Computational pathology and deciphering the complexity of HER2 expression in breast cancer - Atallah, Nehal Mohammed (2025)
Background Breast cancer (BC) is the most common cancer and second leading cancer-related death in women. Despite significant advancement in the detection, prognostic and predictive classification of BC, there remains a proportion of patients who recur and die of their disease. Further research on BC diagnosis and classification is needed. Digital pathology (DP) and molecular assays, including whole slide imaging (WSI), digital image analysis (DIA) and artificial intelligence (AI), are revolutionising BC diagnosis and treatment, enabling detailed cyto-morphometric assessments that enhance diagnostic accuracy, personalise treatment, and improve outcomes. Although the therapeutic roles of HER2 protein in BC have been defined for more than two decades, the spectrum of HER2 expression in BC is evolving, and therapeutic modalities are expanding. HER2-positive cases are candidates for targeted anti-HER2 therapy, but patient response varies, with recurrence rates ranging from 20-40%. The introduction of HER2-Low BC and antibody-drug conjugates (ADCs) has transformed the management of HER2-negative BC but added complexity to HER2 assessment, particularly regarding the lower limits of ADC therapy eligibility. This thesis evaluates the integration of computational pathology and advanced molecular approaches in identifying novel BC prognostic parameters and deciphering the complexity of HER2 expression, including its biological and clinical characterisation. It highlights differential responses to targeted anti-HER2 therapies among HER2-positive classes, the role of hormone receptor (HR) expression, and refines the definition of HER2-Low BC. Methods and Results A large cohort of 40,160 breast WSIs was analysed to assess the accuracy and utility of WSI compared to traditional microscopy. The assessment focused on the presence, frequency, location, and tissue type of missing tissue and its clinical implications. Additionally, scanning time, specimen type, time to implement WSIs and quality control measures were evaluated. The results indicated