Development and validation of a multivariable model for prediction of malignant transformation and recurrence of oral epithelial dysplasia.

BACKGROUND: Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma which is amongst the top ten cancers worldwide. Prognostic significance of conventional histological features in OED is not well established. Many additional histological abnormalities are seen in OED, but are insufficiently investigated, and have not been correlated to clinical outcomes. METHODS: A digital quantitative analysis of epithelial cellularity, nuclear geometry, cytoplasm staining intensity and epithelial architecture/thickness is conducted on 75 OED whole-slide images (252 regions of interest) with feature-specific comparisons between grades and against non-dysplastic/control cases. Multivariable models were developed to evaluate prediction of OED recurrence and malignant transformation. The best performing models were externally validated on unseen cases pooled from four different centres (n = 121), of which 32% progressed to cancer, with an average transformation time of 45 months. RESULTS: Grade-based differences were seen for cytoplasmic eosin, nuclear eccentricity, and circularity in basal epithelial cells of OED (p < 0.05). Nucleus circularity was associated with OED recurrence (p = 0.018) and epithelial perimeter associated with malignant transformation (p = 0.03). The developed model demonstrated superior predictive potential for malignant transformation (AUROC 0.77) and OED recurrence (AUROC 0.74) as compared with conventional WHO grading (AUROC 0.68 and 0.71, respectively). External validation supported the prognostic strength of this model. CONCLUSIONS: This study supports a novel prognostic model which outperforms existing grading systems. Further studies are warranted to evaluate its significance for OED prognostication.

Immune status is prognostic for poor survival in colorectal cancer patients and is associated with tumour hypoxia.

BACKGROUND: Immunohistochemical quantification of the immune response is prognostic for colorectal cancer (CRC). Here, we evaluate the suitability of alternative immune classifiers on prognosis and assess whether they relate to biological features amenable to targeted therapy. METHODS: Overall survival by immune (CD3, CD4, CD8, CD20 and FOXP3) and immune-checkpoint (ICOS, IDO-1 and PD-L1) biomarkers in independent CRC cohorts was evaluated. Matched mutational and transcriptomic data were interrogated to identify associated biology. RESULTS: Determination of immune-cold tumours by combined low-density cell counts of CD3, CD4 and CD8 immunohistochemistry constituted the best prognosticator across stage II-IV CRC, particularly in patients with stage IV disease (HR 1.98 [95% CI: 1.47-2.67]). These immune-cold CRCs were associated with tumour hypoxia, confirmed using CAIX immunohistochemistry (P = 0.0009), which may mediate disease progression through common biology (KRAS mutations, CRIS-B subtype and SPP1 mRNA overexpression). CONCLUSIONS: Given the significantly poorer survival of immune-cold CRC patients, these data illustrate that assessment of CD4-expressing cells complements low CD3 and CD8 immunohistochemical quantification in the tumour bulk, potentially facilitating immunophenotyping of patient biopsies to predict prognosis. In addition, we found immune-cold CRCs to associate with a difficult-to-treat, poor prognosis hypoxia signature, indicating that these patients may benefit from hypoxia-targeting clinical trials.

A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia.

Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p < 0.001). Nuclear analyses elucidated the presence of peri-epithelial and intra-epithelial lymphocytes in highly predictive patches of transforming cases (p < 0.001). This is the first study to propose a completely automated, explainable, and externally validated algorithm for predicting OED transformation. Our algorithm shows comparable-to-human-level performance, offering a promising solution to the challenges of grading OED in routine clinical practice.

Identification of a prognostic signature in colorectal cancer using combinatorial algorithm-driven analysis.

Colorectal carcinoma is one of the most common types of malignancy and a leading cause of cancer-related death. Although clinicopathological parameters provide invaluable prognostic information, the accuracy of prognosis can be improved by using molecular biomarker signatures. Using a large dataset of immunohistochemistry-based biomarkers (n = 66), this study has developed an effective methodology for identifying optimal biomarker combinations as a prognostic tool. Biomarkers were screened and assigned to related subsets before being analysed using an iterative algorithm customised for evaluating combinatorial interactions between biomarkers based on their combined statistical power. A signature consisting of six biomarkers was identified as the best combination in terms of prognostic power. The combination of biomarkers (STAT1, UCP1, p-cofilin, LIMK2, FOXP3, and ICOS) was significantly associated with overall survival when computed as a linear variable (χ2  = 53.183, p < 0.001) and as a cluster variable (χ2  = 67.625, p < 0.001). This signature was also significantly independent of age, extramural vascular invasion, tumour stage, and lymph node metastasis (Wald = 32.898, p < 0.001). Assessment of the results in an external cohort showed that the signature was significantly associated with prognosis (χ2  = 14.217, p = 0.007). This study developed and optimised an innovative discovery approach which could be adapted for the discovery of biomarkers and molecular interactions in a range of biological and clinical studies. Furthermore, this study identified a protein signature that can be utilised as an independent prognostic method and for potential therapeutic interventions.

HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks.

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. This generally requires consultation of documentation for multiple coding libraries, as well as trial and error to ensure that the techniques used on the images are appropriate. Herein we introduce HistoClean; a user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. HistoClean is an application that aims to help bridge the knowledge gap between pathologists, biomedical scientists and computer scientists by providing transparent image augmentation and pre-processing techniques which can be applied without prior coding knowledge. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. This study demonstrates how HistoClean can be used to improve a standard deep learning workflow via classical image augmentation and pre-processing techniques, even with a relatively simple convolutional neural network architecture. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.

A robust multiplex immunofluorescence and digital pathology workflow for the characterisation of the tumour immune microenvironment.

Multiplex immunofluorescence is a powerful tool for the simultaneous detection of tissue-based biomarkers, revolutionising traditional immunohistochemistry. The Opal methodology allows up to eight biomarkers to be measured concomitantly without cross-reactivity, permitting identification of different cell populations within the tumour microenvironment. In this study, we aimed to validate a multiplex immunofluorescence workflow in two complementary multiplex panels and evaluate the tumour immune microenvironment in colorectal cancer (CRC) formalin-fixed paraffin-embedded tissue. We stained CRC and tonsil samples using Opal multiplex immunofluorescence on a Leica BOND RX immunostainer. We then acquired images on an Akoya Vectra Polaris and performed multispectral unmixing using inform. Antibody panels were validated on tissue microarray sections containing cores from six normal tissue types, using qupath for image analysis. Comparisons between chromogenic immunohistochemistry and multiplex immunofluorescence on consecutive sections from the same tissue microarray showed significant correlation (rs  > 0.9, P-value < 0.0001), validating both panels. We identified many factors that influenced the quality of the acquired fluorescent images, including biomarker co-expression, staining order, Opal-antibody pairing, sample thickness, multispectral unmixing and biomarker detection order during image analysis. Overall, we report the optimisation and validation of a multiplex immunofluorescence process, from staining to image analysis, ensuring assay robustness. Our multiplex immunofluorescence protocols permit the accurate detection of multiple immune markers in various tissue types, using a workflow that enables rapid processing of samples, above and beyond previous workflows.