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Molecular signatures of multiple myeloma progression through single cell RNA-Seq
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 grou...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6318319/ https://www.ncbi.nlm.nih.gov/pubmed/30607001 http://dx.doi.org/10.1038/s41408-018-0160-x |
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author | Jang, Jin Sung Li, Ying Mitra, Amit Kumar Bi, Lintao Abyzov, Alexej van Wijnen, Andre J. Baughn, Linda B. Van Ness, Brian Rajkumar, Vincent Kumar, Shaji Jen, Jin |
author_facet | Jang, Jin Sung Li, Ying Mitra, Amit Kumar Bi, Lintao Abyzov, Alexej van Wijnen, Andre J. Baughn, Linda B. Van Ness, Brian Rajkumar, Vincent Kumar, Shaji Jen, Jin |
author_sort | Jang, Jin Sung |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6318319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63183192019-01-04 Molecular signatures of multiple myeloma progression through single cell RNA-Seq Jang, Jin Sung Li, Ying Mitra, Amit Kumar Bi, Lintao Abyzov, Alexej van Wijnen, Andre J. Baughn, Linda B. Van Ness, Brian Rajkumar, Vincent Kumar, Shaji Jen, Jin Blood Cancer J Article 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. Nature Publishing Group UK 2019-01-03 /pmc/articles/PMC6318319/ /pubmed/30607001 http://dx.doi.org/10.1038/s41408-018-0160-x Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Jang, Jin Sung Li, Ying Mitra, Amit Kumar Bi, Lintao Abyzov, Alexej van Wijnen, Andre J. Baughn, Linda B. Van Ness, Brian Rajkumar, Vincent Kumar, Shaji Jen, Jin Molecular signatures of multiple myeloma progression through single cell RNA-Seq |
title | Molecular signatures of multiple myeloma progression through single cell RNA-Seq |
title_full | Molecular signatures of multiple myeloma progression through single cell RNA-Seq |
title_fullStr | Molecular signatures of multiple myeloma progression through single cell RNA-Seq |
title_full_unstemmed | Molecular signatures of multiple myeloma progression through single cell RNA-Seq |
title_short | Molecular signatures of multiple myeloma progression through single cell RNA-Seq |
title_sort | molecular signatures of multiple myeloma progression through single cell rna-seq |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6318319/ https://www.ncbi.nlm.nih.gov/pubmed/30607001 http://dx.doi.org/10.1038/s41408-018-0160-x |
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