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

University of Arkansas at Little Rock, Little Rock, Arkansas, United States
Blood (Impact Factor: 10.45). 05/2007; 109(8):3177-88. DOI: 10.1182/blood-2006-09-044974
Source: PubMed


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.

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    • "In theory, any relevant system could be used for learning. As a proof-of concept we used pRRophetic to build models on two arms (025 and 040) of the bortezomib clinical trial (discussed above; as per [5]) and predicted drug sensitivity on the remaining arm (039). Using this approach, the predicted drug sensitivity for trial defined “sensitive” and “resistant” patients was significantly different (P = 0.02 from t-test), suggesting that some signal is being captured and that this is a viable application of this tool. "
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    ABSTRACT: We recently described a methodology that reliably predicted chemotherapeutic response in multiple independent clinical trials. The method worked by building statistical models from gene expression and drug sensitivity data in a very large panel of cancer cell lines, then applying these models to gene expression data from primary tumor biopsies. Here, to facilitate the development and adoption of this methodology we have created an R package called pRRophetic. This also extends the previously described pipeline, allowing prediction of clinical drug response for many cancer drugs in a user-friendly R environment. We have developed several other important use cases; as an example, we have shown that prediction of bortezomib sensitivity in multiple myeloma may be improved by training models on a large set of neoplastic hematological cell lines. We have also shown that the package facilitates model development and prediction using several different classes of data.
    PLoS ONE 09/2014; 9(9):e107468. DOI:10.1371/journal.pone.0107468 · 3.23 Impact Factor
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    • "Next, we applied our approach to a larger publicly available clinical phase II/III trial dataset, which assessed response to bortezomib in relapsed multiple myeloma patients [19]. In the original study, a pretreatment bone marrow aspirate was collected and enriched for tumor cells, which underwent microarray expression profiling. "
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    • "Finally, available on-line data was included from 156 patients in the APEX phase-3 trial investigating the efficacy of bortezomib vs. high-dose dexamethasone for relapsed MM patients. This trial acted as a negative control cohort in order to exclude the RI predicted response to treatments not containing HDM [18], [19]. Response to treatment was defined primarily by the European Group for Blood and Marrow Transplantation (EBMT) criteria for evaluating disease response and progression in patients treated by high-dose therapy and haemopoietic stem cell transplantation [20], but with the International Myeloma Working Group (IMWG) criteria for very good partial response (VGPR) added [21]. "
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