Jacqueline A. James

The pituitary neuroendocrine tumour (PitNET) immune microenvironment (IME) has become a recent focus of research interest. Multiplex immunofluorescence combined with digital image analysis is a powerful technique to interrogate the IME. Processes such as DNA damage response and cellular senescence have been implicated in somatotroph tumour pathogenesis, and these have in turn been linked with immunological responses to neoplasia. The objectives of this study were to compare the immune cell infiltrate in PitNETs to normal pituitary, investigate how the PitNET-IME may vary according to treatment with somatostatin receptor ligand, tumour invasiveness, tumour proliferation, and AIP mutation status. Two bespoke multiplex panels were developed and applied to a range of PitNET subtypes before being interrogated with deep learning algorithms and QuPath image analysis software. Tumours had significantly higher total macrophage infiltrate (P < 0.001), STING+ macrophages (P < 0.001) and STING positivity (P < 0.001) compared with normal pituitary tissue. Macrophage infiltration and STING positivity were positively correlated with tumour Ki-67 proliferation index (rs = 0.305, P = 0.003; and rs = 0.263, P = 0.010, respectively). Results suggested that AIP-mutated PitNETs may have higher CD8+ T-lymphocyte infiltrate, macrophage infiltrate, including STING+ macrophages and overall STING positivity compared with AIP-negative tumours. The application of machine learning to multiplex immunofluorescence images identifies macrophages and STING as upregulated in these subgroups of functioning PitNETs. This immunological response may indicate that DNA damage, cellular senescence and autophagy could be features of these tumours.

Lifestyle factors such as smoking and alcohol may influence colon cancer (CC) survival, but it is unclear whether their effects vary by tumour-infiltrating immune biomarkers. This study examined CC-specific survival by smoking and alcohol status, stratified by immune cell density, in a large population-based cohort. The study included 661 individuals who underwent surgery for stage II or III CC between 2004 and 2008 within two Health and Social Care (HSC) Trusts in Northern Ireland. Representative formalin-fixed, paraffin-embedded (FFPE) tumour blocks were retrieved, and immunohistochemistry (IHC) was performed on tissue microarrays constructed from both the central tumour and the invasive margin. Cox proportional hazards models were used to assess CC-specific survival, adjusting for key clinical and demographic confounders. Ever smoking, compared to never smoking, was associated with poorer CC-specific survival among individuals with lower densities of CD3+, CD4 +, and FOXP3 + tumour-infiltrating immune cells. Among those with higher CD8 + cell density in the central tumour, ever smoking was linked to worse outcomes. Similar patterns were seen in the invasive margin, although these were not all statistically significant. No significant associations were observed between alcohol use and survival across any immune biomarker subgroups. Smoking was associated with poorer survival among patients with CC, and this association appears to be modified by the density of tumour-infiltrating immune cells.

Background: Oral epithelial dysplasia (OED) is a potentially malignant histopathological diagnosis given to lesions of the oral cavity that are at risk of progression to malignancy. Manual grading of OED is subject to substantial variability and does not reliably predict prognosis, potentially resulting in sub-optimal treatment decisions. Method: We developed a Transformer-based artificial intelligence (AI) pipeline for the prediction of malignant transformation from whole-slide images (WSIs) of Haematoxylin and Eosin (H&E) stained OED tissue slides, named ODYN (Oral Dysplasia Network). ODYN can simultaneously classify OED and assign a predictive score (ODYN-score) to quantify the risk of malignant transformation. The model was trained on a large cohort using three different scanners (Sheffield, 358 OED WSIs, 105 control WSIs) and externally validated on cases from three independent centres (Birmingham and Belfast, UK, and Piracicaba, Brazil; 108 OED WSIs). Results: Model testing yielded an F1-score of 0.96 for classification of dysplastic vs non-dysplastic slides, and an AUROC of 0.73 for malignancy prediction, gaining comparable results to clinical grading systems. Conclusions: With further large-scale prospective validation, ODYN promises to offer an objective and reliable solution for assessing OED cases, ultimately improving early detection and treatment of oral cancer.

Background: A causal role of high-risk HPV in oesophageal adenocarcinoma development has been hypothesised, but longitudinal evidence is limited. This study aims to investigate a potential causal role of infectious agents in the malignant progression of Barrett’s oesophagus. Methods: Using a retrospective nested case-control study design, index Barrett’s biopsies were retrieved for individuals within the Northern Ireland Barrett’s oesophagus register who subsequently progressed to oesophageal adenocarcinoma (n = 150) and matched non-progressors (n = 298). Index Barrett’s biopsies were assessed for the presence of 142 infectious agents by multiplex polymerase chain reaction using the Luminex platform. RNA in-situ hybridisation assessed persistent transcriptional activity in subsequent tissue samples, for infectious agents detected more frequently in progressors. Results: High-risk HPV genotypes (HPV16 and HPV18) were only identified in the index biopsies of progressors but not non-progressors (4% [5/150] versus 0% [0/298], P = 0.004), though no signs of persistence or transcriptional activity were observed in subsequent tissue. Prevalence of infections did not differ between progressors and non-progressors for any other infectious agents, including Helicobacter Pylori and Herpes. Conclusion: Despite a higher prevalence of high-risk HPV in progressors than non-progressors, no evidence of transcriptionally active high-risk HPV was observed in subsequent samples, indicating presence in Barrett’s is likely non-causal.

Background: The last decade has witnessed major advances in the development of artificial intelligence (AI) technologies for use in health care. One of the most promising areas of research that has potential clinical utility is the use of AI in pathology to aid cancer diagnosis and management. While the value of using AI to improve the efficiency and accuracy of diagnosis cannot be underestimated, there are challenges in the development and implementation of such technologies. Notably, questions remain about public support for the use of AI to assist in pathological diagnosis and for the use of health care data, including data obtained from tissue samples, to train algorithms. Objective: This study aimed to investigate public awareness of and attitudes toward AI in pathology research and practice. Methods: A nationally representative, cross-sectional, web-based mixed methods survey (N=1518) was conducted to assess the UK public’s awareness of and views on the use of AI in pathology research and practice. Respondents were recruited via Prolific, an online research platform. To be eligible for the study, participants had to be aged >18 years, be UK residents, and have the capacity to express their own opinion. Respondents answered 30 closed-ended questions and 2 open-ended questions. Sociodemographic information and previous experience with cancer were collected. Descriptive and inferential statistics were used to analyze quantitative data; qualitative data were analyzed thematically. Results: Awareness was low, with only 23.19% (352/1518) of the respondents somewhat or moderately aware of AI being developed for use in pathology. Most did not support a diagnosis of cancer (908/1518, 59.82%) or a diagnosis based on biomarkers (694/1518, 45.72%) being made using AI only. However, most (1478/1518, 97.36%) supported diagnoses made by pathologists with AI assistance. The adjusted odds ratio (aOR) for supporting AI in cancer diagnosis and management was higher for men (aOR 1.34, 95% CI 1.02-1.75). Greater awareness (aOR 1.25, 95% CI 1.10-1.42), greater trust in data security and privacy protocols (aOR 1.04, 95% CI 1.01-1.07), and more positive beliefs (aOR 1.27, 95% CI 1.20-1.36) also increased support, whereas identifying more risks reduced the likelihood of support (aOR 0.80, 95% CI 0.73-0.89). In total, 3 main themes emerged from the qualitative data: bringing the public along, the human in the loop, and more hard evidence needed, indicating conditional support for AI in pathology with human decision-making oversight, robust measures for data handling and protection, and evidence for AI benefit and effectiveness. Conclusions: Awareness of AI’s potential use in pathology was low, but attitudes were positive, with high but conditional support. Challenges remain, particularly among women, regarding AI use in cancer diagnosis and management. Apprehension persists about the access to and use of health care data by private organizations.

Oral epithelial dysplasia (OED) is a potentially malignant histopathological diagnosis given to lesions of the oral cavity that are at risk of progression to malignancy. Manual grading of OED is subject to substantial variability and does not reliably predict prognosis, potentially resulting in sub-optimal treatment decisions. We developed a Transformer-based artificial intelligence (AI) pipeline for the prediction of malignant transformation from whole-slide images (WSIs) of Haematoxylin and Eosin (H&E) stained OED tissue slides, named ODYN ( Oral Dysplasia Network ). ODYN can simultaneously classify OED and assign a predictive score (ODYN-score) to quantify the risk of malignant transformation. The model was trained on a large cohort using three different scanners (Sheffield, 358 OED WSIs, 105 control WSIs) and externally validated on cases from three independent centres (Birmingham and Belfast, UK, and Piracicaba, Brazil; 108 OED WSIs). Model testing yielded an F1-score of 0.96 for classification of dysplastic vs non-dysplastic slides, and an AUROC of 0.73 for malignancy prediction, gaining comparable results to clinical grading systems. With further large-scale prospective validation, ODYN promises to offer an objective and reliable solution for assessing OED cases, ultimately improving early detection and treatment of oral cancer.

Pituitary neuroendocrine tumour Ki-67 proliferation index varies according to the number of tumour cells assessed. Consistent Ki-67 scoring approaches and re-evaluation of the recommended Ki-67 3% cut-off are required to clarify controversies in pituitary neuroendocrine tumour Ki-67 proliferation index assessment.