Spatial transcriptomics identifies a suppressive, T-cell excluded tumor microenvironment in extramedullary myeloma
Nicholas Eardley Bingham, Julie R Boiko, Daniel C Jones, Daniel Wong, Tiffany Khong, Sridurga Mithraprabhu, Kathleen S. Ensbey, Anna E Elz, Evan W. Newell, Andrew Spencer, Geoffrey R HillExtramedullary disease (EMD) in multiple myeloma (MM) is associated with poor outcomes due to aggressive disease kinetics and therapy resistance. The bone marrow (BM) tumour microenvironment (TME) promotes cell survival and drug resistance in marrow restricted MM, but little is known about the TME in EMD. To characterize the tissue architecture of these tumors, we performed spatial transcriptomics on tumour biopsies. Like BM-restricted MM, we identified significant inter-patient heterogeneity in plasma cell (PC) expression profiles, although still maintaining a characteristic plasma cell transcriptome. The TME was dominated by macrophages with a suppressive "M2" phenotype. CD8+ T-cells frequently expressed exhaustion markers LAG3 and TIGIT, consistent with a suppressive TME. We identified three recurrent TME niches based on immune-cell composition: immune excluded, immune suppressed and immune permissive. The immune excluded niche comprised the bulk of tumors, reflecting spatial exclusion of immune cells. The suppressive extracellular matrix from abundant tumour-associated fibroblasts in the immune suppressive niche may further contribute to T-cell exclusion. Cell-cell interaction modelling revealed a complex, bidirectional network: PC modulated the TME through PGE2 and VEGFB, while receiving canonical pro-survival signals through CD38, CXCR4 and BCMA. Notably, TME cells, particularly fibroblasts and macrophages, are predicted to collectively promote the suppressive macrophage phenotype and fibrotic extracellular matrix, reinforcing an immunosuppressive milieu that supports local disease progression. These findings reveal the spatial organization of EMD, highlighting niche substitution rather than niche independence, and identify clinically tractable microenvironmental niches and signaling networks in this high-risk myeloma subset.