Project 1: Pan-Cancer Master Regulatory Gene Networks
Each human cancer type possesses a unique set of genetic and epigenetic signatures, which likely explain each cancer’s disparate behaviors and response to various forms of therapy. However, most, if not all, cancer types also share many molecular and signaling similarities, irrespective of the tissue or organ of origin, suggesting there could be a common or universal regulatory gene network underpinning malignant transformation in general. This could provide the key to understanding cancer origin and developing cancer type-agnostic therapies.
This project focuses on identifying such a universal signature by leveraging the predictive robustness of nSCORE and novel machine learning workflows upon the GeneRep-created reference gene networks of 28 major human cancers in the Cancer Genome Atlas (TCGA), will eventually be deposited in the Network Data Exchange, followed by experimental validation in both in vitro and animal models.
Project 2: Liquid Biopsy Screening for Early Detection of Cancer
- Non-invasive novel cancer screening assay to detect cancer by analyzing key genes controlling cancer initiation and progression of a specific cancer type
- Abnormal chromosomal accessibility and expression profile of cancer master gene regulators
- More comprehensive and sensitive compared to current treatment
- Detects cancer that are of rare and novel mutations
Project 3: Cancer Single Cell Analysis
In collaboration with Florida Center for Brain Tumor Research (FCTBR) we are planning to collect comprehensive genetic data from the high grade glioma (HGG) patients and create a database supporting ongoing and future research, to promote understanding of glioblastoma pathology. We will use novel computational platform (NETZEN – GeneRep), developed in our laboratory, to identify key factors and master regulators responsible for tumor progression, responsiveness and resistance to treatment. In addition to identifying and validating complex networks of glioblastoma master regulators, we will focus on single cell gene expression analysis, identify rare cell types, and understand cellular heterogeneity and cell to cell interactions in cancer