Abstract 2529: T-cell phenotype and differentiation vary in the tumor microenvironment of follicular lymphoma and are associated with patient outcome
Authors: Yang Zhi-Zhang , Kim Hyo Jin , Wu Hongyan , Tang Xinyi , Krull Jordan , Mondello Patrizia , Villasboas Jose , Novak Anne , Ansell Stephen 
Published: June 2022

Previous studies have found that the prevalence of subtypes of T cells may be associated with patient outcomes, but a comprehensive analysis of the immune profile in the TM of FL has not been done. In the present study, we identified groups of FL patients with discretely unique TMEs and determined whether the T-cell phenotypes in the TME differed among groups. Using a cohort of 82 FL patients with biopsy specimens collected before treatment, we defined the type of TME based on the content of major lineages (T, B, monocytes/macrophages and NK cells) determined by CyTOF analysis. Hierarchical clustering of this cohort stratified patients into 4 groups with different TME: group 1 (G1) included patients with a high percentage of monocyte/macrophages/NK cells; patients from G2 and G3 were enriched for intratumoral T and B cells, respectively. Patients with intermediate numbers of T and B cells were included in G4. CITRUS analysis revealed that T-cell clusters with phenotypes expressing KLRG1, CD57, PD-1dim and rich in TEMRA cells were significantly more abundant in G1 when compared to G2, G3 or G4. In contrast, T-cell clusters with phenotypes containing CD127, CD45RA, CCR7 or PD-1high cells were significantly less abundant in G1 when compared to other groups. When compared to G3 or G4, 2 classes of T cell clusters, all from CD4+ T cells, were significantly more (CD127+KLRG1-) or less (CD57+PD-1high) abundant in G2, respectively. Clusters with a phenotype rich in short-lived effector cells (SLEC) (from CD8+) were upregulated in G3 when compared to G4. These results suggest that patient groups with distinct TME exhibited variable T-cell phenotypes. To determine the role of T-cell differentiation in predicting patient outcome in FL, we identified T-cell subsets using tSNE plots based on the expression of T-cell maturation markers. We identified 18 subsets (S1-S18) of T cells (11 from CD4, 6 from CD8 and 1 from CD4-CD8-) in each sample. Four subsets - S4 (CD4+TN), S5 (CD4+TCM), S7 (CD8+TN) and S10 (CD8+MEPCs) - were considered to be naïve cells or cells in the early stages of differentiation. Four additional subsets - S8 (CD57+TFH), S9 (CD4+TEXH), S13 (CD8+SLECs) and S17 (CD4+PD-1+Treg) - were memory cells with expression of surface markers indicting late-stage differentiation. We found that the 4 subsets (S4: p=0.08, S5: p=0.01, S7: p=0.04 and S10: p&lt;0.01) with an early-stage phenotype were associated with a favorable clinical outcome. In contrast, subsets (S8: p=0.02, S9: p=0.04, S13: p=0.07 and S17: p=0.06) with a late-stage differentiation phenotype had an unfavorable survival. Supporting this finding, we observed that increased numbers of CD45RA+ T cells correlated with a favorable survival. These results indicate that the differentiation stage may determine the role of T cells in predicting patient outcome in FL.

Citation Format: Zhi-Zhang Yang, Hyo Jin Kim, Hongyan Wu, Xinyi Tang, Jordan Krull, Patrizia Mondello, Jose Villasboas, Anne Novak, Stephen Ansell. T-cell phenotype and differentiation vary in the tumor microenvironment of follicular lymphoma and are associated with patient outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2529.