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AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and correlates with survival in breast cancer

SABCS 2022

Study Background

Morphological features of cancer cell nuclei are routinely used to assess disease severity and prognosis, and prior work has linked cancer nuclear morphology to genomic alterations. In addition, the cellular composition of cancer-associated stroma (CAS) has been linked to prognosis in several cancer types, including breast cancer.
• Quantitative analyses of 1) nuclear features of cancer cells and other tumor-resident cell types, such as cancer-associated fibroblasts (CAFs), and 2) composition of CAS may reveal novel biomarkers for prognosis and treatment response.
• Here, we applied a nucleus detection and segmentation algorithm, a cell classification model, and a stromal subdivision model to hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of breast cancer specimens, enabling the assessment of features related to nuclear morphology and stromal composition.
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Abel et al.