# Integrative network modeling of regulatory modules in Large Granular Lymphocyte Leukemia

> **NIH NIH F30** · UNIVERSITY OF VIRGINIA · 2021 · $49,083

## Abstract

The broad long term objectives of this project are to elucidate the regulatory mechanisms 
contributing to the pathogenesis of cancer. By understanding how and why cancers are dysregulated, 
we hope to develop better therapeutics and design more effective treatment strategies. Our lab 
studies a blood cancer called large granular lymphocyte (LGL) leukemia. Currently it is 
thought that leukemic LGL cells accumulate activating genetic mutations in the context of 
pathologically persistent inflammation. These activating mutations, acting in concert with 
inflammatory signaling, lead to chronic proliferation and expansion of these LGL cells. This then 
contributes to clinical burden in the form of decreased white blood cells, enlarged spleen or 
liver, bone marrow disorders, and/or autoimmune manifestations. There is currently no reliable cure 
and most patients are managed on lifelong immunosuppressive therapy. Therefore, we hope to pursue 
mechanistic studies that address this unmet need. Often, cancers have inappropriate activation of 
master gene regulators such as transcription factors (TF). Like many other cancers, LGL Leukemia 
cells have hyper-activation of a TF called STAT3, with 30-40% of patients carrying an activating 
mutation in this gene. Because many patients do not have a STAT3 mutation, and because STAT3 
activation can be part of a healthy immune response, we reasoned that there may be other factors 
leading to inappropriate leukemic proliferation. Therefore, we propose to dissect the regulatory 
mechanisms at play in LGL Leukemia. In this study, we will characterize changes that occur in the 
genome at multiple regulatory levels and correlate those changes with gene expression programming 
(Aim 1). This will help us identify regulatory modules important in this disease, as well as 
 contrast functional differences between patient samples in a personalized way. In 
addition, we will integrate a computational model of the salient gene signaling network 
based on our experimental findings and existing scientific knowledge (Aim 2). By leveraging this 
network model, we expect to identify key nodes or motifs in the regulatory network that are 
important for disease progression, and therefore, identify critical targets for drug 
intervention. If successful, the findings from this project are expected to contribute to 
new treatment strategies in LGL leukemia and more broadly, to our understanding of cancer 
dysregulation.

## Key facts

- **NIH application ID:** 10163135
- **Project number:** 5F30CA225046-04
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Jeffrey Chunlong Xing
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $49,083
- **Award type:** 5
- **Project period:** 2018-07-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10163135

## Citation

> US National Institutes of Health, RePORTER application 10163135, Integrative network modeling of regulatory modules in Large Granular Lymphocyte Leukemia (5F30CA225046-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10163135. Licensed CC0.

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