Tracing the lineage histories and differentiation trajectories of individual cancer cells in myeloproliferative neoplasms

追踪骨髓增生性肿瘤中单个癌细胞的谱系历史和分化轨迹

• 批准号: 10097469
• 负责人: Sahand Hormoz
• 金额: $ 44.25万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2026-01-14
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10097469
• 关键词: Acute; Acute leukemia; Allogeneic Bone Marrow Transplantation; Animal Model; Atlases; Blood; Blood Cells; Bone Marrow; Bone marrow biopsy; CRISPR/Cas technology; Cell Communication; Cell Death; Cell Differentiation process; Cell division; Cells; Cessation of life; Cicatrix; DNA; Data; Development; Diagnosis; Disease; Disease Outcome; Disease Progression; Engineering; Erythrocytes; Event; Gene Expression; Genealogy; Genome; Genotype; Growth; Hematologic Neoplasms; Hematopoietic; Hematopoietic Neoplasms; Hematopoietic stem cells; Heritability; Heterogeneity; Human; In Situ; In Vitro; Individual; JAK2 gene; Malignant Neoplasms; Measurement; Measures; Microscopy; Molecular; Mothers; Mus; Mutate; Mutation; Myeloproliferative disease; Nature; Nucleotides; Patients; Pattern; Phenotype; Platelet Count measurement; Point Mutation; Population; Procedures; Process; Proliferating; Recording of previous events; Research; Resolution; Role; Series; Somatic Mutation; Symptoms; Technology; Testing; Therapeutic Studies; Time; Tissues; Trees; United States; cancer cell; cancer stem cell; disease phenotype; genome sequencing; individual patient; mouse model; patient population; patient subsets; progenitor; programs; stem cells; targeted treatment; therapeutic evaluation; time use; tool; transcriptome; transcriptomics; treatment strategy; tumor progression; whole genome

Project Summary In some cancers, intriguingly, the same mutation results in drastically different disease phenotypes in different patients. An example is a type of blood cancer, known as myeloproliferative neoplasm (MPN), where a single nucleotide change in the JAK2 gene, may result in either an increase in the number of red blood cells, an increase in the number of platelets, or scarring of bone marrow tissue, in different patients. The disease outcome is just as unpredictable. Some patients show no symptoms for decades whereas others rapidly progress to acute leukemias. This disconnect between genotype and phenotype may be due to the identity of the hematopoietic stem cell (HSC) in which the mutation first occurs. Not all HSCs are equivalent and some may preferentially give rise to certain types of blood cells. Additionally, the subsequent expansion of the population of mutated stem cells may be different in different patients. Therefore, to understand the heterogeneity in disease presentation, we would like to know when and in which cell the cancer mutation first occurred in each patient, how the population of mutated HSCs expanded, and to what extent the differentiation trajectory of the cancer cells deviates from that of the healthy cells. Here, we propose a comprehensive research program to make these measurements in individual MPN patients. To understand the difference between the cancer cells and the healthy cells in each patient, we will profile each cell individually. Bulk measurements average over the cancer cells and healthy cells, and obscure different cell states along the differentiation trajectory. We have recently developed a technology platform to simultaneously read out the full transcriptome and the cancer mutation in single cells. We will apply this platform to cells obtained from bone marrow biopsies of MPN patients. To obtain the history of the expansion of the cancer stem cells in each patient, we will reconstruct the lineage tree of the HSCs by sequencing the somatic mutations in the whole genomes of individual HSCs. Somatic mutations occur randomly at each cell division and are passed on to a cell’s descendants. Critically, we will also trace the differentiation trajectories of the progenies of each HSC by identifying the somatic mutations that uniquely mark each HSC in our single-cell transcriptomic data. Taken together, these measurements will provide the most detailed molecular picture of MPN at a single-cell resolution and the most comprehensive molecular history of cancer progression in individual patients. Finally, to identify and test potential therapies for MPN, we will engineer animal models whereby lineage histories of individual cells can be obtained without whole genome sequencing. We will engineer a mouse model of MPN in which individual cells record their lineage histories in their own DNA by using Cas9 to induce heritable mutations in synthetic target arrays that are transcribed and read out using sequencing. Our proposal will answer some of the most outstanding and fundamental questions about MPNs and blood development. Ultimately, our measurements should reveal patient-specific targeted therapies that preferentially eradicate the cancer stem cells or hinder their differentiation.

项目摘要 有趣的是，在某些癌症中，相同的突变会导致不同的疾病表型截然不同。 病人。一种被称为骨髓增生性肿瘤(MPN)的血癌就是一个例子，在这种肿瘤中，单个 JAK2基因的核苷酸变化，可能导致红细胞数量的增加，增加 不同患者的血小板数量，或骨髓组织的疤痕形成。疾病的结局就是 是不可预测的。一些患者几十年来没有任何症状，而另一些患者则迅速发展为急性 白血病。这种基因和表型之间的脱节可能是由于造血者的同一性。 首先发生突变的干细胞(HSC)。并不是所有的HSC都是等价的，有些可能会优先给予 上升到某些类型的血细胞。此外，突变茎的种群随后的扩张 不同患者的细胞可能不同。因此，要了解疾病表现的异质性， 我们想知道癌症突变最早发生在每个患者的什么时候以及哪个细胞中， 突变的HSC群体扩大，以及癌细胞的分化轨迹达到什么程度 与健康细胞的情况有所不同。在这里，我们提出了一个全面的研究计划，以使这些 对MPN患者个体进行测量。为了了解癌细胞和癌细胞之间的区别 对于每个患者的健康细胞，我们将分别对每个细胞进行分析。在癌症上的平均体积测量 细胞和健康细胞，并沿着分化轨迹模糊不同的细胞状态。我们最近做了 开发了一种技术平台，可以同时读取完整的转录组和癌症突变 单细胞。我们将把这个平台应用于从MPN患者的骨髓活检中获得的细胞。为了获得 每名患者的肿瘤干细胞扩增历史，我们将重建 通过对单个HSCs的整个基因组中的体细胞突变进行测序。发生体细胞突变 在每个细胞分裂时随机产生，并传递给细胞的后代。重要的是，我们还将跟踪 通过识别唯一标记的体细胞突变来研究每个HSC后代的分化轨迹 我们的单细胞转录数据中的每个HSC。总而言之，这些测量将提供最大的 MPN在单细胞分辨率下的详细分子图像和最全面的分子史 个别患者的癌症进展情况。最后，为了确定和测试MPN的潜在治疗方法，我们将设计 无需全基因组测序即可获得单个细胞的谱系历史的动物模型。 我们将设计一种MPN的小鼠模型，在该模型中，单个细胞在自己的DNA中记录它们的谱系历史 通过使用Cas9在合成靶点阵列中诱导可遗传突变，这些靶点阵列被转录并使用 测序。我们的提案将回答有关MPN的一些最突出和最基本的问题 和血液发育。最终，我们的测量应该揭示出针对患者的靶向治疗 优先根除肿瘤干细胞或阻碍其分化。