The Biostatistics and Bioinformatics Core B will provide statistics and bioinformatics support and expertise in experimental design, data analysis and interpretation ofthe results as needed by the Projects and other Cores to achieve their Specific Aims. The studies in this POl require a variety of statistical and bioinformatic data analysis strategies such as modeling time course experiment data, tesfing synergisfic effect of kinase inhibitor combination, and analyzing genomic profiling data of RNA expression and DNA copy number. In addition. Core B will develop and maintain a bioinformatics infrastructure to enable collaboration and data sharing among research projects. This infrastructure includes: 1) a gene signature database 2) a somafic mutation database and functional characterization tools, and 3) a virtual cell line repository. A broad range of bioinformatics, computational, and statistical techniques will be applied to create this infrastructure.

Public Health Relevance

The Biostatistics and Bioinformatics Core B forms an integral part ofthe P01 and will provide services that are essential for many of the different projects within the POl, assisting the POl investigators in their achievement of the overall research objective: developing new targets for therapy for carcinomas of the lung.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
2P01CA129243-06
Application #
8393595
Study Section
Special Emphasis Panel (ZCA1-RPRB-J (M1))
Project Start
2007-07-23
Project End
2017-08-31
Budget Start
2012-09-12
Budget End
2013-08-31
Support Year
6
Fiscal Year
2012
Total Cost
$143,832
Indirect Cost
$51,979
Name
Sloan-Kettering Institute for Cancer Research
Department
Type
DUNS #
064931884
City
New York
State
NY
Country
United States
Zip Code
10065
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Westover, D; Zugazagoitia, J; Cho, B C et al. (2018) Mechanisms of acquired resistance to first- and second-generation EGFR tyrosine kinase inhibitors. Ann Oncol 29:i10-i19
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Mo, Qianxing; Shen, Ronglai; Guo, Cui et al. (2018) A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19:71-86
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Gao, Yijun; Chang, Matthew T; McKay, Daniel et al. (2018) Allele-Specific Mechanisms of Activation of MEK1 Mutants Determine Their Properties. Cancer Discov 8:648-661
Arbour, Kathryn C; Jordan, Emmett; Kim, Hyunjae Ryan et al. (2018) Effects of Co-occurring Genomic Alterations on Outcomes in Patients with KRAS-Mutant Non-Small Cell Lung Cancer. Clin Cancer Res 24:334-340
Gallant, Jean-Nicolas; Lovly, Christine M (2018) Established, emerging and elusive molecular targets in the treatment of lung cancer. J Pathol 244:565-577
Hellmann, Matthew D; Nathanson, Tavi; Rizvi, Hira et al. (2018) Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer. Cancer Cell 33:843-852.e4

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