Simon A. Fox

Confocal laser endomicroscopy (CLE) enables real-time diagnosis of oral cancer and potentially malignant disorders by in vivo microscopic tissue examination. One impediment to the widespread clinical adoption of this technology is the need for operator expertise in image interpretation. Here we review the application of AI to automatic tissue classification of CLE images and discuss the opportunities for integrating this technology to advance the adoption of real-time digital pathology thus improving speed, precision and reproducibility.

High-grade serous ovarian carcinoma (HGSOC) is genetically unstable and characterised by the presence of subclones with distinct genotypes. Intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and poor prognosis. Here, we use spatial transcriptomics to identify HGSOC subclones and study their association with infiltrating cell populations. Visium spatial transcriptomics reveals multiple tumour subclones with different copy number alterations present within individual tumour sections. These subclones differentially express various ligands and receptors and are predicted to differentially associate with different stromal and immune cell populations. In one sample, CosMx single molecule imaging reveals subclones differentially associating with immune cell populations, fibroblasts, and endothelial cells. Cell-to-cell communication analysis identifies subclone-specific signalling to stromal and immune cells and multiple subclone-specific autocrine loops. Our study highlights the high degree of subclonal heterogeneity in HGSOC and suggests that subclone-specific ligand and receptor expression patterns likely modulate how HGSOC cells interact with their local microenvironment.

Objectives: Confocal laser endomicroscopy (CLE) is a novel non‐invasive point‐of‐care optical biopsy technology that enables real‐time in vivo microscopic visualisation of cellular and tissue architecture. In this study, we assessed the diagnostic accuracy of a hand‐held fluorescence single‐fibre distal‐scanning CLE (fsdCLE) platform for diagnosing oral epithelial dysplasia (OED) and oral squamous cell carcinoma (OSCC).Materials and Methods: Forty‐seven patients presenting with 63 distinct oral mucosal lesions were subjected to optical biopsy using a miniaturised fsdCLE system (ViewnVivo®, Optiscan Imaging Ltd) and topical exogenous acriflavine hydrochloride contrast agent before undergoing tissue biopsy and histopathological consensus review by four pathologists. CLE images were captured in vivo in real‐time during clinical examination and assessed on‐the‐fly for the presence of cellular and architectural features of OED/OSCC offering an instantaneous diagnosis. Predicted optical diagnoses were compared to definitive consensus tissue histopathology. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated for the presence/absence of dysplasia/malignancy on optical biopsy. Percentage agreement, Fleiss' kappa, and intraclass correlation coefficient (ICC) were calculated for each assessment stage during the consensus histopathology process.Results: Diagnostic accuracy was extremely high at 88.9%. Other metrics were sensitivity 86.8%, specificity 92%, PPV 94.3% and NPV 82.1%. One hundred percent of carcinoma cases were detected accurately using CLE in the clinic.Conclusion: fsdCLE is a highly accurate, easy‐to‐use, rapid and slide‐free point‐of‐care in vivo optical technology for diagnosing OED/OSCC and discriminating between dysplastic and non‐dysplastic pathology. It demonstrates near‐perfect agreement with traditional consensus histopathology without the need for physical tissue biopsy.

This study aimed to develop an in vitro three-dimensional (3D) cell culture model of oral carcinogenesis for the rapid, scalable testing of chemotherapeutic agents. Spheroids of normal (HOK) and dysplastic (DOK) human oral keratinocytes were cultured and treated with 4-nitroquinoline-1-oxide (4NQO). A 3D invasion assay using Matrigel was performed to validate the model. RNA was extracted and subjected to transcriptomic analysis to validate the model and assess carcinogen-induced changes. The VEGF inhibitors pazopanib and lenvatinib were tested in the model and were validated by a 3D invasion assay, which demonstrated that changes induced by the carcinogen in spheroids were consistent with a malignant phenotype. Further validation was obtained by bioinformatic analyses, which showed the enrichment of pathways associated with hallmarks of cancer and VEGF signalling. Overexpression of common genes associated with tobacco-induced oral squamous cell carcinoma (OSCC), such as MMP1, MMP3, MMP9, YAP1, CYP1A1, and CYP1B1, was also observed. Pazopanib and lenvatinib inhibited the invasion of transformed spheroids. In summary, we successfully established a 3D spheroid model of oral carcinogenesis for biomarker discovery and drug testing. This model is a validated preclinical model for OSCC development and would be suitable for testing a range of chemotherapeutic agents.

Relapse after surgery for oral squamous cell carcinoma (OSCC) contributes significantly to morbidity, mortality and poor outcomes. The current histopathological diagnostic techniques are insufficiently sensitive for the detection of oral cancer and minimal residual disease in surgical margins. We used whole-transcriptome gene expression and small noncoding RNA profiles from tumour, close margin and distant margin biopsies from 18 patients undergoing surgical resection for OSCC. By applying multivariate regression algorithms (sPLS-DA) suitable for higher dimension data, we objectively identified biomarker signatures for tumour and marginal tissue zones. We were able to define molecular signatures that discriminated tumours from the marginal zones and between the close and distant margins. These signatures included genes not previously associated with OSCC, such as MAMDC2, SYNPO2 and ARMH4. For discrimination of the normal and tumour sampling zones, we were able to derive an effective gene-based classifying model for molecular abnormality based on a panel of eight genes (MMP1, MMP12, MYO1B, TNFRSF12A, WDR66, LAMC2, SLC16A1 and PLAU). We demonstrated the classification performance of these gene signatures in an independent validation dataset of OSCC tumour and marginal gene expression profiles. These biomarker signatures may contribute to the earlier detection of tumour cells and complement existing surgical and histopathological techniques used to determine clear surgical margins.