Rewiring of regulatory networks in breast cancer by transcription factor isoforms

项目摘要 癌症系统生物学的最终目标之一是生成预测和动态模型， 通过鉴定和量化癌细胞系统中所有受干扰的功能相互作用来评估肿瘤发生。 该CSBC U 01应用的中心假设是，在多种类型的 功能扰动，那些出现从癌症特异性基因表达的替代亚型， 对肿瘤发生至关重要。基因组改变，如扩增、缺失、易位和突变， 通常被认为是癌症进展的主要事件。然而，癌症特异性异构体产生于 选择性剪接、转录起始的选择性位点和/或选择性转录终止 位点，也已显示对肿瘤发生具有功能性影响。 特别是，转录因子（TF）亚型引起的基因调控网络（GRNs）的变化已被广泛研究。 显示在多种类型癌症的肿瘤发生和转移中起主要作用。虽然有几个例子 已经报道了驱动癌症特异性TF亚型的功能特征，但仍不清楚 是正常组织和癌组织之间TF同种型的差异影响总体GRNs的程度， 这种调控网络的重新布线如何导致癌症中基因表达程序的改变。事实上， 在正常和癌症样本之间已经鉴定出差异TF同种型，但绝大多数 在功能层面上仍然没有特征。 在这个项目中，我们提出了实现这一长期目标的第一步，包括表征和 在癌症相互作用组的背景下模拟大量乳腺癌特异性TF同种型的作用 网络.我们的目标是结合联合收割机网络建模和高通量系统的实验策略， 分子水平的蛋白质-蛋白质和蛋白质-DNA相互作用来预测癌症驱动因子和抑制因子。的 由此产生的假设将在乳腺癌中使用各种大规模功能测定进行实验测试 作为一个模型系统。作为实验测试的一部分，我们将建立最先进的基因组编辑 用于测试同种型特异性扰动对哺乳动物细胞中GRNs的影响的方法。 总而言之，这一项目将是朝着实现长期目标迈出的重要一步， 使乳腺癌中涉及的大量TF同种型功能化。此外，还应总结从 数据分析和整合将导致识别新的癌症驱动因子和抑制因子， 生成癌症中GRN重新布线的机制模型，并为设计新的 治疗学

Project summary One of the ultimate goals of cancer systems biology is to generate predictive and dynamic models of tumorigenesis by identifying and quantifying all perturbed functional interactions in a cancerous cellular system. The central hypothesis of this CSBC U01 application is that, among the combined effects of multiple types of functional perturbations, those emerging from cancer-specific gene expression of alternative isoforms are crucial for tumorigenesis. Genome alterations such as amplification, deletion, translocations and mutations, are often considered primary events of cancer progression. However, cancer-specific isoforms resulting from alternative splicing, alternative sites of transcriptional initiation, and/or alternative transcriptional termination sites, have also been shown to have functional impact on tumorigenesis. In particular, changes in gene regulatory networks (GRNs) by transcription factor (TF) isoforms have been shown to play a major role in tumorigenesis and metastasis in multiple types of cancer. While a few examples of functional characterization of driver cancer-specific TF isoforms have been reported, what remains unclear is the extent to which differences in TF isoforms between normal and cancer tissue affect global GRNs and how such regulatory network rewiring leads to altered gene expression programs in cancer. Indeed, hundreds of differential TF isoforms have been identified between normal and cancer samples, but the vast majority remain uncharacterized at the functional level. In this project, we propose an initial step toward this long-term goal, which consists of characterizing and modeling the effect of large numbers of breast cancer-specific TF isoforms in the context of cancer interactome networks. We aim to combine network modeling and high-throughput systematic experimental strategies at the level of molecular protein-protein and protein-DNA interactions to predict cancer drivers and suppressors. The resulting hypotheses will be tested experimentally using various large-scale functional assays in breast cancer as a model system. As part of the experimental testing, we will establish state-of-the-art genome editing methodologies for testing the effects of isoform-specific perturbations on GRNs in mammalian cells. Altogether, this project will constitute an important step towards the long-term goal of contextualizing and functionalizing large numbers of TF isoforms implicated in breast cancer. Further, the lessons learned from the data analysis and integration will lead to the identification of novel cancer drivers and suppressors, the generation of mechanistic models of GRN rewiring in cancer, and provide a framework for the design of novel therapeutics.