Identifying Malignant Cell Signaling from Protein Interactions an Polyomic Data

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): The proposed award plan combines didactic training and hands-on research to supplement Dr. Fertig's postdoctoral experience in Johns Hopkins Oncology Biostatistics with biological proficiency, complementing her mathematical background. Moreover, this training will enable Dr. Fertig to pursue pertinent research questions and fruitful, multi- disciplinary collaborations in her future career as an independent computational oncologist. The primary focus of this proposal is the development of quantitative models of the biological processes underlying the development and maintenance of tumors Mentors Michael Ochs, PhD of Oncology Biostatistics, and Joseph Califano, MD of Head and Neck Research, at Johns Hopkins will foster Dr. Fertig's proposed training and hands-on research, including providing insight to statistical methods in computational biology and to the biology and clinical treatment of head and neck cancer respectively. Dr. Fertig will apply her experience in merging dynamic models with indirect measurements from Numerical Weather Prediction to inferring relevant biological processes from patient measurements. With the support of her mentors, she will develop algorithms that infer driver processes underlying malignancies in an individual patient's tumor. In the proposed techniques, Dr. Fertig will infer transcription factor activity in head and neck cancer downstream of the malignant processes by integrating gene expression measurements with epigenetic measurements, EGFR protein-protein interaction network structure, and therapeutic strategy. By merging these diverse data sources, this tool is hypothesized to have the statistical power to accurately represent the probability of activation of specific transcription factors resulting from the modeled processes in head and neck cancer, which will be further validated through targeted cell line experiments. Thus, this research will develop tools that, when migrated to the clinic, will assist clinicians in identifying the appropriate choice of targeted therapeutics to treat an individual's cancer.