The development of solid phase single HLA-antigen bead (SAB) assays represents a major technical improvement in the ability to detect, identify, and semiquantify HLA antibodies. The impact on the field of organ transplantation has been significant.1-3 Single-antigen bead assays provide a sensitive and semiquantitative method for monitoring donor-specific antibodies (DSA) posttransplant, greatly improving our understanding of the dynamics and significance of DSA development during humoral rejection.4-6
However, SAB assays present technical limitations and artifacts, which are significant challenges for accurate interpretation and clinical utilization of SAB test results.7 Day-to-day and lab-to-lab variability in the performance and interpretation of single-antigen bead assays also limits our ability to accurately assess the clinical risk of HLA antibodies, especially in patients with low level DSA.7
There is a growing number of studies trying to improve inter and intra laboratories variation of solid phase HLA antibodies testing.8,9 Nevertheless, intensity varied according to manufacturer, kit, bead type and lot. To date, no published study has compared the performance and accuracy to detect DSA in the posttransplantation period of the 2 currently available SAB tests, One Lambda Labscreen SAB and Immucor Lifescodes SAB, class I and II.
The diagnosis of antibody-mediated rejection (AMR) in kidney transplantation is based on clinical criteria, histological criteria according to Banff classification10 (microvascular inflammation with or without peritubular C4d staining). Donor specific antibody detection (DSA) with Luminex assays is required to confirm the diagnosis of AMR and to start treatment as early as possible.
The present study compared the performance (sensitivity and specificity) of the tests from the 2 manufacturers to detect DSA in the posttransplant period, analyze the correlation and agreement between the 2 tests and try to identify the optimal cutoff value of intensity criteria for the confirmation of AMR with both tests.
MATERIALS AND METHODS
One hundred adult kidney transplant recipients followed up in Rouen University Hospital Nephrology Department were included. Patients were enrolled on the basis of a kidney allograft biopsy performed between 2007 and 2013, showing either a highly suspected AMR according to Banff classification (score of microvascular inflammation of glomerulitis and peritubular capilaritis (g + cpt) ≥ 3) (group 1 [G1], n = 50), or no evidence for AMR (g + cpt = 0) (group 2 [G2], n = 50).10 Group 2 was matched regarding the age of the patients, the number of transplantations (mainly first transplantation) and the time between kidney transplantation and graft biopsy. We chose a microvascular inflammation score strictly greater than 2 to obtain the best chance to detect DSA.11
Institutional review board approval was obtained.
Serum samples collected at biopsy were selected from an inventory (stored at −80°C). In the absence of biopsy day sample, the serum from the closest date available was selected. Sera of all patients were retrospectively and simultaneously tested with both One Lambda Labscreen (test 1) and Immucor Lifecodes (test 2) luminex single antigen (LSA)1 and LSA2.
Identification of antibody specificity was carried out using a LABScan 200 Flow analyzer (Luminex Corporation, Austin, TX). The reagents used were LABScreen Single Antigen HLA Class I and Class II (One Lambda, Canoga Park, CA) and LIFECODES LSA Class I and Class II (Immucor, Stamford, CT). The tests were carried out according to the manufacturers’ instructions, and the analysis was performed with One Lambda and Immucor software.
The same lot number for the Luminex products was used for all sera to avoid any lot to lot variability:
- –LSA Class I 12044E and LSA Class II 03054B for Immucor for G1.
- –LSA Class I 05265D and LSA Class II 05285A for Immucor for G2.
- –LSA Class I lot 009 and LSA Class II lot 010 for One Lamba for both G1 and G2.
Single-antigen Bead Assay
Briefly, 10 μL of each serum sample and 40 μL of HLA class I and class II Single Antigen Luminex beads were mixed and incubated in the dark for 30 minutes at room temperature. After washing with the wash buffer, 50 μL of phycoerythrin-conjugated goat antihuman IgG was added to the beads and incubated for 30 minutes in the dark at room temperature. The fluorescence intensities of the samples were measured by using a Luminex 200 system. Data analysis was performed by using Match IT software provided by the manufacturer (Immucor).
The positive control beads were coated with human IgG and were designed to yield mean fluorescence intensity (MFI) values greater than 2000 when incubated with the positive or negative control sera. Conversely, the negative control beads (CON1, CON2, and CON3) typically yield low MFI values (lot specific) when treated with the positive or negative control sera. To identify positive bead reactions, the background MFI value was subtracted from the raw MFI value to generate the adjusted value 1 (background corrected MFI: BCM) for each individual bead. Adjusted value 1 was then divided by the MFI value from the calculated control (CalcCON) of its respective locus to generate adjusted value 2 (BCR). The CalcCON for each locus was considered the raw MFI value of the lowest ranked antigen bead for that locus. Adjusted value 3 (AD-BCR) was generated by normalization of adjusted value 2 to the amount of antigen on each bead, as indicated in the lot-specific recording sheet. Positive results were assigned when 2 of 3 criteria (BCM > 1500, BCR > 4.0, or AD-BCR > 5.0) were fulfilled.
Kits were composed of 95 class I and 82 class II specific beads and incorporated 1 positive control and 3 negative control beads for each set.
Twenty microliters of test serum was incubated with 5 μL of LABScreen beads. Any HLA antibodies present in the test serum bound to the antigens on the beads and were then labeled with R-phycoerythrin–conjugated goat antihuman IgG. The LABScan 200 flow analyzer simultaneously detected the fluorescent emission of phycoerythrin and a dye signature from each bead, allowing almost real-time data acquisition. To assign HLA specificity, the reaction pattern of the test serum was compared to the lot-specific worksheet defining the antigen array. Cutoff value used was MFI greater than 500.
Kits were composed of 97 class I–specific and 95 class II–specific beads and incorporated 1 positive control and a negative control beads for each set.
In the second part of the study, we attempted to determine sensitivity and specificity of MFI, BCM, BCR, and AD-BCR (for immunodominant DSA and sum of the DSA) for the diagnosis of humoral rejection, using receiver operating characteristic (ROC) curve. Immunodominant DSA (iDSA) was defined as the DSA with the highest intensity. The sum of the DSA (sDSA) was defined as the sum of the crude parameters (MFI or BCM, BCR, AD-BCR) of all beads corresponding to the potential DSA of the patients (A, B, Cw, DR, DQ, DP).
Statistics were performed using Statview version 5.0 (SAS Institute Inc., Brie Comte Robert, France). Quantitative variables were expressed as mean ± standard deviation (SD), whereas qualitative variables were expressed in numbers and percentages. To demonstrate a correlation between MFI and BCM for the DSA detected, we performed a Pearson correlation test, with a correlation coefficient (r): r < 0.25 indicating low correlation, 0.25 < r < 0.5 moderate correlation, 0.5 < r < 0.75 strong correlation, and r > 0.75 excellent correlation. Relative reliability was measured using the intraclass correlation coefficient (ICC). We defined ICC values of less than 0.4 as having poor reliability, 0.40 to 0.75 fair to good reliability, and scores above 0.75 as having excellent reliability.12
An ROC curve was used to calculate the area under the ROC curve (AUC) to identify the optimal cut-off value for DSA intensity value (iDSA or sDSA) for AMR prediction in kidney transplant recipients. Receiver operating characteristic curve was done for all intensity parameters (MFI for One Lambda and BCM, BCR, and AD-BCR for Immucor).
First Part: Results With Usual Cutoff
Group 1: Context of AMR
Fifty patients aged 43.7 ± 2.4 years (16.4–76.6 years), mainly male, had a kidney graft biopsy performed 2.9 years (0.03–9.3 years) after kidney transplantation. Banff classification showed a mean microvascular inflammation g + cpt score of 3.5 (3–6) with a positive peritubular capillary C4d staining on the biopsy in 32% cases (16/50). Forty-four percent of the patients lost their graft (22/50), on average 57 months after graft biopsy.
In group 1, 56% (28/50) of the sera were collected on the day of the biopsy, and the remaining 22 before the biopsy (median, 23 days; maximum, 35 days) and before any specific treatment for AMR.
Test 1 identified at least 1 DSA in 44 (88%) patients and test 2 in 39 (78%) patients. Twenty-nine patients had more than 1 (58%) DSA. All DSAs were de novo DSA.
In 33 of 50 cases, the iDSA was the same. In 2 cases, AMR was confirmed only by test 2 (case 1: BCM, 774; BCR, 5.1; AD-BCR, 6.7 and case 2: BCM, 2143; BCR, 6.7; AD-BCR, 7.7). In 7 cases, AMR was confirmed only with test 1 (MFI between 500 and 1000, 2 cases; MFI between 1000 and 1500, 1 case; MFI > 1500, 4 cases). In 4 cases, the 2 tests identified at least 1 DSA but iDSA was different. Finally, in 4 cases, no DSA was detected with either test.
Test 1 reported 113 DSA, of which 45 in class I (mean MFI, 2374 ± 450) and 68 in class II (mean MFI, 7144 ± 790). Test 2 identified 69 DSA, of which 15 in class I (mean BCM, 3541 ± 769; mean BCR, 15.8 ± 2.9; mean AD-BCR, 19.8 ± 4.4) and 52 in class II (mean BCM, 6318 ± 906; mean BCR, 58.7 ± 9.4; mean AD-BCR, 72.4 ± 11.8). Fifty-one DSAs were identified only with test 1 (30 in class I and 21 in class II) versus 7 identified only with test 2 (2 in class I and 5 in class II). Among DSAs identified only by test 1, 18 of 50 had an MFI greater than 1500 (Figure 1).
Group 2, No AMR
Fifty patients aged 49.0 ± 2.2 years (22.1–78.3 years), mainly male (sex ratio: male/female, 2.5), experienced kidney graft biopsy 2.9 years (0.02–8.7 years) after kidney transplantation for kidney graft dysfunction. Banff classification of these biopsies did not show any sign of AMR.
Test 1 identified at least 1 DSA in 16 (32%) patients and test 2 in 7 (14%) patients. In only 5 cases, this identification was concordant between the 2 tests.
Test 1 reported 21 DSA of which 7 in class I and 14 in class II. Test 2 identified 7 DSAs, all in class II. Thus, 16 DSAs were determined only with test 1 (7 in class I and 9 in class II) versus 2 identified with only test 2, both in class II. Among the 16 DSA identified only by One Lambda, most of them (13/16, 81.1%) had an MFI lower than 1500 (Figure 2).
In group 2, only 3 (18.7%) of 16 patients in which DSAs were identified had a biopsy performed later: these 3 biopsies did not reveal any sign of AMR. Unfortunately, DSAs have not been analyzed later with One Lambda provider.
Sensitivity and Specificity of the Test With Usual Cutoff
For One Lambda, using a threshold of 500 for the MFI, sensitivity and specificity of the test for AMR diagnosis was 88% and 68%, respectively. Table 1 reports sensitivity and specificity for a range of different MFI cutoff values, from 500 to 2000.
For Immucor, sensitivity and specificity of the test was 78% and 86%, respectively.
Correlation and Agreement Between MFI and BCM, BCR, AD-BCR of All the DSA Detected
Correlation and agreement was found in both class I (Figure 3) and class II (Figure 4) between MFI and BCM, for DSA identified by the 2 manufacturers, but were better in class II (r = 0.80 [0.75–0.84] P < 0.0001 and ICC, 0.79 [0.73–0.83]).
Second Part: Determining the Best Cutoff
For iDSA, the ROC curve determined that the optimal cutoff value of MFI for predicting AMR in patients with kidney transplantation was 1294. The AUC for MFI was 0.844 (95% confidence interval [CI], 0.805–0.940; P < 0.0001), with a sensitivity of 78% and a specificity of 90%. For sDSA, the ROC curve determined that the optimal cutoff value of the sum of MFI for predicting AMR in patients with kidney transplantation was 2705. The AUC for the sum of MFI was 0.892 (95% CI, 0.814–0.945; P < 0.0001), with a sensitivity of 80% and a specificity of 94% (Figure 5).
For iDSA, the ROC curve determined that the optimal cutoff value of BCM for predicting AMR in patients with kidney transplantation was 90. The AUC for BCM was 0.917 (95% CI, 0.844–0.963; P < 0.0001), with a sensitivity of 91.8% and a specificity of 77.5%. For sDSA, the ROC curve determined that the optimal cutoff value of the sum of BCM for predicting AMR in patients with kidney transplantation was 473. The AUC for the sum of BCM was 0.930 (95% CI, 0.861–0.971; P < 0.0001), with a sensitivity of 86% and a specificity of 88% (Figure 5).
For iDSA, the ROC curve determined that the optimal cutoff value of BCR for predicting AMR in patients with kidney transplantation was 0.9. The AUC for BCR was 0.912 (95% CI, 0.838–0.960; P < 0.0001), with a sensitivity of 94% and a specificity of 75%. For sDSA, the ROC curve determined that the optimal cutoff value of the sum of BCR for predicting AMR in patients with kidney transplantation was 3.44. The AUC for the sum of BCR was 0.926 (95% CI, 0.856–0.969; P < 0.0001), with a sensitivity of 86% and a specificity of 86% (Figure 5).
For iDSA, the ROC curve determined that the optimal cutoff value of AD-BCR for predicting AMR in patients with kidney transplantation was 0.71. The AUC for AD-BCR was 0.912 (95% CI, 0.837–0.960; P < 0.0001), with a sensitivity of 98% and a specificity of 71.5%. For sDSA, the ROC curve determined that the optimal cutoff value of the sum of AD-BCR for predicting AMR in patients with kidney transplantation was 6.79. The AUC for the sum of AD-BCR was 0.926 (95% CI, 0.856–0.969; P < 0.0001), with a sensitivity of 86% and a specificity of 88% (Figure 5).
To our knowledge, this is the first study comparing the performance of the SAB assays available for the detection of DSA and for the diagnosis of AMR in the posttransplant setting. We found that the 2 LSA tests were not equivalent and not comparable with the usual cutoff value: One lambda test provides better sensitivity but poor specificity and Immucor better specificity but lower sensitivity. Nevertheless, we showed that MFI and BCM were correlated, and we demonstrated a good reliability between the 2 criteria. We performed ROC curve to find optimal cutoff values for both tests. Modifying the usual cutoff values, we improved sensitivity and specificity for both tests, and the use of the sum the intensity of all beads corresponding to the HLA loci of the donor (sDSA) led to propose the following cutoff value:
- –for One Lambda: MFI sDSA greater than 2705.
- –for Immucor: BCM sDSA greater than 473 or BCR sDSA greater than 3.44 or AD-BCR sDSA greater than 6.79.
Using these cutoff values, the 2 tests seem to me more comparable.
The use of Lifecodes single-antigen test for HLA antibody detection in kidney transplantation seems to be less worldwide developed compared with One Lambda Labscreen. Moreover, a major part of the literature on HLA antibodies, DSA detection and intensity cutoff are based on the One Lambda test.13 A very small number of studies have evaluated the impact of DSA detected by LIFECODES single antigen with standard SAB test14,15 and with complement fixing DSA (LIFECODES C3d assay) as described by Sicard et al.16 There are too scarce data in the literature on the Immucor test to draw solid conclusions and recommendations in the context of antibody mediated rejection. As shown in our study, the results of Lifecodes single-antigen test cannot be interpreted with One Lambda usual criteria.
Recently, in a letter published in the American Journal of Transplantation, Battle et al17 reported the use and the comparison of the 2 Luminex SAB in a patient listed for a second transplant following failure of a renal allograft due to antibody-mediated rejection. There was a distinct profile between the 2 tests and one Lambda test shown clearly a more important prozone effect than Immucor test. The mechanism they suspect for the lack of interference with Immucor is a dilution-type effect within the assay protocol that prevents the blocking seen with test 1. One Lambda uses 4 μL of patient serum per 1 μL of test beads, whereas Immucor uses 0.25 μL of patient serum per 1 μL of beads. It is important to note, however, that the dilution-type effect in Immucor may affect kit sensitivity. This point could partly explain our results. As of 2018, Immucor recommends the use of 0.5 instead of 0.25-μL serum for 1 μL of beads, which could result in a higher sensitivity of the test.
Thus, the determination of a positivity MFI cutoff value for One Lambda test has a major impact on HLA antibody detection. Nowadays, there is no consensus regarding this cutoff before kidney transplantation (impact on immunization degrees of patients referred for kidney transplantation, preformed DSA and AMR risk after transplantation, HLA desimmunization for highly immunized patients) and after transplantation (for subclinical de novo DSA detection and for AMR diagnosis).7 A too low cutoff might overestimate the relevance of a DSA, and lead to a high number of false positive results. Consequences in the pretransplantation setting may include limited access to kidney transplantation.18 In contrast, a too high cutoff might underestimate the risk of AMR and, for example, lead to underestimation of the occurrence of de novo DSA after kidney transplantation. Using different cutoff for iDSA determination (Table 1), we demonstrated that the lower the cutoff, the higher the sensitivity, but on the opposite the lower the specificity. This point is confirmed by Riethmüller et al19 for the prediction of AMR according to different strengths for DSA. Nevertheless, the introduction of solid-phase single-antigen bead (SAB) antibody assessment brought the belief that MFI was a quantifiable value.7,20 In their study, Tambur et al21 provided data indicating that neat MFI values do not always accurately depict antibody strength with one Lambda kits. Therefore, some researchers are trying to find other criteria to better define the risk factors of these DSA including complement-fixing ability21 and immunoglobulin isotype analysis.22,23 One of the potential concerns of Immucor tests is that positivity criteria are provided by the manufacturer but there is no intensity notion. Another concern is the normalization of MFI to the background MFI, due to bead variation, specific to each lot, and also the normalization to the relative amount of the antigens present on each bead. These criteria could explain the lower sensitivity of the test but might lead to a better specificity, as seen in the second group of our study. Clerkin et al24 recently compared detection of HLA antibodies by the 2 providers in heart and lung transplant recipients. Most antibodies with moderate to high MFI titer (> 4000) were detected by both assays. Although correlation between the assays was present, significant variance was shown. Lastly, Reed et al9 reported MFI variation to be greater with One Lambda than Immucor. Factors that could contribute to the higher variance include the lack of experience with one manufacturer, different dilution protocols, wash methods and bead manufacturing procedures. The degree of agreement in HLA antibody specificity assignment between the 2 manufacturers as assessed by ROC analysis was excellent at the antigen level in this study. The MFI-positive cutoffs ranging from 1000 to 1500 yielded a high level of agreement (>90%) in antigen specificity assignment. However, examination of % CVs across different MFI strata showed larger differences in lower MFI ranges (0–3000). The use of sDSA with their own cutoff could improve the agreement between the 2 tests in lower MFI ranges. Nevertheless, we have to be aware and very cautious when both providers are used in centralized allocation for kidney grafts or in clinical studies. Even though a good correlation exists, the 2 providers are not equivalent and positivity criteria are clearly different.
Despite the originality and the assets of our study, there are some limits. First, the power of our study is weak and does not allow us to draw solid conclusions and recommendations on which test is better to identify DSA in a context of highly suspected AMR. Another limit of our study is the absence of strategy to reduce the prozone effect25 for One Lambda test. Both assays were performed per the manufacturer’s recommendations; however, the Immucor protocol includes dilution of the sample. Dilution is known to reduce assay interference due to the prozone effect17 (resulting from the presence of high titer HLA Ab). Diluting the sera restored the MFI value in conventional IgG testing. Retesting diluted serum is costly and alternatives, such as addition of ethylenediaminetetraacetic acid or dithiothreitol21 to serum before testing have been proposed. Unfortunately, we did not use any protocol at that time to avoid the prozone effect, and it is an important limit in our study. Now, we use ethylenediaminetetraacetic acid treatment for One Lambda test. Nevertheless, among the 17 high-titer DSA detected with Immucor (BCM > 10 000), only 3, probably affected by the prozone effect, have an MFI of less than 10 000 (1372, 3468, and 6110). On the opposite, a variable percentage of the recombinant HLA molecules bound to commercially available micro particles appear to be in a denatured state26 and could therefore give rise to false-positive results of unknown clinical significance. The use of acid could help to avoid this problem.27 Unfortunately, due to financial reasons, we could not use this strategy.
In summary, we report the first study directly comparing the performance of Luminex single antigen tests of the 2 manufacturers available for the detection of DSA and the diagnosis of AMR in the posttransplant setting. This study shows a good correlation and agreement between One Lambda MFI and Immucor BCM. However, One Lambda’s test seems to be better for detecting low intensity antibodies to confirm AMR diagnosis but clearly has a lower specificity in stable situation. Using sDSA with their own cutoff could help to improve sensitivity and specificity of the 2 tests to make them more comparable. A study with a larger cohort and a greater number of sera tested is necessary to determine whether one of the SAB tests is superior overall, or which combination of assays is most suitable to distinguish false-positive from true-positive results.
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