Novel insights into Hodgkin lymphoma biology by single-cell analysis

Abstract

The emergence and rapid development of single-cell technologies mark a paradigm shift in cancer research. Various technology implementations represent powerful tools to understand cellular heterogeneity, identify minor cell populations that were previously hard to detect and define, and make inferences about cell-to-cell interactions at single-cell resolution. Applied to lymphoma, recent advances in single-cell RNA sequencing have broadened opportunities to delineate previously underappreciated heterogeneity of malignant cell differentiation states and presumed cell of origin, and to describe the composition and cellular subsets in the ecosystem of the tumor microenvironment (TME). Clinical deployment of an expanding armamentarium of immunotherapy options that rely on targets and immune cell interactions in the TME emphasizes the requirement for a deeper understanding of immune biology in lymphoma. In particular, classic Hodgkin lymphoma (CHL) can serve as a study paradigm because of its unique TME, featuring infrequent tumor cells among numerous nonmalignant immune cells with significant interpatient and intrapatient variability. Synergistic to advances in single-cell sequencing, multiplexed imaging techniques have added a new dimension to describing cellular cross talk in various lymphoma entities. Here, we comprehensively review recent progress using novel single-cell technologies with an emphasis on the TME biology of CHL as an application field. The described technologies, which are applicable to peripheral blood, fresh tissues, and formalin-fixed samples, hold the promise to accelerate biomarker discovery for novel immunotherapeutic approaches and to serve as future assay platforms for biomarker-informed treatment selection, including immunotherapies.

Graphical abstract

Figure 1.

Typical workflow for single-cell analysis. Target cell populations are purified from patient samples and constructed libraries from sorted cell populations are sequenced. Next, raw data is preprocessed to remove poor quality cells followed by normalization. Subsequently, batch correction is performed to remove technical variations due to multiple experiments. Finally, data are visualized by dimensionality reduction methods, and further downstream analyses are performed including differential expression analysis, cell-to-cell communication prediction and trajectory analysis.

Figure 2.

Overview of single-cell and tissue architecture analyses. Two major specimen types are depicted to which single-cell technologies can be applied: (1) liquid biopsies (eg, blood) and disaggregated cell suspensions and (2) histologically intact tissues such as FFPET allowing for single-cell measurements in a spatial context. These 2 approaches work synergistically.

Figure 3.

Overview of cross talk between tumor cells and noncancerous immune cells in the TME of CHL. Interaction between HRS cells and numerous nonmalignant immune cells in the TME are shown according to cell type. These interactions play an important role promoting acquired immune evasion and activation of pathways that support the growth and survival of HRS cells through (1) ligand and receptor interactions, such as PD-1/PD-L1, LAG-3/MHC-II and (2) cytokines and chemokine milieus (eg, CXCL13, TGF-β). Several cross talk mechanisms and related treatment targets are highlighted. CXCL13, chemokine C-X-C motif ligand 13; CXCR5, C-X-C chemokine receptor type 5; IL-3, interleukin 3; TGF- β, transforming growth factor beta.