Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach - Sundaram, Srinand; Kim, Eun Na; Jones, Georgina M.; Sivagnanam, Shamilene; Tripathi, Monika; Miremadi, Ahmad; Di Pietro, Massimiliano; Coussens, Lisa M.; Fitzgerald, Rebecca C.; Chang, Young Hwan; Zhuang, Lizhe (Frontiers in Immunology, 2022)
Esophageal adenocarcinoma (EAC) develops from a chronic inflammatory environment across four stages: intestinal metaplasia, known as Barrett’s esophagus, low- and high-grade dysplasia, and adenocarcinoma. Although the genomic characteristics of this progression have been well-defined via large scale DNA sequencing, the dynamics of various immune cell subsets and their spatial interactions in their tumor microenvironment remain unclear. Here, we applied a sequential multiplex immunohistochemistry (mIHC) platform with computational image analysis pipelines that allows for the detection of 10 biomarkers in one formalin-fixed paraffin-embedded (FFPE) tissue section. Using this platform and quantitative image analytics, we studied changes in the immune landscape during disease progression based on 40 normal and diseased areas from endoscopic mucosal resection specimens of chemotherapy treatment naïve patients, including normal esophagus, metaplasia, low- and high-grade dysplasia, and adenocarcinoma. The results revealed a steady increase of FOXP3+ T regulatory cells and a CD163+ myelomonocytic cell subset. In parallel to the manual gating strategy applied for cell phenotyping, we also adopted a sparse subspace clustering (SSC) algorithm allowing automated cell phenotyping of mIHC-based single cell data. The algorithm successfully identified comparable cell types, along with significantly enriched FOXP3 T regulatory cells and CD163+ myelomonocytic cells as found in manual gating. In addition, SCC identified a new CSF1R+CD1C+ myeloid lineage, was previously unknown in this disease, but also increases with advancing disease stages. This study revealed immune dynamics in EAC progression and highlighted the potential application of a new multiplex imaging platform, combined with computational image analysis on routine clinical FFPE sections to investigate complex immune populations in tumor ecosystems.