Integrative framework for identifying dysregulated mechanisms in the tumor-immune microenvironment

识别肿瘤免疫微环境失调机制的综合框架

• 批准号: 10159875
• 负责人: Elham Azizi
• 金额: $ 15.39万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10159875
• 关键词: ATAC-seq; Accounting; Acute Myelocytic Leukemia; Address; Affect; Automobile Driving; Bayesian Modeling; Bone Marrow; Cancer Biology; Cancer Patient; Cells; Chronic Myeloid Leukemia; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Communication; Complex; Computing Methodologies; Data; Data Set; Development; Diagnosis; Doctor of Philosophy; Donor Lymphocyte Infusion; Epigenetic Process; Epithelial; Equilibrium; Faculty; Foundations; Future; Genetic Transcription; Hematopoiesis; Hematopoietic Neoplasms; Hematopoietic System; Heterogeneity; Immune; Immune system; Immunotherapy; Impairment; Institutes; Knowledge; Lead; Leadership; Learning; Machine Learning; Malignant Neoplasms; Malignant neoplasm of ovary; Mammalian Oviducts; Measurement; Memorial Sloan-Kettering Cancer Center; Mentors; Mentorship; Methods; Modeling; Mutation; Neoplasm Metastasis; Noise; Outcome; Outcomes Research; Patients; Phase; Population; Population Heterogeneity; Relapse; Research; Research Personnel; Resistance; Resolution; Sampling; Serous; Solid Neoplasm; System; Techniques; Time; Training; Treatment Failure; Tumor-infiltrating immune cells; Work; base; biological heterogeneity; cancer heterogeneity; cancer stem cell; cancer therapy; career; cell type; cohort; computer framework; computerized tools; genome-wide; heuristics; high dimensionality; improved; individualized medicine; insight; interest; leukemic stem cell; longitudinal analysis; malignant breast neoplasm; mutant; neoplastic cell; novel; patient subsets; personalized medicine; programs; refractory cancer; response; self-renewal; single cell analysis; single cell technology; single-cell RNA sequencing; skills; stem cell population; success; therapy design; therapy resistant; tool; transcriptomics; tumor; tumor heterogeneity; tumor immunology; tumor microenvironment; tumor-immune system interactions; tumorigenesis

Project Summary/Abstract Current cancer therapies provide targeted treatments attacking specific cells, however, tumor cells are heterogeneous and evolving. To develop personalized treatments, we need to understand the composition of cell types in the tumor and the disrupted regulatory mechanisms that lead to cancer stem cells (CSCs). CSCs are resistant to standard therapies and have the ability to form new tumors leading to relapse and metastasis. Immunotherapies harnessing the immune system can be particularly successful in targeting CSCs, however, their mechanisms of action are not well understood. I hypothesize that an unbiased study of the complex tumor microenvironment containing elusive resistant CSCs and interacting immune populations can be achieved with high-dimensional genome-wide data, such as state-of-the-art single-cell resolution transcriptional integrated with epigenetic measurements, using Bayesian statistical tools that are ideal for distinguishing technical noise from biological heterogeneity and integrating different data types. I capitalize on our previous work in collaboration with the Alexander Rudensky Lab on characterizing immune cell populations in breast cancer tumors, using a computational method we developed in the Dana Pe’er Lab for clustering cells in single-cell transcriptomic data while simultaneously normalizing cells and correcting batch effects. In my PhD work, I showed the power of incorporating epigenetic data in inferring regulatory programs. Hence, in my K99 mentored phase, I aim to develop a computational framework for integrating epigenetic data with single-cell transcriptomic data to infer leukemic stem cells and dysregulated mechanisms in Acute Myeloid Leukemia in collaboration with Ross Levine (Aim 1). I have chosen AML as it involves enrichment of epigenetic mutations and the normal hematopoiesis system is well-characterized and would serve as a reference. As an independent investigator in the R00 phase, I will extend this framework to infer CSCs and dysregulations in the tumor as well as composition of immune cells and their reprogramming in under-characterized solid tumors, in collaboration with Benjamin Neel and others in my future institute (Aim 2). I then aim to use this toolbox to study the impact of immunotherapy treatments on the tumor-immune microenvironment in collaboration with Catherine Wu and my future institute (Aim 3). We expect that our results lead to insights into regulatory mechanisms that are disrupted in cancer and drive heterogeneous populations. We would also infer mechanisms of action of immunotherapies in the tumor-immune microenvironment. This proposal describes a training plan to advance my career to an independent investigator at the interface of machine learning and cancer biology. During the K99 phase, I will be supported by an outstanding and interdisciplinary team of advisors and collaborators with expertise in all aspects of the proposed research. Together with institutional support from Memorial Sloan Kettering Cancer Center and formal coursework and training, I will bridge my knowledge gap in cancer biology and gain the communication and leadership skills vital for my transition.

项目摘要/摘要 当前的癌症疗法提供攻击特定细胞的有针对性治疗，但是，肿瘤细胞是 异质和发展。要开发个性化治疗，我们需要了解 肿瘤中的细胞类型以及导致癌细胞（CSC）的破坏调节机制。 CSC 对标准疗法有抵抗力，并具有形成新肿瘤的能力，导致缓解和转移。 但是，利用免疫系统的免疫疗法在针对CSC方面可能特别成功 他们的作用机制尚不清楚。我假设对复合物的无偏研究 含有难以捉摸的抗性CSC和相互作用的免疫种群的肿瘤微环境可以是 通过高维全基因组数据（例如最先进的单细胞分辨率转录）实现 使用贝叶斯统计工具与表观遗传测量进行集成，非常适合区分 生物异质性和整合不同数据类型的技术噪声。我利用我们以前的 与Alexander Rudensky Lab合作工作，以表征乳房中的免疫细胞种群 癌症肿瘤，使用我们在Dana Pe'er实验室开发的计算方法来聚类细胞 单细胞转录组数据同时正常化细胞并纠正批处理效应。在我的博士学位 工作，我展示了将表观遗传数据纳入推断监管程序的能力。因此，在我的K99中 指导阶段，我旨在开发一个计算框架，以将表观遗传数据与单细胞集成 转录组数据推断白血病干细胞和急性髓样白血病的机制失调 与罗斯·莱文（AIM 1）合作。我选择了AML，因为它涉及富集表观遗传突变 正常的造血系统已被充分表征，并将作为参考。作为 在R00阶段的独立研究者，我将扩展此框架以推断CSC和失调 肿瘤以及免疫细胞的组成及其在特征较低的实体瘤中重编程，在 与本杰明·尼尔（Benjamin Neel）和其他人在我未来的研究所的合作（AIM 2）。然后，我的目标是将此工具箱用于 研究免疫疗法治疗对肿瘤免疫微环境的影响 凯瑟琳·吴（Catherine Wu）和我未来的研究所（AIM 3）。我们期望我们的结果会导致对监管的见解 在癌症中破坏并推动异质种群的机制。我们也会推断 免疫疗法在肿瘤免疫微环境中的作用机理。该提议描述了 培训计划将我的职业发展到机器学习界面的独立调查员和 癌症生物学。在K99阶段，我将得到一个杰出和跨学科的支持 顾问和合作者在拟议研究的各个方面具有专业知识。与机构一起 纪念斯隆·凯特林（Sloan Kettering）癌症中心以及正式课程和培训的支持，我将桥接我的 癌症生物学的知识差距，并获得对我过渡至关重要的沟通和领导能力。