In a new study, published in GEN Biotechnology titled, “Changes in Gene Network Interactions in Breast Cancer Onset and Development,” researchers from Georgia Institute of Technology have identified differential gene-network changes characteristic of the three most prevalent molecular subtypes of breast cancer, Luminal A, Luminal B, and the highly metastatic Basal-like subtype. In contrast to previous studies, the authors expanded their analysis beyond genes differentially expressed between normal and cancer samples, as differential gene expression may not be a prerequisite for changes in gene-gene interactions. 

The identification of clinically significant targets remains critical for cancer gene therapy. Although cancer is recognized as a polygenic disease, precision cancer therapy is often limited to individual gene targets identified as “cancer drivers.” As a result, significant regulatory changes underlying cancer onset and progression can go undetected, as many changes in gene-gene interactions are not associated with coordinated changes in gene expression.  

The computational network study led by corresponding author, John McDonald, PhD, professor emeritus in the School of Biological Sciences and founding director of the Georgia Tech Integrated Cancer Research Center (ICRC), revealed eight extensively connected network modules acquired in the aggressive Basal-like subtype. The Basal-like subtype was associated with the most dramatic changes in gene-gene network structure compared to Luminal A and Luminal B. Functional analysis of these Basal-like modules uncovered 19 genes enriched for cancer hallmark functions, including regulation of cell proliferation and motility.  

“The components of any complex system, like the human genome, are certainly important,” emphasized McDonald. “The way in which these independent components interact with one another is also critical.” 

Notably, the researchers also uncovered neural pathways unique to the Basal-like subtype that have not been previously associated with breast cancer. Positive regulation of synaptic communication and emphasized glutamatergic signaling were suggested to play a premetastatic role in primary breast tumors. 

McDonald’s research group takes an integrated systems approach to the study of cancer. Additional research projects in the lab have focused on developing generalized cancer diagnostics, small non-encoding RNAs as therapeutic cancer agents, and understanding the significance of mRNA splice variants in the onset and progression of cancer. In January, the McDonald lab published a study that applied machine learning on patient metabolic profiles to identify biomarker patterns for personalized ovarian cancer diagnosis. 

The authors stated that the insights from this gene network analysis further elucidate the molecular processes underlying Basal-like breast cancer and can facilitate new targets for chemotherapy. In addition, identifying unique network features between varying breast cancer subtypes provides a path toward personalized treatment plans. 

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