MAY 09, 2018 01:30 PM PDT
Bimodal Gene Expression in Breast Cancer
CONTINUING EDUCATION (CME/CE/CEU) CREDITS: CME | CEU | P.A.C.E. CE | Florida CE
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Speakers:
  • Assistant Professor, Department of Systems & Computational Biology, Assistant Professor, Department of Epidemiology & Population Health, Albert Einstein College of Medicine
    Biography
      Jessica Mar is an Assistant Professor at Albert Einstein College of Medicine in the Department of Systems and Computational Biology in the Bronx, New York. The focus of the Mar lab is to understand how variability in gene expression contributes to the regulation of cellular phenotypes. Around the topic of variability, her work involves applications in single cell genomics, stem cells, genetics and cancer biology. Jessica Mar received her Bachelor of Science degree in Mathematics at the University of Queensland in Brisbane, Australia and First Class Honors in Statistics in 2002. She got her PhD in Biostatistics from Harvard University in 2008. Previously she was a postdoctoral research fellow at the Dana-Farber Cancer Institute in Boston, and a visiting scientist at the European Bioinformatics Institute in the UK. Since July 2016, Dr. Mar holds a joint appointment with the Australian Institute for Bioengineering and Nanotechnology at the University of Queensland, Australia as a Group Leader.

    Abstract:

    Tumors are often categorized into standard molecular subtypes. However, largescale studies have demonstrated that patient heterogeneity in the regulatory make-up of tumors remain. At the transcriptional level, one example of heterogeneity in a patient population is the presence of bimodally-expressed genes. Bimodality in expression signifies the presence of potentially new patient sub-groups. Here, we present a new statistical approach called oncomix, that models transcriptional heterogeneity in tumor and adjacent normal (i.e. tumor-free) using bimodality to find oncogene candidates. Oncomix was applied to RNA-sequencing data from the breast cancer cohort of the Cancer Genome Atlas, and a set of oncogene candidates that were over-expressed in only a subset of tumors was identified. 

    Intronic DNA methylation was strongly associated with the overexpression of chromobox 2 (CBX2), an oncogene candidate that was identified using our method but not through other approaches. CBX2 overexpression in breast tumors was associated with the upregulation of genes involved in cell cycle progression and is associated with poorer 5-year survival. The predicted function of CBX2 was confirmed in vitro providing the first experimental evidence that CBX2 promotes breast cancer cell growth. Modeling mRNA expression heterogeneity in tumors through bimodal profiles is a novel powerful approach with the potential to uncover therapeutic targets that benefit subsets of cancer patients.


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