Ruth Dannenfelser will present her general exam on Tuesday, May 17, 2016 at 11:30am in Genomics, CIL 253. The members of her committee are Olga Troyanskaya (adviser), Mona Singh, and Barbara Engelhardt. Everyone is invited to attend her talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Her abstract and reading list follow below. ABSTRACT Large-scale genomic studies now give more predictive power than ever, allowing us to profile the composition of tissues, study cellular functions, and understand organismal traits at an unprecedented level of detail. This is particularly important for studying complex diseases, such as cancer, where small patient specific differences play critical roles in development and progression. The intratumoral immunological landscape has been shown to be an important determinant of outcome with functions in both promotion and inhibition of tumor growth. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining measurements by isolating cells from newly excised tumors and sorting them using flow cytometry. These technical challenges, combined with the substantial overlap between cell surface and secreted proteins, make routine profiling of tumor-infiltrating lymphocytes difficult. We propose a solution to this problem by leveraging over 2,000 samples from existing gene expression studies in a machine learning framework to derive robust markers capable of quantification. Our method is generalizable, can be used to profile the levels of any cell type of interest, and allows for retroactive analysis of published data without requiring additional experimentation. Herein we focus on quantifying the key lymphocytes in the immune response across breast cancer studies. Most notably we find that estrogen receptor activity and genomic complexity are the key factors driving variation in lymphocyte infiltration across individual tumors. Furthermore, we discuss the clinical implications of our findings, specifically preliminary changes in normal breast tissue associated with increased cancer risk and the indirect beneficial effect of anti-estrogen therapy. Reading List: Books: 1. An Introduction to Bioinformatics Algorithms by Jones and Pevzner 2. Artificial Intelligence: A Modern Approach by Russell and Norvig (Chapters: 13 Uncertainty, 14 Probabilistic Reasoning, 20 Statistical Learning Methods) Papers: 3. Irizarry et al., Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics (2003). 4. Piccolo et al., Multiplatform single-sample estimates of transcriptional activation. PNAS (2013). 5. Newman et al., Robust enumeration of cell subsets from tissue expression profiles. Nature Methods (2015). 6. Chikina and Troyanskaya., Accurate quantification of functional analogy among close homologs. PLoS Comp Bio (2011). 7. Greene et al., Understanding multicellular function and disease with human tissue-specific networks. Nature Genetics (2014). 8. McGary et al., Systematic discovery of nonobvious human disease models through orthologous phenotypes. PNAS (2010). 9. Smedley et al., PhenoDigm: analyzing curated annotations to associate animal models with human diseases. Database (2013). 10. Rooney et al., Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell (2015). 11. Curtis et al., The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature (2012).