Computational Prediction of Biomarkers, Pathways, and New Target Drugs in the Pathogenesis of Immune-Based Diseases Regarding Kidney Transplantation Rejection

Alfaro, Rafael; Martínez-Banaclocha, Helios; Llorente, Santiago; Jiménez-Coll, Víctor; Galián, José; Botella, Carmen; Moya‐Quiles, María Rosa; Parrado, Antonio; Muro-Pérez, Manuel; Minguela, Alfredo; Legáz, Isabel; Muro, Manuel

doi:10.3389/fimmu.2021.800968

Cited by 13 publications

(11 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a need for novel biomarkers informing risk for immune and non-immune-mediated outcomes in kidney transplantation ( 34 , 35 ). With the wider acceptance of molecular mismatch ( 5 ), or EMM load, as a predictor of transplant outcomes, there has been a growing interest in determining how the T-cell counterpart may contribute to transplant outcomes.…”

Section: Discussionmentioning

confidence: 99%

Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study

et al. 2022

View full text Add to dashboard Cite

IntroductionKidney transplantation is the optimal treatment in end-stage kidney disease, but de-novo donor specific antibody development continues to negatively impact patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatching between donors and recipients’ Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch at the level of eplets (EMM) as well as the PIRCHE-II score have shown promise in predicting transplant outcomes. In this manuscript, we sought to study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF).MethodsWe studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable Accelerated Failure Time (AFT) models assessed the association between each TcEMM and DCGF. To discriminate between TcEMMs most predictive of DCGF, we fit multivariable Lasso penalized regression models. We identified co-expressed TcEMMs using weighted correlation network analysis (WGCNA). Finally, we conducted sensitivity analyses to address PIRCHE and IMGT/HLA version updates.ResultsA total of 118,309 donor:recipient pairs meeting the eligibility criteria were studied. When applying the PIRCHE-II algorithm, we identified 1,935 distinct TcEMMs at the population level. A total of 218 of the observed TcEMM were independently associated with DCGF by AFT models. The Lasso penalized regression model with post selection inference identified a smaller subset of 86 TcEMMs (56 and 30 TcEMM derived from HLA Class I and II, respectively) to be highly predictive of DCGF. Of the observed TcEMM, 38.14% appeared as profiles of highly co-expressed TcEMMs. In addition, sensitivity analyses identified that the selected TcEMM were congruent across IMGT/HLA versions.ConclusionIn this study, we identified subsets of TcEMMs highly predictive of DCGF and profiles of co-expressed mismatches. Experimental verification of these TcEMMs determining immune responses and how they may interact with EMM as predictors of transplant outcomes would justify their consideration in organ allocation schemes and for modifying immunosuppression regimens.

show abstract

Section: Discussionmentioning

confidence: 99%

Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Our study is another important contribution to the field of computational prediction of biomarkers in the pathogenesis of kidney allograft rejection. The PIRCHE-II score will likely be important in the assessment of immunological pathways as well as in the evaluation of the therapeutic response to new target drugs ( 35 ).…”

Section: Discussionmentioning

confidence: 99%

PIRCHE-II scores prove useful as a predictive biomarker among kidney transplant recipients with rejection: An analysis of indication and follow-up biopsies

et al. 2022

View full text Add to dashboard Cite

BackgroundIndication biopsies for deterioration of kidney allograft function often require follow-up biopsies to assess treatment response or lack of improvement. Immune-mediated injury, namely borderline rejection (BLR), T-cell mediated rejection (TCMR), or antibody-mediated rejection (ABMR), results from preformed or de novo alloreactivity due to donor and recipient HLA-mismatches. The impact of HLA-mismatches on alloreactivity is determined by highly immunogenic HLA-epitopes.MethodsWe analyzed 123 kidney transplant recipients (KTRs) from 2009 to 2019 who underwent a first indication and a follow-up biopsy. KTRs were divided into three groups according to the first biopsy: No rejection (NR)/BLR (n=68); TCMR (n=21); ABMR (n=34). The HLA-derived epitope-mismatches were calculated using the Predicted Indirectly Recognizable HLA-Epitopes (PIRCHE-II) algorithm.ResultsGroup NR/BLR: KTRs with higher total PIRCHE-II scores were more likely to develop TCMR in the follow-up biopsy (p=0.031). Interestingly, these differences were significant for both HLA-class I- (p=0.017) and HLA-class II-derived (p=0.017) PIRCHE-II scores. Group TCMR: KTRs with ongoing TCMR in the follow-up biopsy were more likely to show higher total PIRCHE-II scores (median 101.50 vs. 74.00). Group ABMR: KTRs with higher total PIRCHE-II scores were more likely to show an increase in the microvascular inflammation score in the follow-up biopsy. This difference was more pronounced for the HLA-class II-derived PIRCHE-II scores (median 70.00 vs. 31.76; p=0.086).ConclusionsPIRCHE-II scores may prove useful as a biomarker to predict the histopathological changes of immune-related injury from a first indication to a follow-up biopsy. This immunological risk stratification may contribute to individualized treatment strategies.

show abstract

“…In the current cohort, these risk factors were also evaluated, and none of them demonstrated significant relevance to rejection in multivariate analysis, which might be attributed to the short-term follow-up and small population size that is not enough to prove these immunological associations. Graft matching remains a key element in allocation algorithm and selection of immunosuppressive agents, and poor HLA matching is associated with unwanted transplant outcomes ( 14 ). However, apart from HLA-DQ mismatch as an independent predictor of rejection that was confirmed (OR, 2.197; 95% CI, 1.119–4.317; P = 0.022), we also noted that several unexpected indicators from hemato-biochemical work-up of recipients showed statistical significance in the regression model.…”

Section: Discussionmentioning

confidence: 99%

Serum uric acid as a risk factor for rejection after deceased donor kidney transplantation: A mono-institutional analysis of paired kidneys

et al. 2022

View full text Add to dashboard Cite

BackgroundDeceased donor kidney transplantation (DDKT) is a major therapeutic option for patients with end-stage renal diseases. Although medical techniques improved in recent years, acute or chronic rejection after DDKT is not uncommon and often results in poor graft survival. Therefore, the determination of risk factors is very important to stratify patients and to improve outcomes. This study aims to evaluate the risk factors for treated rejection (TR) of patients after DDKT.MethodsClinical data of deceased donors and corresponding recipients were retrospectively collected. The primary outcome was TR defined as the treatment for rejection within 24 months after DDKT. Univariate comparisons of baseline characteristics were performed with Chi-square test, t-test, and Mann–Whitney U test. Logistic regression was constructed to analyze potential risk factors. Receiver operating characteristic (ROC) curve and Jordan index were generated to determine the optimal cutoff value. The association between continuous variables and TR was examined and visualized by using restricted cubic spline (RCS) models.ResultsData of 123 deceased donors and 246 recipients were obtained and analyzed. The median age was 41 (4–62) years for recipients and 39 (1–65) years for donors. The recipients who died or suffered graft loss during the follow-up period were 8 (3.3%) and 12 (4.9%), respectively. After univariate analysis and subsequent multivariate analysis, the preoperative serum uric acid (OR, 2.242; 95% CI, 1.037–4.844; P = 0.040), platelet (OR, 2.163; 95% CI, 1.073–4.361, P = 0.031), absolute neutrophil count (OR, 2.183; 95% CI, 1.025–4.649; P = 0.043), and HLA-DQ mismatch (OR, 2.102; 95% CI, 1.093–4.043; P = 0.026) showed statistical significance. RCS models showed that patients with higher levels of uric acid had increased risk of TR.ConclusionsSerum uric acid and other three indicators were found to be the independent risk factors for TR, which may contribute to stratify patients and develop personalized regimen in perioperative period.

show abstract

Computational Prediction of Biomarkers, Pathways, and New Target Drugs in the Pathogenesis of Immune-Based Diseases Regarding Kidney Transplantation Rejection

Cited by 13 publications

References 91 publications

Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study

Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study

PIRCHE-II scores prove useful as a predictive biomarker among kidney transplant recipients with rejection: An analysis of indication and follow-up biopsies

Serum uric acid as a risk factor for rejection after deceased donor kidney transplantation: A mono-institutional analysis of paired kidneys

Contact Info

Product

Resources

About