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Fibrosis can occur in many organs, where it is a debilitating and preneoplastic condition. The senescence of activated fibroblasts has been proposed to ameliorate fibrosis via the innate immune system but its role in humans has not been investigated. The availability of oral submucous fibrosis (OSMF) biopsies at different stages of disease progression allowed us to test the hypothesis that senescent fibroblasts accumulate with the progression of human fibrosis in vivo, and also to examine the mechanism of senescence. We tested the hypothesis that senescent cells may ameliorate fibrosis by increasing the secretion of matrix metalloproteinases (MMPs). We have used a combination of in situ immunodetection techniques, drug treatments, fluorescence-activated cell sorting and enzyme-linked absorbance assays on tissue samples and fibroblast cultures. We report a novel panning technique, based on fibronectin adhesion rates, to enrich and deplete senescent cells from fibroblast populations. Senescent fibroblasts, as determined by the presence of senescence-associated heterochromatic foci, accumulated with OSMF progression (R(2) = 0.98) and possessed a reduced replicative lifespan in vitro. Unlike wounds, however, OSMF fibroblasts were quiescent in vivo and consistent with this observation, possessed functional telomeres of normal length. Senescence was associated in vivo and in vitro with oxidative damage, DNA damage foci and p16(INK4A) accumulation and required the production of reactive oxygen species (ROS), perhaps from damaged mitochondria, but not the continuous presence of the disease stimulus (areca nut and tobacco), the tissue environment or other cell types. Depletion of OSMF fibroblasts of senescent cells showed that these cells accounted for 25-83 times more MMP-1 and -2 than their pre-senescent counterparts. The results show that the accumulation of senescent fibroblasts in human fibrosis occurs by a telomere-independent mechanism involving ROS and may locally ameliorate the condition by the increased expression of MMPs prior to clearance by the immune system.

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The clinical and histologic features alone cannot accurately predict whether potentially malignant disorders of the oral mucosa remain stable, regress or progress to malignancy. Some of them, with or without epithelial dysplasia, may transform to invasive oral squamous cell carcinomas (OSCC). Identification of molecular markers which can predict disease progression is necessary to improve the management of these disorders. Many genes and signaling pathways have been shown to be involved in the development of OSCC. This review summarizes some molecular markers researched in the detection of pre-cancer. We highlight selected markers that are reported to be significantly associated with progression of potentially malignant disorders to OSCC. These include alterations in genes/pathways which control cellular signaling, cell cycle, apoptosis, genomic stability, cytoskeleton, angiogenesis, etc. However, these genetic tumor markers have so far not gained any use in routine diagnosis and their utility in the prediction of risk of malignant transformation remains unknown. It is, however, clear from the large number of studies, some described in this review, that multiple genes/pathways are involved in the progression from normal to metaplastic/dysplastic, and subsequently to cancer. It is therefore necessary to study those significant alterations in multiple genes simultaneously in biopsy samples from large cohorts of subjects.

Oral cancer is a major global health issue accounting for 177,384 deaths in 2018 and it is most prevalent in low-and middle-income countries. Enabling automation in the identification of potentially malignant and malignant lesions in the oral cavity would potentially lead to low-cost and early diagnosis of the disease. Building a large library of well-annotated oral lesions is key. As part of the MeMoSA ® (Mobile Mouth Screening Anywhere) project, images are currently in the process of being gathered from clinical experts from across the world, who have been provided with an annotation tool to produce rich labels. A novel strategy to combine bounding box annotations from multiple clinicians is provided in this paper. Further to this, deep neural networks were used to build automated systems, in which complex patterns were derived for tackling this difficult task. Using the initial data gathered in this study, two deep learning based computer vision approaches were assessed for the automated detection and classification of oral lesions for the early detection of oral cancer, these were image classification with ResNet-101 and object detection with the Faster R-CNN. Image classification achieved an F1 score of 87.07% for identification of images that contained lesions and 78.30% for the identification of images that required referral. Object detection achieved an F1 score of 41.18% for the detection of lesions that required referral. Further performances are reported with respect to classifying according to the type of referral decision. Our initial results demonstrate deep learning has the potential to tackle this challenging task. INDEX TERMS Composite annotation, deep learning, image classification, object detection, oral cancer, oral potentially malignant disorders.