Cluster Analysis According to Immunohistochemistry is a Robust Tool for Non–Small Cell Lung Cancer and Reveals a Distinct, Immune Signature-defined Subgroup. Issue 4 (April 2020) - Sterlacci, William; Fiegl, Michael; Juskevicius, Darius; Tzankov, Alexandar
Abstract : Clustering in medicine is the subgrouping of a cohort according to specific phenotypical or genotypical traits. For breast cancer and lymphomas, clustering by gene expression profiles has already resulted in important prognostic and predictive subgroups. For non–small cell lung cancer (NSCLC), however, little is known. We performed a cluster analysis on a cohort of 365 surgically resected, well-documented NSCLC patients, which was followed-up for a median of 62 months, incorporating 70 expressed proteins and several genes. Our data reveal that tumor grading by architecture is significant, that large cell carcinoma is likely not a separate entity, and that an immune signature cluster exists. For squamous cell carcinomas, a prognostically relevant cluster with poorer outcome was found, defined by a high CD4/CD8 ratio and lower presence of granzyme B+ tumor-infiltrating lymphocytes (TIL). This study shows that clustering analysis is a useful tool for verifying established characteristics and generating new insights for NSCLC. Importantly, for one "immune signature" cluster, the signature of the TIL (especially the amount of CD8+ TIL) was more crucial than the histologic or any other phenotypical aspect. This may be an important finding toward explaining why only a fraction of eligible patients respond to immunomodulating anticancer therapies.