Nancy Lan Guo
Associate Professor, Mary Babb Randolph Cancer Center, Adjunct Faculty of Computer Science
West Virginia University, USA
Dr. Guo got her Ph.D. in Computer and Information Science in 2004 and B.S. in Biochemistry and Molecular Biology in 1997. She is Program Assistant Director of West Virginia Clinical & Translational Science Institute for Biomedical Informatics. She had 21 peer-reviewed publications in the past three years. Her work in lung cancer gene signatures was nominated for 2007 CDC Excellence in Science Awards. Her work in breast cancer gene signatures was selected to feature the cover of Clinical Cancer Research [April, 2007] which received national news coverage from Newswise, Medical News Today, NewsRx, etc. She has five pending patents for the gene signatures she identified for personalized therapy. She was awarded a $1M R01 grant from National Library of Medicine (LM009500) in 2008 and a $1M NIH stimulus grant to advance translational research (NCRR P20 RR16440) in 2009.
Traditional prognostic factors for cancer are imperfect. In part, they lack the information about the biological diversity of cancer and have not reflected the complexity of molecular mechanism of the diseases. In addition, they focus on predicting for populations instead of for individuals. Recent advances in the knowledge of human genomics and proteomics, as well as bioinformatics, have revolutionized the ways in which researchers are able to identify molecular signatures of cancer recurrence and metastases. Genome-wide studies will guide hypothesis-driven experimentation and aid clinical decision-making. Bioinformatics is the key to identifying new disease biomarkers and making accurate predictions in molecular diagnosis and prognosis. My research interests include applying bioinformatics methods to clinical research, specifically, to the identification of novel biomarkers for the diagnosis, prognosis, and therapeutics prediction of human diseases. Furthermore, we will employ genomic and proteomic analysis of clinical specimens to validate the biomarkers identified in genome-wide association studies. We are also interested in constructing disease-mediated genome-wide co-expression networks. Identifying gene products within one or a few specific pathways could potentially enhance the prognostic value and reveal therapeutic targets for intervention.