Evaluation of the pathogenetic role of cellular subsets of the Follicular Lymphoma Microenvironment

Follicular lymphoma (FL) is a B cell neoplasm, composed of follicle center cells, that accounts for about 20% of all lymphomas, with the highest incidence reported in the USA and western Europe. FL has been considered a virtually incurable disease, with a high response rate alternated with frequent post-therapy relapses or progression towards more aggressive lymphomas. Due to the extreme variability in outcome, many efforts were made to predict prognosis, the need for therapy, and the likelihood of evolution. Even if clinical scores turned out to be robust and easy to use in clinical practice for patient risk stratification, marked heterogeneity in outcome remains within each group and further insights into the biology of FL are needed. The genome-wide approach underscored the pivotal role of the FL microenvironment in the evolution of the disease. In 2004, a landmark study by Dave et al. first described the microenvironment impact on tumor biology. By gene expression profiling they identified two different immune response signatures, involving T-cells and macrophages which seemed to independently predict FL outcome, but their exact is not completely understood and different studies led to variable results. Subsequently, many workgroups identified in amount and distribution pattern of these different cell subsets features which can impact prognosis, this leading to hypothesizing the use of these parameters as surrogate markers of the molecular signature. We aimed to assess the possible contributions of micro-environmental components to FL transformation or progression, its relevance as a prognostic/predictive tool, and its potential role as an innovative therapeutic target. We used immunohistochemical techniques, focusing specifically on macrophages and T-cells subsets, and then found correlations between the presence, proportions, and distribution of these reactive cells and the clinical outcomes leading to the future development of a reliable tool for upfront risk stratification of patients affected by FL.