# Research Specialist in Cancer Genomics to Integrate Basic Research and Clinical Data

> **NIH NIH R50** · FRED HUTCHINSON CANCER CENTER · 2024 · $112,973

## Abstract

ABSTRACT
Human cancers are complex diseases with pathobiology driven by heritable genetics, environmental exposures,
somatic genomic and epigenomic alterations, and contributions for immunological and other responses in the
host micro- and macroenvironment. Cancer therapeutics overlay another dimension with respect to particular
vulnerabilities that underly concepts of precision medicine. Currently, integrating genome-scale data from
mechanistic ‘cause-effect’ laboratory-based studies to the pathobiology of in vivo tumors in the context of clinical
care remains challenging. This proposal is designed to provide support for a highly skilled and productive
scientist with expertise across laboratory, bioinformatics, and clinical medicine. The objectives are (i) to provide
biostatistical and bioinformatics skills for a range of program investigators that seek to bi-directional integration
for the development and testing of hypotheses involving cancer genomics; (ii) to develop new approaches
(modeling and computational tools) for the analyses and integration of genome-scale data; and (iii) apply rigor
and reproducibility in data annotation for submission/sharing of program genomics data to the research
community.

## Key facts

- **NIH application ID:** 10899604
- **Project number:** 5R50CA274336-02
- **Recipient organization:** FRED HUTCHINSON CANCER CENTER
- **Principal Investigator:** ILSA COLEMAN
- **Activity code:** R50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $112,973
- **Award type:** 5
- **Project period:** 2023-08-15 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10899604

## Citation

> US National Institutes of Health, RePORTER application 10899604, Research Specialist in Cancer Genomics to Integrate Basic Research and Clinical Data (5R50CA274336-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10899604. Licensed CC0.

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