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Blood, 15 April 2007, Vol. 109, No. 8, pp. 3177-3188.
Prepublished online as a Blood First Edition Paper on December 21, 2006; DOI 10.1182/blood-2006-09-044974.
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Submitted September 6, 2006
Accepted December 6, 2006
Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib
George Mulligan*, Constantine Mitsiades, Barb Bryant, Fenghuang Zhan, Wee J. Chng, Steven Roels, Erik Koenig, Andrew Fergus, Yongsheng Huang, Paul Richardson, William L Trepicchio, Annemiek Broyl, Pieter Sonneveld, John D Shaughnessy Jr., P. Leif Bergsagel, David Schenkein, Dixie-Lee Esseltine, Anthony Boral, and Kenneth C. Anderson
Millennium Pharmaceuticals, Inc, Cambridge, MA, United States
Dana-Farber Cancer Institute, Boston, MA, United States
Myeloma Institute for Research and Therapy, University of Arkansas for Medical Sciences, Little Rock, AR, United States
Mayo Clinic, Scottsdale, AZ, United States
Department of Hematology, Erasmus Medical Centre, Rotterdam, Netherlands
* Corresponding author; email: mulligan{at}mpi.com.
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 II and phase III clinical trials of bortezomib and consented to genomic analyses of pre-treatment tumor samples. Bone marrow aspirates were subject to a negative-selection procedure to enrich for tumor cells, and these samples were utilized 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 upon the risk stratification provided by the International Staging System. Predictive models and biological 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 anti-cancer agents.
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