# Targeting minimal residual disease in AML by using single-cell morphological and biophysical analysis with deep learning

> **NIH NIH U01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2024 · $647,458

## Abstract

Summary
 Conventional chemotherapy regimens induce complete remission in the majority of patients with acute
myeloid leukemia (AML) and results in >95% reduction in tumor burden. However, the persistence of minimal
or measurable residual disease (MRD) is considered to be a key determinant of relapse and poor patient
prognosis in AML. The current clinical strategy at complete remission with MRD positivity is, depending on the
level, to either provide additional dose(s) of the initial treatment or switch to a new therapy at the time of
relapse. Our long-term goal is to instead target MRD by selecting a more optimal therapy before relapse as
there are many advantages to treating patients with MRD-only disease rather than waiting for clinical relapse.
This would be a transformative clinical advance for patients with persistent MRD if selective targeting of MRD
either forestalled or completely obviated clinical relapse. We hypothesize that an optimal assay performed at
the time of complete remission would not only predict therapeutic response but also identify relevant
heterogeneity that is present within the patient’s MRD. We are proposing to achieve this assay by integrating
Deepcell’s high-content label-free brightfield imaging and AI powered sorting with MIT’s high precision
biophysical measurements and DFCI’s clinical resources and expertise. We expect to deliver a novel platform
to enable real-time functional assessment of AML MRD therapeutic vulnerabilities with meaningful clinical
impact. Furthermore, technical advances and experience gained here will facilitate future application of a
similar device to the MRD state in other cancers.

## Key facts

- **NIH application ID:** 10878458
- **Project number:** 1U01CA282163-01A1
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Michael Hemann
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $647,458
- **Award type:** 1
- **Project period:** 2024-09-09 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10878458

## Citation

> US National Institutes of Health, RePORTER application 10878458, Targeting minimal residual disease in AML by using single-cell morphological and biophysical analysis with deep learning (1U01CA282163-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10878458. Licensed CC0.

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