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

项目总结/摘要 目前的癌症疗法提供了攻击特定细胞的靶向治疗，然而，肿瘤细胞是不稳定的。 异质性和进化性。为了开发个性化治疗，我们需要了解 肿瘤中的细胞类型以及导致癌症干细胞（CSC）的破坏的调节机制。CSCS 对标准疗法具有抗性，并具有形成新肿瘤的能力，导致复发和转移。 然而，利用免疫系统的免疫疗法在靶向CSC方面特别成功， 它们的作用机制还不清楚。我假设一个对复杂的无偏见的研究 含有难以捉摸的耐药CSC和相互作用的免疫群体的肿瘤微环境可以 用高维全基因组数据实现，例如最先进的单细胞分辨率转录 与表观遗传测量相结合，使用贝叶斯统计工具， 生物异质性和整合不同数据类型的技术噪音。我利用我们以前的 与亚历山大鲁登斯基实验室合作，研究乳腺癌中免疫细胞群的特征。 癌症肿瘤，使用我们在Dana Pe'er实验室开发的一种计算方法， 单细胞转录组数据，同时归一化细胞和校正批次效应。在我读博士的时候 在我的工作中，我展示了将表观遗传数据纳入推断监管程序的力量。在K99中 在指导阶段，我的目标是开发一个计算框架，将表观遗传数据与单细胞 转录组学数据推断急性髓系白血病中白血病干细胞和失调机制 与Ross Levine合作（目标1）。我选择AML是因为它涉及表观遗传突变的富集 并且正常的造血系统被很好地表征并将用作参考。作为 作为R 00阶段的独立研究者，我将扩展这一框架，以推断CSCs和失调， 肿瘤以及免疫细胞的组成和它们在特征不足的实体瘤中的重编程， 与本杰明·尼尔和我未来研究所的其他人合作（目标2）。然后我打算用这个工具箱 研究免疫治疗对肿瘤免疫微环境的影响，与 Catherine Wu和我的未来研究所（目标3）。我们希望我们的研究结果能帮助我们了解监管方面的问题， 这些机制在癌症中被破坏并驱动异质群体。我们还可以推断 免疫疗法在肿瘤免疫微环境中的作用机制。该提案描述了一个 培训计划将我的职业生涯提升为机器学习界面的独立调查员， 癌症生物学在K99阶段，我将得到一个优秀的跨学科团队的支持， 在拟议研究的各个方面具有专业知识的顾问和合作者。与机构 从纪念斯隆凯特琳癌症中心的支持和正式的课程和培训，我将桥梁我的 在癌症生物学的知识差距，并获得沟通和领导技能，我的过渡至关重要。

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.