bioRxivCancer BiologyPreprint
Multidimensional characterization of cellular ecosystems in Hodgkin lymphoma
Tomohiro AokiiD, Gerben DunsiD, Shinya RaiiD, Aixiang JiangiD, Andrew Lytle, Yifan YiniD, Makoto KishidaiD, Michael Yu LiiD, Denise SmorraiD, Laura K. HiltoniD, Stefan AligiD, Mohammad Shahrokh EsfahaniiD, Clémentine SarkozyiD, Stacy S. HungiD, Katy MilneiD, Adèle TeleniusiD, Luke O’BrieniD, Celia StrongiD, Talia GoodyeariD, Chantal Di VitoiD, Cassandra LuksikiD, Glenn EdiniD, Laura Gonzalez, Juan Patino Rangel, Michael HongiD, , Christian SteidliD
Abstract
The tissue architecture of classic Hodgkin Lymphoma (CHL) is unique among cancers and characterized by rare malignant Hodgkin and Reed-Sternberg cells that co-evolve with a complex ecosystem of immune cells in the tumor microenvironment (TME). The lack of a comprehensive systems-level interrogation has hindered the description of disease heterogeneity and clinically relevant molecular subtypes. Here, we employed an integrative, multimodal approach to characterize CHL tumors using malignant cell sequencing, spatial transcriptomics and imaging mass cytometry. We identified four molecular subtypes (CST, CN913, STB, and CN2P), each characterized by distinct clinical features, mutational patterns, malignant cell gene expression profiles, and spatial architecture involving immune cell populations. Functional modeling of CSF2RB mutations, a characteristic feature of the CST subtype, revealed dysregulated oncogenic signaling and unique TME crosstalk. These findings highlight the significance of multi-dimensional profiling in elucidating patterns of molecular alterations that drive immune ecosystems and underlie therapeutically exploitable vulnerabilities.
- organism: human
- assay: spatial transcriptomics
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 1
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