Multimodal discovery of distinct tumor ecosystems for classic Hodgkin lymphoma Free

Introduction: Classic Hodgkin lymphoma (cHL) is distinguished by a unique tumor microenvironment (TME), which is comprised of rare Hodgkin-Reed-Sternberg (HRS) cells surrounded by immune cells. Although single cell and spatial techniques have advanced the characterization of the TME in cHL, prior studies lack comprehensive cohorts spanning the age and geographic spectrum. To address this challenge, we aimed to conduct a multimodal characterization of the TME in cHL using international patient cohorts of pediatric and adult cases including from EBV-endemic areas.

Methods: We identified a training cohort of 198 patients diagnosed with cHL from 2013-2025 diagnosed at 4 international institutions with either formalin-fixed, paraffin-embedded (FFPE) blocks (n=188) or fresh-frozen excisional biopsies (n=10). FACS purification and single cell RNA-seq (scRNA-seq) were performed on 8 cryopreserved tissues, and 2 were profiled using single nucleus RNA-seq (snRNA-seq). Using sc- and snRNA-seq data, we constructed and benchmarked a cHL-specific signature matrix for use with CIBERSORTx. Subsequently, we performed digital deconvolution of 188 bulk RNA-seq cases. Finally, we applied a machine-learning framework (EcoTyper) to discover transcriptionally-defined cell states and ecosystems. Validation of the EcoTyper model was performed using an independent microarray dataset (n=130) and Visium spatial transcriptomics data (n=4).

Results: The median age in the training cohort was 28 years (IQR=14-49), and 75 cases were EBV+ (44% of 169 tested). The median follow-up was 5.9 years (IQR=2.7-8.2). From sc- and snRNA-seq data, we profiled 44,769 cells and identified 16 distinct cell phenotypes including classical and plasmacytoid dendritic cells, gamma-delta T cells, natural killer cells, and HRS cells to derive the cHL-specific signature matrix. We performed benchmarking of the signature matrix using using several methods: pseudo-bulk resampling of scRNA-seq data, correlation of cell abundances from digital cytometry with spatial proteomics, and comparison of tumor genotyping allele frequencies versus HRS abundances. All approaches showed significant positive correlations (P<0.05). After performing digital deconvolution of the 188 FFPE samples, and using EcoTyper, we discovered 28 unique cell states clustered within 2 cellular ecosystems or “Hodgkin lymphoma ecotypes” (HLE). 56% of cases were predominantly HLE1 and were characterized by EBV+ HRS cells overexpressing oncogene BCAS1 and anti-apoptotic IL22RA, fibroblasts promoting endothelial development (VEGF, VCAM1), and abundant B cells and natural killer cells. Conversely, 44% of cases were predominantly HLE2, characterized by EBV- HRS cells expressing ECM remodeling genes (LUM, MMP2, COL1A1, FN1), fibroblasts promoting extracellular matrix reorganization (COL5A2, COL3A1, COL5A1, COL16A1, COL1A1), and macrophages expressing CXCL8 (a tumor migration marker). HLE2 was associated with worse freedom from progression (FFP) in our cohort (HR=3.18, P=0.048) after multivariable adjustment for the age, gender, and clinical stage. We used additional external validation cohorts profiled with Visium spatial transcriptomics (n=4) and RNA microarray (n=130, Steidl et al. NEJM 2010). We recovered the 2 HLE in the validation datasets and found a significant spatial colocalization of the cell states (median Z=25.4) in the Visium dataset, as well as a worse FFP for HLE2 after multivariable adjustment for the age, sex, and disease stage in the RNA microarray cohort (HR=1.81, P=0.02).

Conclusions: In a large cohort containing pediatric, elderly, and EBV+ cHL, we leveraged multimodal approaches to identify HRS, immune, and stromal cell signatures found within clinically relevant HLEs. We show that approximately half of cHL cases were predominantly HLE2 characterized by EBV- migratory HRS cells, fibroblasts promoting extracellular remodeling, and abundant macrophages. HLE2 is associated with worse freedom from progression in both training and validation cohorts. Further prospective validation of our HLEs is crucial particularly in the era of checkpoint inhibition and will aid in individualized therapeutic approaches.