# Proteomic signatures to predict drug response in cancer

> **NIH NIH R21** · UNIVERSITY OF WASHINGTON · 2024 · $218,089

## Abstract

Abstract
 Precision medicine in oncology requires accurate prediction of drug response to guide personalized
treatment decisions. We developed a pharmacoproteomics platform that links kinome activity in liver cancer
cell lines to kinase inhibitor drug response. Preliminary results indicate that our KI-Predictor’s ability to identify
dysregulated kinase signaling is highly dependent on cellular context. To examine and optimize our predictive
model, we will use samples of normal human liver tissue and other inputs and evaluate its performance using
additional liver cell lines and the Genomics of Drug Sensitivity in Cancer (GDSC) datasets. In parallel, we
propose to evaluate our KI-Predictor by profiling kinome activity in clinical liver cancer samples, selecting
candidate kinase inhibitors for testing in organotypic slice cultures derived from each individual patient’s tumor.
By comparing the predicted drug response with the observed response in the organotypic slice culture models,
the accuracy and reliability of the KI-Predictor model can be evaluated. This validation step enables the
assessment of the model's ability to capture the complexities of the tumor microenvironment, including
interactions between cancer cells, stromal cells, and the extracellular matrix.

## Key facts

- **NIH application ID:** 10862955
- **Project number:** 1R21CA288806-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Shao-En Ong
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $218,089
- **Award type:** 1
- **Project period:** 2024-04-17 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862955

## Citation

> US National Institutes of Health, RePORTER application 10862955, Proteomic signatures to predict drug response in cancer (1R21CA288806-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10862955. Licensed CC0.

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