Zainab Arshad, Bioinformatics Thesis Defense

In partial fulfillment of the requirements for the degree of 
Doctor of Philosophy in Bioinformatics
in the School of Biological Sciences

Zainab Arshad
Defends her thesis:
Differential gene co-expression network characteristics of cancer
 
Thursday, December 1, 2022
1:00 PM Eastern Time
IBB Suddath Seminar Room (room #1128)
Zoom link:  https://gatech.zoom.us/j/92842512518 
 
Thesis Advisor: 
Dr. John F. McDonald
School of Biological Sciences
Georgia Institute of Technology
 
Committee Members:
Dr. Cassie S. Mitchell
Biomedical Engr, GT/Emory 
Georgia Institute of Technology
  
Dr. I. King Jordan
School of Biological Sciences
Georgia Institute of Technology

Dr. Jeffrey Skolnick
School of Biological Sciences
Georgia Institute of Technology

Dr. Jung H. Choi
School of Biological Sciences
Georgia Institute of Technology

Summary:
The transformation from a healthy state to a disease state in cancer is dictated in large part by structural and regulatory abnormalities in genes. While the molecular features underlying this transition have been investigated for some time, allowing groundbreaking advancements in cancer research, a majority of these efforts are focused on mutational and expression changes of individual genes. The recent advancement of network-based analytic methods affords an additional route through which disease pathophysiology and biologic regulation can be investigated. Furthermore, with the development of high-throughput technologies and the availability of large biobanks, gene interaction changes, and their functional consequences can be reliably interpreted from a systemic perspective, in a context specific manner. Towards this end, my research investigates gene co-expression changes, derived from transcriptomic case-control data, that underlie cancer onset and progression relative to healthy tissue. 
For the first study, global network changes associated with cancers of nine different tissues of origin were investigated. Network complexity generally dropped in the transition from normal precursor tissues to corresponding primary tumors, whereas cross-tissue cancer network similarity overall increased in early-stage cancers followed by a subsequent loss in similarity as tumors reacquire cancer-specific network complexity in late-stage cancers. In addition, gene-gene connections remaining stable through cancer development were found enriched for ‘‘housekeeping’’ gene functions, whereas newly acquired interactions were associated with established cancer-promoting functions. For the second study, gene-network characteristics of the molecular subtypes (Luminal A, Luminal B and Basal) of Breast Cancer (BC) were outlined based on a comparative analysis relative to precursor normal breast tissue. Basal was identified as the most highly connected yet dissimilar subtype to normal control. We discovered eight extensively connected network modules acquired in Basal BCs that harbored 19 genes found significantly associated with survival and encoding cancer hallmark functions including regulation of cell proliferation and motility, as well as neural pathways that have not been previously associated with basal BCs. Finally, the consensus approach of network construction for an unbiased differential analysis of gene co-expression networks used in these studies was published as a step-by-step protocol. 
Altogether, this thesis highlights gene-network changes characteristic of individual cancer types, molecular subtypes and disease stages that informs their diverse progression patterns and clinical outcomes. Furthermore, it underscores the importance and demonstrates the utility of gene co-expression networks in identifying key genes, gene interactions and functional characteristics of cancers that maybe undiscovered by standard molecular analysis approaches.