# Systems biology analyses to identify driver genes in Down syndrome-related ALL

> **NIH NIH R03** · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · 2020 · $374,456

## Abstract

Abstract
Down syndrome (DS) is one of the strongest risk factors for acute lymphoblastic leukemia (ALL), conferring a
20-fold increased risk compared to children without DS. Survival for children with DS-ALL remains 10-20%
lower than that of non-DS-ALL patients, due to both relapse and treatment-related mortality. Therefore, there is
an unmet need to understand the biology of DS-ALL. To address this, we have recently led collaborative efforts
to perform on the largest genomic profiling studies of DS-ALL, through the INCLUDE and KidsFirst initiatives.
Capitalizing on this invaluable NIH investment, we proposes to apply our novel systems biology analyses to
these datasets to characterize signaling networks in DS-ALL with a particular focus on features that distinguish
DS-ALL from ALL in children without DS. This project will address fundamental questions of why children with
DS have an increased risk of ALL and how their leukemia differs from that of children without DS. Findings
from this study may lead to new insights into genes driving DS-ALL and may guide development of targeted
therapies for this unique but vulnerable group of patients.

## Key facts

- **NIH application ID:** 10106530
- **Project number:** 1R03HD103908-01
- **Recipient organization:** ST. JUDE CHILDREN'S RESEARCH HOSPITAL
- **Principal Investigator:** Jun J. Yang
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $374,456
- **Award type:** 1
- **Project period:** 2020-09-17 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10106530

## Citation

> US National Institutes of Health, RePORTER application 10106530, Systems biology analyses to identify driver genes in Down syndrome-related ALL (1R03HD103908-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10106530. Licensed CC0.

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