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High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease
Published on: October 31, 2012
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.
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.
Abstract:
The National Institutes of Health Consensus criteria for chronic graft-versus-host disease (cGVHD) diagnosis can be challenging to apply in children, making pediatric cGVHD diagnosis difficult. We aimed to identify diagnostic pediatric cGVHD biomarkers that would complement the current clinical criteria and help differentiate cGVHD from non-cGVHD. The Applied Biomarkers of Late Effects of Childhood Cancer (ABLE) study, open at 27 transplant centers, prospectively evaluated 302 pediatric patients after hematopoietic cell transplant (234 evaluable). Forty-four patients developed cGVHD. Mixed and fixed effect regression analyses were performed on diagnostic cGVHD onset blood samples for cellular and plasma biomarkers, with individual markers declared relevant if they met 3 criteria: an effect ratio ≥1.3 or ≤0.75; an area under the curve (AUC) of ≥0.60; and a P value <5.814 × 10-4 (Bonferroni correction) (mixed effect) or <.05 (fixed effect). To address the complexity of cGVHD diagnosis in children, we built a machine learning-based classifier that combined multiple cellular and plasma biomarkers with clinical factors. Decreases in regulatory natural killer cells, naïve CD4 T helper cells, and naïve regulatory T cells, and elevated levels of CXCL9, CXCL10, CXCL11, ST2, ICAM-1, and soluble CD13 (sCD13) characterize the onset of cGVHD. Evaluation of the time dependence revealed that sCD13, ST2, and ICAM-1 levels varied with the timing of cGVHD onset. The cGVHD diagnostic classifier achieved an AUC of 0.89, with a positive predictive value of 82% and a negative predictive value of 80% for diagnosing cGVHD. Our polyomic approach to building a diagnostic classifier could help improve the diagnosis of cGVHD in children but requires validation in future prospective studies. This trial was registered at www.clinicaltrials.gov as #NCT02067832.
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