Cancer onset and progression involves disruptions of complex networks of biomolecular interactions in cells, as a result of genomic and epigenomic aberrations. To achieve a deeper understanding of the genetic basis of cancer and to identify novel translational directions requires moving beyond detecting simple associations between genomic aberrations and clinical endpoints. However, little is known about how the states of disease-perturbed regulatory networks are altered by such aberrations, or how aberrations ultimately lead to cellular dysfunction. The goal of the Center for Systems Analysis of the Cancer Regulome is to address this challenge by developing and applying advanced algorithms and model-based approaches to enable the interpretation of TCGA data at the level of regulatory mechanisms;and to use such knowledge for enabling principled and systematic approaches for drug target discovery and therapeutic intervention in a clinically relevant manner. The Center will build three computational pipelines that will carry out integrated analyses of TCGA-derived high-throughput experimental data sets. These pipelines will: 1) identify potential mechanisms of genomic-transcriptomic regulation associated with specific cancers and clinical parameters;2) infer networks that explain cancer type-specific transcriptional profiles, and identify molecules that may be important control nodes in these networks as a means to prioritize drug targets for therapeutic intervention;and 3) compare cancer-associated features across all cancer types within TCGA to gain insight into the regulatory basis for cancer progression in different cancer types. The results of these pipelines will be rapidly disseminated through TCGA, together with visualization tools that facilitate the exploration and evaluation of each derived regulatory mechanism.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Resource-Related Research Projects--Cooperative Agreements (U24)
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Special Emphasis Panel (ZCA1-SRLB-U (O1))
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Lee, Jerry S
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Institute for Systems Biology
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