The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications
Krueger, F., & Andrews, S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr167 · Cited by 5,324
GENCODE: The reference human genome annotation for The ENCODE Project
Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B. L., Barrell, D., Zadissa, A., Searle, S., et al. (2012) · Genome Research · doi:10.1101/gr.135350.111 · Cited by 4,585
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137
Comparison of phylogenetic trees
Robinson, D. F., & Foulds, L. R. (1981) · Mathematical Biosciences · doi:10.1016/0025-5564(81)90043-2 · Cited by 2,647
Tumour evolution inferred by single-cell sequencing
Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., et al. (2011) · Nature · doi:10.1038/nature09807 · Cited by 2,598
An atlas of active enhancers across human cell types and tissues
The FANTOM Consortium, Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., Chen, Y., Zhao, X., Schmidl, C., et al. (2014) · Nature · doi:10.1038/nature12787 · Cited by 2,550
Quantifying similarity between motifs
Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L., & Noble, W. S. (2007) · Genome Biology · doi:10.1186/gb-2007-8-2-r24 · Cited by 2,058
Mass-spectrometry-based draft of the human proteome
Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M., Lieberenz, M., Savitski, M. M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., et al. (2014) · Nature · doi:10.1038/nature13319 · Cited by 1,820
MEME-ChIP: motif analysis of large DNA datasets
Machanick, P., & Bailey, T. L. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr189 · Cited by 1,815
Hudson, R. R., & Kaplan, N. L. (1985) · Genetics · doi:10.1093/genetics/111.1.147 · Cited by 1,555
Evolution and impact of subclonal mutations in chronic lymphocytic leukemia
Landau, D. A., Carter, S. L., Stojanov, P., McKenna, A., Stevenson, K., Lawrence, M. S., Sougnez, C., Stewart, C., Sivachenko, A., Wang, L., et al. (2013) · Cell · doi:10.1016/j.cell.2013.01.019 · Cited by 1,379
Self-citationEpigenetic plasticity and the hallmarks of cancer
Flavahan, W. A., Gaskell, E., & Bernstein, B. E. (2017) · Science · doi:10.1126/science.aal2380 · Cited by 1,340
Mutations driving CLL and their evolution in progression and relapse
Landau, D. A., Tausch, E., Taylor-Weiner, A. N., Stewart, C., Reiter, J. G., Bahlo, J., Kluth, S., Božić, I., Lawrence, M. S., Böttcher, S., et al. (2015) · Nature · doi:10.1038/nature15395 · Cited by 1,081
Self-citationIncreased methylation variation in epigenetic domains across cancer types
Hansen, K. D., Timp, W., Bravo, H. C., Sabunciyan, S., Langmead, B., McDonald, O. G., Wen, B., Wu, H., Liu, Y., Diep, D. H., et al. (2011) · Nature Genetics · doi:10.1038/ng.865 · Cited by 1,062
A Big Bang model of human colorectal tumor growth
Sottoriva, A., Kang, H., Ma, Z., Graham, T. A., Salomon, M. P., Zhao, J., Marjoram, P., Siegmund, K. D., Press, M. F., Shibata, D. K., et al. (2015) · Nature Genetics · doi:10.1038/ng.3214 · Cited by 1,037
Repbase Update: a database and an electronic journal of repetitive elements
Jurka, J. (2000) · Trends in Genetics · doi:10.1016/s0168-9525(00)02093-x · Cited by 883
Herishanu, Y., Pérez‐Galán, P., Liu, D., Biancotto, A., Pittaluga, S., Vire, B., Gibellini, F., Njuguna, N., Lee, E., Stennett, L. S., et al. (2010) · Blood · doi:10.1182/blood-2010-05-284984 · Cited by 817
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750
A general and flexible method for signal extraction from single-cell RNA-seq data
Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S., & Vert, J.-P. (2018) · Nature Communications · doi:10.1038/s41467-017-02554-5 · Cited by 718
Self-citationIn vivo measurements document the dynamic cellular kinetics of chronic lymphocytic leukemia B cells
Messmer, B. T., Messmer, D., Allen, S. L., Kolitz, J. E., Kudalkar, P., Cesar, D., Murphy, E. J., Koduru, P. R. K., Ferrarini, M., Zupo, S., et al. (2005) · Journal of Clinical Investigation · doi:10.1172/jci23409 · Cited by 598
De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data
Grün, D., Muraro, M. J., Boisset, J.-C., Wiebrands, K., Lyubimova, A., Dharmadhikari, G., van den Born, M., van Es, J., Jansen, E., Clevers, H., et al. (2016) · Cell Stem Cell · doi:10.1016/j.stem.2016.05.010 · Cited by 586
Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics
Ludwig, L. S., Lareau, C. A., Ulirsch, J. C., Christian, E., Muus, C., Li, L. H., Pelka, K., Ge, W. H., Oren, Y., Brack, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.022 · Cited by 554
Self-citationOrphan CpG Islands Identify Numerous Conserved Promoters in the Mammalian Genome
Illingworth, R. S., Gruenewald-Schneider, U., Webb, S., Kerr, A. R. W., James, K. D., Turner, D. J., Smith, C., Harrison, D. J., Andrews, R., & Bird, A. P. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001134 · Cited by 550
Guo, H., Zhu, P., Wu, X., Li, X., Wen, L., & Tang, F. (2013) · Genome Research · doi:10.1101/gr.161679.113 · Cited by 518
A compendium of gene expression in normal human tissues
Hsiao, L., Dangond, F., Yoshida, T., Hong, R., Jensen, R. V., Misra, J., DILLON, W., LEE, K. F., Clark, K., Haverty, P. M., et al. (2001) · Physiological Genomics · doi:10.1152/physiolgenomics.00040.2001 · Cited by 500
A multiprotein supercomplex controlling oncogenic signalling in lymphoma
Phelan, J. D., Young, R. M., Webster, D. E., Roulland, S., Wright, G. W., Kasbekar, M., Shaffer, A. L., Ceribelli, M., Wang, J. Q., Schmitz, R., et al. (2018) · Nature · doi:10.1038/s41586-018-0290-0 · Cited by 416
Landau, D. A., Clement, K., Ziller, M. J., Boyle, P., Fan, J., Gu, H., Stevenson, K. E., Sougnez, C. L., Wang, L., Li, S., et al. (2014) · Cancer Cell · doi:10.1016/j.ccell.2014.10.012 · Cited by 401
Self-citationSingle-cell multiomics sequencing and analyses of human colorectal cancer
Bian, S., Hou, Y., Zhou, X., Li, X., Yong, J., Wang, Y., Wang, W., Yan, J., Hu, B., Guo, H., et al. (2018) · Science · doi:10.1126/science.aao3791 · Cited by 369
Burger, J. A., Landau, D. A., Taylor-Weiner, A., Bozic, I., Zhang, H., Sarosiek, K., Wang, L., Stewart, C., Fan, J., Hoellenriegel, J., et al. (2016) · Nature Communications · doi:10.1038/ncomms11589 · Cited by 345
Self-citationRassenti, L. Z., Jain, S., Keating, M. J., Wierda, W. G., Grever, M. R., Byrd, J. C., Kay, N. E., Brown, J. R., Gribben, J. G., Neuberg, D. S., et al. (2008) · Blood · doi:10.1182/blood-2007-05-092882 · Cited by 324
The Influence of Rate Heterogeneity among Sites on the Time Dependence of Molecular Rates
Soubrier, J., Steel, M., Lee, M. S. Y., Sarkissian, C. S. I. D., Guindon, S., Ho, S. Y. W., & Cooper, A. (2012) · Molecular Biology and Evolution · doi:10.1093/molbev/mss140 · Cited by 316
The BCL11 gene family: involvement of BCL11A in lymphoid malignancies
Satterwhite, E., Sonoki, T., Willis, T. G., Harder, L., Nowak, R., Arriola, E. L., Liu, H., Price, H. P., Gesk, S., Steinemann, D., et al. (2001) · Blood · doi:10.1182/blood.v98.12.3413 · Cited by 314
Oakes, C. C., Seifert, M., Assenov, Y., Gu, L., Przekopowitz, M., Ruppert, A. S., Wang, Q., Imbusch, C. D., Serva, A., Koser, S. D., et al. (2016) · Nature Genetics · doi:10.1038/ng.3488 · Cited by 309
Landan, G., Cohen, N. M., Mukamel, Z., Bar, A., Molchadsky, A., Brosh, R., Horn-Saban, S., Zalcenstein, D. A., Goldfinger, N., Zundelevich, A., et al. (2012) · Nature Genetics · doi:10.1038/ng.2442 · Cited by 305
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology · doi:10.1186/s13059-018-1406-4 · Cited by 228
Self-citationWang, L., Brooks, A. N., Fan, J., Wan, Y., Gambe, R. G., Li, S., Hergert, S., Yin, S., Freeman, S. S., Levin, J. Z., et al. (2016) · Cancer Cell · doi:10.1016/j.ccell.2016.10.005 · Cited by 213
Self-citationHerman, S. E. M., Niemann, C. U., Farooqui, M., Jones, J. A., Mustafa, R. Z., Lipsky, A. H., Saba, N. S., Martyr, S. E., Soto, S., Valdez, J. M., et al. (2014) · Leukemia · doi:10.1038/leu.2014.122 · Cited by 197
The reference epigenome and regulatory chromatin landscape of chronic lymphocytic leukemia
Beekman, R., Chapaprieta, V., Russiñol, N., Vilarrasa‐Blasi, R., Verdaguer-Dot, N., Martens, J. H. A., Duran‐Ferrer, M., Kulis, M., Serra, F., Javierre, B. M., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0028-4 · Cited by 196
Guo, H., Zhu, P., Guo, F., Li, X., Wu, X., Fan, X., Wen, L., & Tang, F. (2015) · Nature Protocols · doi:10.1038/nprot.2015.039 · Cited by 188
Using ChIP-Seq Technology to Generate High-Resolution Profiles of Histone Modifications
O’Geen, H., Echipare, L., & Farnham, P. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-316-5_20 · Cited by 174
Fidelity of the Methylation Pattern and Its Variation in the GenomePreprint
Ushijima, T., Watanabe, N., Okochi, E., Kaneda, A., Sügimura, T., & Miyamoto, K. (2003) · Genome Research · doi:10.1101/gr.969603 · Cited by 174
Zhou, X., Shen, X.-X., Hittinger, C. T., & Rokas, A. (2018) · Molecular Biology and Evolution · doi:10.1093/molbev/msx302 · Cited by 164
Dynamic and static maintenance of epigenetic memory in pluripotent and somatic cells
Shipony, Z., Mukamel, Z., Cohen, N. M., Landan, G., Chomsky, E., Zeliger, S. R., Fried, Y. C., Ainbinder, E., Friedman, N., & Tanay, A. (2014) · Nature · doi:10.1038/nature13458 · Cited by 159
Siegmund, K. D., Marjoram, P., Woo, Y., Tavaré, S., & Shibata, D. K. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810276106 · Cited by 155
Xu, J., Nuno, K. A., Litzenburger, U. M., Qi, Y., Corces, M. R., Majeti, R., & Chang, H. Y. (2019) · eLife · doi:10.7554/elife.45105 · Cited by 147
Rendeiro, A. F., Schmidl, C., Strefford, J. C., Walewska, R., Davis, Z., Farlik, M., Oscier, D., & Bock, C. (2016) · Nature Communications · doi:10.1038/ncomms11938 · Cited by 146
Herndon, T. M., Chen, S.-S., Saba, N. S., Valdez, J., Emson, C. L., Gatmaitan, M., Tian, X., Hughes, T. E., Sun, C., Arthur, D., et al. (2017) · Leukemia · doi:10.1038/leu.2017.11 · Cited by 134
de Bourcy, C. F. A., Angel, C. J. L., Vollmers, C., Dekker, C. L., Davis, M. M., & Quake, S. R. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617959114 · Cited by 132
Van den Berge, K., Soneson, C., Robinson, M. D., & Clement, L. (2017) · Genome Biology · doi:10.1186/s13059-017-1277-0 · Cited by 126
Raval, A., Lucas, D. M., Matkovic, J. J., Bennett, K. L., Liyanarachchi, S., Young, D. C., Rassenti, L. Z., Kipps, T. J., Grever, M. R., Byrd, J. C., et al. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.02.196 · Cited by 97
p63 consensus DNA-binding site: identification, analysis and application into a p63MH algorithm
Perez, C. A., Ott, J., Mays, D. J., & Pietenpol, J. A. (2007) · Oncogene · doi:10.1038/sj.onc.1210561 · Cited by 95
Caron, G., Hussein, M., Kulis, M., Delaloy, C., Chatonnet, F., Pignarre, A., Avner, S., Lemarié, M., Mahé, E. A., Verdaguer-Dot, N., et al. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.09.051 · Cited by 81
Shlush, L. I., Chapal-Ilani, N., Adar, R., Pery, N., Maruvka, Y. E., Spiro, A., Shouval, R., Rowe, J. M., Tzukerman, M., Bercovich, D., et al. (2012) · Blood · doi:10.1182/blood-2011-10-388629 · Cited by 72
Sequence-specific transcriptional repression by an MBD2-interacting zinc finger protein MIZF
Sekimata, M. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh249 · Cited by 56
Dadashian, E. L., McAuley, E., Liu, D.-L., Shaffer, A., Young, R. M., Iyer, J. R., Kruhlak, M., Staudt, L., Wiestner, A., & Herman, S. E. M. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-0781 · Cited by 50
A generic, cost-effective, and scalable cell lineage analysis platformPreprint
Biezuner, T., Spiro, A., Raz, O., Amir, S., Milo, L., Adar, R., Chapal-Ilani, N., Berman, V., Fried, Y., Ainbinder, E., et al. (2016) · Genome Research · doi:10.1101/gr.202903.115 · Cited by 41
Infinite-allele model and infinite-site model in population genetics
Tajima, F. (1996) · Journal of Genetics · doi:10.1007/bf02931749 · Cited by 40
Qu, Y., Siggens, L., Cordeddu, L., Gaidzik, V. I., Karlsson, K., Bullinger, L., Döhner, K., Ekwall, K., Lehmann, S., & Lennartsson, A. (2016) · Blood · doi:10.1182/blood-2016-07-726877 · Cited by 40
Mutation and epigenetic molecular clocks in cancer
Shibata, D. K. (2010) · Carcinogenesis · doi:10.1093/carcin/bgq239 · Cited by 32
Arsura, M., Hofmann, C. S., Golay, J., Introna, M., & Sonenshein, G. E. (2000) · Blood · doi:10.1182/blood.v96.3.1013 · Cited by 21
Protection of the intestinal mucosa during ischaemia by intraluminal perfusion
MIRKOVITCH, V., Menge, H., & Robinson, J. W. L. (1975) · Research in Experimental Medicine · doi:10.1007/bf01851184 · Cited by 18
Chen, T. L., Tran, M., Lakshmanan, A., Harrington, B. K., Gupta, N., Goettl, V. M., Lehman, A. M., Trudeau, S. J., Lucas, D. M., Johnson, A. J., et al. (2017) · Blood · doi:10.1182/blood-2017-01-761130 · Cited by 14
Cited by
Sorted by citations
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037
Intratumor Heterogeneity: The Rosetta Stone of Therapy Resistance
Marusyk, A., Janiszewska, M., & Polyak, K. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.03.007 · Cited by 1,009
Intratumoral heterogeneity in cancer progression and response to immunotherapy
Vitale, I., Shema, E., Loi, S., & Galluzzi, L. (2021) · Nature Medicine · doi:10.1038/s41591-021-01233-9 · Cited by 848
Single-cell multiomics: technologies and data analysis methods
Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
