Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib

被引:321
作者
Mulligan, George
Mitsiades, Constantine
Bryant, Barb
Zhan, Fenghuang
Chng, Wee J.
Roels, Steven
Koenig, Erik
Fergus, Andrew
Huang, Yongsheng
Richardson, Paul
Trepicchio, William L.
Broyl, Annemiek
Sonneveld, Pieter
Shaughnessy, John D., Jr.
Bergsagel, P. Leif
Schenkein, David
Esseltine, Dixie-Lee
Boral, Anthony
Anderson, Kenneth C.
机构
[1] Millennium Pharmaceut Inc, Clin Res Translat Med, Cambridge, MA 02139 USA
[2] Dana Farber Canc Inst, Boston, MA 02115 USA
[3] Univ Arkansas Med Sci, Myeloma Inst Res & Therapy, Little Rock, AR 72205 USA
[4] Mayo Clin, Scottsdale, AZ USA
[5] Erasmus MC, Dept Hematol, Rotterdam, Netherlands
关键词
D O I
10.1182/blood-2006-09-044974
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
The aims of this study were to assess the feasibility of prospective pharmacogenomics research in multicenter international clinical trials of bortezomib in multiple myeloma and to develop predictive classifiers of response and survival with bortezomib. Patients with relapsed myeloma enrolled in phase 2 and phase 3 clinical trials of bortezomib and consented to genomic analyses of pretreatment tumor samples. Bone marrow aspirates were subject to a negative-selection procedure to enrich for tumor cells, and these samples were used for gene expression profiling using DNA microarrays. Data quality and correlations with trial outcomes were assessed by multiple groups. Gene expression in this dataset was consistent with data published from a single-center study of newly diagnosed multiple myeloma. Response and survival classifiers were developed and shown to be significantly associated with outcome via testing on independent data. The survival classifier improved on the risk stratification provided by the International Staging System. Predictive models and biologic correlates of response show some specificity for bortezomib rather than dexamethasone. Informative gene expression data and genomic classifiers that predict clinical outcome can be derived from prospective clinical trials of new anticancer agents.
引用
收藏
页码:3177 / 3188
页数:12
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