Translating Insights in Follicular Lymphoma for Improved Diagnosis and Classification - PROJECT SUMMARY/ABSTRACT Follicular lymphoma (FL) is an indolent, virtually incurable B-cell lymphoma and the most common small B-cell lymphoma in the United States. Most FL patients experience long-term survival rates similar to the general population; however, approximately 20% experience early disease progression within 24 months (POD24) of frontline chemoimmunotherapy (CIT)), often with histologic transformation (HT) to high-grade lymphoma and face a poor prognosis. When initiated early, intensified frontline treatment can improve outcomes in these high- risk patients, but currently, there are no reliable predictive biomarkers to guide frontline risk stratification and assignment to a risk-adapted treatment protocol at the time of diagnosis. The challenge of predictive biomarker discovery in FL is rooted, in significant part, in its heterogeneity both genetically and in the composition of the tumor microenvironment (TME). Consistent with these observations, the identification of recurrent genetic alterations in FL has led to only modest improvements in the predictive value of established clinical risk models. The m7-FLIPI, for example, currently represents one of the most robust clinicogenetic FL risk models to date for the prediction of POD24, yet performs with only 77% accuracy and a positive predictive value under 50%, suggesting that the determinants of early progression in FL extend beyond the clinicogenetic parameters that inform the current risk-classification paradigm. In addition, multiple in-depth genomic analyses of FL have failed to identify a single unifying genetic driver of transformation, supporting the concept that it more likely represents the culmination of multiple, progressively acquired genetic alterations. Several studies have identified components of the TME that contribute to disease pathogenesis or are correlated with clinical outcomes, including tumor-associated macrophage, T cells, and even components of the stroma, but these findings have not been translated to routine clinical practice. We have shown that the FL TME is enriched for transcriptional and functional subtypes of neoplastic B cells and non-B-cells and that TME alterations in cell composition and/or function can predict FL patient outcomes following CIT. To characterize the TME changes that are predictive of clinical outcomes, we will utilize bulk transcriptome, single-cell resolution protein, transcriptional profiling, and flow cytometry (FC) approaches to characterize pretreatment tissues and peripheral blood from well-annotated FL patient samples to identify biomarkers of CIT response. We will also elucidate the role of neutrophilic myeloid-derived suppressor cells (PMN-MDSCs) in FL development and therapy response using a novel mouse model of FL. In addition to identifying biomarkers of high-risk FL that can serve as the basis of novel predictive transcriptomic, immunohistochemical (IHC), or FC assays for use in the clinical setting, we expect these studies to improve our understanding of the biological mechanisms driving FL development and treatment outcomes.
