A diagnostic classifier for pediatric chronic graft-versus-host disease: results of the ABLE/PBMTC 1202 study

Geoffrey D E Cuvelier1, Bernard Ng2, Sayeh Abdossamadi3

  • 1Pediatric Blood and Marrow Transplantation, Manitoba Blood and Marrow Transplant Program, CancerCare Manitoba, University of Manitoba, Winnipeg, MB, Canada.

Blood Advances
|October 11, 2022
PubMed

Insights

Diagnosing chronic graft-versus-host disease (cGVHD) in children is challenging. New biomarkers and a machine learning classifier show promise for improving pediatric cGVHD diagnosis and differentiating it from other conditions.

Area of Science:

  • Hematology
  • Immunology
  • Pediatric Oncology

Background:

  • The National Institutes of Health Consensus criteria for diagnosing chronic graft-versus-host disease (cGVHD) present challenges in pediatric populations.
  • Accurate diagnosis of pediatric cGVHD is crucial for timely and appropriate management.
  • Current diagnostic methods may not fully capture the nuances of cGVHD in children.

Purpose of the Study:

  • To identify novel diagnostic biomarkers for pediatric cGVHD.
  • To develop a classifier that aids in differentiating cGVHD from non-cGVHD in children.
  • To complement existing clinical criteria for cGVHD diagnosis in pediatric patients.

Main Methods:

  • Prospective evaluation of 302 pediatric patients post-hematopoietic cell transplant within the Applied Biomarkers of Late Effects of Childhood Cancer (ABLE) study.
  • Analysis of cellular and plasma biomarkers from diagnostic cGVHD onset blood samples using mixed and fixed effect regression models.
  • Development of a machine learning-based classifier integrating multiple biomarkers and clinical factors.

Main Results:

  • Specific biomarkers including decreased regulatory natural killer cells, naïve CD4 T helper cells, and naïve regulatory T cells were identified.
  • Elevated levels of CXCL9, CXCL10, CXCL11, ST2, ICAM-1, and soluble CD13 (sCD13) characterized cGVHD onset.
  • The developed cGVHD diagnostic classifier demonstrated strong performance with an AUC of 0.89, 82% positive predictive value, and 80% negative predictive value.

Conclusions:

  • A polyomic approach combining biomarkers and clinical data can significantly improve pediatric cGVHD diagnosis.
  • The identified biomarkers and machine learning classifier offer a promising tool to aid clinicians in diagnosing cGVHD in children.
  • Further validation in prospective studies is recommended to confirm the utility of this diagnostic approach.