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: 9042284
• 负责人: Jerald Patrick Radich
• 金额: $ 58.6万
• 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/9042284
• 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; Health; 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; Role; Sampling; Staging; Stem cells; Stratification; Structure; Sum; Techniques; Therapeutic; Trastuzumab; Tretinoin; Tumor Tissue; Validation; Variant; Work; bcr-abl Fusion Proteins; cancer cell; cancer initiation; cancer subtypes; cancer therapy; cancer type; chemotherapeutic agent; chemotherapy; computer framework; computerized tools; cost; cost effective; design; leukemia; malignant breast neoplasm; neoplastic; normal aging; novel; response; single cell analysis; stem; therapy resistant; tool; tumor; tumor heterogeneity; 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的贡献之和。最近的数据表明，肿瘤内的细胞差异可能是理解患者之间对治疗的不同反应的关键。我们有效治疗癌症的能力的一个主要限制是患者之间对治疗的反应的固有差异，即，预测对于给定疗法将复发/难治的那些患者和将实现缓解的那些患者。因此，开发稳健而快速的细胞间异质性测定对于我们了解正常和异常生物学以及癌症表型在治疗期间如何发展和演变至关重要。 在本提案中，我们将完善强大的方法来分析和量化细胞异质性使用造血系统的模型。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，前者是一种源自正常造血干细胞的白血病，后者是一种可进展为AML的骨髓恶性肿瘤。为此，我们将1）开发稳健的单细胞测定和分析方法。我们将扩展我们目前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。这些检测的统计和技术验证将与计算工具的开发一起进行，以处理和比较单细胞遗传数据。2)测量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，以区分哪些改变组合代表肿瘤性“命中”，哪些是中性变化。通过比较MDS与AML中的异质性模式，我们将能够识别骨髓恶性肿瘤中常见的特征，以及疾病特异性特征和与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尺度方法根据定义受到规模的限制，并且因此受到成本和样品处理挑战的限制，而这些瓶颈可以通过微观尺度方法来补救。我们将设计和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，使更多AML和MDS临床样本的进一步单细胞分析变得现实，快速和具有成本效益。 关于克隆进化的癌症治疗的当前治疗策略的含义是实质性的并且广泛适用于许多癌症类型。了解肿瘤内的克隆结构及其在化疗中的变化将使我们能够1）开发诊断方法，以在诊断和治疗期间快速量化克隆，2）应用适应性治疗以应对克隆进化。开发所需的分子工具是理解克隆进化在癌症中的作用的关键第一步。