# Predicting Post-treatment Relapse in Pediatric Acute Myeloid Leukemia Using Single-cell Proteomics

> **NIH NIH F31** · STANFORD UNIVERSITY · 2020 · $39,490

## Abstract

Project Summary
 Pediatric Acute Myeloid Leukemia (AML) is the most lethal hematologic malignancy in childhood, with a
probability of 5-year survival at only 60%. Most children diagnosed with AML initially respond well to standard
chemotherapy; however, nearly 40% eventually develop relapsed disease, which responds poorly to treatment
and is fatal in the majority of patients. Although age at diagnosis, response to induction chemotherapy, and
cytogenetic status have been identified as coarse prognostic factors in pediatric AML, it is still unclear what
molecular features lead certain patients to relapse over others. Thus, developing an enhanced understanding of
the mechanistic drivers underlying relapse in pediatric AML represents a significant area of clinical need.
 Many reports indicate that there are rare, hematopoietic stem cell-like subpopulations in AML patients
that resist chemotherapy and drive relapse. However, the exact characteristics of these relapse-associated
cells—often called “leukemic stem cells” (LSCs)—are a matter of contention, with reported phenotypes spanning
much of the known hematopoietic developmental continuum and differing significantly between patients and
throughout the course of disease. As such, the identity and importance of these relapse-associated cells as well
as their relationship to normal hematopoietic developmental processes remain mysterious.
 The proposed project will examine the relationship between single-cell AML phenotypes, clinical
outcomes, and normal myeloid development in 60 clinically-annotated primary samples from pediatric AML
patients in order to identify relapse-associated cellular subtypes. To achieve this, we will leverage the versatility
of mass cytometry, a 40-parameter single-cell proteomics platform, and machine learning in simultaneously
studying the complex surface and signaling phenotypes of millions of leukemic cells from patients’ diagnostic
and relapse bone marrow samples relative to healthy controls.
Central hypothesis: We hypothesize that high-dimensional molecular profiling of primary AML cells will reveal
consistent, functional phenotypes associated with relapse-driving subpopulations that computationally align with
particular stages of healthy hematopoietic development and represent points of future therapeutic intervention.
Aim 1: Develop methods to computationally align high-dimensional, single-cell AML phenotypes with their
 most analogous developmental state along the healthy myeloid continuum.
Aim 2: Utilize predictive modeling to determine the surface, signaling, and functional phenotype of AML
 subpopulations predicting relapse and functionally validate these characteristics in vitro and in vivo.

## Key facts

- **NIH application ID:** 9994724
- **Project number:** 5F31CA239365-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Timothy James Keyes
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $39,490
- **Award type:** 5
- **Project period:** 2019-09-17 → 2021-09-16

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994724

## Citation

> US National Institutes of Health, RePORTER application 9994724, Predicting Post-treatment Relapse in Pediatric Acute Myeloid Leukemia Using Single-cell Proteomics (5F31CA239365-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9994724. Licensed CC0.

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