The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) will generate a complete compendium of all cancer-associated genomic alterations with the goal of identifying and prioritizing the most promising therapeutic targets and diagnostic biomarkers. The output from these large-scale efforts in the last 2 years is radically transforming the way cancer science is conducted. At the same time, these efforts are uncovering a staggering level of genome complexity in cancer, making it clear that the effective translation of our new-found genomic knowledge into cancer therapeutics and diagnostics will require not only sophisticated computational analyses but, importantly, experimental systems to inform the functional activity of targets in the relevant biological context. The collective experience in cancer gene discovery and drug development has taught the field that an annotation of functionality alone is not sufficient to make informed decisions in cancer drug development. Rather, a productive drug development effort requires mechanistic understanding of a target's cancer-relevant activity, the specific biological and genotypic context in which it operates, and the clinical context in which to test the ultimate hypothesis, i.e. rational design of clinical trials. Given the hundreds and thousands of potential candidates from obtained by genomic efforts, it is imperative that an efficient prioritization pipeline is in place to filter and prioritize for downstream studies. Here we propose a CTD2 Center that will bring to the CTD2 Network multi-level functional and pharmacological assessments of biological importance, in both cell-based and in vivo settings, for somatic mutations identified by TCGA. Such """"""""ground-truth"""""""" will be incorporated iteratively into computational models developed and refined to identify """"""""driver mutations"""""""" with increasing specificity and sensitivity. In addition to these functional and pharmacological data and prediction algorithms, this Center has also developed novel approaches to rapidly and efficiently engineer somatic mutations in diverse vector systems which will support the activities of other centers in the Network and in the general cancer research community. Specific, we will pursue the following Aims: (1) Develop an algorithmic framework for identification of driver events through integrative and iterative analyses of genomic, functional and pharmacological response data;(2) Implement a high throughput platform for engineering somatic mutations in candidate genes identified by TCGA data for downstream functional studies;(3) Pharmacologically assess the therapeutic consequences conferred by candidate driver events in cell- based viability assays;(4) Functionally identify oncogenic driver events through in vivo Context-Specific screen for tumorigenicity.

Public Health Relevance

A central challenge we face in implementing next generation sequencing into cancer patient care involves determining which aberrations in a given tumor are drivers that determine tumor behavior versus passengers that result from the inherently unstable nature of cancer genomes. This proposal aims to address the need for algorithms and practical high-throughput platforms to help prioritize the vast genomic data being generated by TCGA, ICGC and other similar efforts, and will enable the identification and selection of optimal therapies targeting driver aberrations.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA168394-03
Application #
8657939
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Gerhard, Daniela
Project Start
2012-05-01
Project End
2017-04-30
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Texas MD Anderson Cancer Center
Department
Biology
Type
Hospitals
DUNS #
City
Houston
State
TX
Country
United States
Zip Code
77030
Lu, Hengyu; Villafane, Nicole; Dogruluk, Turgut et al. (2017) Engineering and Functional Characterization of Fusion Genes Identifies Novel Oncogenic Drivers of Cancer. Cancer Res 77:3502-3512
Li, Jun; Akbani, Rehan; Zhao, Wei et al. (2017) Explore, Visualize, and Analyze Functional Cancer Proteomic Data Using the Cancer Proteome Atlas. Cancer Res 77:e51-e54
St├Ądler, Nicolas; Dondelinger, Frank; Hill, Steven M et al. (2017) Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study. Bioinformatics 33:2890-2896
Wang, Yumeng; Xu, Xiaoyan; Yu, Shuangxing et al. (2017) Systematic characterization of A-to-I RNA editing hotspots in microRNAs across human cancers. Genome Res 27:1112-1125
Robertson, A Gordon; Kim, Jaegil; Al-Ahmadie, Hikmat et al. (2017) Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell 171:540-556.e25
Zhang, Yiqun; Kwok-Shing Ng, Patrick; Kucherlapati, Melanie et al. (2017) A Pan-Cancer Proteogenomic Atlas of PI3K/AKT/mTOR Pathway Alterations. Cancer Cell 31:820-832.e3
Li, Jun; Zhao, Wei; Akbani, Rehan et al. (2017) Characterization of Human Cancer Cell Lines by Reverse-phase Protein Arrays. Cancer Cell 31:225-239
Yi, Song; Lin, Shengda; Li, Yongsheng et al. (2017) Functional variomics and network perturbation: connecting genotype to phenotype in cancer. Nat Rev Genet 18:395-410
Mitra, Shreya; Montgomery, Jeffrey E; Kolar, Matthew J et al. (2017) Stapled peptide inhibitors of RAB25 target context-specific phenotypes in cancer. Nat Commun 8:660
Fiziev, Petko; Akdemir, Kadir C; Miller, John P et al. (2017) Systematic Epigenomic Analysis Reveals Chromatin States Associated with Melanoma Progression. Cell Rep 19:875-889

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