33 · see citing works
Sahinyan, K., Blackburn, D. M., Simon, M.-M., Lazure, F., Kwan, T., Bourque, G., & Soleimani, V. D. (2022) · eLife · doi:10.7554/elife.72792 · Cited by 32 · see citing works
Regulatory elements in molecular networks
Doane, A. S., & Elemento, O. (2017) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1374 · Cited by 36 · see citing works
Pölönen, P., Mehtonen, J., Lin, J., Liuksiala, T., Häyrynen, S., Teppo, S., Mäkinen, A., Kumar, A., Malani, D., Pohjolainen, V., et al. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-2970 · Cited by 36 · see citing works
“…of several polycomb group proteins (SFMBT1, CBX7 and EZH1) in CLL that could be targeted by small molecules, in line with chromatin state data (46), and their mutation (45) frequencies, highlighting the importance to consider the spectrum of genetic and epigenetic changes in these…”
BackgroundCinquina, V., Calvigioni, D., Farlik, M., Halbritter, F., Fife, V. E. R., Shirran, S. L., Fuszard, M. A., Botting, C. H., Poullet, P., Piscitelli, F., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0580-4 · Cited by 38 · see citing works
Self-citationGenomic and Epigenomic Alterations in Chronic Lymphocytic Leukemia
Nadeu, F., Díaz‐Navarro, A., Delgado, J., Puente, X. S., & Campo, E. (2020) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-012419-032810 · Cited by 34 · see citing works
A neural network based model effectively predicts enhancers from clinical ATAC-seq samples
Thibodeau, A., Uyar, A., Khetan, S., Stitzel, M. L., & Ucar, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-34420-9 · Cited by 32 · see citing works
Eckert, C., Groeneveld‐Krentz, S., Kirschner‐Schwabe, R., Hagedorn, N., Chen, C. I.-U., Bader, P., Borkhardt, A., Cario, G., Escherich, G., Panzer‐Grümayer, R., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.19.01694 · Cited by 31 · see citing works
“Ideally, those markers may serve as suitable novel targets for signal transduction inhibitors or immunotherapeutics.(22-24)”
BackgroundHing, Z. A., Walker, J. S., Whipp, E. C., Brinton, L. T., Cannon, M. J., Zhang, P., Sher, S., Cempre, C. B., Brown-Burke, F., Smith, P. L., et al. (2023) · Nature Communications · doi:10.1038/s41467-022-35778-1 · Cited by 29 · see citing works
Deciphering the genetics and mechanisms of predisposition to multiple myeloma
Went, M., Duran-Lozano, L., Halldorsson, G. H., Gunnell, A., Ugidos-Damboriena, N., Law, P., Ekdahl, L., Sud, A., Thorleifsson, G., Thodberg, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50932-7 · Cited by 30 · see citing works
Zhao, X., Peng, T., Cao, X., Hou, Y., Li, R., Han, T., Fan, Z., Zhao, M., Chang, Y., Chen, H., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111342 · Cited by 30 · see citing works
Chromatin accessibility landscape of pediatric T‐lymphoblastic leukemia and human T‐cell precursors
Erarslan-Uysal, B., Kunz, J. B., Rausch, T., Richter‐Pechańska, P., van Belzen, I. A. E. M., Frismantas, V., Bornhauser, B. C., Ordoñez‐Rueda, D., Paulsen, M. S., Beneš, V., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012104 · Cited by 25 · see citing works
“As a result, epigenetic analyses carry the potential to define the lineage and identity of cells more accurately than conventional methods (Corces et al, 2016; Rendeiro et al, 2016).”
BackgroundFleur-Lominy, S. S., Evensen, N. A., Bhatla, T., Sethia, G., Narang, S., Choi, J., Ma, X., Yang, J. J. J., Kelly, S., Raetz, E. A., et al. (2020) · Cancer Research · doi:10.1158/0008-5472.can-20-1145 · Cited by 25 · see citing works
Towards precision medicine in lymphoid malignancies
Bühler, M. M., Martín-Subero, J. I., Pan‐Hammarström, Q., Campo, E., & Rosenquist, R. (2021) · Journal of Internal Medicine · doi:10.1111/joim.13423 · Cited by 27 · see citing works
Life‐long impairment of glucose homeostasis upon prenatal exposure to psychostimulants
Korchynska, S., Krassnitzer, M., Maleńczyk, K., Prasad, R. B., Tretiakov, E. O., Rehman, S., Cinquina, V., Fife, V. E. R., Farlik, M., Petersen, J., et al. (2019) · The EMBO Journal · doi:10.15252/embj.2018100882 · Cited by 23 · see citing works
“Open chromatin mapping was performed with the assay for transposase accessible chromatin (ATAC-seq) (Buenrostro et al , 2015) with minor modifications (Rendeiro et al , 2016).”
Self-citationRogerson, C., Ogden, S., Britton, E., Ang, Y. S., & Sharrocks, A. D. (2020) · eLife · doi:10.7554/elife.57189 · Cited by 24 · see citing works
“Changes to the chromatin landscape have been implicated in many cancers and chromatin accessibility changes during tumourigenesis are a major factor in altering regulatory element activity (Britton et al., 2017; Corces et al., 2018; Davie et al., 2015; Denny et al., 2016; Kelso et al., 2017; Rendeiro et al., 2016; Tom…”
Salma, M., Andrieu‐Soler, C., Deleuze, V., & Soler, É. (2023) · Blood Cells Molecules and Diseases · doi:10.1016/j.bcmd.2023.102745 · Cited by 18 · see citing works
Ehf and Fezf2 regulate late medullary thymic epithelial cell and thymic tuft cell development
Lammers, S., Barrera, V., Brennecke, P., Miller, C. N., Yoon, J. H., Balolong, J., Anderson, M. S., Sui, S. H., Steinmetz, L. M., von Andrian, U. H., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1277365 · Cited by 21 · see citing works
“…binding to the promoter regions of genes associated with late mTEC development We used an ultrasound-based nuclei extraction method (Nexson: Nuclei Extraction by Sonni fi cation) (41) followed by ChIPmentation sequencing to analyze the binding sites of the identi fi ed transcription factors (34, 35).”
MethodDrug‐microenvironment perturbations reveal resistance mechanisms and prognostic subgroups in CLL
Bruch, P., Giles, H. A. R., Kolb, C., Herbst, S., Becirovic, T., Roider, T., Lu, J.-Y., Scheinost, S., Wagner, L., Huellein, J., et al. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110855 · Cited by 13 · see citing works
Özçelik, F., Dündar, M. S., Yildirim, A. B., HENEHAN, G. T. M., Vicente, O., Sánchez-Alcázar, J. A., Gökçe, N., Yildirim, D. T., Bingol, N. N., Plaseska‐Karanfilska, D., et al. (2024) · Functional & Integrative Genomics · doi:10.1007/s10142-024-01417-9 · Cited by 21 · see citing works
“In a study by Liu et al., a logistic showcased its promise in biomedical research by uncover - ing extensive variability in chromatin connections among CLL samples (Rendeiro et al. 2016; Cao et al. 2021).”
Nucleosome repositioning in chronic lymphocytic leukemiaPreprint
Piroeva, K. V., McDonald, C., Xanthopoulos, C., Fox, C., Clarkson, C. T., Mallm, J., Vainshtein, Y., Ruje, L., Klett, L. C., Stilgenbauer, S., et al. (2023) · Genome Research · doi:10.1101/gr.277298.122 · Cited by 18 · see citing works
“…accessibility, and transcription factor (TF) binding, as well as long-range chromatin interactions (Ferreira et al. 2014; Kulis et al. 2015; Queirós et al. 2015; Oakes et al. 2016; Rendeiro et al. 2016; Beekman et al. 2018; Mallm et al. 2019; Vilarrasa-Blasi et al. 2021).”
Chronic lymphocytic leukaemia genomics and the precision medicine era
Ghamlouch, H., Nguyen‐Khac, F., & Bernard, O. (2017) · British Journal of Haematology · doi:10.1111/bjh.14719 · Cited by 17 · see citing works
Bagacean, C., Tempescul, A., Dantec, C. L., Bordron, A., Mohr, A., Saad, H., Olivier, V., Zdrenghea, M., Cristea, V., Cartron, P., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.20081 · Cited by 17 · see citing works
“Epigenome-wide association studies (EWAS) have contributed to better understanding the significance of DNA methylation in CLL by showing: (1) global DNA hypomethylation combined with local hypermethylation in CLL B cells as observed in other cancers [22]; (2) a link between the methylome profile heterogeneity, an incr…”
Bagacean, C., Dantec, C. L., Berthou, C., Tempescul, A., Saad, H., Bordron, A., Zdrenghea, M., Cristea, V., Douet‐Guilbert, N., & Renaudineau, Y. (2017) · Clinical Epigenetics · doi:10.1186/s13148-017-0422-7 · Cited by 17 · see citing works
BiFET: sequencing Bi as-free transcription factor F ootprint E nrichment T est
Youn, A., Márquez, E. J., Lawlor, N. T., Stitzel, M. L., & Ucar, D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1117 · Cited by 17 · see citing works
“immunosenescence (11) or cancer subtypes (12).”
Labrèche, K., Daniau, M., Sud, A., Law, P., Royer-Perron, L., Holroyd, A., Broderick, P., Went, M., Benazra, M., Ahle, G., et al. (2019) · Neuro-Oncology · doi:10.1093/neuonc/noz088 · Cited by 16 · see citing works
Teixeira, S. R., Abreu, C. M., Parkes, L., Davies, J., Yao, S. N., Sawhney, M. A., Margarit, L., González, D., Pinto, I. M., Francis, L. W., et al. (2019) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2019.111386 · Cited by 16 · see citing works
Core-binding factor leukemia hijacks the T-cell–prone PU.1 antisense promoter
van der Kouwe, E., Heller, G., Czibere, A. G., Pulikkan, J. A., Agreiter, C., Castilla, L. H., Delwel, R., Ruscio, A. D., Ebralidze, A. K., Forte, M., et al. (2021) · Blood · doi:10.1182/blood.2020008971 · Cited by 16 · see citing works
Targeting Nuclear NOTCH2 by Gliotoxin Recovers a Tumor-Suppressor NOTCH3 Activity in CLL
Hubmann, R., Schnabl, S., Araghi, M. H. D., Schmidl, C., Rendeiro, A. F., Hilgarth, M., Demirtas, D., Ali, F., Staber, P. B., Valent, P., et al. (2020) · Cells · doi:10.3390/cells9061484 · Cited by 15 · see citing works
“Accessible-chromatin mapping on the CLL cells was performed using the ATAC-seq method with minor modifications together with the ATAC-seq processing pipeline, as described previously [34,35].”
MethodSelf-citationHow low can you go? Pushing the limits of low-input ChIP-seq
Dahl, J. A., & Gilfillan, G. D. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx037 · Cited by 15 · see citing works
Clinical implications of chromatin accessibility in human cancers
Liu, Y. (2020) · Oncotarget · doi:10.18632/oncotarget.27584 · Cited by 15 · see citing works
“A study reported a small number of chromatin accessibility differences between two CLL subtypes with striking difference in prognosis [14].”
BackgroundMulti-omics analysis of naïve B cells of patients harboring the C104R mutation in TACI
Ramirez, N. J., Posadas‐Cantera, S., Langer, N., Caballero-Oteyza, A., Proietti, M., Keller, B., Zhao, F., Fife, V. E. R., Pecoraro, M., Eibel, H., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.938240 · Cited by 14 · see citing works
CLL intraclonal fractions exhibit established and recently acquired patterns of DNA methylation
Bartholdy, B., Wang, X., Yan, X., Pascual, M., Fan, M., Barrientos, J. C., Allen, S. L., Martinez-Climent, J. A., Rai, K. R., Chiorazzi, N., et al. (2020) · Blood Advances · doi:10.1182/bloodadvances.2019000817 · Cited by 14 · see citing works
Ucar, D., & Benayoun, B. A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-811060-7.00001-2 · Cited by 12 · see citing works
Epigenomic technologies for precision oncology
Weichenhan, D., Lipka, D. B., Lutsik, P., Goyal, A., & Plass, C. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.08.004 · Cited by 13 · see citing works
Decoding transcriptional states in cancer
Wouters, J., Atak, Z. K., & Aerts, S. (2017) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2017.01.003 · Cited by 13 · see citing works
Chen, I.-W., Ronzitti, E., Lee, B. R., Daigle, T. L., Zeng, H., Papagiakoumou, E. I., & Emiliani, V. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/250795 · Cited by 12 · see citing works
Advances in Epigenomic Sequencing and Their Applications in Cancer Diagnostics
Lin, L., & Liu, Y. (2025) · Precision Chemistry · doi:10.1021/prechem.5c00014 · Cited by 11 · see citing works
Chromatin accessibility landscape of relapsed pediatric B-lineage acute lymphoblastic leukemia
Wang, H., Sun, H., Liang, B., Zhang, F., Yang, F., Cui, B., Ding, L., Wang, X., Wang, R., Cai, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42565-z · Cited by 10 · see citing works
Salem, M., Al-Tobasei, R., Ali, A., An, L.-Q., Wang, Y., Bai, X., Bi, Y., & Zhou, H.-J. (2024) · GigaScience · doi:10.1093/gigascience/giae092 · Cited by 7 · see citing works
“Regions of open c hr omatin identified by A T AC-seq, combined with expression analysis, allow for associating functional/regulatory ele-Recei ments with transcribed genes [ 7–9 ].”
Kulis, M., & Martín-Subero, J. I. (2022) · British Journal of Haematology · doi:10.1111/bjh.18465 · Cited by 11 · see citing works
Jiang, Q., Stachelscheid, J., Bloehdorn, J., Pacholewska, A., Aszyk, C. M., Grotenhuijs, F., Müller, T. A., Önder, Ö., Wagle, P., Herling, C., et al. (2023) · Blood · doi:10.1182/blood.2022017230 · Cited by 10 · see citing works
Global chromatin landscapes identify candidate noncoding modifiers of cardiac rhythm
Bhattacharyya, S., Kollipara, R. K., Orquera-Tornakian, G., Goetsch, S. C., Zhang, M., Perry, C., Li, B., Shelton, J. M., Bhakta, M., Duan, J., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci153635 · Cited by 13 · see citing works
“229 Combining motif identification algorithms with chromatin accessibility data can be used to construct 230 putative cell-type specific gene regulatory networks (GRNs)(43).”
MethodYan, H., Tian, S., Kleinstern, G., Wang, Z., Lee, J., Boddicker, N. J., Cerhan, J. R., Kay, N. E., Braggio, E., & Slager, S. L. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa165 · Cited by 9 · see citing works
Advances in Epigenetics and Epigenomics in Chronic Lymphocytic Leukemia
Xanthopoulos, C., & Kostareli, E. (2019) · Current Genetic Medicine Reports · doi:10.1007/s40142-019-00178-3 · Cited by 9 · see citing works
TEAD4 antagonizes cellular senescence by remodeling chromatin accessibility at enhancer regions
Zhang, D., Zhu, Y., Ju, Y., Zhang, H., Zou, X., She, S., Zhu, D., & Guan, Y. (2023) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-023-04980-9 · Cited by 8 · see citing works
Zhang, D., Zhang, C., Zhu, Y., Xie, H., Yue, C., Li, M., Wei, W., Peng, Y., Yin, G., Guo, Y., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad506 · Cited by 8 · see citing works
Bioinformatics of Epigenomic Data Generated From Next-Generation Sequencing
Hsu, F., Gohain, M., Chang, P. P.-C., Lu, R. J.-H., & Chen, P. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812215-0.00004-2 · Cited by 8 · see citing works
Koyama, T., Rhrissorrakrai, K., & Parida, L. (2019) · BMC Cancer · doi:10.1186/s12885-019-5313-1 · Cited by 8 · see citing works
“It may be possible to assess whether these variants in SWI/SNF genes contribute to tumorigenesis by studying epigenetic signatures using techniques like ATAC-seq [46].”
Environmental Epigenetics of Diesel Particulate Matter Toxicogenomics
Bilinovich, S. M., Lewis, K., Thompson, B. L., Prokop, J. W., & Campbell, D. B. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17207386 · Cited by 7 · see citing works
“ATAC-seq has been widely used to probe biological questions of gene regulation including normal human tissues [12], differences between cancerous and healthy cells [13], effects of therapeutics on cells [14], nucleosome mapping [15], enhancer identification [16], and transcription factor binding sites [17].”
MethodDissecting CLL through high-dimensional single-cell technologies
Gohil, S. H., & Wu, C. J. (2019) · Blood · doi:10.1182/blood-2018-09-835389 · Cited by 7 · see citing works
- Authors
- Rendeiro, A. F., Schmidl, C., Strefford, J. C., Walewska, R., Davis, Z., Farlik, M., Oscier, D., & Bock, C.
- Affiliations
- Austrian Academy of Sciences (AT)
- University of Southampton (GB)
- Royal Bournemouth Hospital (GB)
- Corresponding author
- Bock, Christoph
- ORCID
- Venue
- Nature Communications
- Published
- 2016-06-27
- Volume
- 7
- Issue
- 1
- Pages
- 11938-11938
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5494194
- OpenAlex
- W2470513050
- Cited by
- 146
- References
- 75
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 8 · 2023: 13 · 2022: 10 · 2021: 10 · 2020: 20 · 2019: 29 · 2018: 20 · 2017: 20 · 2016: 4
- FWCI
- 17.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Max Planck Digital Library (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Funders
- Blood Cancer UK (11052, 12036)
- Topics
- Health Sciences › Medicine › Genetics › Chronic Lymphocytic Leukemia Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- chronic lymphocytic leukemia, chromatin accessibility, IGHV mutation status, transcriptional regulatory networks, gene regulatory networks, clinical heterogeneity, chromatin landscape
- MeSH terms
- Machine Learning, Chromatin, Humans, Transcription, Genetic, Leukemia, Lymphocytic, Chronic, B-Cell, Gene Expression Regulation, Neoplastic, Sequence Analysis, RNA, Gene Regulatory Networks, Epigenomics
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