Project 3: Analysis of intrinsic and extrinsic factors that promote prostate neuroendocrine differentiation

项目摘要/摘要 强大的抗雄激素的临床应用促进了新形式的去势的出现-- 耐药前列腺癌(CRPC)，包括高度侵袭性的神经内分泌前列腺癌(NEPC) 而且致命的。到目前为止，人们对REARE规范的监管程序知之甚少 正常前列腺中的神经内分泌细胞，或在神经内分泌过程中它们可能如何重现或改变 前列腺癌的分化程度。因此，我们将对神经内分泌进行比较分析。 在正常和转化的前列腺中的规范以阐明其调控的异同 程序。在前列腺器官发生的初步研究中，我们的发现表明，前列腺神经内分泌 细胞是在器官发生的青春期前阶段产生的，此时雄激素水平较低， 转录因子Ascl1对前列腺神经内分泌细胞的形成是必不可少的。为了补充这些内容 分析，我们也进行了前列腺肿瘤的神经内分泌分化的研究，通过单一的- 小鼠CRPC肿瘤和器官模型的细胞RNA测序以确定候选的非细胞自主 可能调节神经内分泌分化的相互作用，以及识别新的 候选肿瘤神经内分泌分化调节大师。重要的是，我们已经获得了证据 来自微环境的外部信号在促进神经内分泌方面的非细胞自主作用 前列腺肿瘤细胞的分化。 根据我们的初步数据，我们假设正常和 肿瘤环境由来自环境的外部信号控制，以响应低AR活动，以便 调节神经内分泌规范的内在途径。为了研究这一假设，我们将进行一个 体内、体外、分子和计算分析的创新组合，分为三个部分 协同作用的具体目标：(1)研究Ascl1在前列腺发育和癌症中的作用，以寻求比较 分析其在正常和肿瘤环境中的作用；(2)神经内分泌内在调节机制的研究 使用计算机系统方法确定候选MASTER在前列腺癌中的分化 调节器及其功能验证；以及(3)促进神经内分泌的基质信号分析 对雄激素剥夺反应的差异性评估候选信号因子的作用 器官发生和前列腺AR反应中调节神经内分泌分化的微环境 癌症。这些研究将通过识别分子靶标产生强大的翻译影响 是专用于神经内分泌前列腺癌的。此外，这个项目将与其他项目高度整合 通过与Sawyers实验室在分析方面的协作交互，实现此计划项目的组成部分 微环境信号，与阿巴特-申实验室在小鼠模型的研究中，核心A在分析 人体组织样本，以及用于统计支持和数据管理的核心B，

Project Summary/Abstract The clinical use of potent anti-androgens has promoted the emergence of novel forms of castration- resistant prostate cancer (CRPC), including neuroendocrine prostate cancer (NEPC), which is highly aggressive and lethal. To date, relatively little is known about the regulatory programs for specification of rare neuroendocrine cells in the normal prostate, or how they might be recapitulated or altered during neuroendocrine differentiation in prostate cancer. Therefore, we will pursue a comparative analysis of neuroendocrine specification in the normal and transformed prostate to elucidate similarities and differences in their regulatory programs. In preliminary studies of prostate organogenesis, our findings suggest that prostate neuroendocrine cells are generated at pre-pubertal stages of organogenesis, when androgen levels are low, and that the transcription factor Ascl1 is essential for formation of prostate neuroendocrine cells. To complement these analyses, we have also performed studies of neuroendocrine differentiation in prostate tumors through single- cell RNA-sequencing of mouse CRPC tumors and organoid models to identify candidate non-cell autonomous interactions that may regulate neuroendocrine differentiation, as well as computational analyses to identify novel candidate master regulators of neuroendocrine differentiation in tumors. Importantly, we have obtained evidence for a non-cell autonomous role for extrinsic signals from the microenvironment in promoting neuroendocrine differentiation of prostate tumor cells. Based on our preliminary data, we hypothesize that neuroendocrine differentiation in both normal and tumor contexts is governed by extrinsic signals from the environment in response to low AR activity in order to regulate intrinsic pathways of neuroendocrine specification. To investigate this hypothesis, we will pursue an innovative combination of in vivo, ex vivo, molecular, and computational analyses that are organized in three synergistic specific aims: (1) Investigation of Ascl1 in prostate development and cancer to pursue comparative analyses of its role in normal and tumor contexts; (2) Investigation of intrinsic regulators of neuroendocrine differentiation in prostate cancer using computational systems approaches to identify candidate master regulators followed by their functional validation; and (3) Analysis of stromal signals that promote neuroendocrine differentiation in response to androgen deprivation to evaluate the role of candidate signaling factors from the microenvironment in modulating neuroendocrine differentiation in response to AR in organogenesis and prostate cancer. These studies will have strong translational impact through the identification of molecular targets that are specific for neuroendocrine prostate cancer. Furthermore, this project will be highly integrated with the other components of this program project through collaborative interactions with the Sawyers lab in analyzing microenvironmental signals, with the Abate-Shen lab in studies of mouse models, with Core A in analysis of human tissue samples, and Core B for statistical support and data management,