Bernard Fine

Publications (2)7.1 Total impact

• Source

  Available from: John C Castle

  Article: Development of a microarray platform for FFPET profiling: application to the classification of human tumors.

  Sven Duenwald, Mingjie Zhou, Yanqun Wang, Serguei Lejnine, Amit Kulkarni, Jaime Graves, Ryan Smith, John Castle, George Tokiwa, Bernard Fine, Hongyue Dai, Thomas Fare, Matthew Marton
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  ABSTRACT: mRNA profiling has become an important tool for developing and validating prognostic assays predictive of disease treatment response and outcome. Archives of annotated formalin-fixed paraffin-embedded tissues (FFPET) are available as a potential source for retrospective studies. Methods are needed to profile these FFPET samples that are linked to clinical outcomes to generate hypotheses that could lead to classifiers for clinical applications. We developed a two-color microarray-based profiling platform by optimizing target amplification, experimental design, quality control, and microarray content and applied it to the profiling of FFPET samples. We profiled a set of 50 fresh frozen (FF) breast cancer samples and assigned class labels according to the signature and method by van 't Veer et al 1 and then profiled 50 matched FFPET samples to test how well the FFPET data predicted the class labels. We also compared the sorting power of classifiers derived from FFPET sample data with classifiers derived from data from matched FF samples. When a classifier developed with matched FF samples was applied to FFPET data to assign samples to either "good" or "poor" outcome class labels, the classifier was able to assign the FFPET samples to the correct class label with an average error rate = 12% to 16%, respectively, with an Odds Ratio = 36.4 to 60.4, respectively. A classifier derived from FFPET data was able to predict the class label in FFPET samples (leave-one-out cross validation) with an error rate of approximately 14% (p-value = 3.7 x 10(-7)). When applied to the matched FF samples, the FFPET-derived classifier was able to assign FF samples to the correct class labels with 96% accuracy. The single misclassification was attributed to poor sample quality, as measured by qPCR on total RNA, which emphasizes the need for sample quality control before profiling. We have optimized a platform for expression analyses and have shown that our profiling platform is able to accurately sort FFPET samples into class labels derived from FF classifiers. Furthermore, using this platform, a classifier derived from FFPET samples can reliably provide the same sorting power as a classifier derived from matched FF samples. We anticipate that these techniques could be used to generate hypotheses from archives of FFPET samples, and thus may lead to prognostic and predictive classifiers that could be used, for example, to segregate patients for clinical trial enrollment or to guide patient treatment.
  Journal of Translational Medicine 08/2009; 7:65. · 3.41 Impact Factor
• Source

  Available from: Walter K Kraft

  Article: Diurnal variation of the human adipose transcriptome and the link to metabolic disease.

  Andrey Loboda, Walter K Kraft, Bernard Fine, Jeffrey Joseph, Michael Nebozhyn, Chunsheng Zhang, Yudong He, Xia Yang, Christopher Wright, Mark Morris, Ira Chalikonda, Mark Ferguson, Valur Emilsson, Amy Leonardson, John Lamb, Hongyue Dai, Eric Schadt, Howard E Greenberg, Pek Yee Lum
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  ABSTRACT: Circadian (diurnal) rhythm is an integral part of the physiology of the body; specifically, sleep, feeding behavior and metabolism are tightly linked to the light-dark cycle dictated by earth's rotation. The present study examines the effect of diurnal rhythm on gene expression in the subcutaneous adipose tissue of overweight to mildly obese, healthy individuals. In this well-controlled clinical study, adipose biopsies were taken in the morning, afternoon and evening from individuals in three study arms: treatment with the weight loss drug sibutramine/fasted, placebo/fed and placebo/fasted. The results indicated that diurnal rhythm was the most significant driver of gene expression variation in the human adipose tissue, with at least 25% of the genes having had significant changes in their expression levels during the course of the day. The mRNA expression levels of core clock genes at a specific time of day were consistent across multiple subjects on different days in all three arms, indicating robust diurnal regulation irrespective of potential confounding factors. The genes essential for energy metabolism and tissue physiology were part of the diurnal signature. We hypothesize that the diurnal transition of the expression of energy metabolism genes reflects the shift in the adipose tissue from an energy-expending state in the morning to an energy-storing state in the evening. Consistent with this hypothesis, the diurnal transition was delayed by fasting and treatment with sibutramine. Finally, an in silico comparison of the diurnal signature with data from the publicly-available Connectivity Map demonstrated a significant association with transcripts that were repressed by mTOR inhibitors, suggesting a possible link between mTOR signaling, diurnal gene expression and metabolic regulation. Diurnal rhythm plays an important role in the physiology and regulation of energy metabolism in the adipose tissue and should be considered in the selection of novel targets for the treatment of obesity and other metabolic disorders.
  BMC Medical Genomics 03/2009; 2:7. · 3.69 Impact Factor