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    Myeloma Genomics and Microenvironment and immune profiling

    Category: Myeloma Genomics and Microenvironment and immune profiling

    A multi-omics approach to understanding extramedullary multiple myeloma

    (PA-516) A Multi-omics Approach to Understanding Extramedullary Multiple Myeloma

    Friday, September 19, 2025
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    Introduction:      Multiple Myeloma (MM) is usually restricted to the bone marrow (BM). However, in a subset of patients, MM cells form lesions outside of the BM which is known as extramedullary multiple myeloma (EMM). These represent up to 8% of patients at primary diagnosis and up to 40% of patients with relapsed disease. EMM is a poor prognosis factor, with no therapeutic approaches specifically developed for EMM and inferior outcomes for these patients even after receiving new treatment modalities, such as bispecific antibodies and CARTs. Understanding the unique biology of EMM is critical for the development of novel EMM therapeutics. In this project we aimed to profile the genetic clonal evolution in the development of EMM, identify signatures of transcriptional plasticity in EMM, and understand how EMM cells interact with the tumour microenvironment.

    Methods:   We generated matched whole exome sequencing of BM and EMM samples, and matched spatial transcriptomics data, specifically, Nanostring GeoMX gene expression data from BM samples and Visium HD data from EMM samples from a retrospective cohort of 6 EMM patients treated at the Peter MacCallum Cancer Centre in Melbourne, Australia. 

    Results:   We observed both shared and unique mutations in the EMM samples compared to matched BM samples, suggesting clonal evolution in EMM. There was significant diversity in the microenvironments and the transcriptional composition of EMM disease, with EMM cells within a single sample often showing a diversity of gene expression signatures. MM cells had differential patterns of interactions with cells of the microenvironment, with a subset of patients showing more homogeneous spatial patterns of cell interactions, while others showed a diversity of cellular niches of communication. These features were shared between BM and EMM sites, suggesting that interactions with the microenvironment were tied to the unique biology of each MM patient. There was significant heterogeneity in the expression of BCMA, CD38, FCRL5, SLAMF7, GPRC5D.

    Conclusions:   Our study showcases the unique biology of EMM and will enable the identification of EMM-specific evolutionary changes and targets.