# Interrogation of TLR2 Inflammatory Signaling in AML

> **NIH NIH F31** · THOMAS JEFFERSON UNIVERSITY · 2022 · $46,752

## Abstract

PROJECT SUMMARY
 Acute myeloid leukemia (AML) is defined as a clonal expansion of abnormal myeloid blasts which are
impaired to differentiate into mature and functional myeloid cells. Toll-like receptors (TLRs) are pathogen-
associated molecular pattern (PAMP) receptors that specialize in recognizing foreign pathogens and elicit an
innate immune response through promoting myeloid cell differentiation and inflammatory cytokine production.
The role of TLR signaling in AML is poorly understood, and the mechanisms involved in an innate immune
response through TLRs, and whether this induces differentiation and/or cell death of AML blasts is unclear. Two
of the most commonly mutated genes in AML are FLT3 and DNMT3A, where 20% of AML patients can be found
with co-occurring mutations, which results in a poor prognosis. Preliminary data indicate that TLRs are expressed
on the surface of AML cells, and stimulation of these receptors produces a proinflammatory response associated
with AML blast differentiation. In sum, I identify, in DNMT3A-mutant AML, a TLR signaling network that regulates
differentiation of AML and increases the survival of this common and clinically poor AML subtype which can lead
to a prospectively new differentiating/therapeutic agent for AML treatment.

## Key facts

- **NIH application ID:** 10534321
- **Project number:** 1F31CA268843-01A1
- **Recipient organization:** THOMAS JEFFERSON UNIVERSITY
- **Principal Investigator:** Michael Lawler
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $46,752
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10534321

## Citation

> US National Institutes of Health, RePORTER application 10534321, Interrogation of TLR2 Inflammatory Signaling in AML (1F31CA268843-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10534321. Licensed CC0.

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