Karin D. Rodland, PhD.
Talk Title: Proteogenomic Insights for Cancer Biology, Prognosis, and Treatment
Chief Scientist for Biomedical Research,
Pacific Northwest National Laboratory, Richland WA
Dr. Karin Rodland joined the Biological Sciences Division at the Pacific Northwest National Laboratory after serving seventeen years on the faculty of Oregon Health Sciences University. As Chief Scientist for Biomedical Research at PNNL, she has promoted the application of PNNL’s traditional strengths in mass spectrometry, proteomics, and systems biology to important problems in biomedical research. Dr. Rodland’s research focuses on the use of proteomics to study signal transduction pathways that regulate proliferation in normal and malignant cells, with an emphasis on ovarian cancer. She was the first to recognize the role of the calcium-sensing receptor in modulating proliferation in response to small molecules in the extracellular environment. Since joining PNNL, she has adopted a systems biology approach to signal transduction and has become a recognized expert in the field of proteomics and cancer biomarkers.
The flow of information from the genome to the phenome is not linear, but intricately regulated at the level of transcription, translation, post-translational modification and cellular localization. A full understanding of the biological changes driving cancer initiation, progression, and response to therapy requires comprehensive analysis at all levels of information, and meaningful integration into pathways and networks. The Clinical Proteomic Tumor Analysis Consortium has provided a mechanism for adding comprehensive data on protein abundance and post-translational modifications, closely linked to comprehensive genomic and transcriptomic data, in previously analyzed TCGA tumor samples and purpose-collected prospective samples. This talk will present novel insights from the proteogenomic interrogation of high grade serous ovarian carcinoma, including pathways associated with clinical outcome, and describe how the same approaches can be applied to patient samples from on-going clinical trials to provide dynamic protein-level information on the development of drug resistance.