The research proposed by The University of Texas SPORE in Lung Cancer encompasses a broad range of lung cancer translational research activities, including studies in cell lines, xenografts and transgenic animal models, clinically and molecularly annotated tumor and other biospecimens, germline polymorphisms, and clinical trials. These studies will generate many different types of data, including clinical, epidemiological, biochemical, immunohistochemical, dose response, gene expression microarrays, sequencing, and more. The Core provides comprehensive expertise to ensure the statistical integrity, data integrity, data sharing, and data analysis of the studies performed by the SPORE, which are conducted at the University of Texas Southwestern Medical Center (UTSW), the M.D. Anderson Cancer Center (MDACC) and Ohio State University (OSU). The Core has a director at each institution (Xie at UTSW, Baladandayuthapani at MDACC, and Coombes at OSU) and has the flexibility to match personnel to the evolving needs of existing and developmental SPORE Projects. Members of the Core participate in monthly SPORE vide conferences linking researchers at UTSW in Dallas, TX, MDACC in Houston, TX and OSU in Columbus, Ohio, ensuring that proper consideration is taken of biostatistics and data management issues during all phases of SPORE experiments. The Core will develop and maintain systems for data storage, retrieval, analysis, and sharing. It will provide an interface for all SPORE investigators. The Core services are made possible by the accumulated experience, accumulated computer codes and resources, and by innovative, unique, sometimes customized approaches to solving the data analysis and interpretation challenges in the modern data centric research laboratory. To carry out its responsibilities, the Core has the following Specific Aims:
Aim 1 : To provide valid statistical designs of laboratory research, clinical trials and translational experiments arising from the ongoing research of the SPORE.
Aim 2 : To oversee and conduct the innovative statistical modeling, simulations, data analyses and data integration needed by the Projects, Developmental and Career Projects, and the other Cores to achieve their specific aims. A im 3: To ensure that the results of all Projects are based on well-designed experiments, appropriately interpreted, and to assist in the preparation of manuscripts describing these results. A im 4: To provide an integrated site for data storage and distribution for the SPORE Projects, particularly those with genome-wide and other data-dense Projects.

Public Health Relevance

The Biostatistics and Bioinformatics Core ensures that all experiments performed by the core are properly designed, and that the data collected by those experiments are stored safely, analyzed sensibly, and made available to other SPORE investigators (and ultimately to other lung cancer researchers) in order to further the ultimate goal of translating knowledge from the research lab into the clinic.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center (P50)
Project #
2P50CA070907-16A1
Application #
8747071
Study Section
Special Emphasis Panel (ZCA1-RPRB-C (M1))
Project Start
1996-09-30
Project End
2019-08-31
Budget Start
2014-09-23
Budget End
2015-08-31
Support Year
16
Fiscal Year
2014
Total Cost
$278,105
Indirect Cost
$55,727
Name
University of Texas Sw Medical Center Dallas
Department
Type
DUNS #
800771545
City
Dallas
State
TX
Country
United States
Zip Code
75390
Sinicropi-Yao, Sara L; Amann, Joseph M; Lopez, David Lopez Y et al. (2018) Co-Expression Analysis Reveals Mechanisms Underlying the Varied Roles of NOTCH1 in NSCLC. J Thorac Oncol :
Le, Xiuning; Puri, Sonam; Negrao, Marcelo V et al. (2018) Landscape of EGFR-Dependent and -Independent Resistance Mechanisms to Osimertinib and Continuation Therapy Beyond Progression in EGFR-Mutant NSCLC. Clin Cancer Res 24:6195-6203
Wang, Shidan; Chen, Alyssa; Yang, Lin et al. (2018) Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci Rep 8:10393
Gomez, Daniel Richard; Byers, Lauren Averett; Nilsson, Monique et al. (2018) Integrative proteomic and transcriptomic analysis provides evidence for TrkB (NTRK2) as a therapeutic target in combination with tyrosine kinase inhibitors for non-small cell lung cancer. Oncotarget 9:14268-14284
Parra, Edwin R; Villalobos, Pamela; Mino, Barbara et al. (2018) Comparison of Different Antibody Clones for Immunohistochemistry Detection of Programmed Cell Death Ligand 1 (PD-L1) on Non-Small Cell Lung Carcinoma. Appl Immunohistochem Mol Morphol 26:83-93
Yamauchi, Mitsuo; Barker, Thomas H; Gibbons, Don L et al. (2018) The fibrotic tumor stroma. J Clin Invest 128:16-25
Ma, Junsheng; Hobbs, Brian P; Stingo, Francesco C (2018) Integrating genomic signatures for treatment selection with Bayesian predictive failure time models. Stat Methods Med Res 27:2093-2113
Yi, Faliu; Yang, Lin; Wang, Shidan et al. (2018) Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC Bioinformatics 19:64
Song, Kai; Bi, Jia-Hao; Qiu, Zhe-Wei et al. (2018) A quantitative method for assessing smoke associated molecular damage in lung cancers. Transl Lung Cancer Res 7:439-449
Ji, Xuemei; Bossé, Yohan; Landi, Maria Teresa et al. (2018) Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk. Nat Commun 9:3221

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