Prognostic Biomarkers Converge on a Germinal Centre Dark Zone Phenotype As a Determinant of Adverse Outcome in Follicular Lymphoma Patients Treated with Rituximab and Chemotherapy

Introduction: Follicular lymphoma (FL) is a clinically and genetically heterogeneous disease with highly variable patient outcomes. Recently, Huet et al. proposed a 23-gene expression-based risk score for predicting progression-free survival (PFS) in FL patients treated with rituximab and chemotherapy (Huet et al. Lancet Oncology 2018). The m7-FLIPI risk score has also been described as a clinico-genetic model predicting patient outcomes (Pastore et al. Lancet Oncology 2015). Moreover, EZH2 wild-type status and high expression of the FOXP1 transcription factor are associated with increased risk of lymphoma progression (Mottok et al. Blood 2018). This multitude of prognostic tools in FL raises the question whether they identify common biology. The aims of this study were to assess whether the 23-gene predictor score identifies a poor risk group of patients in our own gene expression dataset, and whether commonality exists between the 23-gene score, the m7-FLIPI, EZH2 mutation status and FOXP1 expression.

Methods: In our previous work, we generated Illumina DASL microarray expression profiles for 137 FL patients who were treated with rituximab and CVP chemotherapy (cyclophosphamide, vincristine and prednisone). Using genes from the 23-gene linear risk predictor, we determined each patient's risk score by setting coefficients at -1 and +1 for genes associated with favorable and unfavorable PFS, respectively. We dichotomized the distribution of scores using the maximally selected log-rank statistic. We also performed unsupervised, hierarchical clustering to identify underlying subgroups in an unbiased fashion. Survival analyses were performed using the log-rank test and Cox regression analyses. We used gene set enrichment analysis to identify concordant differences of relevant gene signatures between specimens with either low or high expression of FOXP1.

Results: Twenty genes from the 23-gene predictor (87%) were identified in the DASL gene expression dataset. The coefficients from univariate Cox regression analysis from our data were correlated with coefficients from Huet et al. (Pearson r = .7, P < .001; Spearman r = .44, P = .051). All poor-risk genes from the 23-gene predictor were associated with poor PFS in our data, and vice versa. Concordantly, calculated risk scores were significantly associated with PFS in the univariate Cox regression analysis (P = .007). Dichotomizing the distribution of risk scores identified 68% of cases with high risk score who had inferior PFS and OS compared to 32% of cases with low risk score (5-year PFS 54% vs. 77%, P = .004; 5-year OS 73% vs. 86%, P = .04). Hence, the risk score stratified patients into groups with diverging outcomes. This association was found to be independent of the Follicular Lymphoma Prognostic Index (FLIPI). In addition, the mean risk score was significantly higher in cases with high expression of FOXP1 (P < .001) and in cases with high m7-FLIPI risk score (P = .023). Unsupervised hierarchical clustering identified two main clusters ("cluster 1" and "cluster 2") that were characterized by low and high expression of genes associated with poor outcome, respectively. Patients from "cluster 2" experienced worse PFS compared to patients in "cluster 1" (P = .046; 5-year PFS 54% vs. 68%). The 5-year OS was 72% for patients in "cluster 2", vs. 81% in "cluster 1" (P = .13).

We have previously reported that a germinal centre dark zone signature is enriched in cases with high FOXP1 expression, and the ICA13 signature reported by Huet et al. has been described as being highly expressed in centroblasts. Using gene set enrichment analysis, we found that genes with positive weight and coefficients in the ICA13 and the 23-gene predictor score, respectively, were enriched in the FOXP1-high phenotype (adjusted P = .009 and .005, respectively). GeneMANIA illustrated co-expression interconnectivity among ORAI2, TCF4, AFF3, FOXO1, CXCR4 and FOXP1, suggesting that genes with prognostic significance operate in tightly regulated networks.

Conclusions: Our results exemplify the robustness of the predictor model by Huet et al. Further, we demonstrate biomarker convergence on a common phenotype: FOXP1 expression, EZH2 wild-type status and expression of dark zone-related genes, which characterize a subset of FL cases with adverse outcome following rituximab and chemotherapy.