Exploring the Tumor Microenvironments of Follicular Helper T-Cell Lymphomas of Angioimmunoblastic-Type and Follicular-Type Using the Geomx® Digital Spatial Profiler
Authors: Craig Jeffrey W , Chaudhary Sahil , Pramoonjago Patcharin , Pal Ipsita , Roberts Nathan , Marchi Enrica 
Published: December 2024

Angioimmunoblastic T-cell lymphoma (AITL) and follicular T-cell lymphoma (FTCL) represent the two most well-defined manifestations of follicular helper T-cell lymphoma. The overlapping clinical, immunophenotypic and genetic features of these T follicular helper (TFH) cell-derived neoplasms belie their divergent growth patterns while suggesting that both are part of the same disease spectrum. Recent single-cell transcriptomic analyses provide a comprehensive description of the tumor microenvironment (TME) in AITL, with functional studies further indicating a role for non-malignant populations in AITL pathobiology. Meanwhile, the composition and role of the TME in FTCL remains understudied. In particular, the use of emerging spatial transcriptomics technologies has rarely been applied to follicular helper T-cell lymphomas of any variety.

To better asses the TME composition in FTCL, we performed spatial transcriptomics using the GeoMx® Digital Spatial Profiler (NanoString Technologies) on nodal follicular helper T-cell lymphomas of follicular type (n = 3 tumors) and angioimmunoblastic type (n = 4 tumors) as well as on benign lymphoid follicles from sections of reactive tonsil (n = 4). Regions of interest (ROI; 4 per tumor) were identified through the evaluation of H&E-stained sections as well as routine diagnostic immunohistochemical stains. A trio of morphology markers consisting of a pan-T-cell antigen (CD3) and a pair of sensitive TFH antigens (PD-1 and ICOS) was used to define populations of TFH-like cells for microdissection, while the remaining tissue within each respective ROI was designated as bulk TME.

Compared to their corresponding bulk TME, the TFH-like fractions from both the FTCL and AITL groups exhibited significantly greater expression of genes corresponding to the employed morphology markers (CD3D, CD3E, CD3G, PDCD1, ICOS) as well as genes encoding additional TFH markers (CXCR5 and/or CXCL13) and numerous other TFH-related genes (e.g., BATF, CD40LG, IL21, MAF, TIAM1, TCF7, TNFRSF4, TOX, TOX2), supporting the validity of this approach. Upon dimensional reduction, the bulk TME from the nodular FTCL follicles formed an extended cluster either adjacent to (UMAP) or partially within (tSNE) the bulk TME from tumor cell-rich regions of AITL, while both groups were separate from the microenvironment of benign lymphoid follicles.

Quantification of immune and non-immune cell subtypes was performed using SpatialDecon. Compared to benign lymphoid follicles, the follicles of FTCL were significantly enriched in macrophages, memory CD8+ T cells, NK cells, plasmacytoid dendritic cells and myeloid dendritic cells; however, only memory CD8+ T cells and myeloid dendritic cells remained significant after accounting for the intrinsically B-cell-rich nature of the benign follicles. FTCL follicles were proportionally enriched in naïve B cells compared to tumor cell-rich regions of AITL, in keeping with the presence of mantle zone-type lymphocytes in both the follicular lymphoma-like and progressive transformation of germinal centers-like growth patterns of FTCL. No other statistically significant differences in immune cell subtype composition were observed between FTCL and AITL in this small cohort.

In conclusion, this work represents the successful application of spatial transcriptomics to follicular helper T-cell lymphomas, and is the first such study to include FTCL. Our findings highlight significant differences in the composition of FTCL follicles compared to benign lymphoid follicles as well as substantial overlap between the cellular compositions of FTCL and AITL, in keeping with the plethora of shared biology between these entities. Further analysis will benefit from technologies affording single-cell resolution.