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    Myeloma Genomics and Microenvironment and immune profiling

    Category: Myeloma Genomics and Microenvironment and immune profiling

    Exploration of tumor immune gene expression to stratify progression risk in newly diagnosed multiple myeloma patients from CoMMpass with 1q cytogenetic abnormality

    (PA-230) Exploration of Tumor Immune Gene Expression to Stratify Progression Risk in Newly Diagnosed Multiple Myeloma Patients from CoMMpass with 1q Cytogenetic Abnormality

    Thursday, September 18, 2025
    Introduction: Chromosome 1q copy number variations (+1q) are recognized as high-risk cytogenetic abnormalities by the International Myeloma Working Group. Present in 40% of newly diagnosed multiple myeloma (NDMM) patients, both 1q gain (3 copies) and 1q amplification (4+ copies) are linked to poorer survival outcomes. While previous studies investigated the clinical impact and timing of +1q, ongoing research aims to establish whether +1q is an independent marker of poor prognosis and to enhance patient stratification for individualized therapy. This study examines how gene expression from immune cells in the tumor microenvironment can be utilized to better assess disease risk in patients with +1q.

    Methods: We analyzed NDMM patients’ tumor microenvironments along with their tumors to characterize gene expression patterns associated with +1q and survival outcomes. Immune data were generated using scRNA on CD138- bone marrow (BM) samples from the Multiple Myeloma Research Foundation (MMRF) Immune Atlas Network. Tumor data, including whole genome sequencing and bulk RNA-seq from CD138+ bone marrow (BM) samples, came from the MMRF CoMMpass Study. We used ssGSEA and custom R scripts to score pseudobulk immune samples using immune signatures (Azizi, et al. Cell 2018) and bulk tumor samples using MSigDB oncogenic signatures (Liberzon, et al. Cell Syst 2015). We then clustered immune and oncogenic signature scores simultaneously using k-means and associated clusters with survival.

    Results: From our cohort of 190 subjects with overlapping bulk and immune data, 125 (65.8%) were copy number neutral (2 copies), 54 (28.4%) had +1q gain (3 copies), and 11 (5.8%) had +1q amplification (4+ copies). Clustering of +1q patients yielded five clusters distinguished by patterns of immune and oncogenic gene signature scores. One cluster with significantly better progression free survival (median PFS 2355 days; HR 0.39, 95% CI [0.17, 0.88]; p = 0.0236) showed both low immune and low oncogenic signature scores (low-low group). This low-low group was associated with significantly lower monocyte proportions (6.9% vs. 15.9%; adj p = 0.00014) as well as significantly lower immune signature scores for anti-inflammatory, G1/S, G2/M, lipid mediators, and macrophage polarization. The low-low group also showed significantly higher scores for CD8+ T cell activation and the cytolytic effector pathway. Clustering based solely on oncogenic gene signatures resulted in a poor survival cluster dominated by high expression of four canonical MM pathways, including MTORC1 signaling, MYC targets, G2M checkpoint, and E2F targets. However, simultaneous clustering of immune signatures with oncogenic signatures modified this risk structure, resulting in clusters driven by other oncogenic and immune signatures.

    Conclusions: These results provide preliminary evidence that incorporation of tumor microenvironment immune signatures into cytogenetic risk stratification of NDMM patients can refine prognoses.