# Development of a novel imaging modality for adoptive cell therapy

> **NIH NIH R21** · CASE WESTERN RESERVE UNIVERSITY · 2021 · $413,974

## Abstract

Abstract:
Recently a new therapeutic approach for cancer has exhibited high promise termed adoptive cell therapy. This
approach involves treating patients with immune cells such as T cells or Natural Killer (NK) cells instead of
drug therapy. While some cell therapies have exhibited high promise for at least a subset of cancers, a major
technological challenge in the development of this field is the lack of knowledge of where these cells reside in
the body once they are infused. While it is readily easy to monitor the blood for the presence of the infused
immune cells, there is no satisfactory method to monitor the cells that traffic into tissue (ex. colon tumors). In
this proposal, we will develop a novel method to label immune cells using nanobubbles (NBs). These labelled
cells then can be monitored non-invasively using an ultrasound. As NBs are a component of an already FDA
approved product, we anticipate this approach will not only be safe but can be translated quickly to human
testing. It is hoped that this work will lead to new technology that will enable us to monitor the trafficking of
immune cell therapies so that we can optimally select which patients/tumor types would most benefit from a
given cell therapy product.

## Key facts

- **NIH application ID:** 10316427
- **Project number:** 1R21CA262736-01A1
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Agata A Exner
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $413,974
- **Award type:** 1
- **Project period:** 2021-09-17 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10316427

## Citation

> US National Institutes of Health, RePORTER application 10316427, Development of a novel imaging modality for adoptive cell therapy (1R21CA262736-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10316427. Licensed CC0.

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