The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Prediction of Survival in Multiple Myeloma Based on Gene Expression Profiles Reveals Cell Cycle and Chromosomal Instability Signatures in High-Risk Patients and Hyperdiploid Signatures in Low-Risk Patients: A Study of the Intergroupe Francophone du Myélome
Abstract
Purpose: Survival of patients with multiple myeloma is highly heterogeneous, from periods of a few weeks to more than 10 years. We used gene expression profiles of myeloma cells obtained at diagnosis to identify broadly applicable prognostic markers. Patients and Methods: In a training set of 182 patients, we used supervised methods to identify individual genes associated with length of survival. A survival model was built from these genes. The validity of our model was assessed in our test set of 68 patients and in three independent cohorts comprising 853 patients with multiple myeloma. Results: The 15 strongest genes associated with the length of survival were used to calculate a risk score and to stratify patients into low-risk and high-risk groups. The survival-predictor score was significantly associated with survival in both the training and test sets and in the external validation cohorts. The Kaplan-Meier estimates of rates of survival at 3 years were 90.5% (95% CI, 85.6% to 95.3%) and 47.4% (95% CI, 33.5% to 60.1%), respectively, in our patients having a low risk or high risk independently of traditional prognostic factors. High-risk patients constituted a homogeneous biologic entity characterized by the overexpression of genes involved in cell cycle progression and its surveillance, whereas low-risk patients were heterogeneous and displayed hyperdiploid signatures. Conclusion: Gene expression–based survival prediction and molecular features associated with high-risk patients may be useful for developing prognostic markers and may provide basis to treat these patients with new targeted antimitotics.
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Rosenwald, A., Wright, G., Chan, W. C., Connors, J. M., Campo, E., Fisher, R. I., Gascoyne, R. D., Muller-Hermelink, H. K., Smeland, E. B., Giltnane, J. M., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa012914 · Cited by 3,829
International Staging System for Multiple Myeloma
Greipp, P. R., Miguel, J. S., Durie, B. G. M., Crowley, J. J., Barlogie, B., Bladé, J., Boccadoro, M., Child, J. A., Avet-Loiseau, H., Kyle, R. A., et al. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.04.242 · Cited by 2,974
Self-citationThe spindle-assembly checkpoint in space and time
Musacchio, A., & Salmon, E. D. (2007) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2163 · Cited by 2,227
Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I., Ball, C. A., Alexander, K. E., Matese, J. C., Perou, C. M., Hurt, M. M., Brown, P. O., et al. (2002) · Molecular Biology of the Cell · doi:10.1091/mbc.02-02-0030 · Cited by 1,527
Carter, S. L., Eklund, A. C., Kohane, I. S., Harris, L. N., & Szallasi, Z. (2006) · Nature Genetics · doi:10.1038/ng1861 · Cited by 1,193
On the road to cancer: aneuploidy and the mitotic checkpoint
Kops, G. J. P. L., Weaver, B. A. A., & Cleveland, D. W. (2005) · Nature Reviews Cancer · doi:10.1038/nrc1714 · Cited by 1,185
Feedback control of mitosis in budding yeast
Li, R., & Murray, A. W. (1991) · Cell · doi:10.1016/0092-8674(81)90015-5 · Cited by 1,174
The molecular classification of multiple myeloma
Zhan, F., Huang, Y., Colla, S., Stewart, J. P., Hanamura, I., Gupta, S., Epstein, J., Yaccoby, S., Sawyer, J., Burington, B., et al. (2006) · Blood · doi:10.1182/blood-2005-11-013458 · Cited by 1,133
S. cerevisiae genes required for cell cycle arrest in response to loss of microtubule function
Hoyt, M. A., Totis, L., & Roberts, B. T. (1991) · Cell · doi:10.1016/0092-8674(81)90014-3 · Cited by 1,131
Cited by
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
MAQC Consortium (2010) · Nature Biotechnology · doi:10.1038/nbt.1665 · Cited by 889
“and later on validated in three additional independent data set”
BackgroundThe multiple myelomas — current concepts in cytogenetic classification and therapy
Kumar, S. K., & Rajkumar, S. V. (2018) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-018-0018-y · Cited by 302
A gene expression signature for high-risk multiple myeloma
Kuiper, R., Broyl, A., de Knegt, Y., van Vliet, M. H., van Beers, E. H., van der Holt, B., el Jarari, L., Mulligan, G., Gregory, W., Morgan, G., et al. (2012) · Leukemia · doi:10.1038/leu.2012.127 · Cited by 295
Multiple Myeloma, Version 3.2017, NCCN Clinical Practice Guidelines in Oncology
Kumar, S. K., Callander, N. S., Alsina, M., Atanackovic, D., Biermann, J. S., Chandler, J. C., Costello, C., Faiman, M., Fung, H. C., Gasparetto, C., et al. (2017) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2017.0023 · Cited by 279
Metadata
- Authors
- Decaux, O., Lodé, L., Magrangeas, F., Charbonnel, C., Gouraud, W., Jézéquel, P., Attal, M., Harousseau, J.-L., Moreau, P., Bataille, R., Campion, L., Avet-Loiseau, H., & Minvielle, S.
- Venue
- Journal of Clinical Oncology
- Published
- 2008-10-10
- Volume
- 26
- Issue
- 29
- Pages
- 4798-4805
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- OpenAlex
- W2136810538
- Cited by
- 371
- References
- 32
- FWCI
- 12.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Prediction of Survival in Multiple Myeloma Based on Gene Expression Profiles Reveals Cell Cycle and Chromosomal Instability Signatures in High-Risk Patients and Hyperdiploid Signatures in Low-Risk Patients: A Study of the Intergroupe Francophone du Myélome. https://app.cochat.ai/papers/10.1200/jco.2007.13.8545
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- Decaux, O., Lodé, L., Magrangeas, F., Charbonnel, C., Gouraud, W., Jézéquel, P., Attal, M., Harousseau, J.-L., Moreau, P., Bataille, R., Campion, L., Avet-Loiseau, H., & Minvielle, S. (2008). Prediction of Survival in Multiple Myeloma Based on Gene Expression Profiles Reveals Cell Cycle and Chromosomal Instability Signatures in High-Risk Patients and Hyperdiploid Signatures in Low-Risk Patients: A Study of the Intergroupe Francophone du Myélome. Journal of Clinical Oncology. https://doi.org/10.1200/jco.2007.13.8545
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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.
