# Elucidating the role of the Branched Chain Aminotransferases (BCATc and BCATm) as novel metabolic checkpoints of anti-lymphoma T cell immunity

> **NIH NIH R15** · DES MOINES UNIV OSTEOPATHIC MEDICAL CTR · 2021 · $443,548

## Abstract

PROJECT SUMMARY
New immunotherapies targeting lymphomas delivered promising results during recent clinical trials. However,
these therapies were only effective in a small subset of patients with short periods of remission. The results from
these studies suggested the existence of immunosuppression in the tumor microenvironment. Indeed, the
lymphoma microenvironment is a very dynamic network between lymphoma cells and non-malignant
components that may promote tumor growth and consequently drug resistance. Progress in T cell metabolism
has demonstrated that T cells experience a metabolic disadvantage in the tumor microenvironment, which often
manifests in T cell exhaustion that jeopardizes their potential to destroy cancer cells. This reveals a critical need
to explore new (metabolic) approaches to improve T cell performance. Our research team proposes to target the
metabolism of the branched chain amino acids (BCAAs) as a novel metabolic checkpoint of T cell activation in
the lymphoma microenvironment. Our rationale stems from the findings that the BCAA, leucine, is indispensable
for T cells activation, while BCAA metabolism, initiated by the cytosolic (BCATc) and mitochondrial (BCATm)
branched-chain aminotransferases, is a means to direct leucine toward degradation. The objective in this
application is to determine whether a loss of expression of BCATc and BCATm is beneficial for the durability and
functional integrity of T cells during lymphoma eradication in unique pre-clinical mouse models created in our
laboratory. The long-term goal of this application is to provide new means to improve the T cell-mediated immune
response and to address the challenges with T cell-driven anti-lymphoma immunotherapy. The central
hypothesis is that BCATc, supported by BCATm, serves to provide checkpoint control on T cell function by being
a part of a negative feedback loop regulation of T cell activation. Deletion of the BCAT genes from T cells,
individually or in combination, may provide a metabolic advantage of T cells allowing them to remain activated
and to successfully combat lymphoma growth. To test the central hypothesis, we identified three specific aims:
(1) Investigate how the expression of BCATc and BCATm changes upon T cell subset differentiation and whether
the BCAT proteins are essential for T cell lineage commitment and function, (2) Determine whether a blockage
in the transamination of BCAAs enhances the T cell response to lymphoma tumors, and (3) Investigate whether
a loss of expression of BCATc in mouse T cells can overcome the lymphoma resistance to anti-CTLA4 therapy.
Completion of this project will not only provide the opportunity to improve the current treatment options for
lymphoma patients but will also engage students in pre-clinical cancer studies. The students will highly benefit
from acquiring hands-on research experience in cancer, which can be translated into enhanced research skills,
scientific reasoning, and better understanding ...

## Key facts

- **NIH application ID:** 10291201
- **Project number:** 1R15CA249796-01A1
- **Recipient organization:** DES MOINES UNIV OSTEOPATHIC MEDICAL CTR
- **Principal Investigator:** Elitsa Antonova Ananieva-Stoyanova
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $443,548
- **Award type:** 1
- **Project period:** 2021-08-16 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10291201

## Citation

> US National Institutes of Health, RePORTER application 10291201, Elucidating the role of the Branched Chain Aminotransferases (BCATc and BCATm) as novel metabolic checkpoints of anti-lymphoma T cell immunity (1R15CA249796-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10291201. Licensed CC0.

---

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