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.

Public Health Relevance

Advanced head and neck tumors have a 50% cure rate in spite of combined treatment modalities. This project will merge biological structure with head and neck tumor measurements to pinpoint specific pathways that drive individual tumor development, and thus assess personalized treatment plans for that patient. Although applied to head and neck cancers, the developed tools will have broad clinical implications.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Mentored Quantitative Research Career Development Award (K25)
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Subcommittee G - Education (NCI)
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Jakowlew, Sonia B
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Johns Hopkins University
Internal Medicine/Medicine
Schools of Medicine
United States
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Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas et al. (2016) Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods 13:310-8
Guo, Theresa; Gaykalova, Daria A; Considine, Michael et al. (2016) Characterization of functionally active gene fusions in human papillomavirus related oropharyngeal squamous cell carcinoma. Int J Cancer 139:373-82
Cheng, Haixia; Fertig, Elana J; Ozawa, Hiroyuki et al. (2015) Decreased SMAD4 expression is associated with induction of epithelial-to-mesenchymal transition and cetuximab resistance in head and neck squamous cell carcinoma. Cancer Biol Ther 16:1252-8
Fertig, Elana J; Lee, Esak; Pandey, Niranjan B et al. (2015) Analysis of gene expression of secreted factors associated with breast cancer metastases in breast cancer subtypes. Sci Rep 5:12133
Afsari, Bahman; Fertig, Elana J; Geman, Donald et al. (2015) switchBox: an R package for k-Top Scoring Pairs classifier development. Bioinformatics 31:273-4
Lee, Esak; Fertig, Elana J; Jin, Kideok et al. (2014) Breast cancer cells condition lymphatic endothelial cells within pre-metastatic niches to promote metastasis. Nat Commun 5:4715
Fertig, Elana J; Stein-O'Brien, Genevieve; Jaffe, Andrew et al. (2014) Pattern identification in time-course gene expression data with the CoGAPS matrix factorization. Methods Mol Biol 1101:87-112
Afsari, Bahman; Geman, Donald; Fertig, Elana J (2014) Learning dysregulated pathways in cancers from differential variability analysis. Cancer Inform 13:61-7
Fortin, Jean-Philippe; Labbe, Aurélie; Lemire, Mathieu et al. (2014) Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol 15:503
Parker, Hilary S; Leek, Jeffrey T; Favorov, Alexander V et al. (2014) Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction. Bioinformatics 30:2757-63

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