Single-cell DNA sequencing uncovers synergistic co-mutations in multiple myeloma Free

Background: Despite recent advancements in treatment for multiple myeloma (MM), most patients eventually relapse due to the emergence of resistant tumor subclones. Bulk DNA sequencing has identified recurrent resistance-associated mutations and can infer subclonal architecture, but it lacks the resolution to determine whether multiple mutations co-exist within the same malignant cell or arise in separate subclones. This distinction is critical, as multiple somatic variants within individual cells may confer resistance that does not arise from any single alteration alone. We hypothesized that targeted single-cell DNA sequencing of myeloma tumor cells would reveal the co-occurrence of mutations within individual cells that are missed by conventional bulk sequencing approaches.

Methods: Bone marrow aspirates were collected from 46 patients, including 9 with newly diagnosed and 37 with relapsed or refractory MM. Two patients were sampled at multiple time points, yielding a total of 48 samples. CD138⁺ plasma cells were enriched using magnetic-activated cell sorting and subjected to single-cell encapsulation for simultaneous genomic and proteomic profiling. Libraries were prepared using the Mission Bio Tapestri Single-Cell Multiple Myeloma Multiomics Assay, which includes amplicons targeting single-nucleotide variants, insertions and deletions, and focal copy number aberrations commonly altered in the disease. In parallel, immunoglobulin heavy- and light-chain V(D)J clonotypes were identified, and cells were labeled with a 20-plex oligo-conjugated antibody panel to quantify surface protein markers associated with cell lineage and immunotherapy targets. Tumor cells were defined as those harboring the dominant immunoglobulin V(D)J sequence within each sample. Reference cells were identified as VDJ-negative and expressing high levels of non–plasma cell markers (CD3, CD19, or CD33). Somatic mutations were identified by comparing variant allele frequencies between tumor and reference cell populations.

Results: Across all 48 samples, 146,946 cells were sequenced, with 68,052 of those being tumor cells. Hallmark MM copy number alterations, such as 17p deletion, 1q amplification, and 13q deletion, were identified using the copy number assay. The most frequent mutations identified were in KRAS, TP53, TENT5C, and CRBN. The two patients with CRBN mutations underwent 27 and 34 prior cycles of immunomodulatory (IMiD) therapy, respectively, compared to a median of 8.0 cycles in patients without CRBN mutations, raising the possibility of treatment-driven selection. In another case, we identified three mutually exclusive KRAS mutations (Q61H, G12V, and G12D) in distinct subclones within a single patient, consistent with convergent evolution toward activation of the MAPK pathway. We also resolved a true “double hit” in another patient, characterized by 58% of tumor cells carrying a deletion of one allele and a somatic mutation in the other, consistent with biallelic inactivation. Finally, we observed CDKN2A more frequently co-mutated with TP53, PTPRT, and TENT5C than as isolated events, suggesting multi-hit inactivation of key cell-cycle checkpoints and DNA-damage response pathways that may drive selective growth advantage or therapeutic resistance. CDKN2A and TP53 are canonical tumor suppressors that regulate G1/S transition and apoptosis, respectively, while PTPRT encodes a phosphatase that negatively regulates STAT3 signaling, and TENT5C is a tumor suppressor involved in mRNA stability and plasma cell homeostasis. These recurrent co-mutations may act synergistically by simultaneously disrupting transcriptional and signaling regulation, thereby conferring a selective tumor cell growth advantage under therapeutic pressure.

Conclusions: Targeted single-cell DNA sequencing reveals co-occurring mutations within individual myeloma cells that are obscured by bulk approaches, providing critical insight into the cooperative and competitive genomic events driving disease progression and resistance. These findings support a model in which myeloma subclones accumulate synergistic alterations within the same cell. Work is ongoing to provide functional validation of key mutation pairs and their integration with transcriptomic data to refine the mechanistic understanding and identify candidates for combination therapy.