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

• 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
• 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

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.

项目摘要 癌症系统生物学的最终目标之一是产生预测性和动态模型 肿瘤发生通过识别和量化癌细胞系统中所有扰动功能相互作用。 该CSBC U01应用的中心假设是，在多种类型的综合效果中 功能扰动，从癌症特异性基因表达中出现的替代同工型的那些是 对于肿瘤发生至关重要。基因组改变，例如扩增，缺失，易位和突变，是 通常被认为是癌症进展的主要事件。但是，由 替代剪接，转录启动的替代位点和/或替代转录终止 位点也已证明对肿瘤发生有功能影响。 特别是，通过转录因子（TF）同工型的基因调节网络（GRN）的变化已已 证明在多种类型的癌症中在肿瘤发生和转移中起主要作用。虽然一些例子 已经报道了驱动癌特异性TF同工型的功能表征，尚不清楚 是正常组织和癌症组织之间TF同工型的差异影响全局GRN和 这种调节网络如何重新启动癌症中基因表达程序的改变。确实，数百个 在正常样本和癌症样品之间已经确定了差异TF同工型，但是绝大多数 保持在功能水平上未表征。 在这个项目中，我们提出了朝着这个长期目标迈出的第一步，该目标包括表征和 在癌症相互作用的背景下建模大量乳腺癌特异性TF同工型的影响 网络。我们旨在将网络建模和高通量系统实验策略结合在 分子蛋白 - 蛋白质和蛋白-DNA相互作用的水平，以预测癌症驱动因素和抑制因子。这 由此产生的假设将使用乳腺癌中各种大规模功能测定法对实验进行测试 作为模型系统。作为实验测试的一部分，我们将建立最新的基因组编辑 测试同工型特异性扰动对哺乳动物细胞GRN的影响的方法。 总之，该项目将构成朝着情境化和的长期目标迈出的重要一步 功能化大量与乳腺癌有关的TF同工型。此外，从 数据分析和整合将导致鉴定新型的癌症驱动因素和抑制器，即 GRN重新布线的机械模型生成癌症，并为新颖的设计提供了一个框架 疗法。