One of the goals of modern cancer research is to decompose the oncogenic state of individual tumors directly in terms of cellular pathways that are aberrantly activated or deregulated. Despite large-scale efforts to systematically map the cancer genome, determining how alterations within a given tumor interact to induce activated cellular states is still unknown. A promising approach is to use transcriptional signatures to determine which specific oncogenic pathways are active. However, significant challenges remain to make this approach effective. Our objective in this project is to develop a general methodology to systematically generate and validate gene expression signatures representing the activation and deregulation of 40 oncogenes and tumor suppressors and to use them to characterize human tumors.
Aim 1 : Generate oncogenic transcriptional signatures using experimental cell models.
Aim 2 : Create statistical scoring models based on the oncogenic signatures of Aim 1.
Aim 3 : Validate the signature models and assess their universality and tissue specificity.
Aim 4 : Create a publicly available database of signatures and tools to characterize tumors. This database will be broadly useful for the cancer research community and will enable a large number of clinically-relevant applications such as functional annotation of cancer genomes, pathway-based classification of individual cell line and tumors, improving clinical prognosis in multiple disease subtypes, better risk stratification of patients, and predicting response for targeted therapeutic interventions. For over a decade we have been identifying transcriptional signatures for a variety of problems including molecular sub-typing, prediction of chemo-sensitivity and clinical outcome in many tumor types and making them available to the research community. Based on our previous experience, preliminary results and a detailed understanding of the experimental, statistical, software and database generation challenges, and the fact that we are bringing together a broad interdisciplinary group of researchers with strength in primary cellular transformation, data and statistical analysis, along with collaborators who can contribute additional, important genomic data and resources, we believe we are well poised to move forward with this project.
Relevance This project will yield a collection of molecular signatures and a computational method to accurately classify human tumors by their activated oncogenic pathways. They will provide functional annotation of cancer genomes, better classification of individual cell line and tumors, and will be broadly useful for the cancer research community. Moreover these signatures, together with the method for identifying them, holds promise for many clinical applications including: improvement of clinical prognosis, stratification of patients in clinical trials, and identification of targeted therapeutic interventions.
|Archer, Tenley C; Ehrenberger, Tobias; Mundt, Filip et al. (2018) Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups. Cancer Cell 34:396-410.e8|
|Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku et al. (2018) Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 6:484-495.e5|
|Viswanathan, Vasanthi S; Ryan, Matthew J; Dhruv, Harshil D et al. (2017) Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway. Nature 547:453-457|
|Reich, Michael; Tabor, Thorin; Liefeld, Ted et al. (2017) The GenePattern Notebook Environment. Cell Syst 5:149-151.e1|
|Kim, Jong Wook; Abudayyeh, Omar O; Yeerna, Huwate et al. (2017) Decomposing Oncogenic Transcriptional Signatures to Generate Maps of Divergent Cellular States. Cell Syst 5:105-118.e9|
|Huang, Franklin W; Mosquera, Juan Miguel; Garofalo, Andrea et al. (2017) Exome Sequencing of African-American Prostate Cancer Reveals Loss-of-Function ERF Mutations. Cancer Discov 7:973-983|
|Zhu, Xiaodong; Girardo, David; Govek, Eve-Ellen et al. (2016) Role of Tet1/3 Genes and Chromatin Remodeling Genes in Cerebellar Circuit Formation. Neuron 89:100-12|
|Hanaford, Allison R; Archer, Tenley C; Price, Antoinette et al. (2016) DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with In Silico Analysis to Identify Novel Therapeutic Targets. Clin Cancer Res 22:3903-14|
|D'Antonio, Matteo; Tamayo, Pablo; Mesirov, Jill P et al. (2016) Kataegis Expression Signature in Breast Cancer Is Associated with Late Onset, Better Prognosis, and Higher HER2 Levels. Cell Rep 16:672-83|
|Rosenbluh, Joseph; Mercer, Johnathan; Shrestha, Yashaswi et al. (2016) Genetic and Proteomic Interrogation of Lower Confidence Candidate Genes Reveals Signaling Networks in ?-Catenin-Active Cancers. Cell Syst 3:302-316.e4|
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