Abstract
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 eosinestained 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 eosinestained 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.
Document Type
Article
Publication Date
8-2023
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.labinv.2023.100176
Funding Information
This work was supported in part by a grant from the American Cancer Society (133123-RSG-19-081-01-TBG) and grants from the American Institute for Cancer Research (NIGMS P20 GM121327 and NCI R01 CA237643). This research was also supported by the Cancer Research Informatics, Biostatistics and Bioinformatics, and Biospecimen Procurement and Translational Pathology Shared Resource Facilities of the University of Kentucky, Markey Cancer Center (P30 CA177558).
Repository Citation
DuCote, Tanner; Naughton, Kassandra J.; Skaggs, Erika M.; Bocklage, Therese J.; Allison, Derek B.; and Brainson, Christine Fillmore, "Using Artificial Intelligence to Identify Tumor Microenvironment Heterogeneity in Non–Small Cell Lung Cancers" (2023). Markey Cancer Center Faculty Publications. 400.
https://uknowledge.uky.edu/markey_facpub/400
Notes/Citation Information
© 2023 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.