Cellular Heterogeneity and Aging in MDS and AML

MDS 和 AML 中的细胞异质性和衰老

• 批准号: 10013078
• 负责人: Austin E Gillen
• 金额: --
• 依托单位: VA EASTERN COLORADO HEALTH CARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10013078
• 关键词: AML/MDS; Acute Myelocytic Leukemia; Affect; Aftercare; Age; Aging; Alternative Splicing; Animal Model; Award; Big Data; Biological Assay; Biological Markers; Blood; Cancer Patient; Cell Culture Techniques; Cells; Cellular Assay; Clinical; Clinical Management; Data; Data Analyses; Development; Disease; Disease Progression; Disease Resistance; Drug resistance; Dysmyelopoietic Syndromes; Environment; Environmental Exposure; Event; Exposure to; Gene Expression Profile; Genes; Goals; Grant; Hematopoietic Neoplasms; Heterogeneity; Human; In Vitro; Incidence; Lead; Link; Malignant Neoplasms; Mentors; Messenger RNA; Methods; Modeling; Molecular; Molecular Profiling; Molecular Target; Mus; Mutate; Mutation; Outcome; Patients; Pharmacotherapy; Phenotype; Play; Poison; Polyadenylation; Population; Population Heterogeneity; Prediction of Response to Therapy; Process; Protein Isoforms; RNA Processing; RNA Splicing; Regulation; Reporting; Resolution; Risk; Role; Sampling; Site; Software Tools; Specimen; Spliceosomes; Survival Rate; Techniques; Testing; The Cancer Genome Atlas; Tissues; Transplant Recipients; Transplantation; Variant; Veterans; age related; cancer cell; clinically relevant; data resource; drug sensitivity; effective therapy; experience; experimental study; fitness; human old age (65+); improved; in vivo; individual patient; insight; mouse model; new therapeutic target; novel; patient response; predict clinical outcome; predicting response; predictive marker; pressure; programs; response; single cell sequencing; single-cell RNA sequencing; software development; therapy resistant; treatment response; tumorigenesis

The goal of this application is to increase the efficacy of existing treatments for myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) by identifying and characterizing RNA processing phenotypes that predict age-related disease progression and drug resistance. Current therapies for both of these devastating diseases are effective in only a small number of patients and predicting responses remains challenging. Improving response rates represents a substantial unmet need for Veterans. Like many other cancers, MDS and AML are most prevalent in aging populations. The Adaptive Oncogenesis model holds that this association is the result of changing selective pressures acting on heterogeneous populations of cells as we age. MDS and AML are also characterized by extensive heterogeneity. This heterogeneity is evident in the molecular profiling of cells from individual patients, as well as in variable response to treatments across patients. Preliminary data show that mRNA processing and regulation are also highly variable in these diseases. As changes to these processes can play a critical role in cancer, this proposal seeks to understand how variability in mRNA processing and regulation drives disease progression and resistance to drug treatment in MDS and AML. The proposed studies use new sequencing methods to capture the full range of mRNA processing and regulation heterogeneity in MDS and AML at single-cell resolution. Using primary human and MDS and AML specimens, as well as murine models of AML, these studies will allow us to identify specific mRNA processing and regulation phenotypes that allow malignant cells to take advantage of changes to tissue environments associated with aging and drug treatment. Targeting the molecular mechanisms underlying these phenotypes will improve treatments and enhance the prediction of clinical outcomes for Veterans with MDS and AML. In addition to studies using primary specimens and animal models, this application will leverage “big data” resources to identify clinically relevant mRNA processing and regulation signatures from that can inform the clinical management of patients. This award will support the continued development of software tools to interpret changes in RNA processing from single-cell sequencing data. Taken together, the proposed studies will provide important new insights into the interplay between mRNA isoform heterogeneity, drug resistance, and aging in MDS/AML and will identify novel therapeutic targets that can be used to improve the treatment of Veterans with these diseases.

该应用的目标是提高骨髓增生异常综合征 (MDS) 现有治疗方法的疗效 和急性髓系白血病 (AML)，通过识别和表征 RNA 加工表型来预测 年龄相关的疾病进展和耐药性是这两种破坏性疾病的当前治疗方法。 仅对少数患者有效，并且预测反应的改善仍然具有挑战性。 与许多其他癌症一样，MDS 和 AML 的缓解率代表了退伍军人的大量未满足的需求。 适应性肿瘤发生模型认为这种关联是其结果。 随着年龄的增长，作用于异质细胞群的选择压力也发生变化。 其特征是广泛的异质性，这种异质性在细胞的分子谱分析中很明显。 初步数据显示，个体患者对治疗的反应各不相同。 mRNA 的加工和调节在这些疾病中也存在很大差异，因为这些过程的改变可能会导致这些疾病的发生。 在癌症中发挥着关键作用，该提案旨在了解 mRNA 加工和调节的变异性如何 促进 MDS 和 AML 的疾病进展和对药物治疗的耐药性。拟议的研究使用了新的方法。 测序方法可捕获 MDS 中全方位的 mRNA 加工和调控异质性 使用原代人类、MDS 和 AML 标本以及小鼠模型进行单细胞分辨率的 AML。 AML，这些研究将使我们能够识别特定的 mRNA 加工和调控表型，从而允许 恶性细胞利用与衰老和药物治疗相关的组织环境的变化。 针对这些表型背后的分子机制将改善治疗并增强疗效 除了使用原始样本的研究外，还预测患有 MDS 和 AML 的退伍军人的临床结果。 和动物模型，该应用程序将利用“大数据”资源来识别临床相关的 mRNA 处理和监管签名可以为患者的临床管理提供信息。 支持软件工具的持续开发，以解释单细胞 RNA 加工的变化 总而言之，拟议的研究将为相互作用提供重要的新见解。 MDS/AML 中 mRNA 亚型异质性、耐药性和衰老之间的关系，并将鉴定新的 可用于改善患有这些疾病的退伍军人的治疗的治疗目标。