# Illuminating understudied druggable proteins using pan-cancer proteogenomics data

> **NIH NIH U01** · BAYLOR COLLEGE OF MEDICINE · 2022 · $475,200

## Abstract

Project Summary
 Proteins in the families of kinases, G protein coupled receptors, and ion channels frequently contribute
to disease pathogenesis and are good candidates for the development of therapeutics. In fact, 41% of the FDA-
approved drugs target proteins in these families. However, each of these protein families has a number of
members about which very little is known. Better understanding of these ‘dark’ members may pave the way to
new methods for treating diseases. Utilizing existing large omics datasets can be a great starting point to
generate new hypotheses on the function and phenotype association of the understudied proteins. Recently, the
NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) program has characterized over 1,000 primary
tumors covering 10 cancer types using multiple omics platforms. While previous large-scale omics datasets have
focused on genomic and transcriptomic data, the CPTAC data also integrate mass spectrometry (MS)-based
proteomics and phosphoproteomics. Published studies by our colleagues and us in the CPTAC consortium have
demonstrated the value of these proteogenomics datasets as a comprehensive resource for reinforcing existing
knowledge, identifying new biological insights, and generating therapeutic hypotheses. The goal of this
application is to illuminate understudied druggable proteins using CPTAC pan-cancer proteogenomics data. We
will achieve this goal by addressing two specific Aims. Aim 1 is built upon our established multi-omics data
analysis portal LinkedOmics. We will extend LinkedOmics into a knowledgebase, LinkedOmicsKB, in which
information derived from harmonized CPTAC pan-cancer proteogenomics data will be organized into gene-
centric web pages with easily browsable sections and effective visualizations. Aim 2 is based on our previous
report that protein profiling data is much more closely aligned with gene function than mRNA profiling data. We
will use CPTAC pan-cancer proteomics data to make function predictions for the understudied druggable
proteins, followed by experimental validation of selected predictions. Data, visualization, and prediction results
from both Aims will be integrated into Pharos, the knowledge portal of the Illuminating the Druggable Genome
(IDG) program, to accelerate our understanding of IDG-eligible understudied proteins.

## Key facts

- **NIH application ID:** 10449905
- **Project number:** 1U01CA271247-01
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Bing Zhang
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $475,200
- **Award type:** 1
- **Project period:** 2022-07-26 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449905

## Citation

> US National Institutes of Health, RePORTER application 10449905, Illuminating understudied druggable proteins using pan-cancer proteogenomics data (1U01CA271247-01). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10449905. Licensed CC0.

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