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Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia
Open accessAbstract
Genetic and epigenetic intra-tumoral heterogeneity cooperate to shape the evolutionary course of cancer 1. Chronic lymphocytic leukaemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution after therapy 2,3. The CLL epigenome is also an important disease-defining feature 4,5, and growing populations of cells in CLL diversify by stochastic changes in DNA methylation known as epimutations 6. However, previous studies using bulk sequencing methods to analyse the patterns of DNA methylation were unable to determine whether epimutations affect CLL populations homogeneously. Here, to measure the epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced-representation bisulfite sequencing to B cells from healthy donors and patients with CLL. We observed that the common clonal origin of CLL results in a consistently increased epimutation rate, with low variability in the cell-to-cell epimutation rate. By contrast, variable epimutation rates across healthy B cells reflect diverse evolutionary ages across the trajectory of B cell differentiation, consistent with epimutations serving as a molecular clock. Heritable epimutation information allowed us to reconstruct lineages at high-resolution with single-cell data, and to apply this directly to patient samples. The CLL lineage tree shape revealed earlier branching and longer branch lengths than in normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history. Integration of single-cell bisulfite sequencing analysis with single-cell transcriptomes and genotyping confirmed that genetic subclones mapped to distinct clades, as inferred solely on the basis of epimutation information. Finally, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells that were preferentially expelled from the lymph node after treatment, marked by distinct transcriptional profiles. The single-cell integration of genetic, epigenetic and transcriptional information thus charts the lineage history of CLL and its evolution with therapy.
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“Another study combined single-cell transcriptome and DNA methylome data to construct a chronic lymphocytic leukaemia (CLL) lineage tree based on random DNA methylation changes, finding that certain CLL lineages were preferentially affected by drug treatment143.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [143]
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Citations
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A., & Minh, B. Q. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msu300 · Cited by 26,046
ModelFinder: fast model selection for accurate phylogenetic estimates
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A., & Jermiin, L. S. (2017) · Nature Methods · doi:10.1038/nmeth.4285 · Cited by 18,449
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Self-citationUFBoot2: Improving the Ultrafast Bootstrap Approximation
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q., & Vinh, L. S. (2018) · Molecular Biology and Evolution · doi:10.1093/molbev/msx281 · Cited by 10,881
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Items 201–263 of 263
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Izzo, F., Prieto, T., Potenski, C. J., & Landau, D. A. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00970-8 · Cited by 2
Self-citationAdvances in single-cell epigenomics of the immune system
Schulte-Schrepping, J., Ferreira, H. J., Saglam, A., Hinkley, E. R., & Schultze, J. L. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817964-2.00008-3 · Cited by 2
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Ware, A. D., Davis, K. E., & Xian, R. R. (2021) · Surgical pathology clinics · doi:10.1016/j.path.2021.06.001 · Cited by 2
Age-Related Variation in DNA Methylation
Keith, K., Issa, J. J., & Panjarian, S. B. (2022) · Springer eBooks · doi:10.1007/978-3-030-94475-9_10 · Cited by 2
Murdaugh, R. L., & Anastas, J. N. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1002296 · Cited by 7
Parrott, A. M., Murty, V. V. V. S., Walsh, C., Christiano, A., Bhagat, G., & Alobeid, B. (2021) · Cancer Medicine · doi:10.1002/cam4.3853 · Cited by 2
Single-Cell Multiomics: Dissecting Cancer
Sambath, J., Patel, K., Limaye, S. A., & Kumar, P. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-2445-5_14 · Cited by 2
Budeus, B., Dampmann, M., Kibler, A., Faßbender, B., Brauser, M., Bronischewski, K., Homp, E., Taudien, S., Johansson, P., Bloehdorn, J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.04.490383 · Cited by 2
Nucleosome repositioning in chronic lymphocytic leukaemiaPreprint
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. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.20.518743 · Cited by 2
Liu, Y., Goretsky, A., Keşküş, A. G., Malikić, S., Ahmad, T., Gertz, E. M., Mehrabadi, F. R., Kelly, M. C., Hernandez, M., Seibert, C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.28.672865 · Cited by 2
The Broad Spectrum of TP53 Mutations in CLL: Evidence of Multiclonality and Novel Mutation Hotspots
Lazarian, G., Leroy, B., Theves, F., Hormi, M., Letestu, R., Éclache, V., Tueur, G., Ameur, A., Bidet, A., Cornillet‐Lefèbvre, P., et al. (2023) · Human Mutation · doi:10.1155/2023/4880113 · Cited by 1
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Lopedote, P., Kittai, A. S., & Danilov, A. V. (2024) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2024.2398483 · Cited by 1
Naddaf, L., & Li, S. (2025) · Aging and Cancer · doi:10.1002/aac2.12078 · Cited by 1
Xu, Y., Ma, S., Xu, M., Zhu, H., Wang, Y., Dong, W., Gan, J., Yusen, Z., Li, X., Wang, S., et al. (2025) · Genes & Diseases · doi:10.1016/j.gendis.2025.101832 · Cited by 1
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Kelly, L. M., Fenouille, N., Wood, K. C., & Puissant, A. (2026) · Trends in cancer · doi:10.1016/j.trecan.2025.12.004 · Cited by 1
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Noorafrooz, M., Noorafrooz, R., Barosi, G., & Gale, R. P. (2026) · Blood Reviews · doi:10.1016/j.blre.2026.101385 · Cited by 1
Bueno-Costa, A., Campillo‐Marcos, I., Casado-Peláez, M., Yassine, K., Noguera‐Castells, A., Zamora, L., Xicoy, B., Solé, F. G., Mata, C., Park, J., et al. (2026) · Leukemia · doi:10.1038/s41375-026-03015-z · Cited by 1
Self-citationKim, I. S. (2026) · Molecular Oncology · doi:10.1002/1878-0261.70303 · Cited by 2
Bukovec, D., Gjorgjioski, B., Simjanoska, M., & Kungulovski, G. (2026) · Epigenetics & Chromatin · doi:10.1186/s13072-026-00704-y · Cited by 1
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Ahn, I. E. (2021) · Clinical Lymphoma Myeloma & Leukemia · doi:10.1016/s2152-2650(21)01241-6 · Cited by 1
Fukano, M., Alzial, G., Lambert, R., & Deblois, G. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-2914-7_19 · Cited by 2
The emerging field of opportunities for single-cell DNA methylation studies in hematology and beyond
Albinati, L., Bianchi, A., & Beekman, R. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1286716 · Cited by 1
Inferring the Cancer Cellular Epigenome Heterogeneity via DNA Methylation Patterns
Li, S. (2023) · Cancer treatment and research · doi:10.1007/978-3-031-45654-1_12 · Cited by 1
Li, X. C., Liu, Y., Schäffer, A. A., Mount, S. M., & Şahinalp, S. C. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf237 · Cited by 1
Blood cancer driver Musashi-2 as therapeutic target in chronic lymphocytic leukemia
Kazianka, L., & Staber, P. B. (2021) · Leukemia · doi:10.1038/s41375-021-01144-1 · Cited by 1
Sturgess, K., Wilson, N. K., & Göttgens, B. (2022) · Blood · doi:10.1182/blood.2022017361 · Cited by 1
Heide, T., Househam, J., Cresswell, G. D., Spiteri, I., Lynn, C., Kimberley, C., Mossner, M., Zapata, L., Gabbutt, C., Ramazzotti, D., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.14.451265 · Cited by 1
Cheng, T., Zhou, C., Pang, J., Tretyakova, N. Y., Tse, W. W., Liu, Y., & Liu, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1432174/v2 · Cited by 1
Magnani, L. (2022) · Research Square · doi:10.21203/rs.3.rs-1432636/v1 · Cited by 1
Targeting the methylome to improve CLL outcome
Gribben, J. G. (2023) · Blood · doi:10.1182/blood.2023020393 · Cited by 1
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Ardakani, F. B., Ashrafiyan, S., Rumpf, L., Hecker, D., & Schulz, M. H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.09.653095 · Cited by 1
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Fonseca, A. V. A., Boniface, C. T., Watson, C. J., Hackett, S., Gabbutt, C., Ramos-Yamasaki, A. D., Tan, A. B., Montoya, J., Strogantsev, R., Hanssen, L. E., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.26.661643 · Cited by 1
Epigenetic Trajectories of the Premalignant-to-Malignant Transition of Chronic Lymphocytic Leukemia
Rossi, D. (2020) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-20-0207
Smith, M. R., Cronin, J. F., & Weiss, R. F. (2021) · Leukemia Research · doi:10.1016/j.leukres.2021.106663
Single Cell Sequencing, Its Application and Future Challenges
Saleem, M., Alam, S., Kainat, I., Iftikhar, R., & Ijaz, A. (2021) · Current Trends in OMICS · doi:10.32350/cto.11.05
Multi-tissue DNA methylation microarray signature is predictive of gene function
Roopnarinesingh, X., Porter, H. L., Giles, C. B., Brown, C. A., Georgescu, C., & Wren, J. D. (2022) · Epigenetics · doi:10.1080/15592294.2022.2036411 · Cited by 1
Cheng, T., Zhou, C., Pang, J., Tretyakova, N. Y., Tse, W. W., Liu, Y., & Liu, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1432174/v1
Biran, A., Yin, S., Kretzmer, H., ten Hacken, E., Parvin, S., Lucas, F. M., Uduman, M., Gutierrez, C., Dangle, N. J., Billington, L., et al. (2023) · doi:10.1158/0008-5472.c.6513540.v1
Self-citationWhat is the Origin of Pancreatic Endocrine Tumors?
Perren, A. A., Michael, I. P., & Marinoni, I. (2023) · Wiley eBooks · doi:10.1002/9781119876007.ch101
Molecular Pathology of Mature Lymphoid Malignancies
Ware, A. D., Davis, K. E., & Xian, R. R. (2024) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2023.08.014
Charting epimutation dynamics in human hematopoietic differentiation
Qin, X., Lu, J., Wu, P., Zhang, C., Shi, L., & Zhu, P. (2024) · Blood Science · doi:10.1097/bs9.0000000000000197
tagtango: an application to compare single-cell annotations
Mora, B. B., Lindsay, H. A., Thiébaut, A., Stuart, K. D., & Gottardo, R. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf012
Advances and breakthroughs in single-cell nucleic acid omics
Sun, Z., Li, Z., Tang, Y., & Dong, F. (2025) · Scientia Sinica Vitae · doi:10.1360/ssv-2025-0107
The Chronic Lymphocytic Leukemia Epigenome
Perez-Castro, A., Abruzzo, L. V., & Oakes, C. C. (2025) · Hematology/Oncology Clinics of North America · doi:10.1016/j.hoc.2025.05.005
Yan, F., Wang, Y., Fan, X., Wei, P., & Shan, Y. (2025) · Brain and Behavior · doi:10.1002/brb3.70996
Wang, P., Feng, R., & Liu, F. (2025) · Epigenetics · doi:10.1080/15592294.2025.2599682 · Cited by 1
Derrien, J., Guérin‐Charbonnel, C., Gaborit, V., Campion, L., Devic, M., Douillard, E., Roi, N., Avet‐Loiseau, H., Decaux, O., Facon, T. T. F., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15139625
Johnson, N. D., Wu, X., Still, C. D., Chu, X., Petrick, A. T., Gerhard, G. S., Conneely, K. N., & DiStefano, J. K. (2021) · Figshare · doi:10.6084/m9.figshare.15122268
Cheng, T., Zhou, C., Bian, S., Sobeck, K., & Liu, Y. (2024) · Figshare · doi:10.6084/m9.figshare.26697467
Kabeer, F., Tran, H., Andronescu, M., Singh, G., Lee, H., Salehi, S., Wang, B., Biele, J., Brimhall, J., Gee, D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26750714
Kabeer, F., Tran, H., Andronescu, M., Singh, G., Lee, H., Salehi, S., Wang, B., Biele, J., Brimhall, J., Gee, D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26333394
Kabeer, F., Tran, H., Andronescu, M., Singh, G., Lee, H., Salehi, S., Wang, B., Biele, J., Brimhall, J., Gee, D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26333247
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Rota, R., Flittner, E., Tartagni, M., Bourdim, M., Westlund, E., McKenzie, J. S., Devlin, A. J., Thankamony, A. P., Pandell, N., Gabel, F., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.10.705126
L’hétérogénéité épigénétique intra et inter-patient dans le cas du myélome multiple
Derrien, J. (2020) · theses.fr (ABES) · doi:10.70675/ca772ec8z1a68z4ddazb884z2870e3f967b6
Kim, H. J., Lee, H. J., Hong, J., & Park, D. (2026) · Molecular Oncology · doi:10.1002/1878-0261.70285
Kazmi, I. A., Rahman, S., Al-Zharani, M., Afzal, M., Rafeeq, M., Nasr, F. A., Alzarea, S. I., Jafer, R., & Sayyed, N. (2026) · International Journal of Laboratory Hematology · doi:10.1111/ijlh.70196
Liu, Y., Goretsky, A., Keşküş, A. G., Malikić, S., Ahmad, T., Gertz, E. M., Mehrabadi, F. R., Kelly, M. C., Hernandez, M. O., Seibert, C., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-75172-9 · Cited by 2
Scalable methylome-transcriptome profiling resolves clonal organization in human hematopoiesisPreprint
Shao, J., Chen, M., Liu, Y., Sun, J., Zhang, G., Zheng-yi, Y., Lin, H., Ren, M., Pei, S., Peng, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.08.757494
PRC2-binding regions are hotspots of DNA methylation entropy in pre-cancer and cancerPreprint
Bukovec, D., Misheva, M. S., Gjorgjievska, M., & Kungulovski, G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.11.758285
Liu, F., Huang, J., Zheng, Y., Fu, L., Wu, X., Zhao, N., Wang, Y., Gnirke, A., Meissner, A., Wang, X., et al. (2026) · Nature Protocols · doi:10.1038/s41596-026-01452-9
Self-citationSethi, R., & Chen, J. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10609309
Kawaguchi, R. K., Ballouz, S., & Gillis, J. A. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.19659477
Metadata
- Authors
- Gaiti, F., Chaligne, R., Gu, H., Brand, R. M., Kothen-Hill, S., Schulman, R. C., Grigorev, K., Risso, D., Kim, K.-T., Pastore, A., Huang, K. Y., Alonso, A., Sheridan, C., Omans, N. D., Biederstedt, E., Clement, K., Wang, L., Felsenfeld, J. A., Bhavsar, E. B., Aryee, M. J., Allan, J. N., Furman, R., Gnirke, A., Wu, C. J., Meissner, A., & Landau, D. A.
- Affiliations
- New York Genome Center (US)
- Broad Institute (US)
- Weill Cornell Medicine (US)
- University of Padua (IT)
- Memorial Sloan Kettering Cancer Center (US)
- City of Hope (US)
- Corresponding author
- Landau, Dan A.
- ORCID
- Venue
- Nature
- Published
- 2019-05-15
- Volume
- 569
- Issue
- 7757
- Pages
- 576-580
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6533116
- OpenAlex
- W2946801856
- Cited by
- 262
- References
- 73
- Citations by year
- 2026: 18 · 2025: 27 · 2024: 34 · 2023: 37 · 2022: 51 · 2021: 51 · 2020: 35 · 2019: 9
- FWCI
- 20.01
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Padua Research Archive (University of Padova) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (1dp2ca239065-01, 5r01ca229902-05)
- National Heart, Lung, and Blood Institute (R01HL145283)
- National Cancer Institute (R01CA240910, DP2-CA239065, R01-CA229902, R01CA216273, UG1CA233338, P30CA008748, P01CA206978, P30CA006516)
- National Institute of Environmental Health Sciences (K01ES025431)
- Topics
- Health Sciences › Medicine › Hematology › Chronic Lymphocytic Leukemia Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- chronic lymphocytic leukemia, epimutations, DNA methylation, epigenetic evolution, intratumoral heterogeneity, clonal evolution, lineage reconstruction, ibrutinib resistance, B cell differentiation, single-cell bisulfite sequencing, single-cell transcriptomics
- MeSH terms
- Epigenome, Base Sequence, Biological Clocks, Humans, Transcription, Genetic, Leukemia, Lymphocytic, Chronic, B-Cell, Gene Expression Regulation, Neoplastic, Sequence Analysis, RNA, Cell Lineage, Evolution, Molecular, DNA Methylation, Epigenesis, Genetic, Single-Cell Analysis, Mutation Rate
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