Introduction

Classical Hodgkin Lymphoma (cHL) is one of the most manageable human cancers. The early identification of patients at risk of relapse following front-line therapy, currently represents an unsolved clinical need. cHL is a complex ecosystem characterized by an intricate and mutual interaction between an extensively dominant tumor microenvironment (TME) and malignant Hodgkin Reed-Sternberg (HRS) cells; disease progression likely reflects some innate features of this organization. Nevertheless, even the most advanced prognostic models fail to fully account for the clinical heterogeneity of the disease. A major limitation lies in the inherent difficulty of profiling HRS cells, given their rarity and unique distribution within the tumor parenchyma. In this study, we employed an integrated multidimensional transcriptomic analysis to explore the peculiar architecture of the cHL tumor ecosystem and its potential relationship with clinical progression and patient outcomes.

Methods

Patient cohort: A retrospective training cohort of untreated cHL patients (N=155), age < 65 years and including all disease stages, was selected. Patients were classified as either relapsed/refractory (R/R+) (N=31) or non-relapsed (R/R-) (N=124) based on a minimum follow-up of 3 years.

A morphology-guided spatial transcriptomics approach was used to analyze the transcriptional profile of HRS and TME components in surgical lymph node resections of a matched R/R-:R/R+ case-control subset of the cohort. A total of 118 Areas of Interest (AOIs) were collected, 56 from the TME (CD45+/CD30-) and 62 from HRS-rich regions (CD30+). Digital barcoding profiling was used to assess the expression of 770 immune-related genes across the entire training cohort. Cox regression analysis was used to identify genes significantly associated with progression-free survival (PFS). IHC was used to confirm the validity of the derived model.

Results

Morphology-guided profiling of HRS cells showed significant differences between R/R+ and R/R- cHLs. Unsupervised clustering based on the transcriptional profile of HRS cells strongly segregated AOIs according to clinical classification. Topographical organization of HRS within the tumor parenchyma, along with paracrine signaling, accounted for HRS differential properties in the two groups. The TME in R/R+ cHLs exhibited an immunosuppressed phenotype, defined by the exclusion of non-tumoral B-cells and overrepresentation of immunosuppressive populations (M2 Macrophages, Tregs and Mast cells). This phenotype was associated with altered immune-modulatory signaling by HRS cells, involving inhibition of stimulatory mediators such as chemotactic cytokines (CXCL9, CXCL10, etc) and IFN signaling and the upregulation of inhibitory molecules including CCL17 (encoding for TARC) and IL13. An inverse correlation was observed between the presence of non-tumoral B-cells and immunosuppressive populations. Digital barcoding gene expression analysis on the entire cohort (N=155) confirmed this model and identified a non-tumoral B-cell specific gene signature (N=18) that significantly correlated with improved PFS and stratified patients according to risk of progression (HR 0.23 95% CI 0.06-0.87; p=0.03)

Conclusion

This study provides, one of the first molecular characterization of HRS cells and of their interplay with the surrounding TME, uncovering mechanisms that drive immune escape and disease progression. Notably, our findings reveal a previously unrecognized protective role of non-malignant B cells residing in the TME of cHL, highlighting their potential utility as biomarkers for risk stratification in cHL. Finally, our data demonstrate that integrating high-resolution transcriptional profiling with morphology enables a more precise understanding of cHL complexity, capturing not only intrinsic variability but also the reciprocal spatial organization between cell populations, which significantly shape the biological properties of the entire tumor ecosystem.

REFERENCES

1. Scott, DW &Gascoyne, RD The tumour microenvironment in B cell lymphomas. Nat. Rev. Cancer 14, 517–534 (2014)

2. Aoki, T. et al. Single-cell transcriptome analysis reveals disease-defining t-cell subsets in the tumor microenvironment of classic hodgkin lymphoma. Cancer Discov.10, 406–421 (2020)

3. Aoki T. et al. Spatially Resolved Tumor Microenvironment Predicts Treatment Outcomes in Relapsed/Refractory Hodgkin Lymphoma. J Clin Oncol. doi: 10.1200/JCO.23.01115. (2024)

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2025
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