NIH-funded research R01 grant

Using machine learning to find early signs of chronic graft-versus-host disease after blood stem cell transplants

Official title: Machine Learning to identify Biomarkers for Risk of Chronic Graft-Versus-Host Disease

Funded at Medical University of South Carolina · NIH-11001469 · Data updated 2026-11-12

This study is looking for clues in blood samples from patients who have had a stem cell transplant to help predict if they might develop chronic graft-versus-host disease (cGVHD), so that doctors can catch it early and provide better treatment.

More Graft Versus Host Disease research and trials

Quick facts

Grant typeR01 grant
Study typeNIH-funded research
Funding institutionMedical University of South Carolina
NIH-funded
Lab location1 site (Charleston, United States)
Project startDecember 2, 2021
Project endNovember 30, 2026
Funding year2025
Award (this year)$693,916
NIH project number5R01CA264921-04
Project IDNIH-11001469 on NIH RePORTER

Who could benefit from this research

Could help

Ideal candidates for this research include individuals who are undergoing or have recently undergone allogeneic hematopoietic cell transplantation.

Less likely to help

Patients who have not undergone blood stem cell transplants or those who are not at risk for cGVHD may not benefit from this research.

What this research studies

This research aims to identify specific biomarkers that can predict the risk of developing chronic graft-versus-host disease (cGVHD) in patients who have undergone allogeneic hematopoietic cell transplantation (HCT). By analyzing previously collected blood samples and using advanced machine learning techniques, the study seeks to uncover proteomic and cellular signatures that indicate the impending onset of cGVHD. The goal is to enhance early detection and improve patient outcomes by enabling preemptive treatment strategies before clinical symptoms appear.

Why it matters

Potential benefit: If successful, this research could lead to earlier identification and treatment of patients at risk for cGVHD, potentially improving survival rates and quality of life.

How similar studies have gone: Previous research has shown promise in using machine learning and biomarker identification for similar conditions, indicating a potential for success in this novel approach.

Where this research is happening

  • Medical University of South Carolina
    Charleston, United States
    Active

Researchers

  • Principal investigator: Paczesny, Sophie — Medical University of South Carolina
  • Study coordinator: Paczesny, Sophie

Related research

Other NIH-funded projects on the same topics.

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.