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Value of Donor–Specific Anti–HLA Antibody Monitoring and Characterization for Risk Stratification of Kidney Allograft Loss

† †‡ | Denis Viglietti,* Alexandre Loupy, Dewi Vernerey,§ Carol Bentlejewski, Clément Gosset,¶ † † †‡ Olivier Aubert, Jean-Paul Duong van Huyen,** Xavier Jouven, Christophe Legendre, † | † Denis Glotz,* Adriana Zeevi, and Carmen Lefaucheur*

Departments of *Nephrology and and ¶Pathology, Saint Louis Hospital and Departments of ‡Kidney Transplantation and **Pathology, Necker Hospital, Assistance Publique Hôpitaux de Paris, Paris, France; †Paris Translational Research Center for Transplantation, Institut National de la Santé et de la Recherche Médicale, UMR-S970, Paris, France; §Methodology Unit (EA 3181) CHRU de Besançon, France; and |University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania

ABSTRACT The diagnosis system for allograft loss lacks accurate individual risk stratification on the basis of donor– specific anti–HLA antibody (anti-HLA DSA) characterization. We investigated whether systematic moni- toring of DSA with extensive characterization increases performance in predicting kidney allograft loss. This prospective study included 851 kidney recipients transplanted between 2008 and 2010 who were systematically screened for DSA at transplant, 1 and 2 years post-transplant, and the time of post– transplant clinical events. We assessed DSA characteristics and performed systematic allograft biopsies at the time of post–transplant serum evaluation. At transplant, 110 (12.9%) patients had DSAs; post- transplant screening identified 186 (21.9%) DSA-positive patients. Post–transplant DSA monitoring im- proved the prediction of allograft loss when added to a model that included traditional determinants of allograft loss (increase in c statisticfrom0.67;95%confidence interval [95% CI], 0.62 to 0.73 to 0.72; 95% CI, 0.67 to 0.77). Addition of DSA IgG3 positivity or C1q binding capacity increased discrimination per- formance of the traditional model at transplant and post-transplant. Compared with DSA mean fluores- cence intensity, DSA IgG3 positivity and C1q binding capacity adequately reclassified patients at lower or higher risk for allograft loss at transplant (category–free net reclassification index, 1.30; 95% CI, 0.94 to 1.67; P,0.001 and 0.93; 95% CI, 0.49 to 1.36; P,0.001, respectively) and post-transplant (category–free net reclassification index, 1.33; 95% CI, 1.03 to 1.62; P,0.001 and 0.95; 95% CI, 0.62 to 1.28; P,0.001, respectively). Thus, pre– and post–transplant DSA monitoring and characterization may improve individual risk stratification for kidney allograft loss.

J Am Soc Nephrol 28: 702–715, 2017. doi: 10.1681/ASN.2016030368

Donor–specificanti–HLA antibodies (anti-HLA Received March 29, 2016. Accepted June 29, 2016. DSAs) have been extensively reported to be D. Viglietti and A.L. are co-first authors and contributed equally strongly associated with increased risks of rejec- to this work. A.Z. and C. Lefaucheur are co-last authors and tion and allograft loss.1–6 Although their value contributed equally to this work. for accurate risk stratification of transplant out- Published online ahead of print. Publication date available at comes has not been determined in the current www.jasn.org. literature, the detrimental influence of anti-HLA DSAs on transplant outcomes has placed anti- Correspondence: Dr. Carmen Lefaucheur, Service de Néphrologie et Transplantation, Hôpital Saint-Louis, 1 Avenue Claude Vellefaux, HLA antibodies at the center of national and 75010 Paris, France. Email: [email protected] local allocation policies in the United States – and Europe.7 10 Copyright © 2017 by the American Society of Nephrology

702 ISSN : 1046-6673/2802-702 J Am Soc Nephrol 28: 702–715, 2017 www.jasn.org CLINICAL RESEARCH

Today, anti-HLA DSAs are considered to be among the most subclasses) to improve individual risk stratification for important biomarkers for predicting allograft injury and loss. allograft loss. However, there is no consensus for defining their pathogenicity and no standard for their evaluation to guide clinical decision making.11,12 The current conventional approach to pre– and RESULTS post–transplant immunologic risk evaluation is on the basis of the assessment by sensitive techniques of anti–HLA antibody Patient Characteristics specificity and strength, most frequently expressed by the This prospective study enrolled 851 patients among 906 mean fluorescence intensity (MFI) provided by single– consecutive recipients undergoing kidney transplantation antigen flow bead techniques.8–10,13 between January 1, 2008 and December 31, 2010. The study Recently, significant advances have occurred in our ability to flow chart is provided in Figure 1. diagnose patients with antibody-mediated rejection (ABMR) and The characteristics of the study population at the time of link anti–HLA antibody characteristics to transplant outcomes. transplantation are summarized in Table 1. The median follow- Theseadvancesincludetheassessmentofthecapacityofanti- up after transplantation was 5.3 years (interquartile range, HLA antibodies to bind complement, particularly C1q binding, 4.6–6.2). and the characterization of their IgG subclass composition. Con- verging evidence has supported that the capacity of anti-HLA Anti–HLA DSA Characteristics According to Time of DSA to bind complement is associated with an increased risk Detection of antibody-mediated injury and poor allograft survival in not Anti–HLA DSA Characteristics at the Time of Transplantation only kidney transplant14–20 but also, heart,21,22 ,23 and lung24 Among the 110 (12.9%) patients with circulating anti–HLA transplant. Furthermore, emerging data have emphasized the DSA at the time of transplantation, the DSA with the highest clinical relevance of the IgG subclass composition of anti-HLA MFI level, the immunodominant donor–specific antibody DSAs and their relationships with allograft injury phenotype25 (iDSA), was HLA class 1 in 52 (47.3%) patients and HLA class and survival in kidney25–27 and liver23,28 transplantation. 2 in 58 (52.7%) patients, with a mean MFI of 5952.164213.4 Considering that one of the most pressing unmet needs in and C1q binding capacity in 35 (31.8%) patients. IgG1 was transplant medicine involves delineating the characteristics of positive for 82 (74.6%) iDSAs, IgG2 was positive for 48 circulating anti–HLA antibodies that confer pathogenesis and (43.6%) iDSAs, IgG3 was positive for 31 (28.2%) iDSAs, influence transplant outcomes, the Transplantation Society An- and IgG4 was positive for 30 (27.3%) iDSAs. tibody Consensus Group issued a call to action in 2013 and encouraged the transplant community to focus future efforts Post–Transplant Anti–HLA DSA Characteristics on clinical trials that include serial anti–HLA DSA monitoring Among the186 (21.9%)patients identified with anti-HLA DSA with the assessment of anti–HLA DSA characteristics, including after transplantation, 86 (46.2%) patients were positive for their complement binding capacity and IgG subclass composi- anti-HLA DSA at the time of a clinical event, 55 (29.6%) tion.9 After decades of studies emphasizing the associations be- patients were identified at 1 year after transplantation, and 45 tween anti-HLA antibodies and kidney transplant outcomes,29 (24.2%) patients were identified at 2 years after transplanta- the key issue today is to evaluate whether systematic anti–HLA tion. In total, the iDSA was HLA class 1 in 76 (40.9%) patients DSA monitoring integrating the assessment of antibody char- and HLA class 2 in 110 (59.1%) patients, with a mean MFI of acteristics might improve risk stratification for allograft loss.30 5746.764627.7 and C1q binding capacity in 57 (30.7%) pa- Stratifying patients by their immunologic risk has the potential tients. IgG1 was positive for 137 (73.7%) iDSAs, IgG2 was to resolve the puzzle of alloimmune conditions determining positive for 80 (43.0%) iDSAs, IgG3 was positive for 42 allograft outcomes and increase long–term allograft and patient (22.6%) iDSAs, and IgG4 was positive for 46 (24.7%) iDSAs. survival by improving the efficacy of allocation policies and The characteristics of post–transplant anti–HLA DSA at the therapeutic strategies.31 time of detection are shown in Table 2. Our hypothesis was that systematic monitoring and precise A comparison of post–transplant anti–HLA iDSA charac- characterization of anti-HLA DSAs, including their comple- teristics according to their preformed/de novo status is shown ment binding capacity and IgG subclass composition, might in Supplemental Table 1. add to the predictive value for allograft loss of the conventional approach on the basis of their detection and strength assessed Clinical and Histologic Characteristics at the Time of by MFI level. To test this hypothesis, we specifically designed Post–Transplant Anti–HLA DSA Detection a prospective study performed in a large and unselected All of the patients with post–transplant anti–HLA DSA (n=186) population of kidney transplant recipients who underwent underwent kidney allograft biopsy at the time of anti–HLA DSA standardized monitoring of anti-HLA DSAs together with detection. Among them, 65 (34.9%) patients had clinical ABMR, systematic allograft biopsies. Weassessed the performance of and 67 (36.0%) patients had subclinical ABMR. The clinical and prospective systematic monitoring and characterization of histologic characteristics, according to the time of post–transplant anti-HLA DSAs (MFI, C1q binding capacity, and IgG anti–HLA DSA detection, are provided in Table 2.

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Figure 1. Prospective post-transplant anti-HLA DSA screening using single-antigen Luminex technique identified 110/851 (12.9%) patients with circulating anti–HLA DSA at the time of transplantation and 186/851 (21.9%) patients with circulating anti-HLA DSA after transplantation. Tx, transplant.

A comparison of clinical and histologic characteristics transplantation (HR, 2.32; 95% CI, 1.40 to 3.84; P=0.001). according to the preformed/de novo status of post–transplant These risk factors constituted the conventional model for allo- anti–HLA iDSA is shown in Supplemental Table 2. loss at the time of transplantation: the day 0 reference model (Table 3, multivariate day 0 reference model). Conventional Determinants of Kidney Allograft Loss at the Time of Transplantation Incremental Effect of Post–Transplant Anti–HLA DSA The 5-year kidney allograft survival rate was 89.1% (95% Monitoring on the Conventional Determinants of confidenceinterval[95%CI],86.7to91.1). Allograft Loss at the Time of Transplantation To build the conventional model for allograft loss at the The addition of post–transplant anti–HLA DSA detected by time of transplantation, we considered all of the tradi- single-antigen Luminex to the day 0 reference model (post–Tx tional recipient, donor, and transplant characteristics at the DSA model) was significantly associated with the time to al- time of transplantation (Table 3, univariate analysis). The fol- lograft loss (adjusted HR, 3.0; 95% CI, 1.8 to 5.0; P,0.001) lowing independent predictors of allograft loss at the time of and improved the ability to discriminate allograft loss, with an transplantation were identified: donor age (per 1-year incre- increase in the c statistic from 0.67 (95% CI, 0.62 to 0.73) for ment; hazard ratio [HR], 1.02; 95% CI, 1.00 to 1.03; P=0.03), the day 0 reference model to 0.72 (95% CI, 0.67 to 0.77) for the cold ischemia time (per 1-hour increment; HR, 1.03; 95% CI, post–Tx DSA model. The mean difference in the c statistic 1.01 to 1.05; P=0.01), donor terminal serum creatinine (per between the day 0 reference model and the post–Tx DSA 1-mmol/L increment; HR, 1.00; 95% CI, 1.00 to 1.01; P=0.01), model was 0.047 (95% CI, 0.046 to 0.049). The integrated and the presence of anti-HLA DSA at the time of discrimination improvement (IDI) between the post–Tx DSA

704 Journal of the American Society of Nephrology J Am Soc Nephrol 28: 702–715, 2017 www.jasn.org CLINICAL RESEARCH

Table 1. Baseline characteristics of the study population Hierarchical Ranking of Anti–HLA DSA Characteristics Characteristics N on the Basis of Their Ability to Classify Patients Recipient characteristics According to their Risk of Allograft Loss Age, yr, mean6SD 851 50.3612.8 At the Time of Transplantation Men, n (%) 851 512 (60.2) In patients with anti-HLA DSA detected at the time of trans- Retransplantation, n (%) 851 143 (16.8) plantation (n=110), we ranked day 0 iDSA characteristics on the Time since dialysis, y, mean6SD 733 4.864.5 basis of their ability to classify patients according to their risk of Blood type, n (%) 851 allograft loss by performing multivariate random survival forest A 386 (46.2) analysis, which integrated all of the day 0 iDSA characteristics B69(8.3)(iDSA HLA class, iDSA MFI level, iDSA C1q binding capacity, O 352 (42.2) and iDSA IgG1–4 subclasses). The relative variable importance AB 28 (3.4) values were as follows: 1 for IgG3 positivity, 0.59 for C1q binding CKD, n (%) 851 capacity, 0.16 for IgG4 positivity, 0.12 for IgG2 positivity, 0.11 Glomerulopathy 222 (26.1) for MFI, 20.02 for IgG1, and 20.03 for HLA class (Figure 2A). Vascular nephropathy 64 (7.5) Chronic interstitial nephropathy 103 (12.1) Malformative uropathy 161 (18.9) Post-Transplantation Diabetes 84 (9.9) The multivariate random survival forest analysis performed on Other 36 (4.2) patients with post–transplant anti–HLA DSA (n=186) (Figure 2B) Not determined 181 (21.3) showed the following relative variable importance values: 1 for Diabetes mellitus, n (%) 851 123 (14.5) IgG3 positivity, 0.42 for C1q binding, 0.16 for IgG1 positivity, 0.16 Body mass index, kg/m2,mean6SD 803 23.664.3 for HLA class, 0.06 for MFI, 20.04 for IgG4 positivity, 20.05 for Donor characteristics IgG2 positivity, and 20.07 for preformed/de novo status. Age, yr, mean6SD 851 51.4615.8 Men, n (%) 851 477 (56.05) Value of Anti–HLA DSA IgG3 Positivity and C1q Type, n (%) 851 Living 156 (18.3) Binding Capacity to Predict Allograft Loss at a Cerebrovascular death 370 (43.5) Population Level Other cause of death 325 (38.2) Because iDSA IgG3 positivity and C1q binding capacity were Diabetes mellitus, n (%) 775 47 (5.5) the most informative anti–HLA iDSA characteristics, we as- Hypertension, n (%) 822 213 (25.0) sessed their discrimination performance for the prediction of Body mass index, kg/m2,mean6SD 835 26.1625.7 allograft loss in the overall study population (n=851). Terminal serum creatinine, mmol/L, mean6SD 851 87.1652.7 Transplant characteristics 6 6 At the Time of Transplantation Cold ischemia time, h, mean SD 851 17.1 9.8 The performances of day 0 anti–HLA DSA, day 0 IgG3–positive PRA, n (%) 851 iDSA, and day 0 C1q binding iDSA in predicting clinical and 0%–20% 746 (87.7) – subclinical ABMR are shown in Table 4. 21% 50% 53 (6.2) – 51%–80% 25 (2.9) Patients with day 0 IgG3 positive iDSA (n=31) had a de- 81%–100% 27 (3.2) creased 5-year allograft survival (41.8%; 95% CI, 23.6 to 59.0) Calculated PRA, n (%) 851 compared with patients without day 0 IgG3–positive iDSA 0%–20% 430 (50.5) (n=820; 90.8%; 95% CI, 88.5 to 92.7; P,0.001) (Supplemental 21%–50% 129 (15.2) Figure 1A). Patients with day 0 C1q binding iDSA (n=35) had a 51%–80% 266 (31.3) 5-year allograft survival of 51.0% (95% CI, 32.4% to 66.8%) 81%–100% 26 (3.1) compared with 90.7% (95% CI, 88.4 to 92.6) in patients without HLA mismatch, mean6SD 851 day 0 C1q binding iDSA (n=816) (Supplemental Figure 1B). 6 A1.00.7 The addition of day 0 iDSA IgG3 positivity to the day 6 B1.20.7 0 reference model yielded a c statistic of 0.72 (95% CI, 0.66 to DR 0.960.7 0.77; mean difference of 0.047; 95% CI, 0.045 to 0.048), whereas Anti-HLA DSA at the time of transplantation, 851 110 (12.9) n (%) the addition of day 0 iDSA C1q binding capacity provided a c PRA, panel reactive antibody. statistic of 0.70 (95% CI, 0.66 to 0.76; mean difference of 0.033; 95% CI, 0.032 to 0.035). The addition of both day 0 iDSA C1q binding capacity and IgG3 positivity to the day 0 reference model model and the day 0 reference model was 0.021 (95% CI, ,0.00 resulted in a c statistic of 0.74 (95% CI, 0.69 to 0.79). to 0.04; P=0.003). The detection of post–transplant anti–HLA DSA significantly reclassified patients at lower or higher risk for Post-Transplantation allograft loss with a category–free net reclassification improve- The performances of post–transplant anti–HLA DSA, post– ment (NRI) of 0.494 (95% CI, 0.28 to 0.71; P,0.001). transplant IgG3–positive iDSA, and post–transplant C1q

J Am Soc Nephrol 28: 702–715, 2017 Anti-HLA DSA and Risk Stratification 705 CLINICAL RESEARCH www.jasn.org

Table 2. Clinical, histologic and immunological characteristics according to the time of anti-HLA DSA detection All 1-yr 2-yr Time of Post–Transplant Characteristics Post–Transplant Post-Transplant Post-Transplant P Valuea Transplantation Clinical Event DSA Screening Screening n 110 186 86 55 45 Characteristics of anti-HLA DSAs All anti–HLA DSAs No., mean6SD 2.061.3 1.861.2 1.961.2 1.861.4 1.761.0 0.52 HLA class specificity, n (%) 0.59 1 32 (29.1) 44 (23.7) 23 (26.7) 12 (21.8) 9 (20.0) 2 32 (29.1) 82 (44.1) 33 (38.4) 25 (45.5) 24 (53.3) 1+2 46 (41.8) 60 (32.3) 30 (34.9) 18 (32.7) 12 (26.7) iDSA HLA class specificity, n (%) 0.04 1 52 (47.3) 76 (40.9) 43 (50.0) 16 (29.1) 17 (37.8) 2 58 (52.7) 110 (59.1) 43 (50.0) 39 (70.9) 28 (62.2) Preexisting, n (%) — 81 (43.6) 39 (45.4) 42 (76.4) 0 ,0.001 MFI, mean6SD 5952.164213.4 5746.764627.7 5971.264941.5 6319.564972.2 4617.463273.5 0.41 C1q binding, n (%) 35 (31.8) 57 (30.7) 34 (39.5) 13 (23.6) 10 (22.2) 0.05 IgG subclasses, n (%) IgG1 82 (74.6) 137 (73.7) 62 (72.1) 42 (76.4) 33 (73.3) 0.85 IgG2 48 (43.6) 80 (43.0) 37 (43.0) 23 (41.8) 20 (44.4) 0.97 IgG3 31 (28.2) 42 (22.6) 38 (44.2) 2 (3.6) 2 (4.4) ,0.001 IgG4 30 (27.3) 46 (24.7) 9 (10.5) 24 (43.6) 13 (28.9) ,0.001 Clinical and histologic characteristics Clinical characteristics eGFR at biopsy, ml/min — 41.2619.9 30.5613.0 49.9620.9 50.7619.8 ,0.001 per 1.73 m2,mean6SD Proteinuria, g/g, mean6SD — 0.560.8 0.961.0 0.260.1 0.260.2 ,0.001 Histologic characteristics Acute/active ABMR, n (%) — 102 (54.8) 52 (60.5) 31 (56.4) 19 (42.2) 0.13 Chronic/active ABMR, n (%) — 30 (16.1) 13 (15.1) 10 (18.2) 7 (15.6) 0.88 TCMR, n (%) — 17 (9.1) 11 (12.8) 2 (3.6) 4 (9.1) 0.18 g+ptcScore,mean6SD — 2.461.8 2.961.9 2.161.4 1.961.7 0.004 i + t Score, mean6SD — 1.161.8 1.462.1 0.661.2 1.161.9 0.19 v Score, mean6SD — 0.260.6 0.360.7 0.160.3 0.160.5 0.012 cg Score, mean6SD — 0.360.8 0.460.9 0.260.4 0.360.8 0.99 IF/TA score, mean6SD — 1.261.0 0.960.8 1.361.0 1.561.0 0.001 cv Score, mean6SD — 1.461.0 1.261.1 1.561.1 1.660.9 0.10 ah Score, mean6SD — 0.860.8 0.760.8 0.960.9 1.060.9 0.07 C4d deposition, n (%) — 59 (31.7) 42 (48.8) 11 (20.0) 6 (13.3) ,0.001 —,notapplicable;TCMR,Tcell–mediated rejection; g, glomerulitis; ptc, peritubular capillaritis; i, mononuclear cell interstitial inflammation; t, tubulitis; v, intimal arteritis; cg, allograft glomerulopathy; IF/TA, interstitial fibrosis/tubular atrophy; cv, vascular fibrous intimal thickening; ah, arteriolar hyaline thickening. aP values are for the comparisons of the patients with post–transplant anti–HLA DSA detected for clinical indication, at 1 year after transplantation, and at 2 years after transplantation. binding iDSA in predicting clinical and subclinical ABMR are The addition of post–transplant iDSA IgG3 positivity and detailed in Table 4. post–transplant iDSA C1q binding capacity to the post–Tx Patients with post–transplant IgG3–positive iDSA DSA model resulted in c statistics of 0.76 (95% CI, 0.72 to (n=42) showed a 5-year allograft survival of 30.0% (95% 0.82; mean difference of 0.046; 95% CI, 0.045 to 0.048) and CI, 16.4 to 44.7) compared with 92.1% (95% CI, 89.9 to 0.76 (95% CI, 0.72 to 0.82; mean difference of 0.045; 95% CI, 93.9) in patients without post–transplant IgG3–positive 0.044 to 0.046), respectively. The addition of both post–transplant iDSA (n=809) (Supplemental Figure 1C). Patients with iDSA C1q binding capacity and IgG3 positivity to the post–Tx post–transplant C1q binding iDSA (n=57) had a 5-year DSA model resulted in a c statistic of 0.81 (95% CI, 0.76 to 0.85). allograft survival of 45.8% (95% CI, 31.9 to 58.8) compared Figure 3 depicts the predictive value for allograft loss of a with 92.1% (95% CI, 89.9 to 93.9) in patients without strategy on the basis of systematic monitoring and precise post–transplant C1q binding iDSA (n=794) (Supplemen- characterization of anti-HLA DSA at the time of transplanta- tal Figure 1D). tion and after transplantation at the population level.

706 Journal of the American Society of Nephrology J Am Soc Nephrol 28: 702–715, 2017 www.jasn.org CLINICAL RESEARCH

Table 3. Conventional determinants of time to kidney allograft loss at the time of transplantation: univariate analysis and multivariate day 0 reference model Variables No. of Patients No. of Events HR 95% CI P Value Univariate analysis Recipient characteristics Age per 1-yr increment 851 86 0.99 0.98 to 1.01 0.46 Sex Women 339 35 1 — Men 512 51 0.95 0.62 to 1.46 0.80 Retransplantation No 708 66 1 — Yes 143 20 1.60 0.97 to 2.64 0.07 Time since dialysis per 1-yr increment 733 81 1.02 0.97 to 1.06 0.46 Diabetes mellitus No 728 72 1 — Yes 123 14 1.20 0.67 to 2.12 0.54 Body mass index per 1-kg/m2 increment 803 80 1.00 0.95 to 1.05 .0.99 Donor characteristics Age per 1-yr increment 851 86 1.02 1.00 to 1.03 0.02 Sex Women 374 39 1 — Men 477 47 0.94 0.62 to 1.44 0.78 Type Living 156 8 1 — Deceased 695 78 2.29 1.10 to 4.73 0.03 Diabetes mellitus No 728 73 Yes 47 5 1.17 0.47 to 2.89 0.74 Hypertension No 608 56 1 — Yes 214 28 1.52 0.97 to 2.40 0.07 Body mass index per 1-kg/m2 increment 835 84 1.00 0.99 to 1.01 0.88 Terminal SCr per 1-mmol/L increment 851 86 1.00 1.00 to 1.01 0.01 Transplant characteristics Cold ischemia time per 1-h increment 851 86 1.04 1.02 to 1.06 ,0.001 PRA per 10% increment 851 86 1.11 1.04 to 1.20 0.004 Calculated PRA per 10% increment 851 86 1.08 1.02 to 1.15 ,0.01 HLA-A/-B/-DR mismatch (continuous) 851 86 0.90 0.79 to 1.04 0.16 Anti-HLA DSA No 741 65 1 — Yes 110 21 2.47 1.51 to 4.05 ,0.001 Multivariate day 0 reference model Donor age per 1-yr increment 851 86 1.02 1.00 to 1.03 0.03 Cold ischemia time per 1-h increment 851 86 1.03 1.01 to 1.05 0.01 Donor terminal SCr per 1-mmol/L increment 851 86 1.003 1.00 to 1.01 0.01 Anti-HLA DSA No 741 65 1 — Yes 110 21 2.32 1.40 to 3.84 0.001 —, not applicable; SCr, serum creatinine; PRA, panel reactive antibody.

Incremental Effect of Anti–HLA DSA IgG3 Positivity 0 iDSA IgG3 positivity improved day 0 iDSA MFI discrimina- and C1q Binding Capacity on MFI Level for Stratifying tion performance, yielding a c statistic of 0.85 (95% CI, 0.79 to the Individual Risk of Allograft Loss in Patients with 0.92) with a mean difference of 0.105 (95% CI, 0.102 to 0.108) Anti-HLA DSA and an IDI of 0.233 (95% CI, 0.12 to 0.35; P,0.001) (Figure At the Time of Transplantation 4A). Day 0 iDSA IgG3 positivity adequately reclassified patients In patients with anti-HLA DSA detected at the time of at lower or higher risk for allograft loss compared with iDSA transplantation (n=110), the day 0 iDSA MFI level showed a MFI level alone, resulting in a category-free NRI of 1.304 (95% c statistic of 0.75 (95% CI, 0.66 to 0.83). The addition of day CI, 0.94 to 1.67; P,0.001). The addition of day 0 iDSA IgG3

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Figure 2. Hierarchical ranking of anti–HLA iDSA characteristics on the basis of their ability to classify patients according to their risk of allograft loss using random survival forest modeling. (A) At the time of transplantation (n=110). (B) Post-transplantation (n=186). positivity to day 0 iDSA MFI level reclassified 75 of 89 patients The addition of both day 0 iDSA C1q binding capacity and (84.3%) in the correct direction in the group without allograft IgG3 positivity to day 0 iDSA MFI level provided a c statistic of loss by predicting a lower probability of allograft loss, whereas 0.87 (95% CI, 0.81 to 0.93). it adequately reclassified 17 of 21 patients (81.0%) in the group with allograft loss by predicting a greater probability of allo- Post-Transplantation graft loss (Figure 5A). In patients with post–transplant anti–HLA DSA (n=186), The addition of day 0 iDSA C1q binding capacity to day iDSA MFI level had a c statisticof0.73(95%CI,0.65to 0 MFI level increased the c statistic to 0.79 (95% CI, 0.69 to 0.80). The addition of post–transplant iDSA IgG3 positivity 0.90; mean difference of 0.040; 95% CI, 0.038 to 0.042) and increased the c statistic to 0.86 (95% CI, 0.80 to 0.91; mean provided an IDI of 0.123 (95% CI, 0.06 to 0.19; P,0.001) difference of 0.130; 95% CI, 0.128 to 0.132). The IDI was (Figure 4A). Day 0 iDSA C1q binding capacity improved pa- 0.328 (95% CI, 0.24 to 0.42; P,0.001) (Figure 4B), and tient classification according to the patients’ risk of allograft the category-free NRI was 1.326 (95% CI, 1.03 to 1.62; loss with a category-free NRI of 0.929 (95% CI, 0.49 to 1.37; P,0.001). The addition of post–transplant iDSA IgG3 pos- P,0.001). The addition of day 0 iDSA C1q binding capacity to itivity to post–transplant iDSA MFI level reclassified 135 of day 0 iDSA MFI level reclassified 71 of 89 patients (79.8%) in 149 patients (90.6%) in the correct direction in patients the correct direction in the group without allograft loss, without allograft loss, whereas it adequately reclassified whereas it adequately reclassified 14 of 21 patients (66.7%) 28 of 37 patients (75.7%) in patients with allograft loss among the patients with allograft loss (Figure 5B). (Figure 5C).

Table 4. Performance of anti-HLA DSA, IgG3–positive anti–HLA iDSA, and C1q binding anti–HLA iDSA to predict clinical and subclinical ABMR in an unselected population of kidney transplant recipients (n=851) Measures of Day 0 Day 0 IgG3 Day 0 C1q Post-Transplant Post–Transplant Post–Transplant Diagnostic Accuracy DSA, % DSA, % DSA, % DSA, % IgG3 DSA, % C1q DSA, % Clinical ABMR Sensitivity 55.4 33.8 32.3 100 58.5 52.3 Specificity 91.0 98.9 98.2 84.6 99.5 97.1 PPV 32.7 71.0 60.0 34.9 90.5 59.6 NPV 96.1 94.8 94.6 100 96.7 96.1 Subclinical ABMR Sensitivity 49.3 4.5 11.9 100 4.5 29.9 Specificity 90.2 96.4 96.6 84.8 95.0 95.3 PPV 30.0 9.7 22.9 35.5 7.1 35.1 NPV 95.4 92.2 92.8 100 92.1 94.1 PPV, positive predictive value; NPV, negative predictive value.

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Figure 3. Predictive value for allograft loss of a strategy on the basis of a systematic monitoring of anti-HLA DSAs and integration of anti–HLA DSA characteristics in an unselected population of kidney transplant recipients (n=851). Predictive value for allograft loss was assessed by Cox model Harrell c statistics in the overall study population (n=851). Day 0 anti–HLA DSA characteristics (IgG3 positivity and C1q binding) were added to the day 0 reference model, which was on the basis of a conventional strategy. Post–transplant anti–HLA DSA characteristics (IgG3 positivity and C1q binding) were added to the post–Tx DSA model. In the day 0 reference model and the post–Tx DSA model, anti-HLA DSAs were detected using the single–antigen Luminex technique. A c statistic of 0.5 indicated that the model is no better than chance at predicting membership in a group, and a value of one indicates that the model perfectly identifies those within a group and those not in a group. Percentile 95% CIs for c statistics were derived using 1000 bootstrap samples. The differences in c statistics were replicated 1000 times using bootstrap samples to derive 95% CIs.

The addition of post–transplant iDSA C1q binding capacity to 0.018). The IDI was 0.190 (95% CI, 0.12 to 0.26; P,0.001) to post–transplant iDSA MFI level provided a c statistic of 0.74 (Figure 4B), and the category-free NRI was 0.948 (95% CI, 0.62 (95% CI, 0.65 to 0.83; mean difference of 0.016; 95% CI, 0.014 to 1.28; P,0.001). The addition of post–transplant iDSA C1q

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Figure 4. Improvement in calculated risk of allograft loss by considering IgG3 and C1q binding anti–HLA DSA status in addition to anti– HLA DSA MFI level at (A) the time of transplantation and (B) post-transplantation. Improvement in calculated risk of allograft loss was assessed by the IDI. The IDI integrates the change in mean predicted probability of allograft loss in patients with allograft loss and those without allograft loss. The change in the mean predicted probability of allograft loss is adequate if it is positive for patients with allograft loss (increased calculated risk) and negative for those without allograft loss (decreased calculated risk). Tx, transplant. binding capacity to post–transplant iDSA MFI level reclassified for allograft loss. Extensive characterization of anti-HLA 127 of 149 patients (85.2%) in the correct direction in patients DSAs allowed us to show that IgG3 subclass positivity or without allograft loss, whereas it adequately reclassified 23 of complement binding capacity further improved pre- and 37 patients (62.2%) in patients with allograft loss (Figure 5D). post-transplant performance in predicting kidney allograft The addition of both post–transplant iDSA C1q binding loss beyond the conventional approach on the basis of the capacity and IgG3 positivity to post–transplant iDSA MFI detection of circulating anti–HLA DSAs and the assessment level provided a c statistic of 0.87 (95% CI, 0.82 to 0.92). of their strength using Luminex technology. We showed that a precise characterization of anti-HLA DSAs, including IgG3 Sensitivity Analyses status or C1q binding status, improved the evaluation of in- The robustness of our results was confirmed by a sensitivity dividual risk for allograft loss in .60% of patients. analysis performed after excluding patients with preformed Our study showed that a significant proportion of patients anti–HLA DSA (n=110). In the population of patients without (100 of 186; 53.8%) did not show allograft dysfunction at the preformed anti-HLA (n=741), post–transplant anti–HLA time of post–transplant anti–HLA DSA detection. In these DSA monitoring improved the day 0 reference model discrim- clinically stable patients, concurrent kidney allograft biopsies ination performance (Supplemental Table 3). The addition of revealed acute/active or chronic/active ABMR in 67 (67.0%) post–transplant anti–HLA iDSA C1q binding capacity and patients, emphasizing the importance of performing allograft IgG3 capacity further improved the model discrimination biopsy at the time of anti–HLA DSA detection in uncovering performance (Supplemental Table 3). In the patients with de subclinical ABMR disease.32 Systematic monitoring of anti- novo anti–HLA DSA (n=105), anti–HLA iDSA C1q binding HLA DSAs might allow for the early diagnosis of ABMR dis- capacity and IgG3 positivity provided better discrimination of ease and subsequent specific treatment and adjustment of allograft loss when added to iDSA MFI level than MFI level immunosuppressive therapy. alone (Supplemental Table 3). This study extended the results of previous work showing relationships between circulating anti–HLA DSA strength and allograft lesion intensity and allograft survival, with higher DISCUSSION levels of circulating anti–HLA DSAs being associated with increased microvascular inflammation, increased C4d depo- In this prospective study performed in an unselected popu- sition in the peritubular capillaries of the allograft,33–35 and lation of 851 kidney transplant recipients, we showed that decreased allograft survival.2 More recently, other properties standardized monitoring of circulating anti–HLA DSAs within of anti-HLA DSAs have been associated with kidney allograft 2 years after transplantation improved the risk stratification loss, including anti–HLA DSA complement binding capacity

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Figure 5. Individual additive value of IgG3 and C1q binding anti–HLA DSA status to MFI level for stratifying the risk of allograft loss at the time of transplantation ([A] IgG3 status and [B] C1q binding status) and post-transplantation ([C] IgG3 status and [D] C1q binding status). Additive value of IgG3 and C1q binding anti–HLA DSA status to MFI level was assessed by category-free NRI. The NRI inte- grates the direction of change in the probability of allograft loss for every individual. The change in individual calculated risk is in the correct direction if it is greater for patients with allograft loss and less for those without allograft loss. Blue lines in patients without allograft loss indicate that IgG3 and C1q binding anti–HLA iDSA status moved the individual predicted probability of allograft loss in the correct (downward) direction. Red lines in patients with allograft loss indicate a correct (upward) change in the predicted probability of allograft loss when adding IgG3 and C1q binding anti–HLA iDSA status to anti–HLA iDSA MFI level. Pts, patients; Tx, transplant.

J Am Soc Nephrol 28: 702–715, 2017 Anti-HLA DSA and Risk Stratification 711 CLINICAL RESEARCH www.jasn.org and IgG subclass composition.14,15,18,19,25–27 However, the pre- HLA DSA screening and allograft biopsies, showed that post- dictive value of anti–HLA DSA characteristics for kidney trans- transplant monitoring of circulating anti–HLA DSA using the plant outcomes assessed at the time of transplantation and after single–antigen flow bead technique improved the individual transplantation had not been previously investigated accu- risk stratification for allograft loss. We also showed that the rately.30,36 This study addressed, for the first time, the dynamic addition of IgG3 or C1q binding anti–HLA DSA status to the and incremental prediction of kidney allograft loss using a pro- conventional approach on the basis of anti–HLA DSA strength spective systematic monitoring and characterization of anti- improved the performance in assessing the individual risk for HLA DSA (MFI, C1q binding capacity, and IgG subclasses) allograft loss in .60% of patients. together with its potential for individual risk reclassification. Using dedicated analyses, we evaluated the added predictive ability of the most informative anti–HLA DSA characteristics CONCISE METHODS (IgG3 and C1q binding status) for the reclassification of indi- vidual risk of allograft loss. The increase in c statistic showed Study Design that the addition of anti–HLA DSA IgG3 and C1q binding We enrolled all consecutive patients who underwent kidney transplan- status to anti–HLA DSA MFI level improved the concordance tation at Saint Louis Hospital (n=429) and Necker Hospital (n=477) between predicted and observed kidney allograft survival. The between January 1, 2008 and December 31, 2010 (n=906). Patients IDI showed significant improvement in the magnitude of the were followed until January 1, 2016. All of the transplants were ABO change in the predicted risk of allograft loss when adding anti– blood group compatible and performed with negative standard National HLA DSA IgG3 and C1q binding status to anti–HLA DSA MFI Institutes of Health and anti–human globulin T and B cell cytotoxicity level, whereas the NRI determined a significant change in the crossmatches. Patients transplanted after desensitization protocols adequate direction of the individual predicted risk of allograft loss. (n=21) and those enrolled in clinical trials (n=34) were excluded. All The risk-stratified approach greatly increases our ability to of the included patients (n=851) were screened for the presence of cir- personalize the clinical management of patients with kidney culating anti–HLA DSA (1) at the time of transplantation, (2)systemat- transplants.31 Our results suggest that the risk of ABMR ically at 1 and 2 years after transplantation, and (3) at the time of a clinical and allograft loss might be significantly reduced by avoiding event occurring within the first 2 years after transplantation (Figure 1). HLA-incompatible transplant across preformed C1q binding All study patients identified with circulating anti–HLA DSA were and/or IgG3–positive anti–HLA DSA. In hypersensitized pa- tested for anti–HLA DSA characteristics, namely DSA specificity, DSA tients with an insufficient flow of donors, specificpretransplant HLA class, DSA MFI level, DSA C1q binding capacity, and DSA IgG1–4 conditioning should be considered to eliminate C1q binding subclass, at two time points: at the time of transplantation and at the and/or IgG3–positive anti–HLA DSA before accepting a trans- time of allograft biopsy (as detailed in Figure 1). plant. In the post-transplant setting, the systematic monitoring The study was approved by the Institutional Review Boards of Saint and the characterization of anti-HLA antibodies provide nonin- Louis Hospital and Necker Hospital. vasive tools to identify patients who are at high risk of ABMR One Lambda, Inc. (Canoga Park, CA) donated reagents but was and allograft loss. Furthermore, risk assessment on the basis not otherwise involved in either the conduct of the study or the of anti–HLA DSA properties could provide a basis for more preparation of the manuscript. personalized pathogenesis–driven therapies. Patients with anti-HLA DSA showing complement binding ability reflected Clinical Data by C1q or IgG3 positivity might benefit from more specific The clinical data regarding donors and recipients were extracted therapeutic protocols using complement-targeting agents.37 from a prospective database: Données Informatiques Validées en More generally, risk stratification should be greatly integrated Transplantation (DIVAT; www.divat.fr). Coding was used to ensure in randomized, controlled trials to apply averaged results of strict donor and recipient anonymity. The data are computerized in real clinical trials to individual patients,38 because their aggre- time and at each transplant anniversary, and they are submitted for an gated results can be misleading when applied to individual annual audit. Each patient in this study provided written informed patients.31 consent to be included in the DIVAT database network. This database The principal limitation of this study pertained to the fact is approved by the National French Commission for Bioinformatics that we were not able to validate our findings in an independent Data and Patient Liberty (Commission Nationale de l’Informatique et population given the uniqueness of our prospective, highly des Libertés registration no. 1016618; validated June 8, 2004). phenotyped cohort integrating systematic immunologic mon- Clinical events were defined by the following: (1) an increase in itoring, protocol biopsies, and extensive assessment of anti- serum creatinine exceeding 15% of the baseline value within a period HLA DSA. Furthermore, specific economic studies are needed of 21 days without ultrasound abnormalities, (2) proteinuria exceed- to evaluate the cost efficiency of systematic anti–HLA DSA ing 0.5 g/g, and (3) an increase in MFI level exceeding 50% compared monitoring policies in kidney transplantation before transla- with the day 0 level in patients with preformed anti–HLA DSA. tion in clinical routine. Renal function was assessed by the eGFR with the abbreviated In conclusion, this prospective study, performed in a large Modification of Diet in Renal Disease formula39 at the time of post– cohort of kidney transplant recipients with systematic anti– transplant anti–HLA DSA detection.

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The protocols and treatment of allograft after transplantation in stable-state patients with persistent anti–HLA rejections after transplantation were similar between the centers. The DSA. Renal tissue was fixed in acetic formol absolute alcohol fixative protocols and treatments are described in Supplemental Material. and stained with Masson trichrome and periodic acid–Schiff. All of the graft biopsies were scored and graded from zero to three accord- – Detection and Characterization of Anti-HLA DSAs ing to the updated Banff criteria40 43 for the following histologic Screening for Anti-HLA DSAs in the Study Population factors: glomerulitis, tubulitis, mononuclear cell interstitial inflamma- All of the kidney transplant recipients (n=851) were tested for circu- tion, intimal arteritis, peritubular capillaritis, allograft glomerulopathy, lating anti–HLA-A, -B, -Cw, -DR, -DQ, and -DP DSAs in serum interstitial fibrosis/tubular atrophy, arteriolar hyaline thickening, and samples obtained at the time of transplantation, systematically at 1 vascular fibrous intimal thickening. C4d staining was performed by and 2 years after transplantation, and at the time of a clinical event immunochemical analysis on paraffin sections using polyclonal human occurring in the first 2 years after transplantation. All of the serum anti–C4d antibodies (Biomedica Gruppe, Vienna, ). samples were treated with EDTA; a 0.1 M solution of disodium EDTA All of the graft biopsies were scored and graded by experienced at pH 7.4 was diluted 1:10 in the serum and incubated for 10 minutes pathologists (C.G. and J.-P.D.v.H.) who were unaware of the patients’ before testing. Single–antigen flow bead assays were used (One clinical and immunologic statuses. Lambda, Inc.) on a Luminex platform. All beads showing a normal- ABMR was classified according to the last update of the Banff ized MFI .1000 were considered positive. The highest MFI value classification.43 Subclinical ABMR was defined by stable renal func- toward a donor-specific allele was considered to be the iDSA. tion and the Banff criteria for acute/active or chronic/active ABMR. HLA typing of all of the kidney transplant donors and recipients Stable renal function was defined as the variability in serum creati- was performed by molecular biology (Innolipa HLA Typing Kit; nine not exceeding 15% of the baseline value within a period of Innogenetics, Gent, ). 21 days before the biopsy.32

Antibody Characterization in the Patients with Positive Statistical Analyses Screening for Anti-HLA DSAs The mean6SD values and frequencies are provided for the descrip- Serum samples from patients with circulating anti–HLA DSA at tion of the continuous and categorical variables, respectively, unless thetimeoftransplantation(n=110)andthetimeofthefirst otherwise stated. The means and proportions were compared using post–transplant circulating anti–HLA DSA detection (n=186) the t test and the chi-squared test, respectively (or Mann–Whitney were analyzed in a blinded fashion at the University of Pittsburgh U test and Fisher exact test if appropriate, respectively). for the presence of C1q binding anti–HLA DSA and the presence of First, we built the day 0 reference model by assessing the determinants IgG1–4 subclasses. oftime to kidney allograft failure at the time of transplantation among The presence of C1q binding anti–HLA DSAs was assessed using traditional risk factors, including recipient, donor, transplant char- single–antigen flow bead assays according to the manufacturer’s pro- acteristics, and the presence of anti-HLA DSA detected by single- tocol (C1q Screen; One Lambda, Inc.). antigen Luminex. Kidney allograft survival was calculated from the The IgG subclass assay was performed as reported previously25 date of transplantation to the date of allograft loss. In patients who using a modified standard single–antigen assay and replacing the died with a functioning graft, graft survival was censored at the phycoerythrin–conjugated antipan–human IgG reporter antibody with time of death. Graft survival analyses were performed for a maxi- mAbs specificforIgG1–4 subclasses (IgG1 clone HP6001, IgG2 clone mum follow-up period of 5 years from the time of transplantation. 31–7-4, IgG3 clone HP6050, and IgG4 clone HP6025; Southern Biotech). Cox proportional hazard models were used to estimate the HRs and The specificity of the iDSA on the basis of pan-IgG reactivity was 95% CIs for kidney allograft loss. We first performed univariate analy- applied to the other tests, including C1q binding and IgG subtype analysis. sis. A P value threshold of 0.20 for entering variables into the multivar- For each patient, we evaluated the number, HLA class, and MFI of iate model was used. Significant risk factors were then entered into a all of the detected anti-HLA DSAs, and for iDSAs, we also considered single multivariate model using backward stepwise elimination to de- the C1q binding capacity and the IgG1–4 subclasses. fine the day 0 reference model. Model calibration and goodness of Post–transplant anti–HLA DSAs were considered preformed fit were assessed by visual examination of a calibration plot. when they were detectable at the time of transplantation and persisted Second, we assessed the change in the discrimination capacity of after transplantation. If anti-HLA DSAs were absent at the time of the day 0 reference model by adding the detection of post–transplant transplantation as determined by solid-phase assay and became de- anti–HLA DSA by single-antigen Luminex (post–Tx DSA model). tectable post-transplant, they were considered de novo DSAs. Harrell c statistic was estimated for the day 0 reference model and the post–Tx DSA model; c-statistic estimations were repeated 1000 times Kidney Allograft Histology using bootstrap samples to derive 95% CIs and assess the difference in Kidney allograft biopsies were performed at the time of the first post– the c statistic between the models with its 95% CI. We used category- transplant anti-HLA DSA detection in patients with de novo anti– free NRI and IDI to assess the incremental effect of post–transplant HLA DSA (clinical event or 1 or 2 years after transplantation). In anti–HLA DSA detection on the day 0 reference model to predict patients with preformed anti–HLA DSA, allograft biopsies were per- allograft loss.36,44 formed at the time of a clinical event (in patients with allograft dys- Third, we hierarchically ranked the anti–HLA iDSA characteristics function, proteinuria, or an MFI increase exceeding 50%) or 1 year at the time of transplantation and after transplantation (iDSA HLA

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class, iDSA MFI, iDSA C1q binding capacity, and iDSA IgG1–4sub- 6. Vo AA, Sinha A, Haas M, Choi J, Mirocha J, Kahwaji J, Peng A, Villicana R, classes) according to their ability to classify subjects who would lose Jordan SC: Factors predicting risk for antibody-mediated rejection and graft their graft from those who would not by performing multivariate loss in highly human leukocyte antigen sensitized patients transplanted after desensitization. Transplantation 99: 1423–1430, 2015 random survival forest modeling. Five thousand trees were generated 7. Heidt S, Witvliet MD, Haasnoot GW, Claas FH: The 25th anniversary of using bootstrapping by sampling with replacement at the root node the Acceptable Mismatch program for highly sensitized before growing trees. The following parameters were applied: the patients. Transpl Immunol 33: 51–57, 2015 minimum number of unique patients in a terminal node was set at 8. Süsal C, Roelen DL, Fischer G, Campos EF, Gerbase-DeLima M, Hönger three, the split rule was log-rank splitting, three variables (square root G, Schaub S, Lachmann N, Martorell J, Claas F: Algorithms for the of the number of variables rounded up) were randomly selected as determination of unacceptable HLA antigen mismatches in kidney transplant recipients. Tissue Antigens 82: 83–92, 2013 candidates for each node split, and the maximum number of split 9. Tait BD, Süsal C, Gebel HM, Nickerson PW, Zachary AA, Claas FH, Reed points randomly chosen among the possible split points for each EF, Bray RA, Campbell P, Chapman JR, Coates PT, Colvin RB, Cozzi E, variable was set at one. Variable importance was calculated using Doxiadis II, Fuggle SV, Gill J, Glotz D, Lachmann N, Mohanakumar T, Breiman–Cutler permutation variable importance.45 Suciu-Foca N, Sumitran-Holgersson S, Tanabe K, Taylor CJ, Tyan DB, Fourth, we assessed the change in the discrimination capacities of Webster A, Zeevi A, Opelz G: Consensus guidelines on the testing and clinical management issues associated with HLA and non-HLA anti- the day 0 reference model and the post–Tx DSA model by adding the bodies in transplantation. Transplantation 95: 19–47, 2013 – fi top anti HLA iDSA characteristics identi ed in survival forest mod- 10. and Transplantation Network policies from the U.S. eling at the time of transplantation and after transplantation. Harrell Department of Health & Human Services. Available at: https://optn. c statistic was estimated for the day 0 reference model, the post–Tx transplant.hrsa.gov/governance/policies/. 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