# Project 3: A Genomic Approach to Improved Diagnosis and Treatment of Neuroendocrine Tumors

> **NIH NIH P50** · UNIVERSITY OF IOWA · 2020 · $189,417

## Abstract

The incidence of Neuroendocrine Tumors (NETs) has increased five-fold over the last three decades, and 
many patients do not develop symptoms until the tumors have metastasized. Although patients with these 
tumors may have prolonged survival despite advanced stage, further understanding of the molecular biologic 
basis of NETs holds the promise for improved diagnosis, imaging, and therapy. We hypothesize that analyzing 
the gene expression profiles of these tumors and their exomes will allow us to identify important genes that will 
facilitate clinical advances for patients with these tumors. This contribution will be significant because it will 
allow us to determine the tumor site of origin in patients presenting with liver metastases and unknown 
primaries, which will lead to more directed surgical exploration and resection; and knowledge of cell surface 
receptors or other genes significantly over-expressed in NETs relative to normal tissues will facilitate the 
development of new targets for detection, imaging, and medical management. Novel targets for therapy will 
also be suggested by the identification of frequently mutated or deleted genes in these tumors or the germline 
of patients with familial NETs.

## Key facts

- **NIH application ID:** 10264530
- **Project number:** 3P50CA174521-05S2
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** JAMES R HOWE
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $189,417
- **Award type:** 3
- **Project period:** 2015-09-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10264530

## Citation

> US National Institutes of Health, RePORTER application 10264530, Project 3: A Genomic Approach to Improved Diagnosis and Treatment of Neuroendocrine Tumors (3P50CA174521-05S2). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10264530. Licensed CC0.

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