# Novel computational approaches for pharmacogenomic discovery

> **NIH NIH R00** · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · 2020 · $246,364

## Abstract

PROJECT SUMMARY/ABSTRACT
Affordable high-throughput genome sequencing technologies had been expected to usher an era of precision
medicine, whereby each patient receives an individualized treatment based on their genetic profile. However,
precision medicine is yet to realize this potential and the number of clinically applied drug biomarkers lags far
behind the number proposed in the scientific literature. Lack of reproducibility of proposed biomarkers has
been highly problematic, and in particular the lack of effectiveness of biomarkers identified in pre-clinical
studies when applied in clinical trial. In cancer, the number of drugs with FDA approved biomarkers remains at
less than 30, and almost all of these arose because a drug was designed to target a specific known driver
gene. Overall, only a handful of clinically actionable biomarkers have been discovered for drugs already in use.
The objective of this project is to develop computational methods that will improve on existing biomarker
discovery, and ultimately improve patient care and survival. My first aim is to develop an approach that will
allow me to impute drug sensitivity in very large clinical datasets, such as TCGA. This imputed data will then
be compared to measured markers in these data (e.g. somatic mutations) in order to identify novel predictors
of drug response. The statistical models used to impute drug sensitivity will be developed in a pre-clinical
disease model, where drug response has been accurately measured. In my second aim I will quantify the
contribution of germline genetic variation to drug response in cancer. “Germline genetic variation” refers to the
common genetic differences that exist between individuals, which are for the most part preserved in tumors.
The contribution of this inter-individual genetic variation to variability in drug response between patients
remains unquantified, although it is becoming clearer that it plays an important role. Finally, I will combine the
information gathered in the first steps and build effective integrative models of drug response. Following the
R00 phase (R01 and beyond), I envision that such models will be tested in patients; subsequently, measured
drug response in these cohorts will be used to further refine future predictions. This will result in a framework
where models actively improve their predictive performance as more data is gathered over time. To ensure the
success of this project, I have assembled an expert mentorship team for the K99 phase, which includes a
statistician, wet-lab biologists and clinicians, thus covering the entire scope of the precision medicine discovery
and implementation pipeline. The University of Chicago, a world-class research institution, will provide the ideal
environment and resources for such a cross-disciplinary, collaborative endeavor.

## Key facts

- **NIH application ID:** 9869022
- **Project number:** 5R00HG009679-03
- **Recipient organization:** ST. JUDE CHILDREN'S RESEARCH HOSPITAL
- **Principal Investigator:** Paul Geeleher
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $246,364
- **Award type:** 5
- **Project period:** 2019-02-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9869022

## Citation

> US National Institutes of Health, RePORTER application 9869022, Novel computational approaches for pharmacogenomic discovery (5R00HG009679-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9869022. Licensed CC0.

---

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