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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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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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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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