Cellular Heterogeneity and Aging in MDS and AML

MDS 和 AML 中的细胞异质性和衰老

• 批准号: 10664830
• 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/10664830
• 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; Link; Malignant Neoplasms; Mentors; Messenger RNA; Methods; Modeling; Molecular; Molecular Profiling; Molecular Target; Mus; Mutate; Mutation; Outcome; Patients; Pharmacotherapy; Phenotype; Play; Poison; Poly A; 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.

该应用的目的是提高骨髓增生综合征（MDS）现有治疗的效率 通过识别和表征预测的RNA加工表型，和急性髓样白血病（AML） 与年龄相关的疾病进展和耐药性。这两种毁灭性疾病的当前疗法 仅在少数患者中有效，并且预测反应仍然是挑战。改进 回应率代表了对退伍军人的巨大需求。像许多其他癌症一样，MD和AML也是 最普遍的人口衰老。自适应肿瘤发生模型认为该关联是结果 随着年龄的增长，对细胞异质种群作用的选择性压力变化。 MD和AML也是 以广泛的异质性为特征。这种异质性是细胞分子分析的证据 个别患者以及对患者治疗的可变反应。初步数据显示 在这些疾病中，mRNA处理和调节也很大。由于这些过程的更改可以 该提案在癌症中发挥关键作用，旨在了解mRNA处理和调节的变异性如何 在MDS和AML中驱动疾病的进展和对药物治疗的抵抗力。拟议的研究使用了新的 测序方法以捕获MDS和 AML单细胞分辨率。使用原代人和MDS和AML标本，以及鼠模型 AML，这些研究将使我们能够确定特定的mRNA处理和调节表型 恶性细胞利用与衰老和药物治疗相关的组织环境的变化。 靶向这些表型基础的分子机制将改善治疗方法并增强 MDS和AML退伍军人的临床结果预测。除了使用主要标本的研究 和动物模型，该应用将利用“大数据”资源来识别临床相关的mRNA 从中处理和调节签名可以告知患者的临床管理。这个奖项将 支持持续开发软件工具来解释单细胞中RNA处理的变化 测序数据。综上所述，拟议的研究将为相互作用提供重要的新见解 在MDS/AML中的mRNA同工型异质性，耐药性和衰老之间 可用于改善这些疾病的退伍军人治疗的治疗靶标。