MAY 09, 2018 1:30 PM PDT

Bimodal Gene Expression in Breast Cancer

C.E. Credits: CEU
Speaker
  • Assistant Professor, Department of Systems & Computational Biology, Assistant Professor, Department of Epidemiology & Population Health, Albert Einstein College of Medicine
    BIOGRAPHY

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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MAY 09, 2018 1:30 PM PDT

Bimodal Gene Expression in Breast Cancer

C.E. Credits: CEU

Specialty

Biotechnology

Immunology

Cancer Diagnostics

Cancer Research

Cell Biology

Dna Sequencing

Big Data

Clinical Diagnostics

Tumor

Bioinformatics

Cancer

Oncology

Clinical Research

Gene Expression

Dna

Geography

North America67%

Europe33%

Registration Source

Website Visitors100%

Job Title

Medical Laboratory Technician67%

Student33%

Organization

Independent Laboratories33%

Hospital33%

Academic Institution33%


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