Title |
Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis
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Published in |
Blood, July 2023
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DOI | 10.1182/blood.2023019758 |
Pubmed ID | |
Authors |
Alexandra M. Poos, Nina Prokoph, Moritz J. Przybilla, Jan-Philipp Mallm, Simon Steiger, Isabelle Seufert, Lukas John, Stephan M. Tirier, Katharina Bauer, Anja Baumann, Jennifer Rohleder, Umair Munawar, Leo Rasche, K. Martin Kortüm, Nicola Giesen, Philipp Reichert, Stefanie Huhn, Carsten Müller-Tidow, Hartmut Goldschmidt, Oliver Stegle, Marc S. Raab, Karsten Rippe, Niels Weinhold |
Abstract |
Intratumor heterogeneity becomes most evident after several treatment lines when multi-drug resistant subclones accumulate. To address this clinical challenge, the characterization of resistance mechanisms at the subclonal level is key to identify common vulnerabilities. Here, we integrate whole genome sequencing, single-cell transcriptomics (scRNA-seq) and chromatin accessibility (scATAC-seq) together with mitochondrial DNA (mtDNA) mutations to define subclonal architecture and evolution for longitudinal samples from 15 relapsed/refractory multiple myeloma (RRMM) patients. We assess transcriptomic and epigenomic changes to resolve the multifactorial nature of therapy resistance and relate it to the parallel occurrence of different mechanisms: (i) Pre-existing epigenetic profiles of subclones associated with survival advantages, (ii) converging phenotypic adaptation of genetically distinct subclones, and (iii) subclone-specific interactions of myeloma and bone marrow microenvironment cells. Our study showcases how an integrative multi-omics analysis can be applied to track and characterize distinct multi-drug resistant subclones over time for the identification of novel molecular targets against them. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Germany | 6 | 20% |
United States | 5 | 17% |
Korea, Republic of | 1 | 3% |
Australia | 1 | 3% |
Russia | 1 | 3% |
Indonesia | 1 | 3% |
Unknown | 15 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 15 | 50% |
Scientists | 14 | 47% |
Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 19 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Unspecified | 2 | 11% |
Professor | 2 | 11% |
Student > Ph. D. Student | 2 | 11% |
Researcher | 2 | 11% |
Student > Master | 2 | 11% |
Other | 4 | 21% |
Unknown | 5 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 6 | 32% |
Unspecified | 2 | 11% |
Computer Science | 2 | 11% |
Chemical Engineering | 1 | 5% |
Biochemistry, Genetics and Molecular Biology | 1 | 5% |
Other | 2 | 11% |
Unknown | 5 | 26% |