# Predicting Relapse at the Time of Diagnosis in Acute Lymphoblastic Leukemia

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $547,763

## Abstract

PROJECT SUMMARY
 Relapse is the major cause of cancer related mortality in children with leukemia. Despite improvements
in overall survival for children with B-cell progenitor acute lymphoblastic leukemia (ALL), for the 600 patients
who will relapse each year, half will die of their disease. The high mortality of patients who relapse underscores
the need for improved risk prediction and treatment strategies to prevent recurrent leukemia. Current
approaches to relapse prediction are limited by insufficient accuracy, delayed prediction and the inability to
make actionable treatment adjustments based on prediction information. To address these limitations, we
applied a single-cell, high-parameter proteomic approach to ALL patient samples at the time of diagnosis,
accurately predicting future relapse based on the presence of pre-B cells with activated signaling. This
approach was 38% more accurate than standard of care relapse prediction methods. We propose that
identifying relapse-predictive cells in ALL at the time of diagnosis using their distinguishing proteomic
and genetic features will result in a clinical risk prediction model that is accurate, immediate, and
actionable. This approach to relapse prediction will change the clinical paradigm of relapse risk in ALL to
reduce the incidence of relapse itself.
 Using large multi-institutional, multimodal cohorts of molecularly and clinically annotated diagnostic
patient samples, we will apply deep proteomic approaches to identify surface proteins uniquely expressed on
relapse predictive pre-B cells enabling direct identification in a diagnosis sample. We will determine how
genomic mutations associate with the presence of relapse predictive cells and examine their genomic
mutational burden using single-cell exome sequencing. Finally, building on our data-driven, machine learning
approaches, we will construct a diagnostic relapse predictor that is more accurate than standard of care
models while informing on leukemia biology and targeted therapeutic options for patients at risk. This will
enable a more precise approach to patient classification and treatment, reducing the number of children facing
relapse and moving closer to precision medicine for children with ALL.

## Key facts

- **NIH application ID:** 10798255
- **Project number:** 5R01CA251858-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Kara Lynn Davis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $547,763
- **Award type:** 5
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10798255

## Citation

> US National Institutes of Health, RePORTER application 10798255, Predicting Relapse at the Time of Diagnosis in Acute Lymphoblastic Leukemia (5R01CA251858-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10798255. Licensed CC0.

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