Using Artificial Intelligence to Identify Tumor Microenvironment Heterogeneity in Non-Small Cell Lung Cancers.

Lung cancer heterogeneity is a major barrier to effective treatments and encompasses not only the malignant epithelial cell phenotypes and genetics but also the diverse tumor-associated cell types. Current techniques used to investigate the tumor microenvironment can be time-consuming, expensive, complicated to interpret, and often involves destruction of the sample. Here we use standard hematoxylin and eosin-stained tumor sections and the HALO AI nuclear phenotyping software to characterize 6 distinct cell types (epithelial, mesenchymal, macrophage, neutrophil, lymphocyte, and plasma cells) in both murine lung cancer models and human lung cancer samples. CD3 immunohistochemistry and lymph node sections were used to validate lymphocyte calls, while F4/80 immunohistochemistry was used for macrophage validation. Consistent with numerous prior studies, we demonstrated that macrophages predominate the adenocarcinomas, whereas neutrophils predominate the squamous cell carcinomas in murine samples. In human samples, we showed a strong negative correlation between neutrophils and lymphocytes as well as between mesenchymal cells and lymphocytes and that higher percentages of mesenchymal cells correlate with poor prognosis. Taken together, we demonstrate the utility of this AI software to identify, quantify, and compare distributions of cell types on standard hematoxylin and eosin-stained slides. Given the simplicity and cost-effectiveness of this technique, it may be widely beneficial for researchers designing new therapies and clinicians working to select favorable treatments for their patients.