The immune cell niche associated with oral dysplastic lesion progression to carcinoma is poorly understood. We identified T regulatory cells (Treg), CD8⁺ effector T cells (Teff) and immune checkpoint molecules across oral dysplastic stages of oral potentially malignant disorders (OPMD). OPMD and oral squamous cell carcinoma (OSCC) tissue sections (N = 270) were analyzed by immunohistochemistry for Treg (CD4, CD25 and FoxP3), Teff (CD8) and immune checkpoint molecules (PD-1 and PD-L1). The Treg marker staining intensity correlated significantly (p < 0.01) with presence of higher dysplasia grade and invasive cancer. These data suggest that Treg infiltration is relatively early in dysplasia and may be associated with disease progression. The presence of CD8⁺ effector T cells and the immune checkpoint markers PD-1 and PD-L1 were also associated with oral cancer progression (p < 0.01). These observations indicate the induction of an adaptive immune response with similar Treg and Teff recruitment timing and, potentially, the early induction of exhaustion. FoxP3 and PD-L1 levels were closely correlated with CD8 levels (p < 0.01). These data indicate the presence of reinforcing mechanisms contributing to the immune suppressive niche in high-risk OPMD and in OSCC. The presence of an adaptive immune response and T-cell exhaustion suggest that an effective immune response may be reactivated with targeted interventions coupled with immune checkpoint inhibition.

Oral potentially malignant disorders (OPMDs) are precursors to over 80% of oral cancers. Hematoxylin and eosin (H&E) staining, followed by pathologist interpretation of tissue and cellular morphology, is the current gold standard for diagnosis. However, this method is qualitative, can result in errors during the multi-step diagnostic process, and results may have significant inter-observer variability. Chemical imaging (CI) offers a promising alternative, wherein label-free imaging is used to record both the morphology and the composition of tissue and artificial intelligence (AI) is used to objectively assign histologic information. Here, we employ quantum cascade laser (QCL)-based discrete frequency infrared (DFIR) chemical imaging to record data from oral tissues. In this proof-of-concept study, we focused on achieving tissue segmentation into three classes (connective tissue, dysplastic epithelium, and normal epithelium) using a convolutional neural network (CNN) applied to three bands of label-free DFIR data with paired darkfield visible imaging. Using pathologist-annotated H&E images as the ground truth, we demonstrate results that are 94.5% accurate with the ground truth using combined information from IR and darkfield microscopy in a deep learning framework. This chemical-imaging-based workflow for OPMD classification has the potential to enhance the efficiency and accuracy of clinical oral precancer diagnosis.

Background: Oral potentially malignant disorder (OPMD) prognostication is limited by uncertainty of which lesions will progress and the aggressiveness of lesion progression. We defined patterns and kinetics of progression in a large cohort of OPMD subjected to serial clinical and histologic examinations. Methods: This 24‐year study evaluated the pattern of disease progression in 668 patients with 1313 OPMDs. Results: The disease progression pattern was grouped as (a) non‐progressive (82.5%), (b) progressive, where initial low‐grade dysplasia advanced to high‐grade dysplasia in subsequent lesions (5.8%), and (c) transformed, where dysplastic lesions transformed into squamous cell carcinoma (11.7%). Progression showed aggressive (< 2 years) and indolent (> 2 years) patterns. An aggressive course occurred in 48.3% of progressive and 57.6% of transformed lesions. Progression was associated with high‐grade lesions, floor of mouth and palate subsites, and tobacco and alcohol use. Conclusions: The data from this study may be used to develop a risk‐stratified approach for OPMD management.