The Department of Biomedical Informatics
presents
Antonina Mitrofanova PhD
Antonina Mitrofanova is an Associate Research Scientist and Prostate Cancer Foundation Young Investigator at Columbia University. Antonina received her PhD in Computer Science from New York University in 2009, with the Best Dissertation award and the Henning Biermann Prize for outstanding contribution to Education at NYU. To support her PostDoctoral work, Antonina received a Computing Innovation Fellowship from the National Science Foundation and a Young Investigator Award from the Prostate Cancer Foundation. Her research interests include Computational Biology, Biomedical Informatics, and inference in biological networks, with a wide range of applications to human disease and healthcare. Antonina’s recent work focuses on discovery of mechanisms of cancer progression and therapeutic strategies for patients with the most aggressive form of this disease.
“Computational Systems Biology Approaches to Investigate Mechanisms of Progression and Therapeutic Response in Human Cancer”
April 30, 2015 2:00 pm – 3:00 pm
Pharmacology Seminar Room
Basic Science Tower, Level 8, Rm. 180
Contact the Department of Biomedical Informatics at (631) 444-8459 with any questions regarding this event.
Abstract:
Cancer is a complex disease driven by the coordinated activation and inactivation of multiple genes, which makes the identification of causal drivers of cancer progression a daunting challenge. Although animal models are often used to study mechanisms of cancer progression and evaluate new cancer therapies, the accurate extrapolation of animal studies to human cancer has been difficult. I will present novel cross-species systems biology algorithms that identify conserved regulatory programs between human and mouse cancer models and inform on therapeutic strategies for human patients with the most aggressive disease. These algorithms can identify causal gene “drivers” of aggressive cancer, which may also serve as biomarkers to categorize patients with poor prognosis. We have generated complementary human and mouse prostate cancer gene regulatory networks (interactomes) assembled from molecular profiles of human tumors and genetically engineered mouse models. Our computational systems biology network-based approaches and subsequent experimental validation have elucidated a synergistic interaction of two genes, FOXM1 and CENPF, that drives prostate cancer aggressiveness. Silencing of both genes, but not either individually, abrogates prostate tumor growth while their co-expression is a highly robust prognostic indicator of cancer outcome. I will demonstrate that these identified drivers are excellent candidates for targeted therapeutics, especially for patients with aggressive prostate cancer. Furthermore, I will describe an innovative computational algorithm to identify drugs and drug combinations that inhibit the activity of these molecular drivers. Experimental validation confirms high efficacy of the top predicted drug combination for inhibiting tumorigenesis in mouse and human prostate cancer models. Although these approaches have been specifically applied to prostate cancer, they also address issues of broad general relevance for the prognosis, diagnosis, and treatment of human disease.