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Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia

机译：慢性淋巴细胞白血病的表观遗传演化与谱系历史

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摘要

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.
机译：遗传和表观遗传内肿瘤异质性合作以塑造癌症的进化过程（1）。慢性淋巴细胞白血病（CLL）是一种高度信息丰富的癌症演变模型，因为它经历了实质性遗传多样化和治疗后的演变（2,3）。 CLL表观蛋白酶也是一个重要的疾病定义特征（4,5），CLL中的细胞群生长在称为缩影（6）中的DNA甲基化的随机变化多样化。然而，先前使用本体测序方法分析DNA甲基化模式的研究无法确定映像是否会均匀影响CLL群体。这里，为了测量单细胞分辨率的换像率，我们将多路复用的单细胞减少效应亚硫酸盐测序从健康供体和CLL患者施加到B细胞。我们观察到CLL的共同克隆起源导致始终如一的增加的上等异构率，具有较低的细胞对细胞变性率的可变性。相比之下，健康B细胞的可变换像速率反映了B细胞分化的轨迹的不同进化年龄，与用作分子时钟的缩醛剖视图一致。可遗传的换句词信息允许我们以高分辨率的高分辨率重建谱系，并将其直接应用于患者样品。 CLL谱系树形状透露出早期的分支和较长的分支长度比在正常的B细胞中，反映了初始恶性转化后的快速漂移和更大的增殖史。单细胞转录om和基因分型的单细胞亚硫酸氢盐测序分析的整合证实了遗传亚克酮映射到不同的曲线，仅仅基于换像信息。最后，为了检查治疗过程中的潜在血统偏见，我们在伊布洛尼蛋白相关淋巴细胞增多期间进行连续样品，并确定优先在处理后从淋巴结排出的细胞的蛹，标有明显的转录谱。因此，遗传，表观遗传和转录信息的单细胞集成，如图所示的CLL谱系历史及其随治疗的演变。

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  • 来源
    《Nature》 |2019年第7757期|576-580|共5页
  • 作者

    Gaiti Federico; Chaligne Ronan; Gu Hongcang; Brand Ryan M.; Kothen-Hill Steven; Schulman Rafael C.; Grigorev Kirill; Risso Davide; Kim Kyu-Tae; Pastore Alessandro; Huang Kevin Y.; Alonso Alicia; Sheridan Caroline; Omans Nathaniel D.; Biederstedt Evan; Clement Kendell; Wang Lili; Felsenfeld Joshua A.; Bhavsar Erica B.; Aryee Martin J.; Allan John N.; Furman Richard; Gnirke Andreas; Wu Catherine J.; Meissner Alexander; Landau Dan A.;

  • 作者单位

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

    Broad Inst MIT & Harvard Cambridge MA 02142 USA;

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

    Weill Cornell Med New York NY 10065 USA;

    Weill Cornell Med New York NY 10065 USA|Univ Padua Dept Stat Sci Padua Italy;

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

    Mem Sloan Kettering Canc Ctr Human Oncol & Pathogenesis Program 1275 York Ave New York NY 10021 USA;

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

    Weill Cornell Med New York NY 10065 USA;

    Weill Cornell Med New York NY 10065 USA;

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

    Broad Inst MIT & Harvard Cambridge MA 02142 USA;

    City Hope Natl Med Ctr Beckman Res Inst Monrovia CA USA;

    Weill Cornell Med New York NY 10065 USA;

    Weill Cornell Med New York NY 10065 USA;

    Broad Inst MIT & Harvard Cambridge MA 02142 USA|Harvard TH Chan Sch Publ Hlth Dept Biostat Boston MA USA;

    Weill Cornell Med New York NY 10065 USA;

    Weill Cornell Med New York NY 10065 USA;

    Broad Inst MIT & Harvard Cambridge MA 02142 USA;

    Broad Inst MIT & Harvard Cambridge MA 02142 USA|Dana Farber Canc Inst Boston MA 02115 USA;

    Broad Inst MIT & Harvard Cambridge MA 02142 USA|Max Planck Inst Mol Genet Berlin Germany;

    New York Genome Ctr New York NY 10013 USA|Weill Cornell Med New York NY 10065 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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