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

Blood ◽  
2006 ◽  
Vol 109 (8) ◽  
pp. 3177-3188 ◽  
Author(s):  
George Mulligan ◽  
Constantine Mitsiades ◽  
Barb Bryant ◽  
Fenghuang Zhan ◽  
Wee J. Chng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Clinical Trials ◽  
Gene Expression Profiling ◽  
Anticancer Agents ◽  
Expression Profiling ◽  
Dna Microarrays ◽  
Selection Procedure ◽  
Staging System ◽  
Trial Outcomes

AbstractThe 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.

Metaphase Cytogenetics Adds to Gene Expression Profiling in the Identification of High Risk Multiple Myeloma.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 114-114
Author(s):  
Guido Tricot ◽  
Fenghuang Zhan ◽  
Bart Barlogie ◽  
Yongsheng Huang ◽  
Jeffrey Sawyer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Prognostic Significance ◽  
Staging System ◽  
Treatment Modalities ◽  
Prognostic Impact ◽  
Newly Diagnosed ◽  
Prognostic Information

Abstract The International Staging System (ISS), based on B2-microglobulin and albumin levels at the time of diagnosis, has now generally been adopted as a new prognostic classification system for multiple myeloma (MM). While readily and widely applicable, ISS does not account for genetic disease features, such as metaphase (CA) and interphase fluorescence in situ hybridization (FISH) cytogenetic abnormalities, which when examined in the context of standard prognostic variables, confer higher hazards of relapse and disease-related death. Recently, gene expression profiling (GEP) uncovered the major prognostic significance for outcome of high expression of CKS1B, a gene mapping to an amplicon at chromosome 1q21. We have performed a comprehensive study of CA, FISH, GEP and ISS staging in 351 newly diagnosed MM patients, treated uniformly on Total Therapy 2. We have analyzed outcome based on a combination of high CKS1B by GEP and CA. GEP-based t(11;14) was prognostically favorable, irrespective of expression of CKS1B and, therefore, was removed from the group of patients with high CKS1B expression. After this adjustment, with the combination of both CA and high CKS1B (approximately 10% of all patients) conferred a very poor outcome with only 24% and 40% of such patients being event-free and/surviving at 3 years, compared with 72% and 84% for the others (p values : <.0001). Such patients fared poorly, irrespective of their ISS stage. Similar prognostic information could be gained by combining CA with FISH-defined amplification of 1q21 and t(11;14). Because of their major prognostic impact, all newly diagnosed patients should be tested for these genetic markers. Novel treatment modalities are justified in the small subgroup of such poor prognosis patients, since they derive only a minor benefit from advances in MM therapy. CKS1B Q4 + CA (with no CCND1) vs. Others CKS1B Q4 + CA (with no CCND1) vs. Others

Preparation of Salmonella enterica Serovar Typhi Genomic DNA Microarrays for Gene Expression Profiling Analysis*

2009 ◽  
Vol 2009 (2) ◽  
pp. 206-212 ◽  
Author(s):  
Xiu-Mei SHENG ◽  
Xin-Xiang HUANG ◽  
Ling-Xiang MAO ◽  
Chao-Wang ZHU ◽  
Shun-Gao XU ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Salmonella Enterica ◽  
Genomic Dna ◽  
Dna Microarrays ◽  
Profiling Analysis

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

Blood ◽  
2010 ◽  
Vol 116 (14) ◽  
pp. 2543-2553 ◽  
Author(s):  
Annemiek Broyl ◽  
Dirk Hose ◽  
Henk Lokhorst ◽  
Yvonne de Knegt ◽  
Justine Peeters ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Medical Science ◽  
Protein Tyrosine Phosphatases ◽  
Molecular Classification ◽  
Newly Diagnosed ◽  
Expression Of Genes

Abstract To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associated genes (n = 15, 4.7%), and hyperdiploid (n = 77, 24.1%). Moreover, the UAMS low percentage of bone disease cluster was identified as a subcluster of the MF cluster (n = 15, 4.7%). One subgroup (n = 39, 12.2%) showed a myeloid signature. Three novel subgroups were defined, including a subgroup of 37 patients (11.6%) characterized by high expression of genes involved in the nuclear factor kappa light-chain-enhancer of activated B cells pathway, which include TNFAIP3 and CD40. Another subgroup of 22 patients (6.9%) was characterized by distinct overexpression of cancer testis antigens without overexpression of proliferation genes. The third novel cluster of 9 patients (2.8%) showed up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 as well as SOCS3. To conclude, in addition to 7 clusters described in the UAMS classification, we identified 3 novel subsets of multiple myeloma that may represent unique diagnostic entities.

scholarly journals Gene expression profiling of wound healing in keratinocytes using DNA microarrays

10.1038/14336 ◽  
1999 ◽  
Vol 23 (S3) ◽  
pp. 54-54
Author(s):  
Claire Johnson ◽  
Frank Burslem ◽  
Jerry Lanfear
Keyword(s):  
Gene Expression ◽  
Wound Healing ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Dna Microarrays

Gene Expression Profiling Using DNA Microarrays

2013 ◽  
pp. 381-391 ◽  
Author(s):  
Kyonoshin Maruyama ◽  
Kazuko Yamaguchi-Shinozaki ◽  
Kazuo Shinozaki
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Dna Microarrays

scholarly journals Erratum: Dose-dense and less dose-intense total therapy 5 for gene expression profiling-defined high-risk multiple myeloma

2016 ◽  
Vol 6 (9) ◽  
pp. e471-e471 ◽  
Author(s):  
Y Jethava ◽  
A Mitchell ◽  
M Zangari ◽  
S Waheed ◽  
C Schinke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Total Therapy ◽  
Dose Dense

Gene Expression Profiling for Discovery of Novel Targets in Human Traumatic Brain Injury

2010 ◽  
Vol 13 (2) ◽  
pp. 140-153 ◽  
Author(s):  
Taura L. Barr ◽  
Sheila Alexander ◽  
Yvette Conley
Keyword(s):  
Gene Expression ◽  
Traumatic Brain Injury ◽  
Clinical Trials ◽  
Brain Injury ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Treatment Strategies ◽  
Biological Pathways ◽  
Human Response ◽  
Novel Targets

Several clinical trials have failed to demonstrate a significant effect on outcome following human traumatic brain injury (TBI) despite promising results obtained in preclinical animal studies. These failures may be due in part to a misinterpretation of the findings obtained in preclinical animal models of TBI, a misunderstanding of the complexity of the human response to TBI, limited knowledge about the biological pathways that interact to contribute to good and bad outcomes after brain injury, and the effects of genomic variability and environment on individual recovery. Recent publications suggest that data obtained from gene expression profiling studies of complex neurological diseases such as stroke, multiple sclerosis (MS), Alzheimer’s and Parkinson’s may contribute to a more informed understanding of what affects outcome following TBI. These data may help to bridge the gap between successful preclinical studies and negative clinical trials in humans to reveal novel targets for therapy. Gene expression profiling has the capability to identify biomarkers associated with response to TBI, elucidate complex genetic interactions that may play a role in outcome following TBI, and reveal biological pathways related to brain health. This review highlights the current state of the literature on gene expression profiling for neurological disease and discusses its ability to aid in unraveling the variable human response to TBI and the potential for it to offer treatment strategies in an area where we currently have limited therapeutic options primarily based on supportive care.

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