Ben Major, PhD

Associate Professor Assistant Professor

Research Interests

  • Proteomics
  • Mass Spectrometry
  • Signal Transduction
  • Computer Science
  • Wnt
  • Oxidative stress

Research Synopsis

My lab studies how perturbation of specific signal transduction pathways contributes to the initiation, progression and dissemination of cancer. We employ a “systems level” integrative discovery platform to characterize pathway dynamics in normal and cancer cell models. More specifically, we use mass spectrometry-based proteomics to define the protein-protein interaction networks for signaling pathways of interest; my lab focuses on the Wnt/b-catenin and KEAP1/NRF2 signaling pathways. We then annotate the nodes within the network for function, as determined by established and novel functional genomic screening technologies and small molecule screens. Integration of these data with cancer-associated mutation data and cancer-associated gene expression data yields a powerful tool for oncological discovery—a cancer annotated physical/functional map for a specific signaling pathway of interest. Critical to our success is the development and implementation of computational scoring algorithms, relational databases and data visualization. Ultimately, the models and hypotheses produced are challenged through mechanistic studies employing cultured human cancer cells, zebrafish, mice and in vitro biochemical systems.

In the coming years, our research will further develop focus and direction along three main fronts: 1) systems network biology and computation guided by mass spectrometry, kinase activity profiling and gain-of-function genomic screens, 2) KEAP1/NRF2 signaling in lung and upper aerodigestive cancers, and 3) WNT/-catenin signaling, FOXP1 and B-cell lymphoma. Our goal remains unchanged—we work to understand how alterations in signal transduction causes human disease, and to use that knowledge to create new disease medicines.



Click for complete list of PubMed publications.

  • Department of Cell Biology & Physiology

  • Department of Computer Science