INTEGRATED NETWORK ANALYSIS OF EWING SARCOMA
1. Project Background and Description
System level treatments & cures for Cancer have been a dream of the medical community for many years, but they have proven elusive. The Hallmarks of Cancer (HoC) provide an approach that could be utilized for systematic treatment, through functional decomposition of cancer. While progress has been made in characterizing various hallmark cancer processes, a comprehensive, detailed & unified Systems Engineering description of cancer that can be used & applied for treatment has yet to be achieved.
Ewing’s is insidious in that kids feel healthy except for what their parents consider “normal growing pains”. Little do they know that the “growing pains” are a symptom of a dangerous & aggressive disease that is attacking them. Routine visits to the doctor will monitor biological data but will not disclose the presence of this horrific cancer. Ewing’s illustrates the need to look beyond the genome, the complete catalog of an organism’s genetic information. In many ways, a genome is like a paper map of the world. That map shows where the cities are. But it doesn’t say anything about which nations trade with each other, which towns have fierce football rivalries or which states will swing for a particular political candidate. To figure out what’s really happening within an organism, or within a particular organ or cell, we have to look beyond simply focusing on individual molecules and develop an integrated Genomics (study of the genome), Proteomics (large scale study of proteins) and Metabolomics (metabolic status & global biochemical events) Multi-omic representation of Ewing’s.
2. Project Scope
Our Research Objective will be to develop an integrated, detailed, systemic mechanistic computational representation of Ewing Sarcoma (ES) using the HoC as an overall guide to more fully understand the disease by including the spectrum of –omics technologies. No such system level, integrated representation of Ewing Sarcoma currently exists. Current research is focused on individual details of how Ewing’s responds to a specific treatment or how specific entities are implicated in portions of the disease genotype, heterogeneity, phenotype or progression. However, no systemic focus is provided nor are these individual details translated into the systemic representation which is required to systematically & comprehensively diagnose, monitor, & treat ES.
Connections between genes, proteins, metabolites, and -omic layers provides a more holistic, human body/ systems level approach to understanding and curing complex diseases like Ewing Sarcoma. This has motivated us to undertake integrated -omic dataset modeling that can reveal a human body systems view of disease and biological activity. In the diagram, a current, limited methods are displayed on the left. Our more thorough, multi-omic approach is on the right.
3. High-Level Requirements
To understand the molecular mechanisms of tumorigenesis & metastatic processes in ES, this model will focus on development of a determination of the molecular mechanisms of ES progression through integration of the human ES biological networks (INs) including Genome-scale metabolic models (GEMs), Signaling Networks (SNs), Transcriptional Regulatory Networks (TRNs) & protein-protein interaction (PPINs) networks.
4. Deliverables
This will reveal, characterize, & help validate a Disease Progression Model (DPM) of ES in order to use it in a Systems Engineering based systematic treatment biomarker, drug repurposing/ development strategy. We will use this model to identify biomarkers for the early detection of ES & tracking of disease progression. We will highlight important biological processes, underestimated contributors to tumorigenesis (such as metabolic pathways) & identify key isoforms & molecular players in ES tumor progression.