Leveraging blood-based transcriptomics to detect acute cellular rejection in lung transplant
Auyon J. Ghosh, MD, MPH,
Matthew Moll, MD, MPH,
Shikshya Shrestha, PhD,
Sergio Poli, MD,
Stephen J. Glatt, PhD,
Hilary J. Goldberg, MD,
Andrew M. Courtwright, MD,
Souheil Y. El-Chemaly, MD
Affiliations
Auyon J. Ghosh, MD, MPH
Division of Pulmonary, Critical Care, and Sleep Medicine, SUNY Upstate Medical University, Syracuse, NY; Corresponding author: Auyon J. Ghosh, MD, MPH, Division of Pulmonary, Critical Care, and Sleep Medicine, SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY 13210.
Matthew Moll, MD, MPH
Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA; Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA; Section on Pulmonary, Critical Care, Allergy, and Sleep Medicine, Veterans Affairs Boston Healthcare System, Boston, MA
Shikshya Shrestha, PhD
Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA
Sergio Poli, MD
Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA
Stephen J. Glatt, PhD
Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY
Hilary J. Goldberg, MD
Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA
Andrew M. Courtwright, MD
Department of Pulmonary and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
Souheil Y. El-Chemaly, MD
Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA
Acute cellular rejection (ACR) is one of the main risk factors for chronic allograft dysfunction, the primary contributor to poor long-term survival in lung transplant recipients. We sought to develop a blood-based transcriptomic risk score (TRS) to detect ACR in lung transplant recipients. We tested for the association of the TRS with ACR in a logistic mixed model. We analyzed 101 samples from 75 individuals. We identified 4 genes after application of the least absolute shrinkage and selection operator. The TRS was significantly associated with ACR (odds ratio (OR) 3.43, 95% confidence interval (CI) 1.86-7.14, p < 0.001). The TRS demonstrates robust discrimination between the 2 groups given an area under receiver operator curve above 0.8, which could lead to less invasive diagnosis of ACR and prediction of individuals at risk for ACR in the future. Further studies with larger sample size are needed to firmly establish the clinical utility of the TRS for ACR.
