Multi-site validation of an AI-based biomarker test for determining ER, PR, and HER2 status from H&E-stained breast cancer slides

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Background: hybridisation (ISH) are widely used, but they are resource-intensive and time-consuming. To address these challenges, we developed PANProfiler Breast (PPB), an artificial intelligence (AI)-based digital test to analyse images of haematoxylin-eosin (H&E)-stained slides for BC biomarker assessment. Materials and methods: PPB was validated on a large, diverse cohort of 3138 unique BC samples from five sites across the UK and United States. The validation included both biopsy and resection specimens, digitally scanned using multiple imaging platforms. Standard performance characteristics were assessed by comparison with IHC and ISH reference assays. Results: PPB consistently demonstrated high performance across multiple cohorts, confirming its effectiveness for establishing the status of ER/PR and assessing HER2 negativity for BC patients. HER2 achieved an average sensitivity of 90.6% (±0.6) and a concordance of 93.7% (±7.8%) when determining the negative status. For ER, we measured an overall sensitivity and specificity of 98.2% (±0.3%) and 62.0% (±7.3%), averaged over all sites, respectively. For PR the averaged sensitivity and specificity across all sites were 97.9% (±1.0%) and 45.7% (±1.6%), respectively. Conclusions: Integrating PPB into routine clinical workflows could enable rapid and accurate assessment of BC biomarkers, improving access to targeted therapies and enhancing clinical decision making.

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