Abstract
Peripheral T-cell lymphomas (PTCL) are aggressive hematologic cancers characterized by complex tumor microenvironments (TME) with rich hematopoietic and non-hematopoietic elements. A deeper understanding of both the malignant T cells and the TME could reveal therapeutic vulnerabilities in these lymphomas, which respond poorly to conventional chemotherapy. However, the cellular architecture, transcriptional programs and tumor cell-TME interactions have not been comprehensively characterized in PTCL.
Here, we performed an integrated, multiomic analysis, including single-nucleus RNA sequencing (snRNA-seq), bulk RNA sequencing, and targeted mutational profiling, on archival samples from 29 frozen PTCL biopsies- including T follicular helper cell lymphoma (TFHL) and PTCL, not otherwise specified (PTCL, NOS), along with six control lymph nodes and tonsils. After rigorous quality control, a total of 230,174 cells were analyzed. Unsupervised clustering following dimensionality reduction revealed 13 distinct benign and malignant T-cell states and 42 transcriptionally unique cell types (14 B-cell subsets, 14 myeloid subsets, 6 endothelial cell subsets, and 8 non-endothelial stromal cell subsets), many of which have not been previously characterized in PTCL.
Analysis of malignant T cells revealed substantial heterogeneity in PTCL, NOS, linked to chromosomal abnormalities, while TFHL tumors exhibited less genetic and transcriptional variability and were associated with specific transcriptional programs driven by RHOA G17V and IDH2 mutations. Both subtypes showed enrichment in MYC targets, TNF-α signaling, mTOR signaling, mitosis, and cell cycle pathways, whereas MAPK and STAT5 signaling were uniquely enriched in TFHL.
TME analysis revealed shared hallmarks across PTCL, NOS and TFHL, including B-cell depletion and expansion of exhausted CD8+ T cells and tumor-associated macrophages (Mφ). In contrast, enrichment of immunoblasts, regulatory T cells, and non-endothelial stromal cells represents a disease-specific alteration in TFHL. Decomposition of transcriptomic profiles from all cell types present in PTCL and control samples using non-negative matrix factorization (NMF) identified 104 gene expression programs, 36 of which showed differential per-sample activity between PTCL TME and normal controls. Correlation analysis of PTCL-specific gene programs revealed five multicellular modules, including two non-overlapping modules: one characterized by follicular dendritic cells (FDCs) and inflammatory Mφ, and the other by cancer-associated fibroblasts (CAFs) and M2-like Mφ. Cell-cell communication analysis further identified TNF-α and TGF-β as key ligands orchestrating these TME modules. Finally, computational modeling and in vivo perturbation of these interactions uncovered therapeutic vulnerabilities, including a previously unrecognized role for the TGF-β pathway in supporting PTCL tumor growth. Together, these findings provide new insights into PTCL biology and establish a foundation for TME-informed therapeutic strategies, offering potential avenues for patient stratification and targeted intervention.
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