# iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC

> **NIH NIH U24** · BAYLOR COLLEGE OF MEDICINE · 2020 · $973,045

## Abstract

Project Summary
 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.

## Key facts

- **NIH application ID:** 9999317
- **Project number:** 5U24CA210954-05
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Bing Zhang
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $973,045
- **Award type:** 5
- **Project period:** 2016-09-20 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999317

## Citation

> US National Institutes of Health, RePORTER application 9999317, iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC (5U24CA210954-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9999317. Licensed CC0.

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