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102 Single-Cell RNA-Sequencing of 245 Tumor Samples from Patients with MGUS and Smoldering Multiple Myeloma Reveals Novel Insights into Malignant Plasma Cell Biology

Program: Oral and Poster Abstracts
Type: Oral
Session: 651. Multiple Myeloma and Plasma Cell Dyscrasias: Basic and Translational I
Hematology Disease Topics & Pathways:
Research, Translational Research, Plasma Cell Disorders, bioinformatics, Diseases, Lymphoid Malignancies, computational biology, emerging technologies, Technology and Procedures, profiling, Study Population, Human, omics technologies
Saturday, December 10, 2022: 10:45 AM

Junko Tsuji, PhD1*, Elizabeth D. Lightbody, PhD1,2,3, Romanos Sklavenitis-Pistofidis, MD1,2,3, Michael P. Agius, PhD1,2,3*, Mahshid Rahmat, PhD3*, Hadley Barr, BA2,3*, Yoshinobu Konishi, MD, PhD1,4*, Ankit K. Dutta, PhD2,3*, Nang Kham Su, MSc2,3*, Cody J. Boehner, BS2,3*, Danielle T. Firer, BS1*, Ting Wu, MS1*, Michelle P. Aranha, PhD1,2,3*, Laura Hevenor, BS3*, Erica Horowitz, BA3*, Jacqueline Perry, MPH2,3*, François Aguet, PhD5*, Nicholas J. Haradhvala, PhD1*, Rebecca Boiarsky, MSc1,6*, Gad Getz, PhD1,7* and Irene Ghobrial, MD2,3

1Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA
2Department of Medicine, Harvard Medical School, Boston, MA
3Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
4Department of Medical Oncology, Dana-Farber Cancer Institute, Brookline, MA
5Artificial Intelligence Lab, Illumina, Foster City, CA
6CSAIL and IMES, Massachusetts Institute of Technology, Cambridge, MA
7Department of Pathology, Massachusetts General Hospital Cancer Center, Boston, MA


Patients with Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM) exhibit variable risk of progression to full-blown Multiple Myeloma (MM), which cannot be fully explained by differences in tumor burden or genetic alterations. Therefore, characterizing non-genetic changes in malignant plasma cells may help to identify novel mechanisms of disease progression and improve prognostication. Our current understanding of transcriptomic alterations in patients with MGUS and SMM is based on bulk RNA-sequencing or microarray studies, which are affected by sample purity and interpatient variability. Here, we present results from the largest single-cell RNA-sequencing cohort (n=245) of tumor samples from patients with MGUS and SMM.


We performed single-cell RNA- and V(D)J-sequencing on 245 samples from 36 patients with MGUS, 136 patients with SMM, 37 patients with MM, and 25 healthy donors. Libraries were prepared with the Chromium Single-cell 5’ and V(D)J enrichment kit by 10X Genomics and sequenced on NovaSeq at the Genomics Platform of the Broad Institute of MIT and Harvard (Cambridge, MA). CellRanger v.6.0.1. was used to extract FASTQ files and produce count matrices. Droplets with more than 15% of UMIs mapped to mitochondrial genes or fewer than 200 detected genes were removed, and doublets were identified using Scrublet, SCDS, and scDblFinder. Samples were integrated using Harmony, and plasma cells were annotated as malignant or normal based on the matched V(D)J clonotype information. Differential expression was performed at the single-cell level using Wilcoxon’s rank-sum test.


Overall, we sequenced 1,318,218 plasma cells, including 960,998 malignant and 357,220 normal plasma cells. Each patient’s tumor formed a unique cluster, and tumors with similar cytogenetics clustered adjacent to each other. By comparing expression levels between malignant and normal plasma cells for each patient, we were able to identify differentially expressed genes per tumor and assess how frequently each gene is dysregulated across the cohort. This approach can provide a rationale for the prioritization of target validation and can potentially uncover clinically meaningful differences between disease stages. As hypothesized, our analysis recovered established markers of plasma cell malignancy that are useful diagnostically, such as the downregulation of CD27 (rank 1), CD99 (rank 2), and CD81 (rank 5), or therapeutically, such as the upregulation of GPRC5D (rank 9). Moreover, we discovered frequent downregulation of SH3BGRL3 (rank 3), a gene associated with cell migration, and SSR4 (rank 9), SELENOK (rank 11), OSTC (rank 15), and SPCS3 (rank 18), genes associated with protein folding and the regulation of endoplasmic reticulum (ER) stress. Interestingly, PDK1 (rank 17), an inhibitor of pyruvate dehydrogenase that is highly expressed in normal plasma cells, was frequently downregulated in malignant plasma cells, suggesting that the balance between oxidative phosphorylation and aerobic glycolysis may be different in malignant plasma cells. The most common upregulated genes were the oncogene MLLT3 (rank 1), the growth factor IGF1 (rank 2), and the proteasome regulator TJP1 (rank 3). By comparing the frequency of dysregulation between patients with SMM and MGUS, we observed that malignant plasma cells from patients with SMM were more likely to upregulate MYC, which is concordant with the known increase in MYC genetic alterations as the disease progresses. Furthermore, patients with SMM are more likely to show upregulation of CD47, which promotes immune evasion. Lastly, patients with MM were more likely to exhibit upregulation of the RNA helicase DDX21, the therapeutic target SLAMF7, the ER factor TXNDC11, and NFkB signaling factors PIM2 and IRF9, compared to patients with SMM.


In conclusion, by leveraging the largest single-cell RNA-sequencing cohort of bone marrow samples from patients with MGUS and SMM, we uncovered novel insights into plasma cell malignancy, as well as differences in gene expression dysregulation between disease stages. Observations gleaned from this dataset could be used to nominate novel targets and prioritize target validation for the development of novel therapeutics for the treatment or prevention of MM.

Disclosures: Aguet: Illumina: Current Employment. Haradhvala: MorphoSys: Consultancy. Getz: IBM: Research Funding; Scorpion Therapeutics: Consultancy, Current equity holder in publicly-traded company, Other: Founder; MSMuTect: Patents & Royalties; Pharmacyclics: Research Funding; MSMutSig: Patents & Royalties; MSIDetect: Patents & Royalties; SignatureAnalyzer-GPU: Patents & Royalties; POLYSOLVER: Patents & Royalties. Ghobrial: Takeda: Honoraria; Adaptive: Honoraria; Window Therapeutics: Other: Advisory board participation; Sanofi: Honoraria; Sognef: Honoraria; Bristol Myers Squibb: Honoraria; Aptitude Health: Honoraria; Veeva Systems: Honoraria; Vor Biopharma: Honoraria; The Binding Site: Honoraria; Amgen: Honoraria; AbbVie: Honoraria; GSK: Honoraria; Huron Consulting: Honoraria; Menarini Silicon Biosystems: Honoraria; Janssen: Honoraria; Pfizer: Honoraria; Oncopeptides: Honoraria; Novartis: Research Funding; Celgene: Research Funding.

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