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    Myeloma Genomics and Microenvironment and immune profiling

    Category: Myeloma Genomics and Microenvironment and immune profiling

    Unravelling the effect of subclonal copy number variants on immune microenvironment and survival

    (PA-265) Unravelling the Effect of Subclonal Copy Number Variants on Immune Microenvironment and Survival

    Thursday, September 18, 2025
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    Introduction: In multiple myeloma, subclones—distinct populations of myeloma cells within a patient's tumor—are crucial for understanding disease progression, treatment response, and potential relapse. Analyzing subclone-level changes in transcriptomic data, copy number variation, and their interactions with immune cells is essential for unraveling tumor heterogeneity and identifying potential targets to enhance myeloma treatment. We investigated the genomic characteristics of these subclones and their interactions with immune cells in newly diagnosed multiple myeloma (NDMM) samples.

    Methods: CD138+ scRNA-seq and scATAC-seq multiomic data from NDMM patients (n=21) were obtained from the published study dbGAP phs003220 (Johnson et al., IMS 2022, PMC11099140). Matched CD138- scRNA-seq data of immune cells were sourced from Sudha et al., IMS 2023. Subclones for each tumor sample were identified using inferCNV (v1.8.1) and a custom-built integration pipeline that utilized both scRNA-seq and scATAC-seq data. Interactions between subclones and immune cell types were identified using CellChat (v1.6.0). Copy Number Abnormality (CNA) profiles between subclones from single-cell and whole genome sequencing (WGS) data (phs003220) were compared, and similarity scores were calculated. Due to differing technologies (RNA-seq vs WGS) and sequencing depth (single cell vs. bulk), this score reflects how well single-cell multiomics captures major chromosomal changes seen in WGS. Subclone-to-immune cell interactions were quantified, and statistical significance of chromosomal abnormalities was analyzed. Survival analysis was performed on the most common myeloma abnormalities.

    Results: On average 45% of copy number variants overlap between WGS and scATAC-seq. Samples with low Jaccard index tend to have more subclones and more complex CNAs. Patients with 1q gain and 13q loss cooccurring in the same clone showed significantly shorter progression-free survival (PFS) (P = 0.03) compared to patients with both 1q gain and 13q loss but in separate subclones. In the bone marrow microenvironment, subclones showed the highest interactions with CD8T cells. Overall, 70% of the patients showed high MIF-CD74/CXCR4 interactions between tumor subclones and immune clusters. Subclones with 13q loss showed significant interactions with all immune cell types (P < 0.05) compared to those with normal 13q. In contrast, subclones with 1q gain showed significant interactions with myeloid and myeloid dendritic cells in comparison to those with normal 1q.

    Conclusions: These findings position subclonal CNA profiling as both a prognostic tool and therapeutic roadmap. Interaction networks—especially myeloid recruitment in 1q+ clones—offer actionable targets to disrupt tumor-immune crosstalk in high-risk MM. Co-occurring 1q gain and 13q loss mark aggressive disease and poor survival, often missed by bulk sequencing. Future work should validate these findings and explore therapies targeting both malignant subclones and immune niches.