Preincubation of donor tissue with a VEGF cytokine trap promotes subsequent high-risk corneal transplant survival

AimsPathological neovascularisation of the host bed and the transplant itself is the main risk factor for graft rejection after corneal transplantation. This study aims to prevent this process by preincubation of the corneal donor tissue ex vivo with an antivascular endothelial growth factor (VEGF) cytokine trap blocking additional postsurgical hemangiogenesis and lymphangiogenesis to promote high-risk graft survival.MethodsThe donor tissue was preincubated with a VEGFR1R2 cytokine trap for 24 hours prior to murine high-risk corneal transplantation (human IgG Fc was used as the control). The distribution of VEGFR1R2 Trap in the cornea was investigated by immunohistochemistry. Corneas were excised to quantify the blood vessels (BVs) and lymphatic vessels (LVs) and draining lymph nodes (dLNs) were harvested to analyse the phenotype of dendritic cells (DCs) and T cells at week 1, 2 and 8 post-transplantation. Graft survival was compared between preincubation with VEGFR1R2 Trap and human IgG Fc in high-risk recipients.ResultsVEGFR1R2 Trap was present in the graft for at least 2 weeks after surgery and additionally diffused into the corneal recipient. BVs, LVs and macrophages in the whole cornea were significantly decreased 1-week and 2-week post-transplantation (p<0.05). In dLNs the frequency of CD11c+DCs was significantly reduced, whereas CD200R+ regulatory DCs were significantly increased after keratoplasty (p<0.05). Furthermore, long-term high-risk graft survival was significantly improved (p<0.01).ConclusionsPreincubation of corneal donor tissue with a VEGFR1R2 cytokine trap can significantly promote subsequent high-risk corneal transplant survival and thereby opens new treatment avenues for high-risk corneal transplantation.

Machine learning support for decision making in kidney transplantation: step-by-step development of a technological solution (Preprint)

BACKGROUND Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. To maximize patient and graft survival, the allocation of donor organs to potential recipients requires careful consideration. OBJECTIVE To develop an innovative technological solution to enable better prediction of kidney transplant survival for each potential donor-recipient pair. METHODS We used de-identified data on past organ donors, recipients and transplant outcomes in the United States from the Scientific Registry of Transplant Recipients (SRTR). To predict transplant outcomes for potential donor-recipient pairs, we used several survival analysis models, including regression analysis (Cox Proportional Hazards), Random Survival Forests (RSF) and several artificial neural networks (DeepSurv, DeepHit, Recurrent Neural Networks (RNN)). We evaluated the performance of each model on their ability to predict the probability of graft survival after kidney transplantation from deceased donors. Three metrics were employed: the C-index, the Integrated Brier Score and the Integrated Calibration Index (ICI), along with calibration plots. RESULTS Based on the C-index metrics, the neural network-based models (DeepSurv, DeepHit, RNN) had better discriminative ability than the Cox model and RSF (0.650, 0.661, 0.659 vs 0.646 and 0.644, correspondingly). The proposed RNN model offered a compromise between the good discriminative ability and calibration and was implemented in a technological solution of TRL-4. CONCLUSIONS Our technological solution based on RNN model can effectively predict kidney transplant survival and provide support for medical professionals and candidate recipients in determining the most optimal donor-recipient pair. CLINICALTRIAL Not applicable.

PD-1 expression in hepatocellular carcinoma predicts liver-directed therapy response and bridge-to-transplant survival

Background Hepatocellular carcinoma (HCC) patients undergo liver-directed therapy (LDT) to control tumor burden while awaiting liver transplantation with response impacting waitlist survival. In this study, we investigate the link between absolute lymphocyte count (ALC) and PD-1 expression with response to LDT and bridge-to-transplant survival. Methods Treatment-naïve HCC patients (n = 86) undergoing LDT were enrolled at a single center from August 2016–March 2020. Response to LDT was determined using mRECIST. Blood samples were collected on the day of LDT and at follow-up. Cells were analyzed for phenotype by flow cytometry. Outcomes were liver transplantation or tumor progression. Results Incomplete response to initial LDT was associated with tumor progression precluding liver transplantation (OR: 7.6, 1.7 – 33.3, P < 0.001). Univariate analysis of baseline T cell phenotypes revealed ALC (OR: 0.44, 0.24–0.85, P = 0.009) as well as intermediate expression of PD-1 on CD4 (OR: 3.3, 1.03–10.3, P = 0.034) and CD8 T cells (OR: 3.0, 0.99–8.8 P = 0.043) associated with incomplete response to LDT. Elevations in PD-1 expression were associated with increased risk of bridge-to-transplant tumor progression (HR: 3.2, 1.2–9.4). In patients successfully bridged to liver transplantation, pre-treatment peripheral PD-1 profile was associated with advanced tumor staging (P < 0.005) with 2/4 of patients with elevations in PD-1 having T3-T4 TNM staging compared to 0 with low PD-1 expression. Conclusion Low lymphocyte count or elevated expression of the PD-1 checkpoint inhibitor is associated with incomplete response to LDT and increased risk of bridge-to-transplant tumor progression. Patients with impaired T cell homeostasis may benefit from PD-1 immunotherapy to improve response to LDT and improve bridge-to-transplant outcomes.