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

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

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 −14 ). 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.

Original languageEnglish (US)
Article number2
JournalBlood cancer journal
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2019

Bibliographical note

Funding Information:
This work was supported in part by the Mayo Clinic—University of Minnesota Partnership Award, Mayo Clinic Cancer Center, and Center for Individualized Medicine A Abyzov and J Jen. We thank members of the Genome Analysis Core for technical support over the course of this study. Special thanks to Dr. Liguo Wang for valuable discussions on TIN calculation and Aditya Bhagwate for initial data processing. Helpful discussions with Drs. HP Li and S Prabhakar regarding the Reference component analysis approach are also appreciated. Current address for Lintao Bi is Department of Hematology and Oncology, China–Japan Union Hospital, JiLin University, JiLin, China. Current address for Jin Jen is Celgene Corporation, 10300 Campus Point, San Diego, CA92121.

Publisher Copyright:
© 2019, The Author(s).

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