# Optimizing Gram-positive bacteria as a candidate for targeted anti-tumor therapy

> **NIH NIH F32** · COLUMBIA UNIV NEW YORK MORNINGSIDE · 2020 · $64,554

## Abstract

PROJECT SUMMARY/ABSTRACT
Cancer is the second leading cause of death globally and many available treatments lack the safety and effica-
cy sought by oncologists and patients alike. Major efforts have been made to use living organisms, such as
bacteria, as vehicles to produce and deliver site-specific therapeutic payloads, however they are mostly cen-
tered on Gram-negative bacteria. In this proposal, we aim to expand the list of candidates available for probi-
otic cancer therapy by exploring the potential for species of Bacillus to colonize tumor microenvironments.
Many Bacillus species are generally regarded as safe (GRAS) and as Gram-positives are capable of high pro-
tein product secretion. Therefore, we will also aim to expand the genetic toolbox for model organism, Bacillus
subtilis, in order to develop Gram-positive production hubs of anticancer therapies. Using the bacteria-in-
spheroid coculture (BSCC) model, we can rapidly screen for optimal isolates and recombinant protein products
and use in vivo mouse models for follow up experiments exploring only the high preforming candidate strains.
With in vitro and in vivo approaches, we aim to develop novel, safe, and effective alternatives to treat tumor
growth and increase the quality of cancer therapy.

## Key facts

- **NIH application ID:** 10068682
- **Project number:** 1F32CA254314-01
- **Recipient organization:** COLUMBIA UNIV NEW YORK MORNINGSIDE
- **Principal Investigator:** Bentley M. Shuster
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $64,554
- **Award type:** 1
- **Project period:** 2020-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10068682

## Citation

> US National Institutes of Health, RePORTER application 10068682, Optimizing Gram-positive bacteria as a candidate for targeted anti-tumor therapy (1F32CA254314-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10068682. Licensed CC0.

---

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