Rimsza, Lisa M.

Abstract Description Follicular Lymphoma (FL) is a germinal center-derived malignancy representing approximately 20% of all non-Hodgkin’s lymphomas. Despite a relatively indolent disease course, FL is a biologically and clinically heterogeneous disease which poses significant clinical challenges due to its propensity for relapse and transformation into aggressive and untreatable forms of lymphoma. Here, we have leveraged computational techniques with WES, RNA-sequencing, and immune profiling data from FL tumors to gain further insight into the biologic basis for FL heterogeneity. We performed computational reconstruction of the B cell receptor repertoire on B cell sorted RNA-sequencing data from normal lymph nodes (n = 5) and FL tumors (n = 89). To gain a better understanding of FL B cell biology at the single cell level, we further investigated the clonal architecture of dominant malignant clonotypes in FL tumors using computational analysis of paired single cell V(D)J and 5’ GEX sequencing data from FL tumors (n = 21) and non-malignant lymph nodes (n = 3), allowing us to characterize the germinal center programs in malignant versus non-malignant B cells. Our data suggest that a subset of FL tumors consist of complex polyclonal B cell populations, some of which represent a pre-malignant FL precursor. Our data provide a deepened understanding of FL biology, including precursor cells, and contributes a refined transcriptomic characterization of malignant and non-malignant B cells in Follicular Lymphoma. Funding Sources This work was supported by the National Institutes of Health (R01 CA212162-01A1 to A.J.N and J.R.C., and P50 CA097274 to J.R.C., and A.J.N) Topic Categories Computational and Systems Immunology (COMP)

Abstract Although follicular lymphoma (FL) typically follows an indolent course, patients with FL who experience early events, such as transformation or progression, have increased risk of death related to lymphoma. The FL24Cx is an algorithm based on a 45-target gene expression profiling (GEP) assay, which was developed and trained using 265 formalin-fixed, paraffin-embedded tissue samples on a reliable platform to predict, at the time of diagnosis, whether a patient will experience an event within 24 months. The modeling also confirmed and relied upon previously reported synergy between immune response (IR) gene expression signatures IR1 and IR2. Once locked, the 5-factor logistic regression FL24Cx model was independently validated in a retrospectively assessed cohort of 232 patients from 2 immunochemotherapy-treated arms of SWOG Cancer Research Network S0016 phase 3 clinical trial, in which it assigned 169 patients to the low-risk group with 29 events before 24 months (17.2%) and 63 patients to the high-risk group with 24 events before 24 months (38.1%). The relative risk of an event within 24 months after registration among patients who were classified into the high-risk group relative to patients who were classified into the low-risk group was 2.2 (95% confidence interval, 1.41 to 3.51). An up-front GEP biomarker, such as the FL24Cx, rigorously validated in a clinical laboratory and with a clinically relevant turnaround time, could identify and steer enrollment of patients at high risk for early events in clinical trials, thus enabling timely interpretation of such trials and increasing the pace of innovation.