| Title |
Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis
|
|---|---|
| Published in |
Blood, July 2023
|
| 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
The data shown below were collected from the profiles of 30 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| 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
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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% |
Attention Score in Context
This research output has an Altmetric Attention Score of 34. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 November 2023.
All research outputs
#1,167,723
of 25,394,764 outputs
Outputs from Blood
#880
of 33,262 outputs
Outputs of similar age
#22,246
of 368,437 outputs
Outputs of similar age from Blood
#4
of 133 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 33,262 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 368,437 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.