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Updated: May 29, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
Published on: May 21, 2019
Microenvironmental genomic alterations reveal signaling networks for head and neck squamous cell carcinoma
Gurkan Bebek1, Mohammed Orloff, Charis Eng
1Genomic Medicine Institute, Cleveland Clinic, 9500 Euclid Avenue, Mailstop NE-50 Cleveland, OH 44195, USA. engc@ccf.org.
Background:
Advanced stage head and neck squamous cell carcinoma (HNSCC) is an aggressive cancer with low survival rates. Loss-of-heterozygosity/allelic imbalance (LOH/AI) analysis has been widely used to identify genomic alterations in solid tumors and the tumor microenvironment (stroma). We hypothesize that these identified alterations can point to signaling networks functioning in HNSCC epithelial-tumor and surrounding stroma (tumor microenvironment).
Results:
Under the assumption that genes in proximity to identified LOH/AI regions are correlated with the tumorigenic phenotype, we mined publicly available biological information to identify pathway segments (signaling proteins connected to each other in a network) and identify the role of tumor microenvironment in HNSCC. Across both neoplastic epithelial cells and the surrounding stromal cells, genetic alterations in HNSCC were successfully identified, and 75 markers were observed to have significantly different LOH/AI frequencies in these compartments (p < 0.026). We applied a network identification approach to the genes in proximity to these 75 markers in cancer epithelium and stroma in order to identify biological networks that can describe functional associations amongst these marker-associated genes.
Conclusions:
We verified the involvement of T-cell receptor signaling pathways in HNSCC as well as associated oncogenes such as LCK and PLCB1, and tumor suppressors such as STAT5A, PTPN6, PARK2. We identified expression levels of genes within significant LOH/AI regions specific to stroma networks that correlate with better outcome in radiation therapy. By integrating various levels of high-throughput data, we were able to precisely focus on specific proteins and genes that are germane to HNSCC.
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