Historically, HLA antigens were identified using common serologic methods. Sera containing HLA antibodies of both single and multiple specificities to broad groups of HLA antigens were obtained from sensitized patients (usually multiparous women) and specifically chosen in combination in panels to cover the most common HLA antigen types in a population. The lymphocytes of the individual to be typed were then mixed with all serum samples in the panel in the presence of complement and a vital dye. If the sera in each reaction resulted in cell death through complement-induced injury visualized by vital dye uptake, it could be inferred that the antibody(ies) in that serum had specificity(ies) to one or more HLA antigens on the cell surface. Careful examination of the pattern of positive reactions across the test sera would yield the predicted HLA antigens on the lymphocytes being tested.

Several molecular methods have been developed to better characterize HLA antigens by determining more precisely the HLA alleles that encode them and, in doing so, more accurately identify the amino acid differences between different HLA antigens (and indeed even between different alleles within the same antigen group). Currently used methods use polymerase chain reaction (PCR) platforms, and options include real-time PCR (rtPCR), sequence-specific primer (SSP) methods, the reverse sequence-specific oligonucleotide (R-SSO) probe method, Sanger-based sequencing, and, most recently, next-generation sequencing. It warrants mention that the sequencing methods are not practically suitable for deceased donor typing, as they can take several days to complete; these are more commonly employed for recipient typing and living donor typing when used. Conversely, rtPCR, SSP, and SSO methods are more commonly used for deceased donor typing because their rapid turnaround times facilitate prompt transplant decision making in allocation. A novel approach, using long-read nanopore sequencing, minimizes inaccuracies introduced when assembling short sequence reads. Given sufficiently rapid turnaround time, resolution of shortcomings in read analysis and interpretation could offer the promise of high-resolution HLA genotyping for deceased donor transplants in the future. The details of each method are not reviewed here, as methods are in rapid evolution and chosen in the context of what is appropriate for a given program’s clinical needs. Readers are directed to their own HLA laboratory for a more complete review of the local methods used. Regardless of the specific molecular platform employed, molecular methods as a group allow for a more precise determination of HLA alleles and their corresponding immunogenicity, where serologic typing would fail to identify many of these small but important differences.

Molecular methodology for HLA typing uses a precise nomenclature to identify HLA alleles; it uses a letter to identify the locus (A, B, C, DRB1, DRB3/4/5, DQA1, DQB1, DPA1, DPB1), an asterisk, which confirms that molecular methods were used, and then a series of numbers separated by colons (called field separators) to identify precisely the unique allele. For example, HLA-A∗03:01:01:02N refers to a molecular defined allele at the A locus, followed by four unique fields, each of which has a precise meaning. Field 1 (in this example 03) identifies the allele group. Field 2 (in this example 01) identifies a precise allele, with at least one amino acid difference in the mature protein that distinguishes it from all other alleles in the allele group. Field 3 (in this example 01) identifies if the genotype of the allele contains a synonymous DNA substitution in a coding region that does not alter the final protein structure, and field 4 (in this example 02) indicates a difference in DNA sequence in a noncoding region of the gene. Additionally, a suffix may be appended to the allele name (in this example N), indicating a variant of expression of the final protein product, where N represents Null or No expression; L indicates Low expression; S indicates a molecule that is Secreted and Soluble but not present on cell surfaces; C is assigned to alleles producing products only detected in the Cytoplasm; A indicates Aberrant expression, where the expression is not confirmed; and Q is assigned to Questionable, where the impact of the mutation on expression is not confirmed but has been seen in other alleles.

For practical purposes in solid organ transplantation, only the first two fields have clinical impact insofar as they determine the protein to be considered in mismatching and antibody production. In addition, the N or L suffix is important in transplantation, as where a protein has null or low expression, it may not have any (null) or reduced (low) relevance in immunogenicity or antibody specificity considerations. Indeed, most commonly at present, molecular typing in kidney transplantation is reported at only the first field level unless the specific allele is needed to quantify differences between a donor and recipient or to interpret a specific antibody pattern as donor specific or not.

Molecular typing nomenclature also permits a more accurate description of the complexity of the class II DQ and DP proteins, where the antigen is named only for the β chain, but the immunologic relevance of the molecule requires identification of the α chain distinctly and in combination with the β chain ( Fig. 68.5 ). Indeed, the authors recommend to always identify DQ and DP antigens by both their α and β chains to properly describe the encoded final protein and any antigen–antibody interactions involving the α chain. The DRA1∗ chain is not sufficiently polymorphic such that naming the DR antigens in accordance to their β chain allele is immunologically enough. Stated in other terms, HLA-DR dimers share an α chain that is essentially invariant.

By way of comparison, HLA antigens (serologically determined) are identified by the locus (with no asterisk) and a single number that indicates the specific protein being expressed (e.g., A2 and B27). This format specifically indicates a protein rather than an allele and, in fact, represents the group of all A2 proteins; that is, the protein product of all the HLA-A∗02 alleles. This nomenclature is particularly important because whenever HLA antibodies are identified (see later), the antibody is most typically named for its target antigen using antigen nomenclature. Generally, the gene group from the molecular name and the antigen number are both familiar and similar (e.g., HLA-A∗02 gene group codes for A2 proteins and HLA-B∗27 group codes for B27 proteins). However, there are some exceptions noted ( Table 68.1 ). Typically, in these cases the molecular typing is followed by the relevant antigen in parentheses (e.g., HLA-DQB1∗03[7]), where the DQB1∗03 gene codes for DQ7 protein.

Donors and recipients may be compared by identifying the differences between their HLA antigens and alleles; however, directionality is critical in describing this accurately. Biologically, the most relevant comparison in transplantation is to describe the donor differences to the recipient from the perspective of the recipient’s immune system and the donor antigen or allele differences in terms of the number of mismatches in the host versus graft (HvG) direction. This is particularly important when either the donor or recipient is homozygous for one or more HLA antigens or alleles ( Table 68.2 ). Newer analytic methods assessing molecular compatibility between donors and recipients, among which HLAMatchmaker and the PIRCHE score are most commonly used, also allow laboratories to compare allele and antigen differences from a donor to a recipient and also identify and count the number of eplet (amino acid differences at positions with immunogenicity potential) and epitope (structure surrounding the eplet where the Fab′ can bind) differences, typically expressed as the identity of these molecular or amino acid polymorphisms and the total number of different eplets or eplet “load.” Methods such as these can also identify specific eplets that are different between donor–recipient pairs that may have greater immunologic relevance.

Serologic methods identify only the broadest of HLA antigens at their most common and strongest humoral targets; they do not distinguish, in most cases, between small but immunologically important differences that may be present at alleles within the same antigen group. This is particularly relevant when HLA antibodies have only a subset of allele-coded proteins within a group (allele-specific antibodies) as their target. In addition, serologic methods only commonly identified with any accuracy the A, B, and DR proteins, giving rise to the paradigm that these six antigens were the most significant in transplantation. This paradigm is now accepted as inaccurate. The authors encourage the reader to interpret any historical data limited to HLA-A, B, DR typing with caution, considering modern-era data supporting the immunogenic role of proteins from the other 11 HLA loci as influencing clinical transplant outcomes. Molecular methods, in general, have advanced the understanding of HLA diversity and its contribution to alloimmunity, but different platforms have different levels of typing resolution and all are under rapid evolution such that any specific discussion of limitation would shortly be obsolete. At present, the major limitation to some methods that result in a high level of resolution is that several days may be required to obtain a result, which is prohibitive in deceased donor transplantation workup and allocation; this, however, is anticipated to change with the rapid improvement of methods and platforms. Rather, when interpreting molecular results, the clinician should be sure to note whether allele-level typing (second field resolution) is necessary to interpret immunologic risk, either at the molecular level or antibody–antigen interaction level. Consultation with histocompatibility specialists should be sought out to determine the level of resolution needed in any given case.

The first step in evaluating a transplant candidate is to determine their own HLA allele and antigen profile using the highest resolution typing available in that center. This information is useful immediately, well before a donor is under consideration, as the recipient’s HLA antibody profile must always be interpreted in the context of their own HLA typing to ensure that the antibodies identified are as accurate as possible (cognizant that one does not normally make HLA antibody to self-HLA antigens or self-epitopes). These considerations are crucial for accurate antibody interpretation, and sufficient resolution of recipient HLA typing is thus of utmost importance. Furthermore, in highly sensitized patients, understanding the population frequency of the candidate’s own HLA alleles and haplotype can aid in the estimate of donor access (likelihood to receive a donor organ), with those patients who have rarer HLA alleles potentially having an even further reduced access compared with those with more common HLA phenotypes.

The number of HLA mismatches between donor and recipient was one of the first recognized predictors of transplant outcome as early as 3 years post transplant, even in the early days of transplantation, when typing was only resolved at the serologic level and assessment was limited to only a few loci. With improved immunosuppression, the impact of mismatching at the antigen level was reduced, and its role in allocation correspondingly diminished. ^, Studies using larger data sets and performed in the era of modern immunosuppression, with a view to longer-term clinically relevant outcomes, however, still show that donor–recipient HLA mismatching, even at the antigen level, can have a deleterious impact on graft survival. Notably, however, all HLA antigens are not equally different at the eplet/molecular level, and simply considering the integer difference in mismatches is at best a crude estimate of immunogenicity ( Table 68.3 ). Eplet analysis is a more granular and biologically relevant estimate of immune differences at the level of the HLA molecule. Early data support this strategy with HLA eplet load being strongly associated with both de novo DSA development ^, and clinically relevant outcomes, such as transplant glomerulopathy. It is further recognized that not all HLA eplets are immunologically equivalent. As such, though better, eplet load is itself also only an estimate of immune difference. There is much interest in determining which eplet differences carry the greatest risk of adverse outcome, ^, but such data require validation with large multicenter datasets with unambiguous allele-level donor and recipient genotyping.

As mentioned earlier, the role of HLA matching in allocation has been reduced in many jurisdictions. This is both because of the reduced impact on short- and medium-term outcomes, as well as concerns about creating biases against individuals of certain races/HLA phenotypes, where their group’s HLA antigens are less commonly found among typical organ donors. There is insufficient evidence available at this time to allocate organs strictly based on eplet load or eplet matching. However, this may evolve with newly emerging data. How then should the clinician interpret HLA mismatch data at the antigen, allele, or T-/B cell epitope level ? It is important to acknowledge that if the mismatches in a given pair are greater, then the immunologic potential for alloimmune recognition and response is also greater. The clinician should use this information to refine the risk assessment along a continuum. Higher-risk states may warrant greater vigilance in follow-up (lab tests, HLA antibody monitoring) or greater caution in reducing immunotherapy.

Foundational to modern organ allocation is evaluating donor HLA typing in the context of a recipient’s HLA to identify if there are any DSAs. This is commonly referred to as virtual crossmatching (VXM), where positive DSA is interpreted as a positive VXM and the absence of DSA is a negative VXM. This is discussed in more detail in the section on virtual crossmatching later. From the perspective of HLA typing, it is critical that donor typing be performed at all loci to which a recipient has detectable anti-HLA antibodies, using enough resolution (up to 2 field) to evaluate allele-specific antibody relevance.

Foundational to the estimate of calculated panel reactive antibody (cPRA) ^, (see section later) is a database of many deceased donor HLA typings to which an antibody profile can be compared. It is essential that these data sets be large and as complete at all loci as possible so that the calculation of cPRA is immunologically relevant and accurately informative of access to blood group and HLA compatible donors.

The term panel reactive antibody (PRA), broadly used to describe the percentage of donors in a candidate donor population to whom a recipient may have DSA, comes from the original serologic methods for detecting HLA antibodies. PRA testing involves exposing a panel of various cells from different donors (selected to represent the common HLA frequencies in a potential donor population) to a recipient’s serum, to which complement and a vital dye are added. The percentage of the cells in the panel that are lysed is an estimate of the percentage of donors from that population against whom the recipient has cytotoxic antibodies (IgG1 or IgG3 of sufficiently high titer to initiate the complement cascade). Due to the limitations of serologic screening including lack of sensitivity, limited specificity with high false-positive rates due to non-HLA antibodies, IgM, and autoantibodies (which are immunologically irrelevant) and volatility of the PRA estimate based on cell panel phenotype variation, regardless of recipient serum changes, this method is not further discussed.

In these assays, purified HLA antigens are covalently bound to inert beads or less commonly to an ELISA platform. The latter has been largely replaced with bead-based assays due to their increased sensitivity and capacity for high-volume testing and is not further discussed. In screening solid-phase assays, beads carry the complete class I or class II HLA profile of an individual and can be detected when the antibody in serum binds, regardless of complement activation, with a secondary fluorescent anti–human IgG detector antibody on a flow cytometric platform. A bead is determined to be positive when its fluorescence is above the threshold level validated by the laboratory and manufacturer. The PRA estimate, in this case, stratified by class, is similar in principle to serology methods as the percentage of total beads with positive fluorescence.

Although PRA testing yields a population-based estimate of the number of donors to whom a recipient may have DSA, for these to be used in transplant decision making and donor selection, greater detail is needed as to which precise antigens the antibodies are directed toward. Single antigen beads (SABs) are similar in platform to HLA solid-phase screening assays, but in this case, each bead has only one distinct type of HLA antigen bound. Also analyzed on flow cytometric platforms, up to 100 unique beads per class can be tested simultaneously. The output of this test is also fluorescence based and defines a list of antibody specificities that then must be reverse engineered into a cPRA using a calculator informed by large numbers of donor genotypes. ^{, ,}

These assays are derived from the classic SAB test and instead of simply detecting any antibody bound to the bead’s antigens, they are designed to detect the binding of C1q or C3d complement by HLA-specific antibody. Complement-binding DSA are associated with an acute rejection phenotype that is more severe and with an increased risk of allograft failure compared with non–complement-binding DSA. ^, These assays will only be positive if IgG 1/3 antibodies are also first bound in sufficient density (high concentration) to antigens on the bead surface. ^{, ,} If low-titer IgG 1/3 antibody is present or IgG 2/4 isotypes dominate the serum antibody profile, then the result will be negative. Isotype-specific assays can also be used to discriminate between all four isotypes within a given SAB reaction and infer similar complement-binding potential but are not commonly used in routine clinical practice. It is notable that almost all antibodies that are detectable on traditional SAB platforms are present in all four isotypes, ^, suggesting that complement activation is at least possible for all antibodies, and its detection is dependent largely on titer of the IgG1/3 components. ^,

Though not yet routinely implemented in all labs as a part of standard testing, commercial assays are now available for several non-HLA antibodies including endothelial antibodies, ^, MICA, ^, and angiotensin type 1 receptor antibodies ^, in renal transplantation. These assays are also solid phase based with correspondingly similar methodologies as other solid-phase assays. The incremental additional value of these tests for population screening remains uncertain ^, and their current utilization potential is determined at the individual patient level.

The numeric output from both screening and SABs is mean fluorescence intensity (MFI), which is compared with both negative control bead and negative control serum fluorescence to identify whether a given bead is of sufficiently high fluorescence to be deemed positive. It is important to note that although MFI output is numeric, it should not be interpreted as quantitative or an equivalent to antibody titer (see “Limitations of Human Leukocyte Antigen–Antibody Testing” later). Other factors can and should be considered in determining the relevance of a given bead reactivity, including sensitization history (and HLA type[s] of the sensitizer[s], if available), a pattern of shared eplets or a known Cross Reactive Group among beads deemed to be fluorescing that would support their positivity as biologically plausible from an antibody-eplet binding perspective, a change in reactivity pattern over time with historical sera (where a bead representing an antigen of interest and previously lower ranked in the assay MFI is now relatively increased, even if below usual thresholds for positive), interval changes in immunosuppression treatments, the presence of autoimmune disease with antibody binding to denatured antigens in the SAB assay, and the administration of any agents that could increase background interference. Once the determination of the list of positive beads has been made, further analysis of these positive reactions is required to determine the list of antibody specificities.

Antibodies are identified most commonly as specific to HLA antigens. These are reported as lists of serologic equivalents in nomenclature (e.g., A2, B7, and DR15). The interpretation by the clinician is correspondingly that the patient has an antibody that will bind all members of the antigen group regardless of specific allele. Biologically, it is inferred that the antibody is directed toward the epitope(s) that are common to all members of the antigen group and not at the epitope(s) that would distinguish unique alleles. Additionally, for class II, although not traditionally considered antigens in their own right, antibodies can form the unique polymorphic alpha chains (coded by DQA1 and DPA1 genes). When these are identified, they will be listed with the α chain specified. For example, DQA1 or DPA2 antibody, distinct from DQ7 or DP3, which infers the antibody is to the β chain (coded by gene DQB1 or DPB1 , respectively).

Although antibodies are most identified toward antigen groups, it is recognized that there are multiple different alleles comprising each antigen group. Sometimes, an antibody may be directed to one of the molecular targets (eplet/epitopes) unique to a certain allele, in which case it will typically be identified using up to four digits; for example, B5102 antibody, where this nomenclature implies an antibody binding to the protein encoded by HLA-B∗51:02 allele but not the other alleles in the B51 chain including B∗51:01 ( Fig. 68.6 ).

Allele differences in protein structure can lead to different immunogenicity.

Proteins coded by B∗51:01 and B∗51:02 alleles differ by a 3 amino acid substitution in the peptide-binding region. Both are referred to as B51 antigens. Antibodies that bind to regions common to all the B51 antigens will bind proteins coded by all alleles in the group. Some antibodies bind only to those specific epitope region(s) that distinguish one allele from the others. These allele-specific antibodies may be identified by their four-digit names, in this example, B5102 antibody.

Given the nature of class II molecules, it can occur that Fab′ of the antibody can bridge the polymorphic alpha and beta chains of an antigen for DQ and DP, resulting in an antibody that is specific to the unique combination of an alpha and beta chain. Wherever this occurs, the antibody is identified using some combination of the alpha and beta nomenclature; for example, DQA5-DQ2 (or similar).

A recipient’s list of antibodies is inherently useful in determining against which donors a recipient has antibodies. However, to estimate a better likelihood of access it is useful to understand to what percentage of donors in a population the recipient bears antibody. To determine this, the list of antibodies is compared with a database of donor typings specific to the population of interest. The percentage of donors to whom a recipient has one or more DSAs represents the cPRA ^, or equivalent (e.g., calculated reaction frequency or cRF in the United Kingdom). The cPRA varies not only on basis of the list of antibodies but also, and more importantly, on the loci included in the donor typing and frequency of target HLA antigens in the donor population of interest. This, in turn, is strongly related to population-based diversity. At the time of this publication, the most widely used cPRA calculators do not incorporate molecular compatibility or allele-specific antibodies.

Although the output of the SAB assay is a numerical MFI on each bead, which has a unique antigen on it, the MFI alone is insufficient to determine whether or not the fluorescence on a bead represents a true antibody being detected, and no universal threshold for positive should ever be assigned. There is a widely varying density of HLA antigens between beads, such that the maximum MFI on any given bead may be determined by antigen density rather than serum level of antibody. In the presence of high levels of complement-binding antibody, C1q may be generated in vivo from soluble complement proteins and interfere with the binding of the antibody to the bead, significantly lowering the MFI of the beads, representing high levels of antibody. Dilution of serum samples when this phenomenon occurs can clearly show that MFI increases with serum dilution, confirming its nonquantitative value on the undiluted serum. Often, groups of HLA antigens share common amino acid residues (or epitopes) to which antibodies are targeted, and when the antibody to a shared target is present at a subsaturating level, it can spread across the multiple beads with antigens sharing this target, thereby reducing the MFI of any given single bead within the group. This single-bead MFI can be misleadingly low as an estimate of antibody. Similarly, some antigens that have several common alleles are represented on more than one bead in the SAB assay; again, an antibody to this antigen at an epitope shared by all alleles can be diluted across the multiple beads, lowering single-bead MFI estimate. For these and other lab-specific validation and reagent-based reasons, it is strongly recommended that the readers consult with their own laboratory as to the analytic strategies employed to decrease interference and optimally identify antibodies and thresholds for positive ( Fig. 68.7 ). ^,

Median fluorescence intensity (MFI) of single antigen bead assays has analytic limitations and cannot be used as a quantitative metric of antibody amount.

(A) An ideal test should always be able to distinguish antibody binding (blue signal) from negative control (white) with a clear threshold and no overlap between the MFI distributions. (B) Decreased density of antigen (Ag) on the surface of the bead will result in MFI measurement that underestimates the amount of the antibody present. (C) In contrast, nonspecific binding to the bead can result in artificially high background and signal MFI, with overestimation of antibody. (D) Interfering substances may prevent the detection of the antibody of interest with lower MFI. (E) Epitopes shared between different beads can dilute the amount of antibody bound to any single bead, with an erroneously low MFI on the given bead of interest.

With permission from Konvalinka A, Tinckam K. Utility of HLA antibody testing in kidney transplantation. J Am Soc Nephrol 2015;26[7]:1489–1502.

A conundrum of the detection of antibody, as for all laboratory results, is that mere identification does not precisely determine relevance at that time or in the future. Whereas higher-titer antibodies, persistent (vs. transient) antibodies based on current and historical sera, and class II antibodies may be associated with worse outcomes post transplant, it would be misleading to classify these as absolute risk states but rather a continuum of risk commencing from potential immunologic risk represented by molecular incompatibility and evident memory response represented by presence of DSA detected by solid-phase assays (virtual crossmatch), flow crossmatch, and complement-dependent cytotoxicity (CDC) crossmatch, with the latter options being deemed contraindications for transplantation. Additionally, the clinical relevance of an antibody is part of a multifactorial patient and donor assessment in the context of patient history, sensitizing events, immunosuppression, current clinical status, and organ function. There are no indisputable features that clearly discriminate antibodies with more deleterious or immediate consequences from those that portend a more benign course.

SAB assays are so sensitive that reactions may be detected in individuals without any history of sensitizing events like prior transplantation, transfusion, or pregnancy. ^, The clinical consequences of these antibodies, the nature of their target antigen (denatured in assay vs. intact in vivo), and whether they should be avoided or considered in donor selection requires further study.

The SAB assay detects circulating antibodies. However, it has been shown, in individuals with a failed allograft in place, that significant antibodies may be removed from circulation and bound to the endothelium, reducing the sensitivity of the assay. Following nephrectomy, antibody levels may increase. ^, If an allograft remains in situ, the full antibody potential may be dampened.

For a cPRA to best estimate the percentage of potential donors to whom a recipient has DSA (and consequently higher immune risk to those donors), antibodies need to be accurately identified and donor typings used must represent the donor population accessible to that individual. As such, a cPRA based on one donor population may not be an accurate assessment of transplant access in other populations of varying genetic diversity and different associated HLA frequencies. In addition, the cPRA may be systematically underestimated when donor typing is not complete at loci where HLA antibodies to those loci are used in donor organ allocation decision making.

In addition to HLA typing of the recipient, waitlist HLA antibody testing is foundational to ongoing patient immunologic assessment. The main goal of waitlist testing is to estimate the transplant candidate’s access to donors from the perspective of avoiding donor antigens to whom they have antibodies. This estimate is quantified as the cPRA, where (100-cPRA) is the percentage of donors otherwise medically and ABO suitable that would be accessible to the recipient. If the candidate has a high cPRA, longer wait times and higher waitlist mortality are anticipated. In that case, strategies to improve access to more donors should be sought. If no potential living donor is available, then desensitization or deceased donor acceptable mismatch allocation strategies should be considered. If a living donor is available, then desensitization or kidney-paired donation programs could improve access. A second goal of waitlist antibody testing is to compile a longitudinal immunologic profile of the candidate; antibodies can wax and wane over time, so repeated testing is indicated (usually every 1–3 months) to ensure the most complete list of antibody potential is compiled for use at the time of a potential donor evaluation. If a patient incurs a sensitizing event, repeat testing 6 weeks after the event is recommended to capture any memory responses or de novo antibodies. It is critical to remember that patients frequently have a long immunologic history predating their entrance to the waitlist, so even repeated HLA antibody testing may not identify all antibody potential; a thorough history of sensitizing events both before and while on the waitlist remains foundational to counseling patients as to their immunologic risk post transplant. Finally, the cPRA and its contributing antibody specificities are, in and of themselves, not a measurement of immunologic risk (only risk of reduced access to HLA compatible donors); immunologic risk is a measurement unique to each donor–recipient pair under consideration based on molecular incompatibility with the donor, presence of DSA by solid-phase assays (virtual crossmatch), and/or cell-based crossmatch.

In conjunction with donor HLA typing, the full history of recipient HLA antibodies may be compared with donor HLA in what has come to be known as a virtual crossmatch (VXM). If any of the current or historical specificities correspond to donor antigens, these DSA confer a positive VXM. Under optimal circumstances, the VXM is intended to be a predictor for an actual cell-based crossmatch performed on the corresponding sera to facilitate allocation and transplant decision making. However, the predictive values of VMX for actual crossmatch (XM) can vary widely depending on XM methodology and the laboratory- and program-specific parameters for identifying antibodies. The current VXM reflects the most recent serum tested before donor offer and most closely reflects the antibodies likely to be present at the time of transplant. It is associated with early and late rejections and reduced allograft survival. Conversely, the historical VXM reflects the cumulative history of DSA. When current VXM is negative but historical is positive, there is an increased risk for memory B cell reactivity after transplant and corresponding adverse allograft outcomes. Despite historical and fewer recent reports of high PRA association with adverse transplant outcomes, the ability to more precisely assess VXM/DSA has revealed that it is, in fact, DSA that drives adverse outcomes, regardless of the level of PRA directed toward third-party antibody, further improving treatment decision making. ^{, ,} In some jurisdictions where regulatory requirements allow, the VXM has replaced the actual XM for many transplant decisions, with comparable transplant outcomes. ^, In addition, the VXM is important for the interpretation of cell-based crossmatches; confirming that a positive cellular crossmatch is immunologically relevant. (see Crossmatching later).

After transplantation, all HLA-mismatched patients are at risk of de novo HLA DSA development. Landmark studies in 2005 and 2007 first identified that post-transplant HLA antibodies had higher rates of graft failure than those without antibody development. ^, In the years since, it has become clear that de novo HLA DSA development is a major risk factor for rejection and premature graft loss. ^, Despite numerous studies, there is no consensus to date as to the optimal timing and frequency of monitoring.

De novo DSAs represent an important independent risk factor for antibody-mediated injury and graft failure. ^, De novo DSAs appear at a median of 3 to 68 months post-transplant and positive de novo DSAs are found in 6% to 38% of patients 3 years after transplantation. Class II antibodies predominate in most series. As a diagnostic tool, the presence of DSA is supportive of a diagnosis of AMR in the context of aligned pathology, ^, but AMR may also occur in the absence of detectable HLA DSA, often dependent on timing of testing, with antibody bound in the failed allograft ^, or rarely to non-HLA antibody. ^{, ,} Late AMR is associated with chronic injury, Class II Ab, and lower response rate to treatment. DSA MFI thresholds do not reliably discriminate prognosis or treatment responsiveness, ^, nor are they a tool to determine duration of treatment as DSA frequently persists well beyond clinical and histopathologic resolution of active (or reversible) AMR. ^,

As a prognostic tool, when detected before graft dysfunction or pathology of AMR, the role of de novo DSA is not clearly defined. The interval between de novo DSA detection and clinical outcome can vary widely from months to years, as can the phenotype of the outcome itself, suggesting diverse pathways of injury at play with many effect modifiers. ^, Data do not presently support empirical treatment for de novo DSAs in the absence of graft dysfunction and/or tissue injury evident on biopsy. At this juncture, asymptomatic de novo DSAs can best be considered a higher-risk state for future adverse events that may warrant closer patient follow-up.

This method employs the same serologic principles described for the earliest typing and antibody detection methods, where cells from the donor are mixed with recipient serum in the presence of complement and cell death detected by vital dye uptake is the marker of a positive test, detecting high-titer complement-binding antibodies likely to result in hyperacute rejection ( Fig. 68.8 ).

Crossmatch methods.

(A) The complement-dependent cytotoxicity (CDC) crossmatch detects high-titer donor-specific antibody (DSA) when it binds to the cell in sufficient density to activate the complement cascade. The resulting formation of the membrane attack complex (MAC) results in cell death, which can be detected via microscopy with a vital dye. A positive CDC crossmatch due to human leukocyte antigen (HLA) antibody is associated with hyperacute or early accelerated rejection. (B) The antihuman globulin (AHG)-enhanced CDC crossmatch increases sensitivity for moderate-titer antibody with the addition of AHG. AHG binds to donor-specific antibody (DSA) already bound to its target antigen, allowing for activation of complement at lower levels of original DSA. The MAC will form again and kill cells with antibody bound to it, deleted under microscopy. A positive AHG-CDC crossmatch due to HLA antibody is associated with hyperacute or early accelerated rejection. (C) The flow cytometry crossmatch can detect even lower-level antibodies using fluorescence detection if the antibody is bound to the cell. Non–complement-binding antibodies are also detected using this methodology. Positive flow cytometry crossmatches may vary in strength. Strongly positive results due to HLA antibodies are associated with not only higher rates of earlier antibody-mediated rejection (AMR) but also subclinical rejection and chronic AMR. However, not all positive flow cytometry crossmatches are clearly associated with an adverse outcome, and methods to distinguish pathologic from nonpathologic antibodies are needed.

With permission from Lapointe I, Tinckam K. H istocompatibility Testing for Kidney Transplantation Risk Assessment . Scientific American Nephrology, Dialysis and Transplantation. ©Decker Intellectual Properties, Inc.; 2017.

By the early 1990s, a more sensitive derivation of the CDC crossmatch was achieved by adding antihuman globulin (AHG) to the test, binding any antibody on the cell surface, increasing overall antibody density, and increasing the probability that complement would become activated with lower levels of HLA antibody. These lower-titer HLA antibodies were nonetheless clinically relevant and associated with earlier antibody-mediated outcomes not predicted by the less sensitive CDC crossmatch (see Fig 68.8 ).

With the awareness that antibody-mediated adverse outcomes could still occur with negative AHG-CDC crossmatch, the flow cytometry crossmatch (FCXM) permitted detection of even very low-level and/or noncomplement-binding antibody on the cell surface, detected by fluorescent anti–human IgG rather than complement activation (see Fig 68.8 ).

The use of CDC XM greatly reduced but did not eliminate early accelerated rejection (false-negative test), which could result from antibodies below the level of detection by complement dependence that still caused damage to the graft endothelium in the hours to days after transplantation.

False-positive results can occur from autoantibodies ^, or immunologically irrelevant antibodies (including IgM antibody) binding non-HLA targets. ^{, , ,}

Patients awaiting transplantation may be treated with antibodies that can interfere with crossmatch testing. Antibodies that are known to interfere include antithymocyte globulin (ATG, a polyclonal antibody that can bind to antigens on T and B cells), alemtuzumab (binds to CD52 on T and B cells), rituximab (binds to CD20 on B cells), and daratumumab (binds to CD38 on B cells or T cells).

The BCR includes constitutively expressed immunoglobulin that can increase the likelihood of a positive B cell crossmatch in the absence of donor-specific HLA antibody. Laboratories routinely account for this by setting cutoffs for positivity and by using Pronase, a commercially available mixture of bacterial proteases, in flow cytometry crossmatching, which can improve the crossmatch specificity.

With increasing sensitivity of diagnostic methods, there tends to be decreased specificity. Additionally, the implication of using more sensitive tools is that a positive test result infers a lower immunologic risk and consequently is not necessarily associated with inferior transplant outcomes. For example, while higher rates of adverse outcomes occur with positive FCXM in isolation, a positive FCXM does not guarantee worse transplant results, and in some cases, these transplants can be performed with short- to medium-term success in the context of augmented immunotherapy or desensitization.

The cell-based crossmatch is not a commercially available assay but rather developed within HLA laboratories frequently using nonstandard incubation times and variable reagents and controls. As such, there are no standard cutoffs for positive results and considerable interlaboratory variability may occur at the lower limit of detection of antibody.