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: 10550185
• 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 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10550185
• 关键词: 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 division; Cells; Cessation of life; Cicatrix; DNA; Data; Development; Diagnosis; Disease; Disease Outcome; Disease Progression; Dissociation; Engineering; Erythrocytes; Event; Gene Expression; Genealogy; Genetic Transcription; 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; Persons; Phenotype; Platelet Count measurement; Point Mutation; Population; Procedures; Process; Proliferating; Recording of previous events; Research; Resolution; Role; Series; Somatic Mutation; Symptoms; Testing; Therapeutic Studies; Time; Tissues; Trees; United States; cancer cell; cancer stem cell; disease phenotype; genome sequencing; hematopoietic stem cell expansion; individual patient; mouse model; overpopulation; patient subsets; programs; stem cells; targeted treatment; technology platform; therapeutic evaluation; 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 在某些癌症中，有趣的是，相同的突变导致不同的疾病表型在不同 患者。一个例子是一种血液癌，称为骨髓增生性肿瘤（MPN），其中一个 JAK2基因中的核苷酸变化，可能导致红细胞数量增加，都会增加 在不同患者的血小板数量或骨髓组织的疤痕中。疾病的结果只是 不可预测。一些患者数十年没有症状没有症状，而另一些患者迅速发展为急性 白血病。基因型和表型之间的这种断开可能是由于造血的认同 突变首先发生的干细胞（HSC）。并非所有HSC都是等效的，有些人可能优先给出 上升到某些类型的血细胞。此外，随后突变的茎种群的扩张 不同患者的细胞可能不同。因此，要了解疾病表现的异质性， 我们想知道每个患者首次以及在哪个细胞中出现何时何时发生细胞，如何 突变的HSC种群扩展，癌细胞的分化轨迹在多大程度上 偏离健康细胞的偏离。在这里，我们提出了一项全面的研究计划，以使这些计划 单个MPN患者的测量。了解癌细胞和 每个患者的健康细胞，我们将单独介绍每个细胞。癌症的批量测量平均水平 细胞和健康细胞，并沿分化轨迹掩盖了不同的细胞态。我们最近有 开发了一个技术平台，以简单地读取完整的转录组和癌症突变 单细胞。我们将将此平台应用于从MPN患者的骨髓活检中获得的细胞。获得 每个患者癌症干细胞膨胀的历史，我们将重建 通过对单个HSC的整个基因组中的体细胞突变进行测序。发生体细胞突变 在每个细胞分裂中随机，并传递给细胞的后代。至关重要的是，我们还将追踪 通过识别独特标记的体细胞突变，每个HSC的后代的分化轨迹 我们的单细胞转录组数据中的每个HSC。综上所述，这些测量将提供最多的 单细胞分辨率的MPN的详细分子图和最全面的分子历史 个别患者的癌症进展。最后，要识别和测试MPN的潜在疗法，我们将设计 可以在没有整个基因组测序的情况下获得单个细胞谱系史的动物模型。 我们将设计一个MPN的鼠标模型，其中各单元将其谱系历史记录在自己的DNA中 通过使用CAS9诱导合成目标阵列中的可遗传突变，这些阵列被转录并使用 测序。我们的建议将回答有关MPN的一些最杰出和最根本的问题 和血液发育。最终，我们的测量值应揭示患者特定的靶向疗法 优先将癌细胞细胞放疗或阻碍其分化。