# iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC

> **NIH NIH U24** · BAYLOR COLLEGE OF MEDICINE · 2022 · $896,717

## Abstract

Project Summary
 By combining mass spectrometry (MS)-based proteomics with genomics, epigenomics, and
transcriptomics, proteogenomics holds great potential to better illuminate cancer complexities than individual
‘omes. During the past 10 years, the Clinical Proteomics Tumor Analysis Consortium (CPTAC) has performed
comprehensive proteogenomic characterization of >1,500 tumors across 10 cancer types. These studies not
only yield novel biological and clinical insights into different cancer types but also produce valuable datasets and
computational tools that can be further used by the broad scientific community. The next phase of the CPTAC
program seeks to expand the current success to more cancer types and translational research focusing on
clinically relevant questions. Our integrative proteogenomic data analysis center (iPGDAC) is one of the current
CPTAC funded PGDACs. We have participated in the studies of all CPTAC cancer types and have played a
leading role in data analysis for several cancer types. This application seeks to continue and enhance our
contribution to the next phase of the CPTAC program. The overarching goal of our PGDAC is to accelerate the
translation of cancer proteogenomic data into better understanding of cancer biology and improved cancer
treatment. We will continue developing and improving our computing tools, workflows, and web portals that have
already been successfully used in the CPTAC studies for sequence-based and pathway/network-based
proteogenomic data integration. In addition, we will address unmet needs in post-translational modification
(PTM)-related analyses by using protein sequence and natural language-based deep learning techniques to
improve PTM peptide identification, to predict genomic variant impact on PTMs, and to connect PTM sites to
existing knowledge. Using unique tools from our team and cutting edge statistical inference and machine learning
algorithms, we will perform integrated analysis on proteogenomic data from the CPTAC studies to: 1) create a
comprehensive molecular and cellular portrait for each patient’s tumor; 2) identify and characterize molecular
and tumor microenvironment/immune subtypes; 3) prioritize functional genomic aberrations using
proteogenomic data; 4) reveal molecular mechanisms of cancer phenotypes; and 5) develop predictive models
for patient prognosis and treatment response. Our PGDAC 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:** 10440591
- **Project number:** 1U24CA271076-01
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Bing Zhang
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $896,717
- **Award type:** 1
- **Project period:** 2022-06-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10440591

## Citation

> US National Institutes of Health, RePORTER application 10440591, iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC (1U24CA271076-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10440591. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*