Amber Luong, MD, PhD, professor and vice chair for research in the Department of Otorhinolaryngology at McGovern Medical School at UTHealth Houston, presented on transcriptomics as a panel member at the American Rhinology Society annual meeting in Los Angeles. In addition to biomarkers, the panel discussion focused on the new technology of omics, including genomics, proteomics, metabolomics, and metagenomics, as well as transcriptomics, which aims to characterize and quantify pools of biological molecules that comprise the structure, function, and dynamics of organisms.
A physician-scientist, Dr. Luong has a particular interest in the pathophysiology of chronic rhinosinusitis (CRS) as a model for studying immune dysregulation of the paranasal sinuses. She also has zeroed in on allergic fungal rhinosinusitis (AFRS), a clinical subtype of chronic rhinosinusitis with nasal polyps, and seeks to identify molecular features unique to AFRS, which accounts for about 7 to 12% of all surgical chronic rhinosinusitis cases. Management of AFRS primarily consists of endoscopic sinus surgery and medical therapy with perioperative systemic corticosteroids and long-term intranasal steroid use directed at suppressing intranasal inflammation.
“We don’t fully understand the disease process of CRS and AFRS, but several environmental triggers or inflammatory stimuli have been identified including viruses, bacteria, and fungi. However, we currently are unable to associate specific triggers to specific individual patients,” Dr. Luong says. “Our research goal is to develop personalized curative treatments through a better understanding of the molecular mechanisms of the disease process, but we haven’t had the tools to classify the categories of disease properly. Even though patient symptoms may be the same, we’ve discerned that our patients come to this disease from different pathways. Transcriptomics, the study of the complete set of RNA transcripts produced by the genome within a cell, is rapidly expanding our understanding of the relationships between the transcriptome and an individual patient’s observable traits. Ultimately, this will give us a better idea of how to design well-defined personalized treatments for the cause of each patient’s disorder rather than just treating the symptoms.”