Wednesday, March 4, 2026 3/4/2026

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

Award Number: U01CA282163
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: COOPERATIVE AGREEMENT
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/09/2024
PERIOD OF PERFORMANCE END DATE: 08/31/2029

Group Awards By:

View Award Description

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $655,219 )
2025	2025	MASSACHUSETTS INSTITUTE OF TECHNOLOGY	77 MASSACHUSETTS AVE	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Cancer Detection and Diagnosis Research	000	2	9/3/2025	NON-COMPETING CONTINUATION	$655,219
														Subtotal = $655,219

Issue Date FY: 2024 ( Subtotal = $647,458 )
2024	2024	MASSACHUSETTS INSTITUTE OF TECHNOLOGY	77 MASSACHUSETTS AVE	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Cancer Detection and Diagnosis Research	000	1	9/9/2024	NEW	$647,458
														Subtotal = $647,458

Grand Total All Awards = $1,302,677

Top

All Categories

About

Search

Reports

Data Submission

Award Information

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

Award Number: U01CA282163

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: COOPERATIVE AGREEMENT

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/09/2024

PERIOD OF PERFORMANCE END DATE: 08/31/2029

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer