Molecular signatures of multiple myeloma progression through single cell RNA-Seq.
Jin Sung Jang, Ying Li, Amit Kumar Mitra, Lintao Bi, Alexej Abyzov, Andre J van Wijnen, Linda B Baughn, Brian Van Ness, Vincent Rajkumar, Shaji Kumar, Jin Jen
Author Information
- Jin Sung Jang: Genome Analysis Core, Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
- Ying Li: Division of Bioinformatics and Biostatistics, Department of Health Science Research, Mayo Clinic, Rochester, MN, USA.
- Amit Kumar Mitra: Department of Drug Discovery and Development, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA.
- Lintao Bi: Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
- Alexej Abyzov: Division of Bioinformatics and Biostatistics, Department of Health Science Research, Mayo Clinic, Rochester, MN, USA.
- Andre J van Wijnen: Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA. ORCID
- Linda B Baughn: Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
- Brian Van Ness: Department of Genetics, Cell Biology & Development, University of Minnesota, Minneapolis, MN, USA.
- Vincent Rajkumar: Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA. ORCID
- Shaji Kumar: Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA. Kumar.Shaji@mayo.edu. ORCID
- Jin Jen: Genome Analysis Core, Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. jen.jin@mayo.edu.
We used single cell RNA-Seq to examine molecular heterogeneity in multiple myeloma (MM) in 597 CD138 positive cells from bone marrow aspirates of 15 patients at different stages of disease progression. 790 genes were selected by coefficient of variation (CV) method and organized cells into four groups (L1-L4) using unsupervised clustering. Plasma cells from each patient clustered into at least two groups based on gene expression signature. The L1 group contained cells from all MGUS patients having the lowest expression of genes involved in the oxidative phosphorylation, Myc targets, and mTORC1 signaling pathways (p < 1.2 × 10). In contrast, the expression level of these pathway genes increased progressively and were the highest in L4 group containing only cells from MM patients with t(4;14) translocations. A 44 genes signature of consistently overexpressed genes among the four groups was associated with poorer overall survival in MM patients (APEX trial, p < 0.0001; HR, 1.83; 95% CI, 1.33-2.52), particularly those treated with bortezomib (p < 0.0001; HR, 2.00; 95% CI, 1.39-2.89). Our study, using single cell RNA-Seq, identified the most significantly affected molecular pathways during MM progression and provided a novel signature predictive of patient prognosis and treatment stratification.
Biopsy
Bone Marrow
Computational Biology
Disease Progression
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Humans
Kaplan-Meier Estimate
Multiple Myeloma
Prognosis
Sequence Analysis, RNA
Single-Cell Analysis
Transcriptome
Workflow
