Single-Cell Methods for Analysis of Clonal Heterogeneity and Evolution in Cancer

用于分析癌症克隆异质性和进化的单细胞方法

• 批准号: 8481108
• 负责人: Jerald Patrick Radich
• 金额: $ 64.65万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8481108
• 关键词: Abnormal Cell; Acute Myelocytic Leukemia; Address; Age; Basic Science; Biological; Biological Assay; Blood; Cancer Biology; Cell Differentiation process; Cell Proliferation; Cell physiology; Cells; Chronic Myeloid Leukemia; Clinical; Clinical Research; Clonal Evolution; Clonality; Clone Cells; Complex; Computer Analysis; Data; Development; Diagnosis; Diagnostic; Discipline; Disease; Disease remission; Dysmyelopoietic Syndromes; Dysplasia; ERBB2 gene; Event; Evolution; Gene Expression; Genetic; Genetic Marker Expression; Genetic Markers; Hematopoietic; Hematopoietic System; Hematopoietic stem cells; Heterogeneity; Imatinib; Individual; Malignant - descriptor; Malignant Neoplasms; Measures; Methods; Microfluidics; Minority; Modeling; Molecular; Molecular Biology; Monitor; Myeloproliferative disease; Neoplasms; Normal Cell; Normal tissue morphology; Patients; Pattern; Phenotype; RNA Splicing; Refractory; Relapse; Resistance; Risk; Roche brand of trastuzumab; Role; Sampling; Staging; Stem cells; Stratification; Structure; Sum; Techniques; Therapeutic; Tretinoin; Tumor Tissue; Validation; Variant; Work; bcr-abl Fusion Proteins; cancer cell; cancer initiation; cancer therapy; cancer type; chemotherapeutic agent; chemotherapy; computer framework; computerized tools; cost; cost effective; design; leukemia; malignant breast neoplasm; neoplastic; normal aging; novel; public health relevance; response; single cell analysis; stem; therapy resistant; tool; tumor; tumorigenesis

DESCRIPTION (provided by applicant): Biological differences between healthy and disease states are the sum of contributions between physiologically normal and abnormal cells. Recent data suggests that intra-tumor cellular differences may be key to understanding the varied responses to therapy between patients. A major limitation in our ability to effectively treat cance is the innate variability of the response to therapy between patients, i.e., predicting those who will relapse/be refractory and those who will achieve remission for a given therapy. The development of robust and rapid assays of intercellular heterogeneity is thus essential for our understanding of both normal and abnormal biology and how cancer phenotypes develop and evolve during therapy. In this proposal we will refine robust methods to assay and quantify cellular heterogeneity using the model of the hematopoietic system. We will compare the heterogeneity in normal stem and progenitor cells to that seen in acute myeloid leukemia (AML), a leukemia that derives from normal hematopoietic stem cells and in myelodysplastic syndrome (MDS), a myeloid malignancy that can progress to AML. To do so, we will 1) Develop robust single-cell assays and analysis methods. We will expand our current single cell molecular biology methods to accurately measure and quantify heterogeneity in genetics and gene expression of single cells. Statistical, and technological validation of these assays will be performed alongside development of computational tools to handle and compare single-cell genetic data. 2) Measure clonal variation in normal and malignant hematopoietic cells. We will assess cell-to-cell heterogeneity in normal and malignant hematopoietic stem and progenitor cells to tease apart what combinations of alterations represent neoplastic "hits" and what are neutral changes. By comparing the patterns of heterogeneity seen in MDS to that in AML will allow us to identify features common in myeloid malignancies, and those that are disease-specific and those related to relapse after therapy. 3) Design high-throughput microfluidic methods to capture and interrogate single cells. Current macroscale methods for single-cell analysis are by definition limited by scale, and thus cost and sample handling challenges, whereas these bottlenecks can be remedied via microscale methods. We will design and implement a microfluidic platform that will capture single cells, and efficiently quantify genetic markers and expression levels, making further single cell of analyses on additional AML and MDS clinical samples realistic, rapid and cost-effective. The implications of current therapeutic strategies for cancer treatment with respect to clonal evolution are substantial and broadly applicable to many cancer types. Understanding the clonal structure within a tumor and its change with chemotherapy would allow us to 1) develop diagnostics to quickly quantify clones at diagnosis and during therapy and 2) apply adaptive therapies responsive to clonal evolution. The development of the molecular tools required is a crucial first step to understanding the role of clonal evolution in cancer.

描述（由申请人提供）：健康和疾病状态之间的生物学差异是生理正常和异常细胞之间贡献的总和。最近的数据表明，肿瘤内细胞的差异可能是理解不同患者对治疗反应的关键。我们有效治疗癌症的能力的一个主要限制是患者对治疗反应的先天可变性，即预测哪些人会复发/难治性，哪些人会在给定的治疗下获得缓解。因此，对细胞间异质性进行强大而快速的检测对于我们理解正常和异常生物学以及癌症表型在治疗过程中如何发展和进化至关重要。在这个建议中，我们将完善稳健的方法来测定和量化使用造血系统模型的细胞异质性。我们将比较正常干细胞和祖细胞的异质性与急性髓性白血病（AML）和骨髓增生异常综合征（MDS）的异质性。急性髓性白血病是一种源自正常造血干细胞的白血病，骨髓增生异常综合征是一种可进展为AML的髓性恶性肿瘤。为此，我们将1)开发强大的单细胞测定和分析方法。我们将扩展现有的单细胞分子生物学方法，以准确测量和量化单细胞遗传学和基因表达的异质性。这些分析的统计和技术验证将与处理和比较单细胞遗传数据的计算工具的开发一起进行。2)测定正常和恶性造血细胞的克隆变异。我们将评估正常和恶性造血干细胞和祖细胞的细胞间异质性，以梳理出哪些变化组合代表肿瘤“命中”，哪些是中性变化。通过比较MDS与AML的异质性模式，我们可以识别髓系恶性肿瘤的共同特征，以及那些疾病特异性和治疗后复发相关的特征。3)设计高通量微流体方法来捕获和询问单细胞。目前用于单细胞分析的宏观方法根据定义受限于规模，因此成本和样品处理方面的挑战，而这些瓶颈可以通过微尺度方法来弥补。我们将设计并实现一个微流控平台，该平台将捕获单细胞，并有效地量化遗传标记和表达水平，从而进一步对其他AML和MDS临床样本进行单细胞分析，现实，快速且具有成本效益。目前癌症治疗策略的克隆进化意义重大，广泛适用于许多癌症类型。了解肿瘤内的克隆结构及其随化疗的变化将使我们能够1)开发诊断方法，以便在诊断和治疗期间快速量化克隆；2)应用对克隆进化有反应的适应性疗法。开发所需的分子工具是了解克隆进化在癌症中的作用的关键的第一步。