Marc Hermanus Gerardus Petrus Raaijmakers

Validation of the myeloma tumor microenvironment using an independent publicly available spatial transcriptomics cohort.

Spatial transcriptomics cellular annotation and quality control.

Neighborhood and interaction dynamics across disease stages and plasma cell architecture.

The pathobiology of multiple myeloma is influenced by cells of the bone marrow (BM), but whether tumor support is organized in a spatially defined tumor microenvironment (TME) remains unclear. Using spatial transcriptomics and imaging mass cytometry, we show that myeloma cells initially exist as scattered cells throughout the marrow, yet with disease evolution condense into a spatially defined TME. Dense tumor nodules contain a cellular ecosystem distinct from the surrounding marrow, enriched for macrophages, conventional dendritic cells, and CD8+ T cells, as well as endothelial cells and THY1+ mesenchymal stromal cells. Conversely, neutrophil lineage cells are absent from this TME but interact with scattered myeloma cells outside of dense nodules. The presence of the dense tumor architecture at diagnosis confers a worse prognosis, emphasizing the relevance of spatial organization of the BM. Together, these findings define a discrete spatial and cellular myeloma TME that provides spatially guided insights into patient stratification and disease pathobiology. Significance:This study provides identification of a topographically defined TME for multiple myeloma, with a specific cellular ecosystem. This TME develops during disease evolution and is linked to worse outcomes in newly diagnosed patients. Our work provides insight into the interplay of tumor evolution and spatial organization in multiple myeloma.

Spatial transcriptomics cellular annotation overview.

Cellular localization and plasma cell-density analysis.

Association of bone marrow nodule growth patterns with clinical outcome.

Cellular composition differences across the myeloma disease spectrum.

Structural annotation and immunofluorescence imaging.

Segmentation and annotation of imaging mass cytometry images.