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.
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. Although genome alterations such as amplification, deletion, translocations and mutations, are often considered primary events of cancer progression, cancer-specific alternative 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 this project, we propose to characterize and model the effect of large numbers of breast cancer-specific transcription factor isoforms on gene regulatory networks (GRNs), with the long-term aim of identifying novel cancer drivers and suppressors, generating mechanistic models of GRN rewiring in cancer, and providing a framework for the design of novel therapeutics.
