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Initial genome sequencing and analysis of multiple myeloma
Open accessAbstract
Multiple myeloma is an incurable malignancy of plasma cells, and its pathogenesis is poorly understood. Here we report the massively parallel sequencing of 38 tumour genomes and their comparison to matched normal DNAs. Several new and unexpected oncogenic mechanisms were suggested by the pattern of somatic mutation across the data set. These include the mutation of genes involved in protein translation (seen in nearly half of the patients), genes involved in histone methylation, and genes involved in blood coagulation. In addition, a broader than anticipated role of NF-κB signalling was indicated by mutations in 11 members of the NF-κB pathway. Of potential immediate clinical relevance, activating mutations of the kinase BRAF were observed in 4% of patients, suggesting the evaluation of BRAF inhibitors in multiple myeloma clinical trials. These results indicate that cancer genome sequencing of large collections of samples will yield new insights into cancer not anticipated by existing knowledge. Multiple myeloma, a malignancy of plasma cells, remains incurable and is poorly understood. Chapman et al. have used next-generation sequencing to compare 38 multiple myeloma genomes with those of normal cells from the same patients. The disease involves mutations of genes with roles in protein translation, histone methylation and blood coagulation. In terms of clinically relevant findings, unexpected activating mutations were found in the kinase BRAF, inhibitors of which have recently shown dramatic clinical activity. This suggests that BRAF inhibitors should be evaluated in patients with BRAF-mutated multiple myeloma. Multiple myeloma, a malignancy of plasma cells, remains incurable and is poorly understood. Using next-generation sequencing of several multiple myeloma genomes reveals that this disease involves mutations of genes involved in protein translation, histone methylation and blood coagulation. The study suggests that BRAF inhibitors should be evaluated in multiple myeloma clinical trials.
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Self-citationThe evolutionary pattern of mutations in glioblastoma reveals therapy-mediated selection
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Direct evidence for a polygenic etiology in familial multiple myeloma
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Metadata
- Authors
- Chapman, M. A., Lawrence, M. S., Keats, J. J., Cibulskis, K., Sougnez, C., Schinzel, A. C., Harview, C. L., Brunet, J.-P., Ahmann, G. J., Adli, M., Anderson, K. C., Ardlie, K. G., Auclair, D., Baker, A., Bergsagel, P. L., Bernstein, B. E., Drier, Y., Fonseca, R., Gabriel, S. B., Hofmeister, C. C., Jagannath, S., Jakubowiak, A. J., Krishnan, A., Levy, J., Liefeld, T., Lonial, S., Mahan, S., Mfuko, B., Monti, S., Perkins, L. M., Onofrio, R., Pugh, T. J., Rajkumar, S. V., Ramos, A. H., Siegel, D. S., Sivachenko, A., Stewart, A. K., Trudel, S., Vij, R., Voet, D., Winckler, W., Zimmerman, T., Carpten, J., Trent, J., Hahn, W. C., Garraway, L. A., Meyerson, M., Lander, E. S., Getz, G., & Golub, T. R.
- Venue
- Nature
- Published
- 2011-03
- Volume
- 471
- Issue
- 7339
- Pages
- 467-472
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2101035019
- Cited by
- 1,433
- References
- 8
- FWCI
- 102.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
