# AI-based AML risk stratification using next generation cytogenomics

> **NIH NIH R44** · PHASE GENOMICS, INC. · 2024 · $1,000,000

## Abstract

ABSTRACT
 Chromosome aberrations are a hallmark of acute myeloid leukemia and offer mechanistic and
prognostic insights into disease. As such, a combination of cytogenetic assays are routinely applied as a part
of the AML diagnostic workflow. While offering invaluable information on disease severity, most chromosome
aberrations fall into the “cytogenetic abnormalities not classified” or “complex karyotype” categories. A range
of studies have shown that, while ambiguous, these variants have prognostic value, suggesting the existence
of cryptic variants of significance or complex epistases that drive the AML phenotype. However, there is
currently no system for translating genome-wide chromosomal aberration information into patient risk.
 To improve the predictive potential of chromosome aberration profiles, we propose the development of
a risk-prediction metric that will add new prognostic value to AML studies. Specifically, we will produce a
method which will establish a patient risk metric that can help guide treatment decisions for patients
traditionally judged as of intermediate risk. This development will employ our scalable cytogenomic tools and
novel machine learning analytics to generate a large collection of cytogenomic datasets and analyze them to
identify patterns linked to AML phenotypes. Once completed, we will have a combined kit and software
solution that will not only improve upon existing cytogenetic applications in AML, but will offer new prognostic
insights beyond what is possible with current tools. This product will deliver high-resolution view of the
chromosome aberration landscape in AML and an offer a data-driven interpretation of how variants will impact
disease severity.

## Key facts

- **NIH application ID:** 10862769
- **Project number:** 5R44CA278140-02
- **Recipient organization:** PHASE GENOMICS, INC.
- **Principal Investigator:** Stephen Matthew Eacker
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,000,000
- **Award type:** 5
- **Project period:** 2023-07-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862769

## Citation

> US National Institutes of Health, RePORTER application 10862769, AI-based AML risk stratification using next generation cytogenomics (5R44CA278140-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10862769. Licensed CC0.

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