Rewiring of regulatory networks in breast cancer by transcription factor isoforms

转录因子同工型对乳腺癌调控网络的重新布线

基本信息

批准号：
10249199
负责人：
MARTHA L BULYK
金额：
$ 100.29万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-17 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10249199
关键词：
Affect Alternative Splicing Binding Proteins Biological Assay Biological Models Breast Cancerous Cells Cloning DNA Binding DNA-Protein Interaction Data Analyses Event Gene Expression Generations Genetic Transcription Genome Genomics Goals Maintenance Malignant Neoplasms Mammalian Cell Maps Mediating Methodology Modeling Molecular Mutation Neoplasm Metastasis Network-based Phenotype Play Positioning Attribute Protein Isoforms Proteins Regulator Genes Reporter Reporting Resources Role Sampling Site Specificity Suppressor Genes System Systems Biology Testing Tissues Transcription Initiation Site Tumor Suppressor Genes cancer cell cancer initiation cancer subtypes cancer type cofactor comparative data integration design differential expression genome editing improved innovation malignant breast neoplasm network models novel novel therapeutics predictive test prevent programs promoter transcription factor transcription termination tumor progression tumorigenesis

项目摘要

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.

项目概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识别和量化癌性细胞系统中所有受干扰的功能相互作用来确定肿瘤发生。该 CSBC U01 应用程序的中心假设是，在多种类型的综合影响中功能扰动，那些由替代亚型的癌症特异性基因表达产生的扰动是对肿瘤发生至关重要。基因组改变，例如扩增、缺失、易位和突变，通常被认为是癌症进展的主要事件。然而，癌症特异性亚型是由选择性剪接、选择性转录起始位点和/或选择性转录终止位点，也已被证明对肿瘤发生具有功能影响。特别是，转录因子（TF）亚型对基因调控网络（GRN）的改变已被证实。显示在多种癌症的肿瘤发生和转移中发挥重要作用。虽然举几个例子驱动癌症特异性 TF 亚型的功能特征已被报道，但仍不清楚是正常组织和癌症组织之间 TF 异构体的差异影响整体 GRN 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。确实有数百正常样本和癌症样本之间已鉴定出 TF 亚型的差异，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，其中包括描述和在癌症相互作用组的背景下模拟大量乳腺癌特异性 TF 亚型的影响网络。我们的目标是将网络建模和高通量系统实验策略结合起来分子蛋白质-蛋白质和蛋白质-DNA 相互作用的水平来预测癌症驱动因素和抑制因素。这由此产生的假设将通过乳腺癌的各种大规模功能测定进行实验检验作为模型系统。作为实验测试的一部分，我们将建立最先进的基因组编辑测试哺乳动物细胞中同种型特异性扰动对 GRN 影响的方法。总而言之，该项目将构成实现情境化和使大量与乳腺癌相关的 TF 同工型功能化。此外，从这次事件中吸取的教训数据分析和整合将导致新的癌症驱动因素和抑制因素的识别，生成癌症中 GRN 重连的机制模型，并为新型药物的设计提供框架疗法。