Pharmacogenomics of bortezomib test-dosing identifies hyperexpression of proteasome genes, especially PSMD4, as novel high-risk feature in myeloma treated with Total Therapy 3

Myeloma Institute for Research and Therapy, University of Arkansas for Medical Sciences, Little Rock AR, USA.
Blood (Impact Factor: 10.45). 05/2011; 118(13):3512-24. DOI: 10.1182/blood-2010-12-328252
Source: PubMed


Gene expression profiling (GEP) of purified plasma cells 48 hours after thalidomide and dexamethasone test doses showed these agents' mechanisms of action and provided prognostic information for untreated myeloma patients on Total Therapy 2 (TT2). Bortezomib was added in Total Therapy 3 (TT3), and 48 hours after bortezomib GEP analysis identified 80 highly survival-discriminatory genes in a training set of 142 TT3A patients that were validated in 128 patients receiving TT3B. The 80-gene GEP model (GEP80) also distinguished outcomes when applied at baseline in both TT3 and TT2 protocols. In context of our validated 70-gene model (GEP70), the GEP80 model identified 9% of patients with a grave prognosis among those with GEP70-defined low-risk disease and 41% of patients with favorable prognosis among those with GEP70-defined high-risk disease. PMSD4 was 1 of 3 genes common to both models. Residing on chromosome 1q21, PSMD4 expression is highly sensitive to copy number. Both higher PSMD4 expression levels and higher 1q21 copy numbers affected clinical outcome adversely. GEP80 baseline-defined high risk, high lactate dehydrogenase, and low albumin were the only independent adverse variables surviving multivariate survival model. We are investigating whether second-generation proteasome inhibitors (eg, carfilzomib) can overcome resistance associated with high PSMD4 levels.

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    • "Orlandi et al., evaluated the association between VEGF-A sequence variants and prostate-specific antigen progression, progression-free survival and overall survival, in advanced castration-resistant prostate cancer patients treated with metronomic CPA (CTX), celecoxib and dexamethasone.[39] Pharmacogenomics of bortezomib has been investigated by John D. Shaughnessy et al.[40] In another study, Mercurio et al., have quantified the expression of several genes (ICAM1, CRK, CD36 and IQGAP1) by reverse transcriptase quantitative polymerase chain reaction in pilocytic astrocytomas and glioblastomas. "
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    Journal of Pharmacology and Pharmacotherapeutics 03/2014; 5(3):186-192. DOI:10.4103/0976-500X.136098
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    • "Finally and most importantly, the so-called predictive classification should be able to estimate individual outcome of a specific therapeutic intervention and allow for selection and elimination of specific drugs. The UAMS group has reported post drug genomic data identifying patterns associated with drug specific responses [32], [33] and in our ongoing process we have planned pharmacogenetic microdosis studies for specific drugs [34]–[36]. Such data, in parallel with the present programme, are expected to evolve into a useful drug specific and predictive classification system. "
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    ABSTRACT: In a conceptual study of drug resistance we have used a preclinical model of malignant B-cell lines by combining drug induced growth inhibition and gene expression profiling. In the current report a melphalan resistance profile of 19 genes were weighted by microarray data from the MRC Myeloma IX trial and time to progression following high dose melphalan, to generate an individual melphalan resistance index. The resistance index was subsequently validated in the HOVON65/GMMG-HD4 trial data set to prove the concept. Biologically, the assigned resistance indices were differentially distributed among translocations and cyclin D expression classes. Clinically, the 25% most melphalan resistant, the intermediate 50% and the 25% most sensitive patients had a median progression free survival of 18, 32 and 28 months, respectively (log-rank P-value = 0.05). Furthermore, the median overall survival was 45 months for the resistant group and not reached for the intermediate and sensitive groups (log-rank P-value = 0.003) following 38 months median observation. In a multivariate analysis, correcting for age, sex and ISS-staging, we found a high resistance index to be an independent variable associated with inferior progression free survival and overall survival. This study provides clinical proof of concept to use in vitro drug screen for identification of melphalan resistance gene signatures for future functional analysis.
    PLoS ONE 12/2013; 8(12):e83252. DOI:10.1371/journal.pone.0083252 · 3.23 Impact Factor
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    • "See Figure 4, GEP Historical DB. Microarray datasets were processed as previously reported [33]. "
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    ABSTRACT: Background Transcriptome analysis by microarrays has produced important advances in biomedicine. For instance in multiple myeloma (MM), microarray approaches led to the development of an effective disease subtyping via cluster assignment, and a 70 gene risk score. Both enabled an improved molecular understanding of MM, and have provided prognostic information for the purposes of clinical management. Many researchers are now transitioning to Next Generation Sequencing (NGS) approaches and RNA-seq in particular, due to its discovery-based nature, improved sensitivity, and dynamic range. Additionally, RNA-seq allows for the analysis of gene isoforms, splice variants, and novel gene fusions. Given the voluminous amounts of historical microarray data, there is now a need to associate and integrate microarray and RNA-seq data via advanced bioinformatic approaches. Methods Custom software was developed following a model-view-controller (MVC) approach to integrate Affymetrix probe set-IDs, and gene annotation information from a variety of sources. The tool/approach employs an assortment of strategies to integrate, cross reference, and associate microarray and RNA-seq datasets. Results Output from a variety of transcriptome reconstruction and quantitation tools (e.g., Cufflinks) can be directly integrated, and/or associated with Affymetrix probe set data, as well as necessary gene identifiers and/or symbols from a diversity of sources. Strategies are employed to maximize the annotation and cross referencing process. Custom gene sets (e.g., MM 70 risk score (GEP-70)) can be specified, and the tool can be directly assimilated into an RNA-seq pipeline. Conclusion A novel bioinformatic approach to aid in the facilitation of both annotation and association of historic microarray data, in conjunction with richer RNA-seq data, is now assisting with the study of MM cancer biology.
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