2. Academic Validation
  3. The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma

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

  • Cancer Cell. 2021 Oct 11;39(10):1422-1437.e10. doi: 10.1016/j.ccell.2021.08.011.
Chloé B Steen 1,  Bogdan A Luca 2,  Mohammad S Esfahani 3,  Armon Azizi 4,  Brian J Sworder 3,  Barzin Y Nabet 5,  David M Kurtz 3,  Chih Long Liu 3,  Farnaz Khameneh 4,  Ranjana H Advani 3,  Yasodha Natkunam 6,  June H Myklebust 7,  Maximilian Diehn 5,  Andrew J Gentles 8,  Aaron M Newman 9,  Ash A Alizadeh 10
Affiliations

Affiliations

  • 1 Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
  • 2 Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
  • 3 Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA.
  • 4 Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
  • 5 Department of Radiation Oncology, Stanford University Medical Center, Stanford, CA 94305, USA.
  • 6 Department of Pathology, Stanford University Medical Center, Stanford, CA 94305, USA.
  • 7 Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for B-cell malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway.
  • 8 Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
  • 9 Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: [email protected].
  • 10 Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: [email protected].
PMID: 34597589 DOI: 10.1016/j.ccell.2021.08.011
Abstract

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

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

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