A Diagnostic Test for Prostate Cancer From Gene Expression Profiling Data

Bueno, Raphael; Loughlin, Kevin R.; Powell, M; Gordon, Gavin J.

doi:10.1097/01.ju.0000095446.10443.52

Cited by 35 publications

(22 citation statements)

References 12 publications

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“…We again show the utility of the gene ratio technique (10,11,14,15) in MPM by designing and testing multiple clinically relevant prognostic tests. Prognostication using typical bioinformatics tools (e.g., hierarchical clustering) is not easily amenable to the analysis of a single patient at a time and without reference to an additional group of patients whose gene expression data was similarly acquired.…”

Section: Discussionmentioning

confidence: 99%

“…We identified new treatmentspecific candidate prognostic molecular markers and created an expression level ratio -based outcome predictor model similar to previous studies (11,14,15) using a subset of the 39 samples profiled in this study as training set. We searched the Affymetrix U133A microarray to identify all genes with expression levels that differed significantly (P V 0.01) and by at least 2-fold between good-outcome (n = 13, survival >17 months) and poor-outcome (n = 10 survival <6 months) training set tumors to identify new treatment-specific prognostic markers.…”

Section: Methodsmentioning

confidence: 99%

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Validation of Genomics-Based Prognostic Tests in Malignant Pleural Mesothelioma

Gordon¹,

Rockwell

Godfrey³

et al. 2005

Clinical Cancer Research

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Purpose: Malignant pleural mesothelioma (MPM) is a highly lethal neoplasm with limited pretreatment prognostication strategies. In this report, we examine the accuracy of a previously proposed prognostic test in an independent cohort of MPM patients. This test uses simple ratios of gene expression levels to provide a novel prognostication scheme. Experimental Design: Gene expression data using high-density oligonucleotide microarrays (f22,000 genes) were obtained for a new cohort of human MPM tumors from patients undergoing similar treatments (n = 39). The relative expression levels for specific genes were also determined using real-time quantitative reverse transcription-PCR. We also used a subset of these tumors associated with widely divergent patient survival (n = 23) as a training set to identify new treatment-specific candidate prognostic molecular markers and gene ratio^based prognostic tests. The predictive nature of these newly discovered markers and gene ratiob ased prognostic tests were then examined in an independent group of tumors (n = 52) using microarray data and quantitative reverse transcription-PCR. Results: Previously described MPM prognostic genes and gene ratio^based prognostic tests predicted clinical outcome in 39 independent MPM tumor specimens in a statistically significant manner. Newly discovered treatment-specific prognostic genes and gene ratio^based prognostic tests were highly accurate and statistically significant when examined in an independent group of 52 tumors from patients undergoing similar treatment. Conclusions:The data support the use of gene ratios in translating gene expression data into easily reproducible, statistically validated clinical tests for the prediction of outcome in MPM.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Validation of Genomics-Based Prognostic Tests in Malignant Pleural Mesothelioma

Gordon¹,

Rockwell

Godfrey³

et al. 2005

Clinical Cancer Research

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“…These changes in brain function likely emanate from alterations in gene expression, which in turn may underlie the cellular adaptations to chronic alcohol abuse (Nestler, 2000). The importance of understanding changes in gene expression in alcoholism can be appreciated by the impact of gene expression profiling in other diseases, most notably cancer, where studies have lead to improved pharmacotherapies (Okutsu et al, 2002;Taxman et al, 2003;Zembutsu et al, 2002) and to a molecular classification of disease which promises to be more accurate and informative than traditional diagnostic tests (Bueno et al, 2004;Kim et al, 2002;Mor et al, 2003). Gene expression profiling is only beginning to be applied to psychiatric illnesses (Geschwind, 2003;Marcotte et al, 2003;Mirnics et al, 2000;Tkachev et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Patterns of Gene Expression in the Frontal Cortex Discriminate Alcoholic from Nonalcoholic Individuals

Liu

Lewohl

Harris

et al. 2005

Neuropsychopharmacol

241

254

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Alcohol dependence is characterized by tolerance, physical dependence, and craving. The neuroadaptations underlying these effects of chronic alcohol abuse are likely due to altered gene expression. Previous gene expression studies using human post-mortem brain demonstrated that several gene families were altered by alcohol abuse. However, most of these changes in gene expression were small. It is not clear if gene expression profiles have sufficient power to discriminate control from alcoholic individuals and how consistent gene expression changes are when a relatively large sample size is examined. In the present study, microarray analysis (B47 000 elements) was performed on the superior frontal cortex of 27 individual human cases (14 well characterized alcoholics and 13 matched controls). A partial least squares statistical procedure was applied to identify genes with altered expression levels in alcoholics. We found that genes involved in myelination, ubiquitination, apoptosis, cell adhesion, neurogenesis, and neural disease showed altered expression levels. Importantly, genes involved in neurodegenerative diseases such as Alzheimer's disease were significantly altered suggesting a link between alcoholism and other neurodegenerative conditions. A total of 27 genes identified in this study were previously shown to be changed by alcohol abuse in previous studies of human post-mortem brain. These results revealed a consistent re-programming of gene expression in alcohol abusers that reliably discriminates alcoholic from non-alcoholic individuals.

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“…B. Vergleich hormonrefraktärer und -sensitiver Prostatakarzinome). Auf diese Weise können charakteristische "Klassenmarker" identifiziert werden, sodass ein kleinerer Satz von Genexpressionsdaten schließlich zur Definition eines bestimmten Phänotyps herangezogen werden kann [26,4].…”

Section: Auswertungunclassified

“…Ziel derartiger Ansätze ist neben der Identifikation therapeutischer Ziele die Entwicklung neuer kleinerer Arrays mit ausgewählten Genen. Untersuchungen solcher Markersets an größeren Fallzahlen werden derzeit in retrospektiven und prospektiven Studien durchgeführt [4,20,28].…”

Section: Microarray-analysen Beim Prostatakarzinomunclassified

Microarrays

2004

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Microarrays allow a simultaneous gene expression analysis of thousands of genes, providing an expression profile of the specimen investigated. Thus, this procedure is well suited to characterize the complex genetic alterations of malignant tumors. Using unsupervised hierarchical cluster analysis, characteristic expression profiles for individuals, organs and tissues, as well as for different cell types, can be identified. Molecular signatures have been observed in tumors compared to normal tissue, for different tumor stages, risk groups or response therapy. In prostate cancer, many tumor-specific gene expression alterations have been identified. Among these, the cell surface protease hepsin and alpha-methyl-acryl-CoA-racemase might gain importance as diagnostic tools. Moreover, gene expression profiles were identified which are associated with advanced tumor stage, poor differentiation or progress after radical prostatectomy. Increased expression of enzymes of steroid biosynthesis and the androgen receptor appears to be part of the molecular signature of hormone refractory prostate cancer.

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A Diagnostic Test for Prostate Cancer From Gene Expression Profiling Data

Cited by 35 publications

References 12 publications

Validation of Genomics-Based Prognostic Tests in Malignant Pleural Mesothelioma

Validation of Genomics-Based Prognostic Tests in Malignant Pleural Mesothelioma

Patterns of Gene Expression in the Frontal Cortex Discriminate Alcoholic from Nonalcoholic Individuals

Microarrays

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