Quantification and molecular correlates of tertiary lymphoid structures in primary prostate cancer

Abstract Objective To morphologically describe tertiary lymphoid structures (TLS) in prostatectomy specimens and correlate them with clinical and transcriptomic features. Methodology A total of 72 consecutive cases of entirely submitted radical prostatectomy (RP) patients tested with the Decipher Genomic Classifier were included in the study. Images were manually annotated using QuPath tools to denote tumor regions and each cluster of TLS. Clusters of lymphocytes that were surrounded on all four sides by tumor were defined as intra‐tumor TLS (IT‐TLS). Clusters of lymphocytes at the leading edge of carcinoma with either the prostatic pseudocapsule or benign parenchyma at one end were defined as peri‐tumor TLS (PT‐TLS). A classification algorithm to distinguish lymphocytes from non‐lymphocytic cells using a supervised machine learning model was used. The associations between TLS formation and 265 gene expression‐based signatures were examined. Results The magnitude of total TLS correlations with primary tumor gene expression signatures was moderate (~0.35–0.5) with several HLA, T‐cell and B‐cell Cluster signatures, showing positive correlation with various metrics for quantification of TLS. On the other hand, immune suppressive signatures (Treg, MDSC) were negatively correlated. While signatures for macrophages, NK cells and other immune cell types were uncorrelated for the most part. PT‐TLS was associated with MHC signatures while IT TLS correlated with MHC and T‐cell signatures. Conclusions Clusters of inflammatory cells in the RP specimen can be divided spatially into PT TLS and IT‐TLS, each with its unique molecular correlates of tumor immune microenvironment. The presence of TLS is positively correlated with MHC signatures, T‐ cell and B‐cell cluster signatures but, negatively correlated with immune suppressive signatures. A subset of prostate cancer demonstrate a robust inflammatory response, and warrant further characterization in larger cohorts.