Statistical Methods for Head and Neck Cancer Proteomics

Medical University Of South Carolina, Charleston SC

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Abstract

DESCRIPTION (provided by applicant): My long-term career goal is to be an independent quantitative researcher focused on: (1) the development and implementation of statistical methodology to address the quantitative and analysis needs of proteomics research, and (2) directing these methods toward identification and quantification of the expressed proteins of head and neck cancer tissue. To achieve these goals, I have designed a career plan that augments my statistical expertise with knowledge of the biochemistry of proteomics and, in particular, the biology of head and neck cancers, hands-on experience in proteomics laboratories, including both two-dimensional gel electrophoresis (2-DE) and mass spectrometry-based platforms, and exposure to the realities of head and neck cancer as it relates to case management and patient care. I am committed to developing as an independent investigator with a research focus on developing statistical methodology in proteomics-based research for head and neck oncology. An NIDCR mentored quantitative research career development award (K25) is the ideal mechanism through which I will realize my career goals. Head and neck squamous cell carcinoma (HNSCC) is the sixth most common neoplasm in the world, and 5-year survival has remained at less than 50% for the last 30 years, despite therapeutic advances, in large part due to the late stage at which HNSCC patients present with their malignancies. The use of proteomics to identify, characterize and quantify protein profiles that differentiate healthy from diseased states offers new hope to HNSCC patients and others diagnosed with cancer having historically poor survival. Scientists and clinicians are optimistic that early disease detection and improved interventions will result directly from the identification of disease biomarkers through proteomic profiling. We hypothesize that Bayesian hierarchical mixture models provide a natural, flexible and powerful quantitative approach to classification and differential expression analyses of proteomics data.