DNA Methylation Research in Autologous Hematopoietic Stem Cell Transplant Population.
Lathika MohanrajDana M LapatoAmir ToorTheresa Swift-ScanlanPublished in: Biological research for nursing (2022)
Despite increased sophistication in DNA methylation (DNAm) measurement and methods, conducting studies in specific populations such as the hematopoietic stem cell transplant (HCT) population, presents unique challenges and study design considerations. In this article, we explain the motivation for investigating DNAm in the HCT population, highlighting important study design features and key findings in a longitudinal prospective pilot study of DNAm in 32 patients undergoing autologous HCT in Central Virginia, USA. We also discuss limitations and challenges to generating robust results. We observed that HCT does not prevent high-quality DNA from being extracted from whole blood for DNAm research and that longitudinal prospective studies that span pre- and 2-months post-HCT are feasible. Critically, we did not observe significant impacts of cancer diagnosis, time since transplant, age, or chromosomal sex on overall DNAm data dimensionality. These observations demonstrate that while extreme care is required to ensure generalizable, accurate, and interpretable results, researchers should not avoid HCT-DNAm research simply for fear that the transplant procedure or presence of a cancer diagnosis will prevent meaningful conclusions from being drawn. DNAm is an attractive biomarker that is understudied in patients undergoing HCT and needs to expand to improve precise prediction of HCT outcomes.
Keyphrases
- cell cycle arrest
- nk cells
- hematopoietic stem cell
- dna methylation
- patients undergoing
- cell death
- healthcare
- genome wide
- bone marrow
- gene expression
- palliative care
- squamous cell carcinoma
- cell therapy
- squamous cell
- mesenchymal stem cells
- minimally invasive
- big data
- single molecule
- pain management
- signaling pathway
- electronic health record
- mass spectrometry
- skeletal muscle
- cell free
- artificial intelligence
- genetic diversity