Urinary C-X-C Motif Chemokine 10 Is Related to Acute Graft Lesions Secondary to T Cell- and Antibody-Mediated Damage

A New Routine Immunity Score (RIS2020) to Predict Severe Infection in Solid-Organ Transplant Recipients

BACKGROUND Infection is a cause of morbidity and mortality in solid-organ transplantation (SOT). We evaluated a new score that is applied during the first month after transplantation. The score comprises biomarkers of innate and acquired immunity to predict infections in SOT. MATERIAL AND METHODS Prospectively collected blood samples from 377 heart, liver, or kidney recipients were analyzed at 2 centers in Madrid (Spain) and Lima (Peru). Biomarkers were tested before transplantation and at days 7 and 30 after transplantation. During the first 6 months after transplantation, 183 (48.5%) patients developed severe infections (bacterial infections and/or CMV disease). Risk for severe infection was assessed using logistic regression analysis. We designed a score, the routine immunity score (RIS2020), which is based on the sum of the hazard ratios (HRs) of each biomarker. RESULTS The risk factors for severe infection were as follows: Moderate IgG hypogammaglobulinemia (IgG <600 mg/dL at days 7 or 30, HR 2.07, 95% CI 1.37-3.12, p=0.0005, 2 points), CD4 <400 cells/uL at day 30 (HR 1.76, 95% CI 1.03-3.04, p=0.039, 2 points), C3 <80 mg/dL at day 30 (HR 2.18, 95%CI 1.16-4.06, p=0.014, 2 points), and CRP >3 mg/dL at day 30 (HR 2.11, 95% CI 1.12-3.97, p=0.02, 2 points). In patients with ≥4 points, the HR for infection was 5.18 (95% CI 3.06-8.75; p<0.001). RIS2020 was an independent predictor of severe infection in multivariate models. CONCLUSIONS An immunological score combining moderate IgG hypogammaglobulinemia and other parameters of innate and acquired immunity could better identify the risk for severe infection in SOT.

Urinary Chemokines CXCL9 and CXCL10 Are Non-Invasive Biomarkers of Kidney Transplant Rejection

BACKGROUND Rejection is the main cause of kidney allograft failure, and kidney biopsy is the criterion standard method to diagnose it. However, non-invasive techniques to detect kidney transplant rejection are necessary. This study aimed to evaluate urinary chemokines CXCL9 and CXCL10 as potential biomarkers of kidney transplant rejection and to analyze chemokine association with allograft prognosis. MATERIAL AND METHODS We collected 117 urine samples from kidney transplant recipients undergoing allograft biopsy. CXCL9 and CXCL10 levels were measured by ELISA and the ratio to urine creatinine was calculated. Histology and other clinical data were collected from medical records. RESULTS The diagnostic performance of urinary CXCL9/cre in discriminating rejection from all other histological groups showed an ROC AUC value of 0.728 (95% CI 0.632-0.824, p<0.001), and a cut-off value 0.11 ng/mmol had the best sensitivity (76.9%) and specificity (73.1%). The ability of CXCL10/cre to discriminate transplant rejection from all other histological groups had ROC AUC value 0.73 (95% CI 0.63-0.84, P<0.001), the cut-off value 0.42 ng/mmol with best sensitivity (71.4%) and specificity (84.6%). CXCL9 and CXCL10 levels were also increased in patients with polyoma BK virus, recurrent AA amyloidosis, and thrombotic microangiopathy. Patients with higher CXCL9/cre (≥0.11 ng/mmol) and CXCL10/cre (≥0.42 ng/mmol) levels were at increased risk of transplant progression to ESRD (HR 3.25, 95% CI=1.27-8.36, P=0.01), irrespective of serum creatinine at the time of biopsy. CONCLUSIONS Urinary CXCL9/cre and CXCL10/cre were able to distinguished between patients with transplant rejection and those without rejection. High levels of urinary CXCL9/cre and CXCL10/cre were associated with worse allograft survival.

A universal urinary cell gene signature of acute rejection in kidney allografts

INTRODUCTION

Acute rejection (AR) undermines the life-extending benefits of kidney transplantation and is diagnosed using the invasive biopsy procedure. T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or concurrent TCMR + ABMR (Mixed Rejection [MR]) are the three major types of AR. Development of noninvasive biomarkers diagnostic of AR due to any of the three types is a useful addition to the diagnostic armamentarium.

METHODS

We developed customized RT-qPCR assays and measured urinary cell mRNA copy numbers in 145 biopsy-matched urine samples from 126 kidney allograft recipients. We determined whether the urinary cell three-gene signature diagnostic of TCMR (Suthanthiran et al., 2013) discriminates patients with no rejection biopsies (NR, n = 50) from those with ABMR (n = 28) or MR (n = 20) biopsies.

RESULTS

The urinary cell three-gene signature discriminated all three types of rejection biopsies from NR biopsies (P < 0.0001, One-way ANOVA). Dunnett's multiple comparisons test yielded P < 0.0001 for NR vs. TCMR; P < 0.001 for NR vs. ABMR; and P < 0.0001 for NR vs. MR. By bootstrap resampling, optimism-corrected area under the receiver operating characteristic curve (AUC) was 0.749 (bias-corrected 95% confidence interval [CI], 0.638 to 0.840) for NR vs. TCMR (P < 0.0001); 0.780 (95% CI, 0.656 to 0.878) for NR vs. ABMR (P < 0.0001); and 0.857 (95% CI, 0.727 to 0.947) for NR vs. MR (P < 0.0001). All three rejection categories were distinguished from NR biopsies with similar accuracy (all AUC comparisons P > 0.05).

CONCLUSION

The urinary cell three-gene signature score discriminates AR due to TCMR, ABMR or MR from NR biopsies in human kidney allograft recipients.

European Society of Organ Transplantation Consensus Statement on Testing for Non-Invasive Diagnosis of Kidney Allograft Rejection

To address the need for improved biomarkers for kidney transplant rejection, European Society of Organ Transplantation (ESOT) convened a dedicated working group comprised of experts in kidney transplant biomarkers to review literature pertaining to clinical and subclinical acute rejection to develop guidelines in the screening and diagnosis of acute rejection that were subsequently discussed and voted on during the Consensus Conference that took place in person in Prague. The findings and recommendations of the Working Group on Molecular Biomarkers of Kidney Transplant Rejection are presented in this article.

Predictors of graft failure after first detection of de novo donor-specific HLA antibodies in kidney transplant recipients

BACKGROUND

De novo donor-specific antibodies (dnDSAs) may cause antibody-mediated rejection and graft dysfunction. Little is known about the clinical course after first detection of dnDSAs during screening in asymptomatic patients. We aimed to assess the value of estimated glomerular filtration rate (eGFR) and proteinuria to predict graft failure in patients with dnDSAs and their potential utility as surrogate endpoints.

METHODS

All 400 kidney transplant recipients with dnDSAs at our centre (1 March 2000-31 May 2021) were included in this retrospective study. The dates of graft loss, rejection, doubling of creatinine, ≥30% eGFR decline, proteinuria ≥500 mg/g and ≥1000 mg/g were registered from the first dnDSA appearance.

RESULTS

During 8.3 years of follow-up, graft failure occurred in 33.3% of patients. Baseline eGFR and proteinuria correlated with 5-year graft loss (area under the receiver operating characteristics curve 0.75 and 0.80, P < .001). Creatinine doubled after a median of 2.8 years [interquartile range (IQR) 1.5-5.0] from dnDSA and the time from doubling creatinine to graft failure was 1.0 year (IQR 0.4-2.9). Analysing eGFR reduction ≥30% as a surrogate endpoint (148/400), the time from dnDSA to this event was 2.0 years (IQR 0.6-4.2), with a positive predictive value (PPV) of 45.9% to predict graft loss, which occurred after 2.0 years (IQR 0.8-3.2). The median time from proteinuria ≥500 mg/g and ≥1000 mg/g to graft failure was identical, 1.8 years, with a PPV of 43.8% and 49.0%, respectively. Composite endpoints did not improve PPV. Multivariable analysis showed that rejection was the most important independent risk factor for all renal endpoints and graft loss.

CONCLUSIONS

Renal function, proteinuria and rejection are strongly associated with graft failure in patients with dnDSA and may serve as surrogate endpoints.

Alloimmune risk assessment for antibody-mediated rejection in kidney transplantation: A practical proposal

Kidney transplantation is the treatment of choice for patients with end-stage renal disease. Although an improvement in graft survival has been observed in the last decades with the use of different immunosuppressive drugs, this is still limited in time with antibody-mediated rejection being a main cause of graft-loss. Immune monitoring and risk assessment of antibody-mediated rejection before and after kidney transplantation with useful biomarkers is key to tailoring treatments to achieve the best outcomes. Here, we provide a review of the rationale and several accessible tools for immune monitoring, from the most classic to the modern ones. Finally, we end up discussing a practical proposal for alloimmune risk assessment in kidney transplantation, including histocompatibility leukocyte antigen (HLA) and non-HLA antibodies, HLA molecular mismatch analysis and characterization of peripheral blood immune cells.

Urinary CXCL10 specifically relates to HLA-DQ eplet mismatch load in kidney transplant recipients

BACKGROUND

Urinary CXCL10 (uCXCL10) is associated with graft inflammation and graft survival, but the factors related to its excretion are not well known. HLA molecular matching at epitope level allow estimating the "dissimilarity" between donor and recipient HLA more precisely, being better related to further transplant outcomes. The relationship between uCXCL10 and HLA molecular mismatch has not been previously explored.

METHODS

HLA class I and class II typing of some 65 recipients and their donors was retrospectively performed by high resolution sequence-specific-primer (Life Technologies, Brown Deer, WI). The HLA-Matchmaker 3.1 software was used to assess eplet matching. Urine samples collected on the day of the 1-year surveillance biopsy were available of these 65 patients. uCXCL10 was measured using a commercial enzyme-linked immunoassay kit.

RESULTS

1-year uCXCL10 was independently associated with HLA-DQB1 eplet mismatch load (β 0.300, 95%CI 0.010-0.058, p = 0.006). Kidney transplant recipients with a HLA-DQB1 eplet mismatch load >3 showed higher values of uCXCL10 at 1-year (p = 0.018) than those with ≤3. Patients with a HLA-DQB1 eplet mismatch load >3 with subclinical AbMR had significantly higher levels of the logarithm of 1-year uCXCL10 (No AbMR 0.88, IQR 0.37; AbMR 1.38, IQR 0.34, p = 0.002) than those without AbMR.

CONCLUSIONS

uCXCL10 specifically relates to HLA-DQ eplet mismatch load. This relationship can partly explain the previously reported association between uCXCL10 excretion and graft inflammation. An adequate evaluation of any potential non-invasive biomarker, such as uCXCL10, must take into account the HLA molecular mismatch.

Getting immunosuppression just right: the role of clinical biomarkers in predicting patient response post solid organ transplantation

INTRODUCTION

Actually, immunosuppression selection isn't based on individual immune alloreactivity, and immunosuppressive drug dosing is mainly based on the development of toxicity and the achievement of specific target concentrations. Since a successful outcome requires optimal patient risk stratification and treatment, several groups have evaluated candidate biomarkers that have shown promise in the assessment of individual immune responses, the prediction of personal pharmacodynamic effects of immunosuppressive drugs and the prognosis and diagnosis of graft outcomes..

AREAS COVERED

This review includes biomarkers that the Scientific Community in Solid Organ Transplantation currently considers to have potential as diagnostic and prognostic biomarkers of graft evolution. We have focused on recent scientific advances and expert recommendations regarding the role of specific and non-specific pharmacodynamic biomarkers that are mainly involved in the T-cell-mediated response.

EXPERT OPINION

Integral pharmacologic monitoring that combines pharmacokinetics, pharmacogenetics and predictive pharmacodynamic biomarkers may provide crucial information and allow personal adjustment of immunosuppressive drugs at an early stage before severe adverse events ensue. Multicentre, randomized, prospective and interventional trials are needed to fine tune the established cut-off values for each biomarker and the optimal monitoring frequency for each biomarker and to accurately evaluate possible clinical confounding factors to enable correct clinical qualification.