Unbiased identification of spliceosome vulnerabilities across cancer

项目概要： 尽管选择性剪接是发育过程中细胞多样性和生长的主要驱动力之一， 在癌症中，剪接机制可以被劫持，以促进转移、免疫逃逸、侵袭和抗肿瘤。 凋亡作用虽然剪接因子突变发生在1-15%的患者中，这取决于癌症， 新出现的数据表明，通常失调的癌基因，如MYC， 在不同的恶性肿瘤中导致癌症促进选择性剪接异构体的加工途径。使用 最近开发的一系列无监督剪接检测和候选剪接调控预测 技术，我们发现剪接在成人和儿童癌症中广泛中断， 明显的剪接因子突变。这些数据表明了一个潜在的范式转变模型，其中 广泛的协调剪接功能障碍发生在癌症中，可能是通过剪接因子的不平衡， 表达、信号传导或基因改变。如果是真的，剪接体定向和上游治疗可能是可行的。 广泛地在癌症中重新利用，专注于特定的剪接特征和涉及的调控 而不仅仅是特定的突变。为了验证这些假设并开发可重用的分析 为增加癌症社区的资源，我们提出以下目标。 目的1：将关键剪接途径的脆弱性与观察到的致癌事件联系起来， 多种癌症我们将在全球范围内用我们现有的整合多基因组技术来描述选择性剪接的特征。 组学计算工作流程跨越数十种癌症和数千个样本。确定的剪接事件 使用新型无监督或监督分析也将在不同癌症内部和之间进行比较 作为不同来源的正常细胞，以定义可再现的肿瘤内在与分化相关的程序。 目的2：定义和验证儿科AML和不同类型AML的核心剪接调控网络 人类癌症我们将建立和验证一个新的学习模型，以定义剪接调控网络， 儿童AML，并最终跨越不同的成人和儿童癌症。我们将调整当前的最佳做法 对于多证据转录调控网络推理，以拼接和严格测试我们的模型， 验证数据。一个大型的实验剪接因子结合数据库将用于改善我们的研究。 预测。这些分析将确定新的剪接调节和RNA识别元件。 目标3：建立一个发现平台，用于精确剪接生物标志物检测和选择性剪接 目标抑制我们将开发一个交互式计算界面来识别特定的RNA异构体 与Aim 1中获得的多种癌症中的不良预后剪接亚型相关。通过整合剪接， 基因表达、蛋白质组学和甲基化数据，我们将能够发现 剪接事件与基因调控的不同模式有关，可能在蛋白质水平上表现出来。 将建立相关亚型相互作用和加权共表达网络，以优先考虑特定剪接 已知的癌症通路中的事件。

PROJECT SUMMARY: Although alternative splicing is one of the major drivers of cellular diversity and growth during development, the splicing machinery can be hijacked in cancer to promote metastasis, immune escape, invasion and anti- apoptotic actions. While splicing factor mutations occur in 1-15% of patients, depending on the cancer, emerging data suggest that commonly dysregulated oncogenes such as MYC indirectly regulate mRNA processing pathways leading to cancer promoting alternative splice isoforms in distinct malignancies. Using a series of recently developed unsupervised splicing detection and candidate splicing regulatory prediction techniques, we discovered that splicing is broadly disrupted in adult and pediatric cancers independent of obvious splicing factor mutations. These data suggest a potentially paradigm shifting model, in which widespread coordinated splicing dysfunction occurs across cancers, likely via imbalances in splicing factor expression, signaling or genetic alternations. If true, spliceosome directed and upstream therapies may be broadly repurposed across cancers, focused on specific splicing signatures and implicated regulatory pathways rather than on specific mutations alone. To test these hypotheses and develop reusable analytical resources for the cancer community, we propose the following aims. Aim 1: Implicate key splicing pathway vulnerabilities with observed oncogenic events across diverse cancers. We will characterize alternative splicing on a global-level with our existing integrative multi- omics computational workflow across dozens of cancers and thousands of samples. Splicing events identified using novel unsupervised or supervised analyses will be compared within and between distinct cancers as well as normal cells of different origins to define reproducible tumor intrinsic vs. differentiation associated programs. Aim 2: Define and validate the core splicing regulatory networks in pediatric AML and diverse human cancers. We will build and validate a novel learning model to define the splicing regulatory network in pediatric AML and ultimately across diverse adult and pediatric cancers. We will adapt current best practices for multi-evidence transcriptional regulatory network inference to splicing and rigorously test our models with validation data. A large library of experimental splicing factor binding datasets will be used to improve our predictions. These analyses will identify novel splicing regulators and RNA recognition elements. Aim 3: Build a discovery platform for precision splicing biomarker detection and selective splicing target inhibition. We will develop an interactive computational interface to identify specific RNA isoforms associated with poor prognosis splicing subtypes in diverse cancers obtained in Aim 1. By integrating splicing, gene expression, proteomics and methylation data on the same patients, we will enable the discovery of splicing events linked to diverse modes of gene regulation, that potentially manifest at the protein level. Associated isoform interactions and weighted coexpression networks will be built to prioritize specific splicing events in known cancer pathways.