Formation of DSA remains a significant challenge for achieving long-term graft survival, contributing to both acute and chronic ABMR in transplant recipients. Sensitization to HLA antigens, leading to DSA development, can result from previous transplants, blood transfusions, pregnancies, or de novo occurrences post-transplantation, even with immunosuppressive therapy (1). This process involves the generation of long-lived plasma cells and HLA-specific memory B cells. Several studies have shown that HLA-specific memory B cells can still be detected against HLA antigens expressed on previous kidney allografts that failed many years prior, therefore suggesting their role in rejection and showing their long-term survival capacity (2, 3).

Historically, immunological risk assessment in solid organ transplantation relied on complement-dependent cytotoxicity crossmatch tests (CDC) and later incorporated more sensitive flow cytometry crossmatching assays (4). Currently, the evaluation of humoral immunity, driven by antigen-specific B cells, primarily involves detecting and characterizing HLA antibodies in recipient serum using solid-phase technology such as Luminex SAB assays (5, 6). Alongside antibody-focused risk assessment techniques, there has been an increasing interest in adaptive cellular memory screening, such as the detection of donor-reactive memory B and T cells. Few studies and a clinical trial (2, 3, 7, 8) have explored the roles of these cells in kidney transplant recipients (KTRs), particularly using ELISpot assays, which have shown promising results in predicting transplant outcomes in small groups of KTRs. However, due to the lack of result reproducibility, these assays have not yet been implemented in routine clinical settings.

In addition to the ELISpot, Wehmeier and colleagues developed another screening assay that uses SAB to detect DSAm by concentrating IgG antibodies from the activated B cells culture supernatant in vitro (2). Their pilot study indicated that DSAm correlated with adverse transplant outcomes, notably biopsy-proven ABMR (including subclinical cases) (2). Given the growing interest in the role of adaptive cellular memory, especially donor-reactive memory B cells, in kidney transplant outcomes, and the need to evaluate the reproducibility of these cell-based assays for clinical implementation, our study aimed to validate and expand upon the findings of Wehmeier and colleagues, examining the clinical implications of DSAm in a larger cohort of kidney transplant recipients with pretransplant DSAs. We focus on biopsy-proven clinical ABMR to assess its association with DSAm similarly to the previous study.

The primary findings of this study revealed that the detection of DSAm had no significant impact on the development of ABMR or rejection in general. Additionally, there was no correlation between DSAm and graft survival, and no association was observed between the levels of DSAm MFI and the occurrence of ABMR. As for the other clinical outcome measures, patients with DSAm showed significantly better graft function at one year and 10 years posttransplant. Interestingly, 13 unique DSAm specificities found in 11 patients were identified in the concentrated IgG eluates derived from polyclonally stimulated B cells that were not detected in serum, which we were able to attribute to a prior, patient-specific sensitizing event (i.e., pregnancy, retransplant, etc). However, the MFI values of this group of DSAm were not significantly different from the MFI values of DSAm that were also detectable in the serum. On the other hand, 11 DSA specificities that were previously detected in the serum were absent or undetectable in the concentrated IgG eluates from the in vitro assay in the DSAm positive group.

Despite significant advancements in immunological risk assessment technologies for detecting circulating anti-HLA antibodies, the contribution of the cellular memory compartment of humoral immunity (i.e., memory B cells) to the immunological risk of KTRs is still a subject of debate (16, 17). Various methodologies have been proposed to identify HLA-specific antibody-producing memory B cells, including flow cytometry analysis and sorting of B cells (18–20), as well as ELISpot assays for in vitro activated memory B cells (21, 22). As it stands, the main drawback of these assays is the lack of result reproducibility that makes it difficult to recommend them for widespread diagnostic use in clinics (17). However, we acknowledge that reproducibility could only be possible when all of the experimental variables have been accounted for, which in itself, can be difficult to achieve.

That said, and as suggested by evidence from previous studies, determining the impact of the memory B cell compartment is important for risk assessment, which is why attempts at increasing the sensitivities of these cell-based assays have been made. For example, B cell stimulation assays allow the production of IgG antibodies from memory B cells with higher concentrations, and their subsequent detection using the highly sensitive Luminex SAB could potentially be adapted to complement the existing transplant risk assessment strategies effectively.

The polyclonal activation of memory B cells for antibody production and subsequent detection using the SAB assays is an effective method for profiling the specificity of HLAm and it enables a direct comparison with serum HLA antibody profiles (2, 13). However, whether this approach will improve pretransplant risk stratification and thus impact clinical decision-making is still unknown. For instance, our study did not show a statistically significant relation between the presence of DSAm and ABMR within the first year after transplantation in a cohort of 60 immunologically high-risk KTRs unlike what was previously published by Wehmeier et al. (2) wherein the main conclusion was that patients with pretransplant DSA who tested positive for DSAm were more likely to experience biopsy-proven ABMR (including biopsies with subclinical ABMR) within the first year, compared to those pretransplant DSA-positive patients without DSAm. However, an important thing to note is that despite the lack of statistical association between the DSAm and ABMR, the DSAm positive group seems to have numerically more ABMR cases as seen in Table 3 (n=11/26; 42%). This could suggest that DSAm does indeed have some contribution to ABMR, however, the magnitude of this contribution was not strong enough to reach statistical significance.

Similar to our findings, Wehmeier did not report any significant differences in long-term graft failure and function. We also observed similar findings for neat culture IgG concentrations (median of 8 μg/mL compared to 8.8 μg/mL), the percentage of patients with detectable HLAm (61% compared to 80%), and the percentage of patients with DSAm (43% compared to 45%). Therefore, we believe that the differing conclusions between the two studies are less likely due to technical assay differences, and more in part, due to differences in the patient groups and the type of and policies of the medical care provided. Additionally, this study used PROCARE’s HLA typing data complemented with next-generation sequencing (if needed) for the determination of DSA (10), while Wehmeier et al. used next-generation sequencing for all HLA typing and EDTA-treated serum for comparison to the DSAm. Apart from this, outcome variation likely arose from diverse patient cohorts and the unavailability of protocol biopsies for subclinical rejection assessment in our study (2). Specifically, Wehmeier et al. conducted protocol biopsies at different time points (3, 6, and 12 months after transplantation), and most of the biopsy-proven ABMR in their study resulted from these biopsies revealing subclinical rejection. In contrast, our cohort lacked protocol biopsies, and therefore, the assessment of subclinical rejection was not possible. Nevertheless, even though protocol biopsies were not conducted, nearly two-thirds of the patients underwent at least one biopsy where over half of the overall indication was due to a decline or delay in kidney function (n=29; 60%). Lastly, graft function (eGFR) was significantly better in the group positive for DSAm one-year posttransplant and at 10-year posttransplant (P=0.006, P=0.04 respectively), which was an unexpected finding and that could partly be explained as the result of differences in the baseline characteristics between the DSAm-positive vs. DSAm-negative groups. At baseline the groups were unbalanced for age (3 year younger at baseline for DSAm-positive group), sex (more female in the DSAm-positive group), and primary kidney disease (glomerulonephritis being more prevalent in the DSAm negative group compared to the DSAm positive).

This study has several limitations. Its retrospective design led to the inclusion of patients based on the availability of a sufficient number of PBMC vials to conduct the polyclonal activation assay, therefore potentially introducing a selection bias. Further, the presence of pretransplant DSA as a prerequisite to the inclusion could be considered a confounding variable as these serum DSAs could have diluted the effect DSAm might have on the outcome. Moreover, the reliance on indication biopsies for diagnosing rejection may have resulted in overlooking patients with subclinical rejection, which could have been influenced by the presence of DSAm pretransplant as demonstrated by Wehmeier et al. (2) Additionally, the study did not assess whether the identified pretransplant DSAm were also detectable posttransplant, as the primary focus was on the risk assessment of pretransplant DSAm. Finally, since all of the included KTRs in the study were transplanted during a time period where DSA assessment with SAB was not performed at the time of transplantation, it is highly likely that the presence of DSA pretransplant was not known or partially known for most of these patients, and therefore, factors such as the immunosuppressive medication (i.e., the use of heterogenous immunosuppressive regimens) might not have been fully taken into account which could have influenced the occurrence of ABMR. Despite these limitations, the study included 60 immunological high-risk patients selected retrospectively from a large, well-characterized cohort, providing high-quality characterization data and long-term follow-up.

To summarize, this retrospective single-center study has shown that evaluating DSAm before transplantation did not improve the ABMR risk assessment in a cohort of 60 KTRs with pretransplant DSA, as no clear associations between the presence of pretransplant DSAm and the incidence of ABMR were observed. Future prospective studies focusing on the role of DSAm in high-risk KTRs are necessary to gain further insights into its potential advantages in predicting transplant outcomes. For example, the value of this assay could be potentially more impactful in groups of sensitized patients without pretransplant serum DSA, or patients with pretransplant HLA antibodies without a (known) sensitizing event. These groups might be more likely to benefit from an expanded risk assessment protocol and should therefore be also included in future studies. However, as it stands, this assay is still in its “research stage” and is not yet ready to enter routine practice.

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.

The medical ethical committee of the University Medical Center Groningen approved the study protocol (METc 2014/077). The human samples used in this study were collected from study participants after they provided informed written consent.

DA: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. JV: Writing – review & editing, Formal analysis, Data curation, Conceptualization. AD: Writing – review & editing, Validation, Resources, Methodology. LB: Writing – review & editing, Validation, Formal analysis, Data curation. DT: Writing – review & editing, Investigation, Data curation. BH: Writing – review & editing, Validation, Supervision, Formal analysis, Data curation. RL: Writing – review & editing, Validation, Supervision, Formal analysis, Data curation. PH: Writing – review & editing, Validation, Supervision, Formal analysis, Conceptualization. SH: Writing – review & editing, Validation, Formal analysis, Conceptualization. HO: Writing – review & editing, Validation, Formal analysis, Data curation. LR: Writing – review & editing, Data curation. GK: Conceptualization, Writing – review & editing. SB: Validation, Supervision, Funding acquisition, Conceptualization, Writing – review & editing. J-SS: Validation, Supervision, Funding acquisition, Conceptualization, Writing – review & editing.