ArticleThe Journal of clinical investigation2024
Chronic graft-versus-host disease detected by tissue-specific cell-free DNA methylation biomarkers.
Article in The Journal of clinical investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06715046 (Cell Free DNA Profiling As a Tool to Monitor Clinically-Relevant Events in Allogeneic Hematopoietic Stem Cell Transplantation), which is not on this map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Cell Free DNA Profiling As a Tool to Monitor Clinically-Relevant Events in Allogeneic Hematopoietic Stem Cell Transplantation
Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- A multi-analyte blood test for acute spinal cord injury.The Journal of clinical investigation · 2025Trial
- Article
- Chronic graft-versus-host disease.Nature reviews. Disease primers · 2026Review
- Advances in graft-versus-host disease: emerging therapeutic strategies, biomarker discoveries, and innovative treatment approaches.Frontiers in immunology · 2026Review
- When Genes Wear Marks: Epigenomic Modulation in the Development and Progression of Obesity.International journal of molecular sciences · 2025Review
- Circulating cell-free DNA methylation patterns indicate cellular sources of allograft injury after liver transplant.Nature communications · 2025Article
- The Art (and Science) of Individualized Selection of Non-Invasive Prenatal Screening (NIPS).International journal of women's health · 2025Article
- Circulating, cell-free methylated DNA indicates cellular sources of allograft injury after liver transplant.bioRxiv : the preprint server for biology · 2024Article
- Article
Corrections and comments
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Authors and funding
18 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate detection of graft-versus-host disease (GVHD) is a major challenge in the management of patients undergoing hematopoietic stem cell transplantation (HCT). Here, we demonstrated the use of circulating cell-free DNA (cfDNA) for detection of tissue turnover and chronic GVHD (cGVHD) in specific organs.
methodsWe established a cocktail of tissue-specific DNA methylation markers and used it to determine the concentration of cfDNA molecules derived from the liver, skin, lungs, colon, and specific immune cells in 101 patients undergoing HCT.
resultsPatients with active cGVHD showed elevated concentrations of cfDNA, as well as tissue-specific methylation markers that agreed with clinical scores. Strikingly, transplanted patients with no clinical symptoms had abnormally high levels of tissue-specific markers, suggesting hidden tissue turnover even in the absence of evident clinical pathology. An integrative model taking into account total cfDNA concentration, monocyte/macrophage cfDNA levels and alanine transaminase was able to correctly identify GVHD with a specificity of 86% and precision of 89% (AUC of 0.8).
conclusioncfDNA markers can be used for the detection of cGVHD, opening a window into underlying tissue dynamics in patients that receive allogeneic stem cell transplants.
fundingThis work was supported by grants from the Ernest and Bonnie Beutler Research Program of Excellence in Genomic Medicine, The Israel Science Foundation, the Waldholtz/Pakula family, the Robert M. and Marilyn Sternberg Family Charitable Foundation and the Helmsley Charitable Trust (to YD).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.