# State-transition and leukemia potential dynamics to inform disease evolution and adaptive therapy

> **NIH NIH U01** · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · 2024 · $590,960

## Abstract

PROJECT SUMMARY
Acute myeloid leukemia (AML) is a group of aggressive and highly heterogeneous malignancies with poor overall
survival. Despite advances in identification of molecular prognostic factors, it remains challenging to predict or
tailor optimal individualized treatment options. We recently reported the application of a state-transition model to
view AML initiation and progression as trajectories of the transcriptome in an AML state-space characterized by
a leukemogenic potential. We successfully constructed a health-to-leukemia transcriptome state-space using
time-sequential RNA-seq data collected from a murine genetic model of AML driven by the CBFB-MYH11 (CM)
leukemogenic fusion gene that is created by inv(16)(p13.1q22), a cytogenetic/molecular subtype accounting for
approximately 8-10% of AML patients. Analysis of transcriptome trajectories in the leukemogenic potential
allowed us to mathematically identify state-transition critical points associated with key leukemogenic events and
to accurately predict disease development and outcome. We now propose to utilize the transcriptome movement
in the leukemia potential as a dynamic biomarker that can be used to design adaptive treatment approaches to
overcome treatment resistance and identify new therapeutic approaches. We have recently developed a
microRNA-126 inhibitor (miRisten) which effectively inhibits a highly treatment resistant leukemia stem cell
population in several leukemia models. Our preliminary time-series RNA-seq data pre- and post-chemotherapy
show that the transcriptome trajectory can accurately predict therapy response in murine models of AML. The
central hypothesis and theoretical concept of this proposal is that the dynamics of the transcriptome and the
leukemia potential can be used to predict therapy response and guide optimization of adaptive therapeutic
approaches to mitigate treatment resistance. Specifically, we will model the transcriptome dynamics following
treatment with anti-leukemia therapies, estimate state-transition critical points, and therapeutic force to optimize
therapy dose and combinations. We propose the following specific aims: Aim 1. Quantify leukemia potential
dynamics driven by different oncogenic signals in murine models. Aim 2. Evaluate the effects of treatment on
the leukemia potential to design and test adaptive therapies in murine AML models. Aim 3. Construct a human
AML transcriptome state-space to identify opportunities for adaptive treatment approaches. Impact. Through
integration of experimental and clinical data, this work will accelerate the implementation of personalized therapy,
inform future clinical trial designs, and improve outcomes of AML patients.

## Key facts

- **NIH application ID:** 10978047
- **Project number:** 1U01CA293853-01
- **Recipient organization:** BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
- **Principal Investigator:** YA-HUEI KUO
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $590,960
- **Award type:** 1
- **Project period:** 2024-09-19 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10978047

## Citation

> US National Institutes of Health, RePORTER application 10978047, State-transition and leukemia potential dynamics to inform disease evolution and adaptive therapy (1U01CA293853-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10978047. Licensed CC0.

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