Cancer Cell
Volume 39, Issue 10, 11 October 2021, Pages 1422-1437.e10
Journal home page for Cancer Cell

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

https://doi.org/10.1016/j.ccell.2021.08.011Get rights and content
Under an Elsevier user license
open archive

Highlights

  • Large-scale profiling of cell states & cellular ecosystems in hematologic malignancies

  • Atlas of malignant B cell states and 12 cell types in the DLBCL tumor microenvironment

  • Nine DLBCL cellular ecosystems & their relationships to molecular subtypes and survival

  • Candidate cellular biomarkers of response to bortezomib in DLBCL

Summary

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.edu/lymphoma).

Keywords

DLBCL
diffuse large B cell lymphoma
digital cytometry
tumor microenvironment
tumor ecosystems
tumor immunology
lymphoma
EcoTyper
CIBERSORTx
expression deconvolution

Data and code availability

  • Single-cell RNA-seq data have been deposited at GEO and are publicly available as of the date of publication. The accession number is listed in the key resources table. This paper also analyzes existing, publicly available data. These accession numbers for the datasets are listed in the key resources table.

  • The original code for EcoTyper is available as of the date of the publication for non-profit academic use. The DOI is listed in the key resources table. Updates to the code will be available at https://ecotyper.stanford.edu/lymphoma.

  • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Cited by (0)

10

These authors contributed equally

11

Senior author

12

Lead contact