Developing Hyperpolarized Gas MRI Signatures to Detect and Manage Acute Cellular Rejection

Description

Lung transplantation (LT) is the only definitive therapy for subjects with end-stage lung diseases. The supply of donors’ lungs is the biggest bottleneck to performing a lung transplant, and many patients die while waiting. Many lung transplant recipients experience at least one acute rejection episode after transplantation. Acute Cellular Rejection (ACR) is a significant risk factor for developing chronic allograft failure, a primary reason for death in this patient population. These observations highlight the importance of early diagnosis and management of ACR to prevent chronic graft failure. The preliminary results support the idea that Hyperpolarized Gas Magnetic Resonance Imaging (HGMRI) signatures have excellent potential to address this clinical gap. In lung transplant patients without suspicion of ACR, HGMRI detected subtle, regional abnormalities in pulmonary physiology that were not detected by pulmonary function tests (PFTs) or high-resolution chest computer tomography (HRCT). Biopsy-proven regions of ACR in these subjects exhibited worse airflow and gas exchange HGMRI signatures, which corroborated well with the tissue pathology diagnosis of ACR. This data demonstrates the potential of HGMRI signatures to detect ACR even when existing clinical tools cannot. By merging anatomic CT and physiologic HGMRI readouts, the previous study developed a method to identify the airways that led to the allograft segments with abnormal HGMRI signatures. Then, a method to sample these areas of allografts is enabled during routine surveillance bronchoscopy by mapping the airways leading to dysfunctional allograft regions to enhance the diagnostic accuracy of clinical bronchoscopy. The primary molecular driver of ACR is the exaggerated host immune response to the donor’s lungs. The anticipated results are that within the same subject, the single-cell transcriptome of cells from lung regions with abnormal HGMRI signatures would be more immunologically abnormal than those with normal HGMRI signatures. The hypothesis is that optimized HGMRI signatures can detect early pathophysiologic derangements in lung allografts consistent with ACR. The second hypothesis is that the optimized HGMRI signatures correlate with single-cell transcriptomic signatures reflecting the dysregulated immune responses underlying ACR. This study proposes: Aim 1: Determine the optimized HGMRI signatures to detect early regional allograft dysfunction consistent with ACR in lung allografts at the baseline Visit 1 (V1); Aim 2: Determine how the within-subject longitudinal changes in regional HGMRI signatures over a 1-year follow-up Visit 2 (V2) correlate with a clinical diagnosis of ACR.