CHARACTERIZATION OF THE INflAMMATORY INfiLTRATES IN ORAL EPITHELIAL DYSPLASIA AND ORAL SQUAMOUS CELL CARCINOMA USING A NEW MFCA METHOD

• Published in: Oral surgery, oral medicine, oral pathology and oral radiology Vol. 128; no. 1; p. e57
• Main Authors: Mahdi, Dr. Hayder, Eymael, Ms. Denise Lopez, Ali, Dr. Aiman, Magalhaes, Dr. Marco
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2019
• ISSN: 2212-4403; 2212-4411
• DOI: 10.1016/j.oooo.2019.02.131

Oral cancer is a devastating disease and tumor associated inflammation is a key component of the tumor microenvironment. Current techniques to evaluate inflammatory infiltrate are based on a visual, operator-based quantification and may not accurately quantify specific inflammatory signatures. To develop a method for characterizing the inflammatory infiltrate associated with oral epithelial dysplasia (OED) and oral squamous cell carcinoma (OSCC) using confocal microscopy and multichannel fluorescent colocalization analysis (MFCA). We performed a retrospective analysis of 49 biopsy samples of lateral tongue lesions with a diagnosis of hyperkeratosis, ED and OSCC. The inflammatory infiltrate was identified using a combination of 2 primary antibodies for each cell type followed by staining with Alexa 488 or 555 tagged secondary antibodies for FIHC. Identification of the inflammatory cells was performed by 2-channel colocalization using a custom-made, semi-automated algorithm in Volocity 6.3. Using our novel analysis technique we identified and quantified neutrophils, TCD8, TCD4, eosinophils, plasma cells, B cells, Macrophages and NK cells in biopsy specimens. T-lymphocytes represented the main component of the inflammatory infiltrate in all specimens and there was a marked increase in inflammatory cell density from benign to OSCC lesions. Our results also showed that the CD4/CD8 ratio and neutrophils/lymphocytes ratio (NLR) had a progressive increase when moving from benign lesions to OSCC. We described a new, method to quantify inflammatory infiltrates in oral biopsies. This semi-automated approach decreases operator bias and provides robust and reproducible data to study inflammation in tissue samples. Using this technique, we provide evidence that cancer progression is mirrored by progressive changes in the inflammatory infiltrate. Understanding specific changes in cancer associated inflammation is essential to develop immune-targeted therapies. This technique and our current results will be further explored as a potential prognostic maker of oral cancer.