# Assessing the power of primary drug-screens to predict clinical response.

> **NIH NIH K99** · OREGON HEALTH & SCIENCE UNIVERSITY · 2021 · $137,956

## Abstract

PROJECT SUMMARY
Precision oncology relies on the hypothesis that further characterizing a patient's tumor will lead to better
predictions of treatment response. While this approach effectively breaks diagnoses into smaller and smaller
subtypes likely to respond to a given targeted inhibitor, the downside is it results in highly fragmented clinical
data spread across multiple treatment arms. The more progress the field makes in assigning new therapies,
the harder it will be to accrue adequate sample size to test another therapy. Direct screening of cancer cell
lines and primary samples on panels of targeted inhibitors is a uniquely promising approach to this problem,
turning every patient sample into a hundred mini experiments, but clinical validation of in vitro drug-response
predictions have been hampered by limited numbers of patients who are screened and actually treated with
any given drug. Such an evaluation is critical to determine the predictive value gained from drug screening of
patient samples.
Over the past seven years the Knight Cancer Institute has performed drug screening paired alongside genomic
and/or RNA sequencing for over 600 primary leukemic samples. Within two years, we will have accumulated
over 200 patients not only screened for in vitro drug response but then treated with matched targeted
inhibitors. I will leverage this existing and growing dataset to interrogate the power of in vitro drug screening
data to predict clinical response using retrospective data. I will establish a robust framework for primary drug
screening and analysis, build interpretable models for clinical decision making, and explore mechanisms
controlling drug response. This project will result in improvements to high-throughput drug screening, a
thorough accounting of the predictive power of in vitro drug screening, and candidates for treatment
combinations in resistant tumors.
My goal is to become an independent investigator and cross-disciplinary leader in patient sample multi-omic
profiling, targeted therapy selection, and translational oncology. During my mentored phase I will be receiving
guidance from Dr. Emek Demir, an expert in computational modeling of systems biology, Dr. Jeffery Tyner, a
leader in patient sample drug screening and validation, and Dr. Brian Druker, a pioneer of targeted cancer
therapy and director of the Knight Cancer Institute. I will also improve my statistical understanding of complex
systems by working with my advisory committee member Dr Tomi Mori, learn to integrate large datasets and
predicting patient outcomes from Dr. Shannon McWeeney, and improve upon existing drug screening
platforms and analysis methods with Dr. Laura Heiser. I am determined to attain an independent faculty
position and my mentors have committed to assisting me in the application and transition process.

## Key facts

- **NIH application ID:** 10092124
- **Project number:** 5K99CA245896-02
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Kevin Matthew Watanabe-Smith
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $137,956
- **Award type:** 5
- **Project period:** 2020-02-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10092124

## Citation

> US National Institutes of Health, RePORTER application 10092124, Assessing the power of primary drug-screens to predict clinical response. (5K99CA245896-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10092124. Licensed CC0.

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