Rejection in the setting of non-HLA antibody: New tools for navigating bench to bedside

A machine learning algorithm improves the diagnostic accuracy of the histologic component of antibody mediated rejection (AMR-H) in cardiac transplant endomyocardial biopsies

BACKGROUND

Pathologic antibody mediated rejection (pAMR) remains a major driver of graft failure in cardiac transplant patients. The endomyocardial biopsy remains the primary diagnostic tool but presents with challenges, particularly in distinguishing the histologic component (pAMR-H) defined by 1) intravascular macrophage accumulation in capillaries and 2) activated endothelial cells that expand the cytoplasm to narrow or occlude the vascular lumen. Frequently, pAMR-H is difficult to distinguish from acute cellular rejection (ACR) and healing injury. With the advent of digital slide scanning and advances in machine deep learning, artificial intelligence technology is widely under investigation in the areas of oncologic pathology, but in its infancy in transplant pathology. For the first time, we determined if a machine learning algorithm could distinguish pAMR-H from normal myocardium, healing injury and ACR.

MATERIALS AND METHODS

A total of 4,212 annotations (1,053 regions of normal, 1,053 pAMR-H, 1,053 healing injury and 1,053 ACR) were completed from 300 hematoxylin and eosin slides scanned using a Leica Aperio GT450 digital whole slide scanner at 40X magnification. All regions of pAMR-H were annotated from patients confirmed with a previous diagnosis of pAMR2 (>50% positive C4d immunofluorescence and/or >10% CD68 positive intravascular macrophages). Annotations were imported into a Python 3.7 development environment using the OpenSlide™ package and a convolutional neural network approach utilizing transfer learning was performed.

RESULTS

The machine learning algorithm showed 98% overall validation accuracy and pAMR-H was correctly distinguished from specific categories with the following accuracies: normal myocardium (99.2%), healing injury (99.5%) and ACR (99.5%).

CONCLUSION

Our novel deep learning algorithm can reach acceptable, and possibly surpass, performance of current diagnostic standards of identifying pAMR-H. Such a tool may serve as an adjunct diagnostic aid for improving the pathologist's accuracy and reproducibility, especially in difficult cases with high inter-observer variability. This is one of the first studies that provides evidence that an artificial intelligence machine learning algorithm can be trained and validated to diagnose pAMR-H in cardiac transplant patients. Ongoing studies include multi-institutional verification testing to ensure generalizability.

Antibody-mediated rejection owing to donor-specific HLA-DQA1 antibodies after renal transplantation: A case report

BACKGROUND

Donor-specific HLA antibodies are important risk factors in antibody-mediated rejection and graft loss after renal transplantation and are associated with higher rejection rates and lower graft survival. Most de novo donor specific antibodies (dnDSA) after renal transplantation are directed toward donor HLA-DQ antigens. An HLA-DQ antigen is a heterodimer consisting of an alpha and beta chain. Traditionally, HLA-DQA1 typing has not been part of pretransplant evaluation. Therefore, DQ alpha proteins are not usually considered in the interpretation of HLA-DQ antibody reactions.

METHODS

The renal transplant recipient had a 0% panel reactive antibody pretransplant. Two years after transplantation, he developed symptoms of abdominal distension and bilateral lower extremity edema. Histopathological findings on renal puncture biopsy showed a combination of T-cell-mediated acute rejection type IIA and antibody-mediated rejection with a trend toward chronicity in the transplanted kidney. DSAs were investigated by HLA-I (HLA-A/B) and HLA-II (HLA-DRB1/DQA1/DQB1) single antigen bead (SAB) assay. HLA typing was performed to explain the antibody reactivity patterns by PCR-SSO and Sequencing-based typing (SBT). HLAMatchmaker analysis was performed to identify eplets that explain antibody reactivity patterns.

RESULTS

HLA-II SAB analysis of the patient's serum at the time of rejection showed positive reactions with all DQB1*03:03-carrying beads with high mean fluorescence intensity (MFI). However, DQB1*03:03 was not a dnDSA antigen. High-resolution HLA typing revealed that HLA-DQA1*05:01 and DQA1*03:02 were mismatched donor antigens. HLA Matchmaker analysis demonstrated reactivity toward 130R and 116 V eplet on DQA1 and DQB1.

CONCLUSIONS

Antibodies specific to DQα chains after renal transplantation were highlighted.

Non-HLA Antibodies in Hand Transplant Recipients Are Connected to Multiple Acute Rejection Episodes and Endothelial Activation

The role of anti-HLA antibodies in transplant rejection is well-known but the injury associated with non-HLA antibodies is now widely discussed. The aim of our study was to investigate a role of non-HLA antibodies in hand allografts rejection. The study was performed on six patients after hand transplantation. The control group consisted of: 12 kidney transplant recipients and 12 healthy volunteers. The following non-HLA antibodies were tested: antibody against angiotensin II type 1 receptor (AT1R-Ab), antibody against endothelin-1 type-A-receptor (ETAR-Ab), antibody against protease-activated receptor 1 (PAR-1-Ab) and anti-VEGF-A antibody (VEGF-A-Ab). Chosen proinflammatory cytokines (Il-1, IL-6, IFNγ) were used to evaluate the post-transplant humoral response. Laboratory markers of endothelial activation (VEGF, sICAM, vWF) were used to assess potential vasculopathy. The patient with the highest number of acute rejections had both positive non-HLA antibodies: AT1R-Ab and ETAR-Ab. The same patient had the highest VEGF-A-Ab and very high PAR1-Ab. All patients after hand transplantation had high levels of laboratory markers of endothelial activation. The existence of non-HLA antibodies together with multiple acute rejections observed in patient after hand transplantation should stimulate to look for potential role of non-HLA antibodies in humoral injury in vascular composite allotransplantation.

Autoantibodies to LG3 are associated with poor long-term survival after liver retransplantation

Autoantibodies are detrimental to the survival of organ transplantation. We demonstrated that Angiotensin II Type I Receptor agonistic autoantibodies (AT1R-AA) were associated with poor outcomes after liver retransplantation. To examine the effect of other autoantibodies, we studied a retrospective cohort of 93 patients who received a second liver transplant. Pre-retransplant sera were tested with Luminex-based solid-phase assays. Among 33 tested autoantibodies, 15 were significantly higher in 48 patients who lost their regrafts than 45 patients whose regrafts were still functioning. Specifically, patients with autoantibodies to the C-terminal laminin-like globular domain of Perlecan (LG3) experienced significantly worse regraft survival (p = .002) than those with negative LG3 autoantibodies (LG3-A). In multivariate analysis, LG3-A (HR = 2.35 [1.11-4.98], p = .027) and AT1R-AA (HR = 2.09 [1.07-4.10], p = .032) remained significant predictors of regraft loss after adjusting for recipient age and sex. There were synergistic deleterious effects on regraft survival in patients who were double-positive for LG3-A and donor-specific antibody (DSA) (HR = 5.26 [2.15-12.88], p = .001), or LG3-A and AT1R-AA (HR = 3.23 [1.37-7.66], p = .008). All six double-positive patients lost their liver regrafts. In conclusion, LG3-A is associated with inferior long-term outcomes of a second liver transplant. Screening anti-HLA antibodies and autoantibodies such as LG3-A/AT1R-AA identifies patients with a higher risk for liver transplantation.