Development of a Spatial Multiomics Platform to Integrate Genomic, Transcriptomic and Proteomic Features for Translational Research in Multiple Myeloma
Authors: Gooding Sarah , Wang Chen-Yi , Baker Warren , Watson Edmund , Davis Simon , Fischer Roman , McCallion Oliver , Hester Joanna , Issa Fadi , Rao Srinivasa , Athanasou Nick , Thakurta Anjan , Ramasamy Karthik , Bjorklund Chad C. , Amatangelo Michael , Gandhi Anita K. , Cribbs Adam P , Lagana Alessandro , Oppermann Udo 
Published: December 2024

Multiple Myeloma (MM) is a genomically heterogeneous cancer of malignant plasma cells (PC) residing in the bone marrow. Despite recent therapeutic advances, it is incompletely understood why some patient populations remain largely therapy-refractory or relapse, even with targeted immunotherapies. Whilst MM classification based on genomic features and gene expression profiling (GEP) is well-described, the role of spatial interactions between tumour cells and the bone marrow tumour microenvironment (TME) in dictating treatment responses is insufficiently defined. Spatial heterogeneity in MM was highlighted by a multi-region whole exome sequencing (WES) study of paired samples from random bone marrow aspirates and distant lesions (Rasche et al Nature Comm 2017, 2022; doi.org/10.1038/s41467-017-00296-y; 10.1038/s41467-022-32145-y), however, it is unknown whether spatially and genomically distant clones interact differently within their immune and stromal compartments. Recent pioneering work, using multiplexed imaging and in situ transcriptomics approaches on MM trephines (Robinson et al, JCI 2023; doi.org/10.1172/JCI167629) demonstrated patterns and mechanisms of tumour-immune cell interactions suggesting TME interactions as a critical therapeutic response determinant.

Isolated in situ ‘omics’ assays provide limited information due to biased selection of marker panels or discordance between transcriptional and proteomic profiles. We hypothesize that a combined, systematic approach to integrate multiple layers of information (multiplexed imaging, unbiased LC/MS proteomics, transcriptomics, genomics) within their spatial context would significantly advance our understanding of MM TME phenotypes and responses to therapy.

Consecutive FFPE sections (plasmacytoma or MM; 4 cases) were subjected to spatial transcriptomics (Xenium, 10X Genomics) combined with multiplexed immunofluorescence (IF) or imaging mass cytometry (IMC), laser-capture guided in situ WGS (Rao et al, Genome Med. 2024 doi: 10.1186/s13073-024-01302-x) and LC/MS based spatial proteomic workflows (Davis et al, Nat Commun. 2023 doi: 10.1038/s41467-023-43520-8). A data analysis pipeline utilising machine-learning was implemented. We have developed a multiomics workflow that (1) maps critical regions of interest (ROI) in the TME (using spatial transcriptomics, IHC, IF) and that provides (2) ROI-specific genomic information from WES and/or targeted sequencing, and (3) unbiased proteomic information (LC/MS).

We mapped tumour, immune and stromal cell types and activation states in the bone marrow environment by using low-plex (30-40 markers) IF or IMC, and a customised probe panel for transcript detection in order to define ROIs. This was followed by laser capture microscopy of defined ROIs to genomically characterise the tumour, with ROI-matched LC/MS proteomics enabled detection of 4000-5000 proteins (current resolution of 200-500 cells per ROI).

In this proof of concept study with limited sample numbers we identified differences in tumour MHC expression as well as novel NK-cell-myeloma spatial interactions (such as HLA-G/LILRB1) as factors that may control myeloma-immune environments and responses. Shallow WGS of excised ROI sections allows detection of CNA-defined clonal heterogeneity; we are now developing this promising approach to a targeted panel of genomic aberrations in MM. Our next translational step is the deployment of this workflow to longitudinal clinical trial trephine samples with correlation to single cell data from bone marrow aspirates. Taken together, application of this translational platform will provide new insights into MM pathobiology and therapeutic response and resistance mechanisms.