The ultimate goal of systems medicine is to enable the use of molecular profiles, such as DNA, RNA, epigenetics, and drug sensitivity profiles, for prognosis and predicting response to therapies. There is substantial need for better ways to choose and predict the outcome of therapy in individual cancer patients. For example, patients over 65 with acute myeloid leukemia have no better prognosis today than they did in 1980. For a growing number of diseases, there is a fair amount of data on molecular profiles from patients. The most important step necessary to realize this goal is to identify molecular features (e.g., expression levels of certain genes) in these data that predict clinical phenotypes such as response to a certain therapy. However, due to the high-dimensionality of the data, it is an open challenge to identify robust molecular features that are consistently predictive of clinical phenotypes across many studies.
In this talk, I will present an integrative approach to reduce the dimensionality of expression data by selecting genes that represent important molecular events based on publicly available expression data. In particular, I will present computational methods for identifying the genes with certain features in the inferred expression network and gene expression signature conserved across vastly different cancer types. I will show how our approach led to novel markers for various important clinical phenotypes, such as survival time, chemosensitivity, histomorphological features and surgical resectability in cancer.
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