Prostate cancer remains one of the most prevalent cancers detected in males, but adequate prognostic biomarkers that can distinguish between indolent and aggressive disease are currently lacking. An inflamed tumour microenvironment can lead to the development of tertiary lymphoid structures (TLS), which can mount adaptive effector and memory immune responses than can inform patient outcomes. In functional TLS, B cells can undergo dynamic tumour antigen-driven development into antibody-secreting cells (ASCs).
In this study, we aimed to develop in-house Opal™-TSA multiplex immunohistochemistry panels to adequately identify B cells, ASCs, and TLS, as well as characterise TLS based on their maturity. We then used these panels to investigate their prognostic value in a prostate cancer cohort of 64 low- and high-grade individuals, using treatment naïve prostatectomy tissue sections.
We successfully developed two in-house mIHC panels, including a B cell and TLS panel. ASCs were more commonly seen surrounding tumour areas (n=57/64 in peritumoural regions vs. n=44/63 in intratumoural regions, chi-square p-value=0.0024). Interestingly, a third of specimens lacked tumour-infiltrating ASCs (31%, n=20/64). ASCs were more abundant in patients with high-grade disease when located in both intratumoural (64% low-grade vs. 79% high-grade, chi-square p-value=0.0188) and peritumoural regions (87% low-grade vs. 100% high-grade, chi-square p-value=0.0001). It was evident that most relapse patients contained abundant intratumoural ASCs (70% of relapse vs. 26% of non-relapse patients, chi-square p-value=0.0248). When investigating TLS, these were detected in most patient samples (73%, n=47/64), in both proximal and distal tumour areas. TLS were mostly mature (92%, n=43/47), and often surrounded by ASCs. Mature TLS were more abundantly seen in relapse patients (100% relapse vs. 90% non-relapse; chi-square p-value=0.0012).
In conclusion, B cells and TLS in the tumour microenvironment of prostate cancer patients can be used to distinguish between low- and high-grade disease and to predict the likelihood of disease relapse.