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, P. Leif Bergsagel, David Schenkein, Dixie Lee Esseltine, Anthony Boral, Kenneth C. Anderson

Research output: Contribution to journalArticlepeer-review

260 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3177-3188
Number of pages12
Issue number8
StatePublished - Apr 15 2007

ASJC Scopus subject areas

  • Biochemistry
  • Immunology
  • Hematology
  • Cell Biology


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