The National Cancer Institute has initiated, or will initiate, a number of large-scale cancer genomics programs under the aegis of the Center for Cancer Genomics (CCG). The overall goal of those programs is to help elucidate the mechanisms of cancer initiation, evolution, and resistance to therapy through detailed molecular characterization of tumor samples across multiple technological platforms. As most therapy targets are proteins, and protein phosphorylation is functionally important, accurate analysis of protein is critical to the effort. Consequently, MD Anderson was awarded the Genome Characterization Center contract to develop and apply a high-throughput reverse-phase protein array (RPPA) pipeline (Contact PD/PI Gordon Mills; PD/PI Rehan Akbani). The present proposal is for the establishment of a Specialized Genome Data Analysis Center (GDAC) at MD Anderson under the same auspices. As its first objective, the GDAC will directly support CCG projects by analyzing RPPA data for the Analysis Working Groups (AWGs). The GDAC will participate in discussions, solicit feedback from the AWGs, and suggest future directions for research. A second objective of the GDAC will be to enhance its current bioinformatic tools to improve the analysis and interpretation of RPPA data, whether developed under the aegis of the CCG or through other community approaches. Specifically, the aims of the GDAC are to (i) Extract high-quality, analysis-ready protein expression measures from the RPPA data; (ii) Cluster RPPA data and conduct integrated analysis by correlating RPPA data with clinical and other molecular data; (iii) Perform knowledge-based and independent pathway analysis of RPPA data to identify proteomic pathways that have been substantially altered in the set of cases in each CCG project; and (iv) Continue to develop innovative bioinformatic and computational tools and methodologies to improve the RPPA data analysis pipeline. The pipeline will be shared publicly for the benefit of other researchers. The GDAC will perform the stated tasks by continuing to develop a fully or semi-automated software pipeline using the Galaxy software infrastructure and their own software modules. A preliminary version of the pipeline, together with the necessary expertise for systems biological interpretation of the results, is already in place and will be available at the beginning of the performance period. Further enhancements of the pipeline will be implemented as the GDAC progresses. The pipeline will input raw or pre-processed RPPA data from a central data repository that is specified by the CCG; perform quality control; remove any batch effects; analyze the data using novel plus traditional algorithms; correlate the data with other molecular/clinical features; visualize the outcome; and then deposit the results back in the repository for use by the AWG. The GDAC will interact and collaborate with other components of the CCG consortium to discover biologically and clinically relevant findings that will shed light on the underlying mechanisms of cancer and offer potential avenues for novel therapeutic approaches.

Public Health Relevance

We propose to establish a Genome Data Analysis Center (GDAC) for the analysis of data obtained by the National Cancer Institute on proteins in cancers. The objectives of the GDAC are: (i) to analyze cancer protein data and support the scientific community in their investigations of the data; (ii) develop new or enhanced software tools to meet the first objective more fully and provide the software to the broader research community. We expect that work of the GDAC will lead to biologically and clinically relevant findings that will shed light on the underlying mechanisms of cancer and offer potential avenues for therapy of cancer patients.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24CA210950-03
Application #
9547791
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Yang, Liming
Project Start
2016-09-13
Project End
2021-08-31
Budget Start
2018-09-01
Budget End
2019-08-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Texas MD Anderson Cancer Center
Department
Biostatistics & Other Math Sci
Type
Hospitals
DUNS #
800772139
City
Houston
State
TX
Country
United States
Zip Code
77030
Liu, Yang; Sethi, Nilay S; Hinoue, Toshinori et al. (2018) Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas. Cancer Cell 33:721-735.e8
Wang, Zehua; Yang, Bo; Zhang, Min et al. (2018) lncRNA Epigenetic Landscape Analysis Identifies EPIC1 as an Oncogenic lncRNA that Interacts with MYC and Promotes Cell-Cycle Progression in Cancer. Cancer Cell 33:706-720.e9
Jayasinghe, Reyka G; Cao, Song; Gao, Qingsong et al. (2018) Systematic Analysis of Splice-Site-Creating Mutations in Cancer. Cell Rep 23:270-281.e3
Taylor, Alison M; Shih, Juliann; Ha, Gavin et al. (2018) Genomic and Functional Approaches to Understanding Cancer Aneuploidy. Cancer Cell 33:676-689.e3
Malta, Tathiane M; Sokolov, Artem; Gentles, Andrew J et al. (2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173:338-354.e15
Ellrott, Kyle; Bailey, Matthew H; Saksena, Gordon et al. (2018) Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst 6:271-281.e7
Campbell, Joshua D; Yau, Christina; Bowlby, Reanne et al. (2018) Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas. Cell Rep 23:194-212.e6
Gao, Qingsong; Liang, Wen-Wei; Foltz, Steven M et al. (2018) Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep 23:227-238.e3
Thorsson, Vésteinn; Gibbs, David L; Brown, Scott D et al. (2018) The Immune Landscape of Cancer. Immunity 48:812-830.e14
Ng, Patrick Kwok-Shing; Li, Jun; Jeong, Kang Jin et al. (2018) Systematic Functional Annotation of Somatic Mutations in Cancer. Cancer Cell 33:450-462.e10

Showing the most recent 10 out of 44 publications