Proteogenomic characterization of human tumors seeks to explain how complex genomic alterations drive the hallmarks of cancer through mass spectrometry based proteomic analysis. The field has been accelerated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) who performed proteomic analyses for breast, colorectal and ovarian tumors and integrated the data with genomic information provided by the Cancer Genome Atlas (TCGA). Our Vanderbilt team conducted the colorectal cancer study and published the first paper on proteogenomic characterization of human cancer in the journal Nature. Data analysis approaches pioneered by us also were used in the CPTAC breast and ovarian cancer studies. Results from all three studies successfully demonstrated the value of integrative proteogenomic analyses in achieving a more complete understanding of cancer biology. Based on this proof of concept, the new CPTAC program seeks to expand the proteogenomic approach to more cancer types and to diverse types of samples including pre- and post-treatment clinical specimens, cultured cells, and patient-derived xenografts (PDXs). This application proposes an integrative proteogenomic data analysis center (iPGDAC) built on our established expertise and resources. The overarching goal of the iPGDAC is to analyze data generated by CPTAC and related resources to better understand cancer biology and to improve cancer treatment. To comprehensively exploit all CPTAC data, we propose three tiers of data analysis. Tier 1 analyses will integrate proteomic and genomic data generated from individual studies. These analyses will identify variant peptides and proteins as candidate biomarkers or therapeutic targets, will predict patient prognosis and response to therapy based on multi-omics data, and will reveal mechanisms of drug action and acquired drug resistance to drive rational drug combinations. Tier 2 analyses will integrate data between preclinical models and human tumors to enable effective translation of experimental findings to the clinic. Tier 3 analyses will integrate data across different cancer types to identify common and cancer type-specific protein signatures and networks. We will make our computational tools and analysis results publically available in two integrated proteogenomic data analysis systems, which will facilitate the collaborative identification of candidate biomarkers by all CPTAC investigators and will broaden the impact of the CPTAC program. The iPGDAC brings to the CPTAC network a fully integrated, completely established program with expertise in all the critical areas specified by the RFA. We have a proven track record of leadership in computational proteogenomics and successful collaboration in the CPTAC network, and we expect to broadly advance the field through this project.

Public Health Relevance

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) seeks to expand the proteogenomic approach demonstrated in three tumor types during the current phase of the program to more cancer types and to clinically relevant problems. This application proposes an integrative proteogenomic data analysis center (iPGDAC) built on our established expertise and resources. The overarching goal of the iPGDAC is to analyze data generated by CPTAC and related resources to better understand cancer biology and to improve cancer treatment.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24CA210954-03
Application #
9544872
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Rodriguez, Henry
Project Start
2016-09-20
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
Baylor College of Medicine
Department
Genetics
Type
Schools of Medicine
DUNS #
051113330
City
Houston
State
TX
Country
United States
Zip Code
77030
Menschaert, Gerben; Wang, Xiaojing; Jones, Andrew R et al. (2018) The proBAM and proBed standard formats: enabling a seamless integration of genomics and proteomics data. Genome Biol 19:12
Yan, Aimin; Ban, Yuguang; Gao, Zhen et al. (2018) PathwaySplice: an R package for unbiased pathway analysis of alternative splicing in RNA-Seq data. Bioinformatics 34:3220-3222
Nguyen, Tuan M; Kabotyanski, Elena B; Dou, Yongchao et al. (2018) FGFR1-Activated Translation of WNT Pathway Components with Structured 5' UTRs Is Vulnerable to Inhibition of EIF4A-Dependent Translation Initiation. Cancer Res 78:4229-4240
Li, Jie; Wang, Weida; Xia, Peng et al. (2018) Identification of a five-lncRNA signature for predicting the risk of tumor recurrence in patients with breast cancer. Int J Cancer 143:2150-2160
Vasaikar, Suhas V; Straub, Peter; Wang, Jing et al. (2018) LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res 46:D956-D963
Wang, Xiaojing; Codreanu, Simona G; Wen, Bo et al. (2018) Detection of Proteome Diversity Resulted from Alternative Splicing is Limited by Trypsin Cleavage Specificity. Mol Cell Proteomics 17:422-430
Mallik, Saurav; Odom, Gabriel J; Gao, Zhen et al. (2018) An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. Brief Bioinform :
Zhang, Menghuan; Wang, Bo; Xu, Jia et al. (2017) CanProVar 2.0: An Updated Database of Human Cancer Proteome Variation. J Proteome Res 16:421-432
Wang, Jing; Vasaikar, Suhas; Shi, Zhiao et al. (2017) WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res 45:W130-W137
Wang, Jing; Mouradov, Dmitri; Wang, Xiaojing et al. (2017) Colorectal Cancer Cell Line Proteomes Are Representative of Primary Tumors and Predict Drug Sensitivity. Gastroenterology 153:1082-1095

Showing the most recent 10 out of 11 publications