The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma

Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine-learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at systems-level resolution and identify opportunities for therapeutic targeting (https://ecotyper.stanford.eduilymphoma).

Automated summary

This study utilized the EcoTyper machine-learning framework to characterize cell states and ecosystems of DLBCL, identifying malignant B cell states with varying prognostic associations and diverse interactions in the tumor microenvironment. The results reveal the clinical heterogeneity captured within DLBCL ecosystems, extending beyond cell-of-origin subtypes and genotypic classes, providing opportunities for therapeutic targeting.