American Journal of Transplantation 2014; 14: 764–778 Wiley Periodicals Inc.
C
Copyright 2014 The American Society of Transplantation and the American Society of Transplant Surgeons doi: 10.1111/ajt.12653
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Transplantation Genetics: Current Status and Prospects B. Almoguera1,*, A. Shaked2,* and B. J. Keating1,2,3,*
Received 07 August 2013, revised 17 December 2013 and accepted for publication 31 December 2013
1
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, PA 2 Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA 3 Department of Pediatrics, School of Medicine University of Pennsylvania, Philadelphia, PA Corresponding authors: Berta Almoguera, [email protected]; Abraham Shaked, [email protected]; and Brendan J. Keating, [email protected]
Over the last decade, advances in genetic technologies have accelerated our understanding of the genetic diversity across individuals and populations. Case– control and population-based studies have led to several thousand genetic associations across a range of phenotypes and traits being unveiled. Despite widespread and successful use of organ transplantation as a curative therapy for organ failure, genetic research has yet to make a major impact on transplantation practice aside from HLA matching. New studies indicate that non-HLA loci, termed minor histocompatibility antigens (mHAs), may play an important role in graft rejection. With several million common and rare polymorphisms observed between any two unrelated individuals, a number of these polymorphisms represent mHAs, and may underpin transplantation rejection. Genetic variation is also recognized as contributing to clinical outcomes including response to immunosuppressants, introducing the possibility of genotype-guided prescribing in the very near future. This review summarizes existing knowledge of the impact of genetics on transplantation outcomes and therapeutic responses, and highlights the translational potential that new genomic knowledge may bring to this field. Keywords: Allograft rejection, genomic variability, non-HLA-related rejection, pharmacogenomics Abbreviations: CNV, copy number variant; GWAS, genome-wide association studies; hdCNV, homozygous-deleted copy number variant; HSCT, hematopoietic stem cell transplantation; LoF, loss-of-function; mHAs, minor histocompatibility antigens; SNP, single nucleotide polymorphism; SNV, single nucleotide variant 764
Introduction Over the last two decades, more than 300 000 solid organ transplantations have been performed in the United States alone (1). However, despite improvements in surgical techniques and the development of more effective immunosuppressant therapies, allograft rejection still affects 60% of transplanted individuals and remains one of the major risk factors of graft loss (2,3). Up to 40% of graft recipients experience some form of rejection within the first postoperative year (4), with lung and heart recipients showing the highest rates of rejection, with 55% and 25% of patients, respectively (1,5,6), and kidney and liver the lowest, with 10% and 17% of patients experiencing rejection, respectively. Rejection can occur where genetic disparities exist between donors and recipients, which may lead to presentation of polymorphic peptides that the recipient’s immune system recognizes as nonself. Although key HLA loci have traditionally been considered to be the main contributor to the genetic variability of allograft rejection, some degree of rejection still occurs in HLAmatched sibling transplantations, which may be the result of noncompatible loci beyond HLA between donor and recipient (7). Indeed, new findings indicate that non-HLA polymorphisms can impact upon transplantation outcomes since they have the potential of generating histoincompatibilities (7–9) influencing allograft rejection (10), and impacting immunosuppressant responses (11). Approximately 3.5 million common and rare polymorphisms exist between two unrelated individuals of European ancestry and up to 10 million variants in individuals of African ancestry (12). However, investigations of non-HLA genetic determinants of clinical outcomes following organ transplantation have yet to be performed in any systematic fashion to date. Recent technological advances in genomics such as genome-wide association studies (GWAS) allow the characterization of hundreds of thousands to several million single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) across the human genome rapidly and inexpensively (11,13). Figure 1 illustrates the concepts of structural variations, SNPs and insertion/deletion polymorphisms (InDels). Furthermore, whole exome and whole genome sequencing, which interrogates the coding regions and the entire human genome, respectively, are quickly
Transplantation Genetics
Figure 1: Illustration of common forms of human genomic variations: structural variation (copy number variants [CNVs]), singlenucleotide variants (SNVs) and insertion–deletion polymorphisms (InDels). (A) Structural variation. CNVs are the most common form of structural variation and include deletions and multiplications (typically duplications) >1000 bp. CNVs can affect whole genes or parts of them and may be present in heterozygosis (e.g. deletion of one copy of gene A in IND2 or duplication of gene B in IND1) or homozygosis (e.g. deletion of one copy of gene D in IND3). (B) SNVs and InDels. The presence of an A in IND1 instead of a C on the reference genome represents a graphic illustration of an SNV and the two red Ns in IND3 represent two nucleotides deletion.
becoming commonly used tools within the clinical diagnostic arena. These second-generation sequencing technologies have the ability to extensively characterize genomewide sources of histoincompatibility between donors and recipients, potentially unraveling specific genetic risk factors influencing rejection and immunosuppressant responses or severe adverse effects (14,15). In this review we aim to overview the current knowledge from existing genetics studies recently conducted for transplantation outcomes and therapeutic responses to immunosuppression therapies and we also discuss the translational components from this genetic knowledge that may be rapidly implemented in this field.
Genetics of Transplantation Outcomes Incompatibility across key HLA alleles has traditionally been considered the main factor influencing rejection in stem cell and solid organ transplants, and has therefore been the focus of extensive investigation. Because of high genetic variability in the HLA locus, a detailed characterization of American Journal of Transplantation 2014; 14: 764–778
HLA mismatches between donors and recipients is not routinely achieved. The effect of specific HLA mismatches in kidney transplantation have been characterized for sometime, whereas the importance of HLA matching on outcomes in organs such as the liver is still under debate (6,16–18). Even in HLA identically matched kidney transplantation, some degree of rejection is still evident. NonHLA or minor histocompatibility antigens (mHAs) resulting from a range of functional polymorphisms in the genome have been suggested to be capable of inducing strong cellular immune responses (reviewed in (7)). In contrast to the established role of HLA, Terasaki (2) estimated that over twice as many graft failures in HLA identical siblings at 10 years posttransplantation (38%) were due to immunological reactions to non-HLA factors compared to graft failures attributable to HLA (18%). Although our current knowledge of non-HLA antigens is still limited to a small number of loci (1,19,20), if one extrapolates the large number of genetic variants observed between two unrelated individuals within a donor–recipient pair, then the number of non-HLA discrepancies between any given donor and recipient would be expected to be very large. 765
Almoguera et al
The most rigorous way of investigating the association of mHAs with organ survival is within HLA-identical sibling donor–recipients, which is mostly restricted to hematopoietic stem cell transplantation (HSCT) and living kidney transplants. Therefore, the role of mHAs in other solid organ rejection may be more difficult to resolve due to incomplete or no matching. The first source of mHAs was identified in the Y chromosome (21) with at least six Y chromosome genes encoding various antigens presented by multiple MHC alleles recognized by donor T cells as foreign peptides after gender mismatched transplantation (19,22,23). Autosomal chromosomes also contain mHAs, with more than 40 described to date (reviewed in (20)). Such antigens arise as a consequence of common genetic variation, especially nonsynonymous SNPs in coding regions of the genome (24), leading to differences in the amino acid sequence proteins between donor and recipient. The number of identified autosomal mHAs identified to date is small considering the abundance of functional variants in the human genome, but it will undoubtedly grow with the recent genomic advances. Recently, Tennessen et al (25) applied whole exome sequencing to 15 585 human protein-coding genes in more than 2000 individuals of European and African ancestry and described more than 500 000 single nucleotide variants (SNVs) among the entire sample with an average of 13 595 variants per individual. The majority of these variants had not been previously described and were rare (minor allele frequency below 0.5%) and population-specific, with 2% predicted to impact the function of more than 300 genes per genome assessed (25), highlighting the need for a deeper genome-wide examination of the donor and recipient polymorphisms for potential mHAs. A distinct form of loss-of-function (LoF) genetic variants, homozygous-deleted CNVs (hdCNVs), is gaining traction as playing a role in transplant rejection. hdCNVs have been identified through large-scale GWAS and sequencing data sets (9,26). Possession of hdCNVs may manifest in a ‘‘normal’’ phenotype, with the term ‘‘disposable’’ or ‘‘dispensable,’’ genes often used to describe such variants. Cases where unaltered phenotypes are observed with carriage of hdCNVs suggest that there is either compensation for the hdCNV(s), or their function is no longer necessary. For example, hdCNVs of UGT2B17, a gene expressed in graft-versus-host disease-affected tissues, have been characterized as mHAs in HSCT (8,27). McCarroll et al (8) recently analyzed six common CNV deletions spanning genes in three HSCT cohorts (totaling 1345 HLAidentical sibling donor–recipient pairs). The authors found that risk of acute graft-versus-host disease was greater in recipients where UGT2B17 hdCNV was mismatched, that is zero gene copies present in the donor but one or two copies present in recipient (OR ¼ 2.5; 95% CI 1.4–4.6) (8). However, they only used markers for CNVs that were detectable on the initial GWAS arrays, which were limited in content, as many CNV regions known now were not 766
adequately probed or captured. Common hdCNVs may thus be important as they may play an active role in graft longevity. Recent whole exome and genome sequencing indicates that each individual carries numerous genetic variants predicted to cause LoF of protein-coding genes. MacArthur et al recently studied 2951 putative LoF variants obtained from whole genome sequencing from 185 human individuals from the 1000 Genome Project (derived from typical population samples), identifying and validating rare and likely deleterious LoF alleles, as well as common LoF variants in nonessential genes in this data set (26). They estimated that a typical human genome contains 100 genuine LoF variants with 20 genes inactivated in both copies, indicating unexpected redundancy in the human genome and suggesting that there are numerous mutations that are ‘‘private’’ to each personal genome. Since the immune system of an individual carrying LoF variants in both copies of a given gene may have had no previous exposure to protein (s) encoded by that gene(s), cellular or humoral immune recognition of that protein as an alloantigen in the grafted organ could very plausibly contribute to risk of rejection. LoF impacting the expression or translation of both copies of a given gene, especially in the transplanted organ of interest, is thus a plausible source of donor–recipient genomic incompatibilities, and may underpin rejection. Need for second-generation sequencing The emergence of large numbers of potential non-HLA incompatibilities with varying potential levels of immunogenicity highlights the clear need for deeper capture and assessment of SNVs, SNPs and CNVs in donors and recipients. Deep sequencing technologies will undoubtedly be key to unraveling common and rare LoFs and hdCNVs in population-scale diversity as well as capturing ‘‘private’’ individual-level polymorphisms to characterize the histocompatibility determinants involved in the biological mechanisms of alloimmunity. It should be noted that while there is low statistical power to detect such LoFs and hdCNVs at an association level, there is a lot of promise to follow up putative individual donor–recipient genomic incompatibility through the use of autoantibody testing in sera and/or tissues from the recipient postoperatively. Such approaches offer the promise of clinical utility through closer monitoring of a priori mHAs or potentially tolerizing recipients to a given gene product. Genetic risk factors for transplantation outcomes beyond HLA Over the last decade there have been unprecedented advances in the assessment of human genomic diversity across the major human populations through the development of high-throughput genotyping and deep sequencing technologies, as well as the development of population scale genomic maps such as the International HapMap Project (28). These tools have been applied to deeply American Journal of Transplantation 2014; 14: 764–778
Transplantation Genetics
characterize the genetic architecture of common and rare diseases and evoked conditions such as drug severe adverse events (29). Unfortunately, to date, the field of organ transplantation has not benefited from many of these advances. While over a thousand genetic association studies on organ rejection have been published so far, they are primarily candidate-gene based and suffer from many of the usual pitfalls of genetic association studies such as lack of adequate sample sizes, retrospective study designs, noninclusion of appropriate covariates such as ethnicity, lack of replication and proper statistical correction when multiple hypotheses are tested. Table 1 shows a review of appropriately designed published studies on organ transplantation outcomes. These studies were initially collated from PubMed and filtered for appropriate study size and design, defining appropriate as having at least a sample size of 200 individuals, including covariates, accounting for ethnicity and adjusting for multiple testing, when necessary. As observed in Table 1, genetic variation in up to 47 genes encoding cytokines, chemokines, celladhesion molecules, components of the renin–angiotensin–aldosterone pathway, coagulation and aggregation factors has been widely investigated and associated with outcomes such as delayed graft function, acute or chronic rejection or graft failure. However, results do not consistently replicate across studies. This lack of replication can be attributed to both the above-mentioned limitations of candidate-gene study designs and the complexity and diversity of clinical phenotypes. In addition to differences in protocols for immunosuppressive regimes, ascertainment of outcomes may differ by study—e.g. the criteria used to define acute rejection and chronic allograft dysfunction might vary among transplant centers: these all introduce further heterogeneity between studies. Specifically, in the case of rejection, studies may not always base the diagnosis on biopsy results and biopsy results themselves may be inaccurate due to interoperator differences. It is essential, therefore, to use objective and standard definitions for transplantation outcomes in order to obtain consistent results to homogenize studies. Thus future studies should aim to define phenotypes with precision and use a rigorous genetic approach, which preferably incorporates a hypothesis-free design such as GWAS to gain the most insight into genetic risk factors for organ transplantation.
Pharmacogenetics of immunosuppressant responses Genetic background is thought to account for as much as 95% of the variability in drug disposition and therapeutic effects (30). Regimes for immunosuppressants are typically characterized by wide pharmacokinetic interindividual variability and narrow therapeutic indexes, which often makes the ideal balance between sufficient immunosuppression and drug toxicity difficult to achieve. Thus, the American Journal of Transplantation 2014; 14: 764–778
discovery of genetic markers responsible for the interindividual variation in response to immunosuppressive therapy is an intensive area of ongoing research in transplantation. Ekberg et al evaluated the immunosuppressant drug exposures in 1645 renal transplant patients randomly assigned to four treatment groups: (1) standard-dose cyclosporine, (2) low-dose cyclosporine, (3) low-dose tacrolimus and (4) low-dose sirolimus (31), and observed that up to 90% of patients experienced at least one adverse event during treatment. Both biopsy-proven acute rejection and severe adverse events were similar in groups, ranging from 2.3% to 37% and 43% to 53%, respectively, depending on the drug, with the highest events observed in the low sirolimus dose group (31). In efforts to avoid such issues, therapeutic drug monitoring is being routinely performed, but it is currently assessed posttransplant and thus is not used for determining the optimal immunosuppressant starting dose, which is still established by an iterative postoperative approach. Alternative strategies incorporating pharmacogenetics hold great promise as complementary tools in drug monitoring to better guide individual therapies and doses. While the number of studies focusing on the pharmacogenetics of immunosuppressants has increased dramatically over the last few years, to date, many of these are underpowered that suffer from the typical candidate gene association study pitfalls. One clear exception is the robust association observed for rs776746 in CYP3A5, the primary enzyme involved in the metabolism of tacrolimus, the most frequently prescribed immunosuppressant drug worldwide. While nongenetic factors such as recipient gender, age, diabetes status and calcium channel blockers, such as some newer class of antifungals and grapefruit juice, influence tacrolimus levels, the rs776746 SNP is very important in tacrolimus clearance, with dosing requirements as well as time to therapeutic concentrations explaining up to 45% of the dose and 30% of clearance variability (13). The rs776746-A form is classically referred to as CYP3A5 1, while rs776746-G is referred to as CYP3A5 3. The latter is a noncoding variant that results in a cryptic splicing site, which causes 131 nucleotides of the intronic sequence to be inserted in the mRNA (32). The consequence is the introduction of a premature stop codon that truncatesCYP3A5 and leads to complete lack of CYP3A5 translation in 3 homozygotes (32). The prevalence of this CYP3A5 3 allele is as high as 90% in individuals of European ancestry while 90% of African Americans carry at least one of the common fully functional CYP3A5 1 alleles ( 1/ 1 or 1/ 3). These frequency differences in CYP3A5 3 make it one the most important genetic markers of interindividual and interethnic differences observed in CYP3A-dependent drug responses and clearance (33). There is also evidence of an additional rare variant, CYP3A5 6, which is associated with tacrolimus trough levels. Tables 2–4 review genetic association studies for tacrolimus, cyclosporine and mycophenolic acid regarding 767
768
Variant
rs4343
rs699
rs5186
rs1044240
rs2230199
C4L/S, CNV
rs1024611
109T/C, rs2107538, rs2280788 (a)
rs1799864
rs1799987 (b), rs333 (c)
rs2569190
rs20417
rs1801239, rs7918972
rs1801157
Gene
ACE
AGT
AGTR1
ALCAM
C3
C4
CCL2
CCL5
CCR2
CCR5
CD14
COX2
CUBN
CXCL12
12201365
22574174
19788502
19741468 17217435 17989610
19561149 15458467 12462338b 12201365c 17989610c
15458467 17989610 12201365 12239249
17989610 12462338a
17989610 12462338 12239249
21164027
22176838 19246358
20220571
AR, GS, PS
Proteinuria
GF
AR AR, BOS, GS CAN
AR AR, CAN AR, GS AR, GS, PS AR, SR
AR, CAN AR, CAN, SR AR, GS, PS GF
AR, CAN, SR AR, GS
AR, CAN, SR AR, GS GF
AR, GF, GL, GS
AR, DGF, PNF AR, GF, PS
GS
GF, serCr
GF, serCr
11923700 11923700
GF
GF GF, serCr RHC
Outcome
12548125
12548125 11923700 21659963
PMID
Table 1: Genetic association studies of organ transplantation outcomes
Liver
Kidney
1142R þ 1186D 207
Kidney
Kidney Lung Kidney
Kidney Kidney Liver Liver Kidney
Kidney Kidney Liver Kidney
Kidney Liver
Kidney Liver Kidney
Kidney
603
216D-R 226 436
243D-R 244D 209 207 436
244D 436 207 232
436 209
436 209 232
1969
Kidney Kidney
Kidney
954 þ 1002 1265D-R 1147
Kidney
Kidney
Kidney
Kidney Kidney Heart
Graft
224
224
210
210 224 532
Sample size
American Journal of Transplantation 2014; 14: 764–778
(Continued)
rs1801157 29.0% in dead patients compared to alive 17.1% and PS time (134 vs 98 months wt vs carriers) (p < 0.034)
ESRD: OR ¼ 1.39, elevated proteinuria 1y post (p < 0.015)
rs20417C and GL HR ¼ 2.43 [1.19-4.97], cohort 1; HR ¼ 1.72 [0.99-3.77], cohort 2 (p < 0.051)
NA Earlier AR, BOS and rs2569190TT: HR ¼ 1.65 [1.03–2.64], p ¼ 0.04 NA
# A alleles (D þ R) and AR, RR AR (p ¼ 0.039) NA NA NA
NA NA NA NA
NA NA
NA NA rs1024611GG vs A and GS: 67 14 vs 95 4 months; p ¼ 0.0052
NA
NA NA
NA
rs5186 and preserved long-term GF (p ¼ 0.037)
Increased Cr/t shorter t-to-sustained doubling of Cr, shorter t-to GL (p < 0.007) NA
NA NA RHC and rs4343: HR ¼ 0.58 [0.36-0.95]; p ¼ 0.031
Results
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
rs1126579, rs4674258
rs2228014
rs1799998
rs2814778
rs1800682
rs763110
rs17514136, rs17549193, rs3124952, rs3124953, rs7851696
CXCR2
CXCR4
CYP11B2
DARC
FAS
FASL
FCN2
(GT)n, rs2071746, rs2071747, rs2071748, rs2071749, rs2285112, rs5755720, rs6518952, rs8140669
rs2430561
rs1800871, rs1800872, rs1800896
HMOX1
IFNG
IL10
rs6025
rs2671222
CXCR1
FV
Variant
Gene
Table 1: Continued
15458467 15599305 20622753 15367225
15458467 17989610 15599305 20622753 21659963
18640487
AR, CAN AR, DGF GF, IIF GS
AR, CAN AR, CAN, SR AR, DGF GF, IIF RHC
DGF
AR, GF
BPAR, DGF, PNF, PS
22173060
11858477
RHC
RHC
AR, DGF
GF, serCr
BPAR, GS
AR
21659963
21659963
15327416
11923701
21304904
21452410
AR
BPAR, GS
21304904 21452410
Outcome
PMID
244D 291R þ 206D 218 2298 þ 1901
244D 436 291R þ 206D 218 532
965R
394
1272
532
532
222
224
335D-R
216
216
335D-R
Sample size
Kidney Kidney Kidney Kidney
Kidney Kidney Kidney Kidney Heart
Kidney
Kidney
Kidney
Heart
Heart
Kidney
Kidney
Kidney
Kidney
Kidney
Kidney
Graft
769
(Continued)
NA R ACCACC, ATAATA, GCCATA AR: OR ¼ 1.9 [1.1–3.1], p ¼ 0.016 rs1800871 TT high IIF grade (OR ¼ 3.27 [1.1–9.8]; p ¼ 0.035) NA
CAN (p < 0.008) NA NA NA NA
NA
GA no GF (RR ¼ 2.87 [1.01-8.26]), #dialysis until GF, risk for at least one AR episode (RR ¼ 3.83 [1.38-10.59]). Cr, protein excretion rate (p < 0.05).
NA
NA
RHC and rs1800682AA: HR ¼ 1.84 [1.25–2.69]; p ¼ 0.002
NA
rs1799998TT vs CC worse GF (p ¼ 0.002)
NA
NA
AR and D rs2671222A (OR ¼ 3.56 [1.37-9.27]; p ¼ 0.009)
D-rs1801157A and BPAR: OR ¼ 0.39, [0.20–0.76] and poor GS: HR ¼ 3.01; [1.19–7.60]; p < 0.020
Results
Transplantation Genetics
770
rs1801131, rs1801133
rs1805087
rs1799983
MTHFR
MTR
NOS3
rs12953, rs1131012, rs668
rs11003125, rs1800450, rs1800451, rs5030737, rs7095891, rs7096206
MBL2
PECAM1
rs72550870
rs1800795
IL6
MASP2
rs1801275
IL4R
rs5918
(CA)m(CT)n rs2069762
IL2
ITGB3
rs1143634, rs16944
IL1B
rs4073
rs187238
IL18
IL8
rs3212227
rs1554286, rs1878672, rs2222202, rs3021094, rs3024493, rs3024494, rs3024498
Variant
IL12B
Gene
Table 1: Continued
20220571
19349296
19349296
19349296
22173059 19104434
22173060
17928472
21452410
15458467 15599305 20622753 21659963
15367225
11981437 15458467
20622753
19077897
GS
GS
GS
GS
BPAR, DGF, PNF, PS BOS, GS
BPAR, DGF, PNF, PS
AR
AR
AR, CAN AR, DGF GF, IIF RHC
GS
AR AR, CAN
GF, IIF
AR
GF, IIF
RHC DGF
21659963 18640487
20622753
Outcome
PMID
Kidney Kidney
954 þ 1002
Kidney
Kidney
Kidney Lung
Kidney
Kidney
Kidney
262D-218R
262D-218R
262D-218R
1271 277 D-R
1272
445
216
Kidney Kidney Kidney Heart
Kidney
2298 þ 1901 244D 291R þ 206D 218 532
Heart Kidney
290 244D
Kidney
Kidney
226 þ 148C 218
Kidney
Heart Kidney
Graft
218
532 965R
Sample size
NA
GS: OR ¼ 9.192; [1.09-76.9]; p ¼ 0.0406
NA
NA
(Continued)
NA D-rs7096206 and GS and BOS free PS (p ¼ 0.007)
NA
rs5918TT and AR: OR ¼ 3.4; p ¼ 0.04
NA
NA NA NA NA
NA
Allele 135 and risk of AR (p < 0.03) NA
–
rs187238GG and AR: OR ¼ 3.653; p ¼ 0.015
NA
rs1800896 RHC: HR ¼ 0.49 [0.27–0.90]; p ¼ 0.020 NA
Results
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
American Journal of Transplantation 2014; 14: 764–778
rs5368
rs1131498, rs2205849, rs2229569
rs1800471, rs1800470 (d), rs1800472 (e)
rs3775290, rs3775291, rs3775296
rs10759932, rs1927914
rs1800629 (f), rs1800628 (g), rs3093662 (h) rs361525
rs1042522, rs12951053, rs1614984, rs1625895, rs17884306, rs4968187, rs9894946
rs1005230, rs2010963, rs3556934, rs699947
SELE
SELL
TGFB1
TLR3
TLR4
TNF
TP53
VEGF
h
21659963
RHC
DGF
AR, GS DGF GF, IIF RHC
15367225 18640487f, g, 20622753 21659963 18640487
AR AR AR, CAN, SR AR, DGF
AR
12682890 15458467 17989610 15599305
19741468
AR
DGF GF, IIF GS
18640487d,e 20622753 15367225 19741468
AR, CAN AR, CAN, SR AR, DGF
GS
20220571
15458467 17989610 15599305
AR
Outcome
21452410
PMID
532
Heart
Kidney
Kidney Kidney Kidney Heart
2298 þ 1901 965R 218 532 965R
Liver Kidney Kidney Kidney
Kidney
Kidney
Kidney Kidney Kidney
210D-R 244D 436 291R þ 206D
216D-R
216D-R
965R 218 2298 þ 1901
Kidney Kidney Kidney
Kidney
964 þ 1002
244D 436 291R þ 206D
Kidney
Graft
216
Sample size
NA
NA
NA NA NA R rs1800629AA and AR: OR ¼ 5.0 [3.0–8.3]; D rs1800629GA and DGF OR ¼ 1.6 [1.1–2.6], p < 0.040 GS rate among retransplants: 63.0% AA vs.79.5%G; p ¼ 0.0116 NA NA NA
R rs10759932C vs TT and AR (OR ¼ 0.25 [0.11-0.57]). rs10759932C higher rejection-free PS rates (p < 0.023)
NA
rs1800470 and AR (p ¼ 0.027) NA R rs1800471GG þD rs1800871T and AR: OR ¼ 1.8 [1.1–3.0], p ¼ 0.027 NA NA NA
NA
NA
Results
Summary of the most relevant studies performed to date reporting genes/variants associated with transplantation outcomes. Low case letters a to h in PMID column indicate that, amongst all the variants, these particular studies investigate exclusively those variants specified by the letters. For the rest of the studies, all variants are investigated. In alphabetical order, abbreviations for outcomes are: AR ¼ acute rejection; BOS ¼ bronchiolitis obliterans syndrome; BPAR ¼ biopsy proven acute rejection; CAN ¼ chronic allograft nephropathy; DGF ¼ delayed graft function; GF ¼ graft function; GL ¼ graft loss; GS ¼ graft survival; IIF ¼ infiltration of inflammatory cells; PNF ¼ primary non-function; PS ¼ patient survival; RHC ¼ rejection with hemodynamic compromise; serCr ¼ serum creatinine; SR ¼ subclinic rejection. In sample size and results columns, D ¼ donor and R ¼ recipient. For results, ESRD ¼ end-stage renal disease; NA ¼ No Association
Variant
Gene
Table 1: Continued
Transplantation Genetics
771
772
Variant
rs1045642 (a), rs1128503, rs2032582 (b)
rs3740066, 1446C > G, rs717620 (c)
rs2740574 (d) and rs28371759, rs35599367 (e), rs4987161, rs55785340, rs4986910
rs776746, rs10264272 (f), rs41303343 (g)
Gene
ABCB1
ABCC2
CYP3A4
CYP3A5
104 151 399D-R 301 185
832
18192894 15592326f 15454731 21806386f,g 21903774
19005401
CNI
185
21903774e
CsA
CNI
104
124 151 832
18192894
CsA
185 338
11375290d 15592326 19005401d
19890249c 19494809
MPA þ CNI
104
136 209D-R
18334918 21677300a 18192894
301 832 103 150D
124 151 294
11375290a 15592326a 20061922 21806386 19005401 16753004a,b 17885626
104
Sample size
18192894
PMID
CsA
Tac
CNI
CsA
Primary IST
Table 2: Pharmacogenetic studies on immunosuppressant therapy pharmacokinetics
Kidney
Kidney Kidney Kidney Kidney
Kidney
Kidney
Kidney Kidney and heart Kidney
Kidney
Kidney Kidney
Kidney
Kidney Kidney
Kidney Kidney Kidney Liver
Kidney and heart Kidney Heart
Kidney
Graft
NA NA rs776746AA Tac Co/D than G (p0.0001) Haplotype rs776746AA (CYP3A5)-rs35599367T or CC higher C than rs776746G (CYP3A5)-rs35599367CC (p < 0.03) rs2740574(CYP3A4)AA-rs776746GG and less time to achieve Tac C and higher C/D/Kg (p < 0.001)
NA
NA rs2740574G higher Cl than AA (p < 0.05) rs2740574AA-rs776746 GG (CYP3A5) less time to Tac C and higher C/D/Kg (p < 0.001) rs35599367T D lower than rs35599367CC (p ¼ 0.018). Haplotype rs776746AA (CYP3A5)-rs35599367T or CC higher C than rs776746G (CYP3A5)-rs35599367CC (p < 0.03)
NA
NA NA
NA
NA NA Higher Co/D-Co/weigh in rs1128503CC-rs2032582GGrs1045642CC than TT-TT-TT (p < 0.05) NA NA rs1045642 explains 3.7% of D variation (p ¼ 0.009) rs1128503T and rs2032582T hepatic C greater than CC and GG (p < 0.035) rs1045642TT lower D than C (p < 0.04) NA
Lower oral B: rs2032582GG, rs1128503TT, rs2229109Grs1128503C-rs2032582G-rs1045642C (p < 0.05)
Results
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
American Journal of Transplantation 2014; 14: 764–778
rs17868320, rs6714486, rs72551330, rs2741049
rs7438135, rs7439366, rs7668282
UGT1A9
UGT2B7
18641546 19494809
MPA MPA þ CNI
19890249
19494809
MPA þ CNI
338
332
185
338
185
180
12490779 19890249
178
150D
103 208 150D 209D-R 136
Sample size
15147425
MPA þ CNI
Tac
17885626
16753004 21566507 17885626f 21677300 18334918
Tac
Tac
PMID
Primary IST
Kidney
Kidney
Kidney
Kidney
Kidney
Liver
Kidney Kidney Liver Kidney Kidney
Graft
NA
NA
NA
rs1042597GG higher CsA AUC0–12; rs6714486A and/or rs17868320T lower CsA AUC0–12 (p < 0.05)
Tac patients rs4149117T decrease in MPAG/MPA; Cmax (p < 0.0003)
1 lower C, delay in achieving target C and 3/ 3 C above target (p < 0.003) CYP3AP1 1 vs 3 reduction in C (p < 0.05)
NA
rs776746A explains 35.3% of D variation (p < 0.001) NA rs776746G higher D than AA (p < 0.05) R rs776746G lower Co, Co/D (p < 0.05) rs776746G lower Co/D (p < 0.003)
Results
Relevant studies investigating genes/variants associated with calcineurin inhibitor and mycophenolic acid pharmacokinetics. Similarly to Table 1, letters a to g in PMID column indicate that particular studies investigate exclusively those variants specified by the letters. For Primary IST, CsA ¼ cyclosporine A; CNI ¼ CsA þ Tac; MPA ¼ mycophenolic acid; Tac ¼ tacrolimus. For sample size, D ¼ donor and R ¼ recipient. In results, AUC0–12 ¼ area under the curve from 0 to 12 hours; B ¼ bioavailability; C ¼ concentration; Cl ¼ clearance; Co ¼ trough concentration; D ¼ dose; MPAG/MPA ¼ MPA glucuronide to MPA ratio; NA ¼ No Asociation.
rs1042597, rs17863762
UGT1A8
1/ 3
1C
rs4149117, rs60140950
Variant
SLCO1B3
CYP3AP1
CYP3A7
Gene
Table 2: Continued
Transplantation Genetics
773
774
rs3740066, 1446C > G, rs717620
rs2740574
ABCC2
CYP3A4
PXR
rs11706052 (g), rs4974081 rs1523130, rs2276707,
IMPDH2
1/ 3
rs11770116, rs2228075, rs2288548, rs2288549, rs2288550, rs2288553, rs4731448 and rs2278293 (e), rs2278294 (f)
IMPDH1
CYP3AP1
rs776746
rs1045642 (a), rs1128503 (b), rs2032582 (c), rs717620 (d)
ABCB1
CYP3A5
Variant
Gene
CNI
MPA
MPA
Tac
CNI MPA þ Tac Tac
CsA
CNI MPA þ Tac
CsA
DGF
AR
AR
AR
GL GS AR CAD AR AR, DGF
AR AR, GL AR CAD
AR
CAD AR, DGF
MPA þ Tac Tac
191 456
191 456 178D-R
17851563g 20679962 22453193
178
227D-259R 399D-R 832 252 136 209D-R
124 227D-259R 832 252
338
17851563 20679962e,f
15147425
20505666 15454731 19005401 19762492 18334918 21677300
11375290 20505666 19005401 19762492
19494809
178D-R 252 209D-R
22453193d 19762492a-c 21677300a
147 832
18510642
DGF
227D-259R
294 170
124
Sample size
19005401
20505666
AR
MPA þ CNI
a
20061922 17198259a,c
11375290
PMID
AR, GL
AR
Primary outcome
CNI
CsA
Primary IST
Table 3: Pharmacogenetic studies of transplantation outcomes
Kidney Kidney Kidney
Kidney Kidney
Kidney
Kidney Kidney Kidney Kidney Kidney Kidney
Kidney Kidney Kidney Kidney
Kidney
Kidney Kidney Kidney
Kidney
Kidney
Kidney
Heart Heart
Kidney
Graft
1 earlier AR (p ¼ 0.005)
NA NA Donor 2276707TT higher risk for DGF (p < 0.05)
rs2278293A and rs2278294A lower risk (p < 0.05) rs2278294A lower risk (p ¼ 0.0075)
NA NA NA NA NA NA
NA NA NA NA
NA
NA rs1045642CC and haplotype rs2032582GG-rs1045642CC higher risk for >3A rejection (p ¼ 0.02) D haplotype rs1128503T-rs2032582T-rs1045642T higher risk for AR (p ¼ 0.018) and GL (p ¼ 0.0019) rs1045642T and rs2032582T higher risk for DGF (p < 0.034) Haplotype rs1128503C-rs2032582G-rs1045642T higher risk for AR (p ¼ 0.038) NA D and R rs104564TT higher IF/TA (p ¼ 0.001) NA
NA
Results
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
Relevant pharmacogenetic studies in transplantation outcomes. Letters a to g in PMID column indicate that particular studies investigate exclusively those variants specified by the letters. For Primary IST, CsA ¼ cyclosporine A; CNI ¼ CsA þ Tac; MPA ¼ mycophenolic acid and Tac ¼ tacrolimus. For sample size, D ¼ donor and R ¼ recipient. For primary outcome, AR ¼ acute rejection; CAD ¼ chronic allograft damage; DGF ¼ delayed graft function; GL ¼ graft loss; GS ¼ graft survival. For sample size, D ¼ donor and R ¼ recipient and in results, NA ¼ No Asociation.
rs7438135, rs7439366, rs7668282 UGT2B7
338 19494809 rs6714486, rs17868320 UGT1A9
AR rs3814055, rs1042597, rs7643645 rs17863762 UGT1A8
MPA þ CNI
Sample size Primary outcome Gene
Table 3: Continued
Variant
Primary IST
PMID
Graft
Kidney
Results
Tac patients with 6714486A and/or rs17868320T higher risk of AR (p < 0.05)
Transplantation Genetics
American Journal of Transplantation 2014; 14: 764–778
pharmacokinetics, transplant outcomes and adverse events. Due to the previously mentioned limitations of the candidate gene approach, we only focus on studies with over 100 individuals and appropriate quality control for phenotype and genotype data. Several studies have confirmed the major impact of CYP3A5 on tacrolimus dose requirements and renal clearance; however, similar to genetic association studies on graft outcomes, it has been challenging to robustly replicate additional findings. Apart from CYP3A5, to date, only the associations of ABCB1 rs1045642, rs1128503, rs2032582 and cyclosporine pharmacokinetics and CYP3A4 rs2740574 and rs3559936 with tacrolimus pharmacokinetics appear to be consistent. All of the rigorously designed studies to date conclude that there is a need to dose tacrolimus patients by the CYP3A5 rs776746 genotype. Thervet et al (15), during the first 6 days posttransplantation, studied the effect of guiding tacrolimus dosing based on the number of CYP3A5 functional alleles, on plasma drug concentration. Patients were randomly assigned to either a standard initial dose of tacrolimus (0.2 mg/kg/day) or a genotype-adjusted dose, who received 0.3 mg/kg/day if carrying allele 1 and 0.15 mg/kg/day if 3/ 3. This randomized clinical trial found significantly higher number of patients achieving optimal dosing in the genotype-based dosing group, more rapidly and with fewer dose modifications (15). However, the trial was not designed to investigate hard clinical outcomes such as graft failure—the acid test of a pharmacogenetic test. Passey et al (34) established a dosing algorithm for tacrolimus including clinical, genetic and ethnic information. From a cohort of more than 600 kidney recipients, compared to those with G/G genotype, those with the rs776746 A/G genotype experienced a 69% increase in tacrolimus clearance and A/A genotype a 100% increase in clearance. The final dosing algorithm included CYP3A5 rs776746 genotype, days posttransplantation, age, steroid and calcium channel blocker use as independent predictors of the outcome (34). The incorporation of CYP3A5 genetic information into tacrolimus dosing algorithm will likely be a first major step toward precision genotype-guided dosing in the transplantation setting. Optimal use of pharmacogenetics will require a deeper knowledge of additional genetics factors governing drug disposition, efficacy and toxicity. The application of the genomic advances to this field has the real potential of optimizing dosing strategies for immunosuppressive drugs, avoiding serious adverse and improving patient management after organ transplantation. Ancestry as a genetic risk factor for transplantation outcomes It is also worth noting the large impact that donor and recipient ancestry has in transplantation. Many studies have reported greater risks of rejection and mortality in African Americans when their organs are used as donor 775
776 MPA
MPA
rs5186
rs2740574
rs776746
rs11770116, rs2228075, rs2288548, rs2288549, rs2288550, rs2288553, rs4731448 and rs2278293 (b), rs2278294 (c)
rs11706052, rs4974081
521T > C, rs2306283, rs4149015, rs4149056
rs4149117
98T > C, rs17868320, rs6714486
rs7438135, rs7439366
CYP3A4
CYP3A5
IMPDH1
IMPDH2
SLCO1B1
SLCO1B3
UGT1A9
UGT2B7
Blood pressure
Diarrhea,leukopenia, anemia, infections
456
332 218
18641546 (diarrhea, leukopenia) 21142914
218
20679962 21142914
191
218 456
191
399D-R
209D-R 304
304
233
17851563 (leukopenia)
21142914b,c 20679962 (leukopenia,infection) b,c
17851563 (leukopenia)
15454731
21677300 20526235
20526235
16477235
218
209D-R 304 218
21677300a 20526235 21142914a 21142914
294
Sample size
20061922
PMID
Kidney
Kidney
Kidney
Kidney
Kidney
Kidney Kidney
Kidney
Kidney
Kidney Kidney
Kidney
Liver
Kidney
Kidney Kidney Kidney
Heart
Graft
NA
NA
rs4149056C reduced risk of adverse events (p ¼ 0.002)
NA
NA
NA rs2278294 higher risk of leukopenia (p ¼ 0.0139)
NA
NA rs776746G higher risk of nephrotoxicity (p ¼ 0.01) NA
NA
rs4343DD higher risk of nephrotoxicity (p < 0.0001)
NA
NA NA NA
NA
Results
Pharmacogenetic studies on IST induced adverse events. Letters a to g in PMID column indicate that particular studies investigate exclusively those variants specified by the letters. For Primary IST, CsA ¼ cyclosporine A; CNI ¼ CsA þ Tac; MPA ¼ mycophenolic acid; Tac ¼ tacrolimus. For sample size, D ¼ donor and R ¼ recipient. For sample size, D ¼ donor and R ¼ recipient and in results, NA ¼ No Association.
MPA
Diarrhea,leukopenia, anemia, infections
Leukopenia, infections
Diarrhea,leukopenia, anemia, infections
CsA MPA
Nephrotoxicity
Nephrotoxicity
Nephrotoxicity
Diarrhea,leukopenia, anemia, infections
Diarrhea,leukopenia, anemia, infections
Nephrotoxicity
Adverse event
Tac
Tac
rs5051
AGTR1
CNI
MPA
AGT
rs2273697, rs3740066, 1446C > G, rs717620
ABCC2
MPA
Tac
CsA
rs4343
rs1045642 (a), rs1128503, rs2032582
ABCB1
Primary IST
ACE
Variant
Gene
Table 4: Pharmacogenetics of immunosuppressant therapy–induced adverse events
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
Transplantation Genetics
grafts or when they are a recipient of a graft (35–37). Callender et al also confirmed lower kidney survival rates among African Americans compared to all other ethnic groups and showed that this ethnic group also harbored the shortest half-life after kidney transplantation: only 5.3 years versus 12.2 years for Asians, 10.2 years for individuals of European descent and 9.0 years for Hispanics (36). In 2010, Li et al (38) published a pivotal study showing that Chinese and African human genomes differed by approximately 5 Mb of unique sequence and over 240 potential genes differing between these sequences. Such ‘‘pangenome’’ data sets illustrate the differences between populations and emphasize the need for further characterization of such genomic differences in the transplantation arena. In addition to the highly polymorphic differences in HLA loci already evident in the different ethnic groups, variants like LoFs or hdCNVs with the potential of acting as incompatibility antigens, or genetic risk factors for postoperative transplant outcomes as well as pharmacogenetic markers, exhibit substantial variability interethnically.
Future Perspectives The current pace of genomic discovery has revolutionized our understanding of the genetic architecture of common diseases (www.genome.gov/gwastudies) (29). In contrast, the pipeline of genomic research in transplantation has been relatively weak. This is surprising because in essence transplantation involves the direct exposure of a foreign donor genome into a recipient, creating unparalleled opportunities to investigate the implications this has on graft function. For example, recipient by donor genomic interactions may play a critical role in graft survival. However, analyses of these types will require large wellcharacterized data sets that have been harmonized for important clinical traits and genotyped using GWAS or preferably second-generation sequencing. While small candidate studies have started the collation of DNA and appropriately phenotyped recipients and donors, very few studies have sufficient numbers to be statistically powered to look at polygenic transplant-related intermediate phenotypes and outcomes. There is a clear need for wider collaborations among all appropriately harmonizable data sets using robust genomic platforms and statistical methods to bolster statistical power among existing studies. With such initiatives, we expect the genetic architecture of transplant rejection ranging from identification of LoF variants, which introduce new epitopes to donors, to genes involved in drug metabolism to change the way transplanted grafts are matched and/or recipients are monitored posttransplant. Furthermore, established genetic techniques such as hypothesis-free analyses and Mendelian randomization can be conducted in this setting to investigate the causal effect of modifiable risk factors on graft function and survival with the potential to discover new therapeutic targets. American Journal of Transplantation 2014; 14: 764–778
Large-scale NIH networks such as The electronic Medical Records and Genomics (eMERGE) network integrating large-scale hospital GWAS biobank resource with EMRs (www.genome.gov/27540473) will allow us a framework to consent, counsel and manage the return of results to physicians and graft recipients in a clinical environment, which will lead to greater patient monitoring and care. As waiting lists for kidney transplantation continue to grow (n ¼ 97 856, September 2013) and with solid organ transplants costing from $200 000 to $850 000 (USD) per patient, efforts such as eMERGE are currently attempting to integrate actionable pharmacogenomics data into patients’ EMRs, and tacrolimus-related dosing is being conducted in at least one eMERGE site thus far, which will likely result in a significant benefit to society in the very near future. Although beyond the scope of this review, a number of protein, RNA and microRNA biomarkers for rejection and graft damage injury are emerging that may aid in the diagnosis of rejection episodes. Specific biomarkers are also under investigation for immune quiescence and minimization of immune-suppressants (reviewed in (39)). We are now entering a period in genomics when tools are becoming available, which will dramatically change our understanding of the molecular mechanisms underpinning graft outcomes, and patient-specific treatments will begin to benefit transplant patient care and to define the future of transplantation.
Disclosure The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
References
Electronic Resources Described A Catalog of Published Genome-Wide Association Studies: www.genome.gov/gwastudies/. The electronic Medical Records and Genomics (eMERGE) network: www.genome.gov/27540473. 1. Spierings E, Hendriks M, Absi L, et al. Phenotype frequencies of autosomal minor histocompatibility antigens display significant differences among populations. PLoS Genet 2007; 3: e103. 2. Terasaki PI. Deduction of the fraction of immunologic and nonimmunologic failure in cadaver donor transplants. Clin Transpl 2003; 449–452. 3. Burton CM, Iversen M, Carlsen J, et al. Acute cellular rejection is a risk factor for bronchiolitis obliterans syndrome independent of post-transplant baseline FEV1. J Heart Lung Transplant 2009; 28: 888–893.
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American Journal of Transplantation 2014; 14: 764–778
C
Copyright 2014 The American Society of Transplantation and the American Society of Transplant Surgeons doi: 10.1111/ajt.12653
Minireview
Transplantation Genetics: Current Status and Prospects B. Almoguera1,*, A. Shaked2,* and B. J. Keating1,2,3,*
Received 07 August 2013, revised 17 December 2013 and accepted for publication 31 December 2013
1
The Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, PA 2 Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA 3 Department of Pediatrics, School of Medicine University of Pennsylvania, Philadelphia, PA Corresponding authors: Berta Almoguera, [email protected]; Abraham Shaked, [email protected]; and Brendan J. Keating, [email protected]
Over the last decade, advances in genetic technologies have accelerated our understanding of the genetic diversity across individuals and populations. Case– control and population-based studies have led to several thousand genetic associations across a range of phenotypes and traits being unveiled. Despite widespread and successful use of organ transplantation as a curative therapy for organ failure, genetic research has yet to make a major impact on transplantation practice aside from HLA matching. New studies indicate that non-HLA loci, termed minor histocompatibility antigens (mHAs), may play an important role in graft rejection. With several million common and rare polymorphisms observed between any two unrelated individuals, a number of these polymorphisms represent mHAs, and may underpin transplantation rejection. Genetic variation is also recognized as contributing to clinical outcomes including response to immunosuppressants, introducing the possibility of genotype-guided prescribing in the very near future. This review summarizes existing knowledge of the impact of genetics on transplantation outcomes and therapeutic responses, and highlights the translational potential that new genomic knowledge may bring to this field. Keywords: Allograft rejection, genomic variability, non-HLA-related rejection, pharmacogenomics Abbreviations: CNV, copy number variant; GWAS, genome-wide association studies; hdCNV, homozygous-deleted copy number variant; HSCT, hematopoietic stem cell transplantation; LoF, loss-of-function; mHAs, minor histocompatibility antigens; SNP, single nucleotide polymorphism; SNV, single nucleotide variant 764
Introduction Over the last two decades, more than 300 000 solid organ transplantations have been performed in the United States alone (1). However, despite improvements in surgical techniques and the development of more effective immunosuppressant therapies, allograft rejection still affects 60% of transplanted individuals and remains one of the major risk factors of graft loss (2,3). Up to 40% of graft recipients experience some form of rejection within the first postoperative year (4), with lung and heart recipients showing the highest rates of rejection, with 55% and 25% of patients, respectively (1,5,6), and kidney and liver the lowest, with 10% and 17% of patients experiencing rejection, respectively. Rejection can occur where genetic disparities exist between donors and recipients, which may lead to presentation of polymorphic peptides that the recipient’s immune system recognizes as nonself. Although key HLA loci have traditionally been considered to be the main contributor to the genetic variability of allograft rejection, some degree of rejection still occurs in HLAmatched sibling transplantations, which may be the result of noncompatible loci beyond HLA between donor and recipient (7). Indeed, new findings indicate that non-HLA polymorphisms can impact upon transplantation outcomes since they have the potential of generating histoincompatibilities (7–9) influencing allograft rejection (10), and impacting immunosuppressant responses (11). Approximately 3.5 million common and rare polymorphisms exist between two unrelated individuals of European ancestry and up to 10 million variants in individuals of African ancestry (12). However, investigations of non-HLA genetic determinants of clinical outcomes following organ transplantation have yet to be performed in any systematic fashion to date. Recent technological advances in genomics such as genome-wide association studies (GWAS) allow the characterization of hundreds of thousands to several million single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) across the human genome rapidly and inexpensively (11,13). Figure 1 illustrates the concepts of structural variations, SNPs and insertion/deletion polymorphisms (InDels). Furthermore, whole exome and whole genome sequencing, which interrogates the coding regions and the entire human genome, respectively, are quickly
Transplantation Genetics
Figure 1: Illustration of common forms of human genomic variations: structural variation (copy number variants [CNVs]), singlenucleotide variants (SNVs) and insertion–deletion polymorphisms (InDels). (A) Structural variation. CNVs are the most common form of structural variation and include deletions and multiplications (typically duplications) >1000 bp. CNVs can affect whole genes or parts of them and may be present in heterozygosis (e.g. deletion of one copy of gene A in IND2 or duplication of gene B in IND1) or homozygosis (e.g. deletion of one copy of gene D in IND3). (B) SNVs and InDels. The presence of an A in IND1 instead of a C on the reference genome represents a graphic illustration of an SNV and the two red Ns in IND3 represent two nucleotides deletion.
becoming commonly used tools within the clinical diagnostic arena. These second-generation sequencing technologies have the ability to extensively characterize genomewide sources of histoincompatibility between donors and recipients, potentially unraveling specific genetic risk factors influencing rejection and immunosuppressant responses or severe adverse effects (14,15). In this review we aim to overview the current knowledge from existing genetics studies recently conducted for transplantation outcomes and therapeutic responses to immunosuppression therapies and we also discuss the translational components from this genetic knowledge that may be rapidly implemented in this field.
Genetics of Transplantation Outcomes Incompatibility across key HLA alleles has traditionally been considered the main factor influencing rejection in stem cell and solid organ transplants, and has therefore been the focus of extensive investigation. Because of high genetic variability in the HLA locus, a detailed characterization of American Journal of Transplantation 2014; 14: 764–778
HLA mismatches between donors and recipients is not routinely achieved. The effect of specific HLA mismatches in kidney transplantation have been characterized for sometime, whereas the importance of HLA matching on outcomes in organs such as the liver is still under debate (6,16–18). Even in HLA identically matched kidney transplantation, some degree of rejection is still evident. NonHLA or minor histocompatibility antigens (mHAs) resulting from a range of functional polymorphisms in the genome have been suggested to be capable of inducing strong cellular immune responses (reviewed in (7)). In contrast to the established role of HLA, Terasaki (2) estimated that over twice as many graft failures in HLA identical siblings at 10 years posttransplantation (38%) were due to immunological reactions to non-HLA factors compared to graft failures attributable to HLA (18%). Although our current knowledge of non-HLA antigens is still limited to a small number of loci (1,19,20), if one extrapolates the large number of genetic variants observed between two unrelated individuals within a donor–recipient pair, then the number of non-HLA discrepancies between any given donor and recipient would be expected to be very large. 765
Almoguera et al
The most rigorous way of investigating the association of mHAs with organ survival is within HLA-identical sibling donor–recipients, which is mostly restricted to hematopoietic stem cell transplantation (HSCT) and living kidney transplants. Therefore, the role of mHAs in other solid organ rejection may be more difficult to resolve due to incomplete or no matching. The first source of mHAs was identified in the Y chromosome (21) with at least six Y chromosome genes encoding various antigens presented by multiple MHC alleles recognized by donor T cells as foreign peptides after gender mismatched transplantation (19,22,23). Autosomal chromosomes also contain mHAs, with more than 40 described to date (reviewed in (20)). Such antigens arise as a consequence of common genetic variation, especially nonsynonymous SNPs in coding regions of the genome (24), leading to differences in the amino acid sequence proteins between donor and recipient. The number of identified autosomal mHAs identified to date is small considering the abundance of functional variants in the human genome, but it will undoubtedly grow with the recent genomic advances. Recently, Tennessen et al (25) applied whole exome sequencing to 15 585 human protein-coding genes in more than 2000 individuals of European and African ancestry and described more than 500 000 single nucleotide variants (SNVs) among the entire sample with an average of 13 595 variants per individual. The majority of these variants had not been previously described and were rare (minor allele frequency below 0.5%) and population-specific, with 2% predicted to impact the function of more than 300 genes per genome assessed (25), highlighting the need for a deeper genome-wide examination of the donor and recipient polymorphisms for potential mHAs. A distinct form of loss-of-function (LoF) genetic variants, homozygous-deleted CNVs (hdCNVs), is gaining traction as playing a role in transplant rejection. hdCNVs have been identified through large-scale GWAS and sequencing data sets (9,26). Possession of hdCNVs may manifest in a ‘‘normal’’ phenotype, with the term ‘‘disposable’’ or ‘‘dispensable,’’ genes often used to describe such variants. Cases where unaltered phenotypes are observed with carriage of hdCNVs suggest that there is either compensation for the hdCNV(s), or their function is no longer necessary. For example, hdCNVs of UGT2B17, a gene expressed in graft-versus-host disease-affected tissues, have been characterized as mHAs in HSCT (8,27). McCarroll et al (8) recently analyzed six common CNV deletions spanning genes in three HSCT cohorts (totaling 1345 HLAidentical sibling donor–recipient pairs). The authors found that risk of acute graft-versus-host disease was greater in recipients where UGT2B17 hdCNV was mismatched, that is zero gene copies present in the donor but one or two copies present in recipient (OR ¼ 2.5; 95% CI 1.4–4.6) (8). However, they only used markers for CNVs that were detectable on the initial GWAS arrays, which were limited in content, as many CNV regions known now were not 766
adequately probed or captured. Common hdCNVs may thus be important as they may play an active role in graft longevity. Recent whole exome and genome sequencing indicates that each individual carries numerous genetic variants predicted to cause LoF of protein-coding genes. MacArthur et al recently studied 2951 putative LoF variants obtained from whole genome sequencing from 185 human individuals from the 1000 Genome Project (derived from typical population samples), identifying and validating rare and likely deleterious LoF alleles, as well as common LoF variants in nonessential genes in this data set (26). They estimated that a typical human genome contains 100 genuine LoF variants with 20 genes inactivated in both copies, indicating unexpected redundancy in the human genome and suggesting that there are numerous mutations that are ‘‘private’’ to each personal genome. Since the immune system of an individual carrying LoF variants in both copies of a given gene may have had no previous exposure to protein (s) encoded by that gene(s), cellular or humoral immune recognition of that protein as an alloantigen in the grafted organ could very plausibly contribute to risk of rejection. LoF impacting the expression or translation of both copies of a given gene, especially in the transplanted organ of interest, is thus a plausible source of donor–recipient genomic incompatibilities, and may underpin rejection. Need for second-generation sequencing The emergence of large numbers of potential non-HLA incompatibilities with varying potential levels of immunogenicity highlights the clear need for deeper capture and assessment of SNVs, SNPs and CNVs in donors and recipients. Deep sequencing technologies will undoubtedly be key to unraveling common and rare LoFs and hdCNVs in population-scale diversity as well as capturing ‘‘private’’ individual-level polymorphisms to characterize the histocompatibility determinants involved in the biological mechanisms of alloimmunity. It should be noted that while there is low statistical power to detect such LoFs and hdCNVs at an association level, there is a lot of promise to follow up putative individual donor–recipient genomic incompatibility through the use of autoantibody testing in sera and/or tissues from the recipient postoperatively. Such approaches offer the promise of clinical utility through closer monitoring of a priori mHAs or potentially tolerizing recipients to a given gene product. Genetic risk factors for transplantation outcomes beyond HLA Over the last decade there have been unprecedented advances in the assessment of human genomic diversity across the major human populations through the development of high-throughput genotyping and deep sequencing technologies, as well as the development of population scale genomic maps such as the International HapMap Project (28). These tools have been applied to deeply American Journal of Transplantation 2014; 14: 764–778
Transplantation Genetics
characterize the genetic architecture of common and rare diseases and evoked conditions such as drug severe adverse events (29). Unfortunately, to date, the field of organ transplantation has not benefited from many of these advances. While over a thousand genetic association studies on organ rejection have been published so far, they are primarily candidate-gene based and suffer from many of the usual pitfalls of genetic association studies such as lack of adequate sample sizes, retrospective study designs, noninclusion of appropriate covariates such as ethnicity, lack of replication and proper statistical correction when multiple hypotheses are tested. Table 1 shows a review of appropriately designed published studies on organ transplantation outcomes. These studies were initially collated from PubMed and filtered for appropriate study size and design, defining appropriate as having at least a sample size of 200 individuals, including covariates, accounting for ethnicity and adjusting for multiple testing, when necessary. As observed in Table 1, genetic variation in up to 47 genes encoding cytokines, chemokines, celladhesion molecules, components of the renin–angiotensin–aldosterone pathway, coagulation and aggregation factors has been widely investigated and associated with outcomes such as delayed graft function, acute or chronic rejection or graft failure. However, results do not consistently replicate across studies. This lack of replication can be attributed to both the above-mentioned limitations of candidate-gene study designs and the complexity and diversity of clinical phenotypes. In addition to differences in protocols for immunosuppressive regimes, ascertainment of outcomes may differ by study—e.g. the criteria used to define acute rejection and chronic allograft dysfunction might vary among transplant centers: these all introduce further heterogeneity between studies. Specifically, in the case of rejection, studies may not always base the diagnosis on biopsy results and biopsy results themselves may be inaccurate due to interoperator differences. It is essential, therefore, to use objective and standard definitions for transplantation outcomes in order to obtain consistent results to homogenize studies. Thus future studies should aim to define phenotypes with precision and use a rigorous genetic approach, which preferably incorporates a hypothesis-free design such as GWAS to gain the most insight into genetic risk factors for organ transplantation.
Pharmacogenetics of immunosuppressant responses Genetic background is thought to account for as much as 95% of the variability in drug disposition and therapeutic effects (30). Regimes for immunosuppressants are typically characterized by wide pharmacokinetic interindividual variability and narrow therapeutic indexes, which often makes the ideal balance between sufficient immunosuppression and drug toxicity difficult to achieve. Thus, the American Journal of Transplantation 2014; 14: 764–778
discovery of genetic markers responsible for the interindividual variation in response to immunosuppressive therapy is an intensive area of ongoing research in transplantation. Ekberg et al evaluated the immunosuppressant drug exposures in 1645 renal transplant patients randomly assigned to four treatment groups: (1) standard-dose cyclosporine, (2) low-dose cyclosporine, (3) low-dose tacrolimus and (4) low-dose sirolimus (31), and observed that up to 90% of patients experienced at least one adverse event during treatment. Both biopsy-proven acute rejection and severe adverse events were similar in groups, ranging from 2.3% to 37% and 43% to 53%, respectively, depending on the drug, with the highest events observed in the low sirolimus dose group (31). In efforts to avoid such issues, therapeutic drug monitoring is being routinely performed, but it is currently assessed posttransplant and thus is not used for determining the optimal immunosuppressant starting dose, which is still established by an iterative postoperative approach. Alternative strategies incorporating pharmacogenetics hold great promise as complementary tools in drug monitoring to better guide individual therapies and doses. While the number of studies focusing on the pharmacogenetics of immunosuppressants has increased dramatically over the last few years, to date, many of these are underpowered that suffer from the typical candidate gene association study pitfalls. One clear exception is the robust association observed for rs776746 in CYP3A5, the primary enzyme involved in the metabolism of tacrolimus, the most frequently prescribed immunosuppressant drug worldwide. While nongenetic factors such as recipient gender, age, diabetes status and calcium channel blockers, such as some newer class of antifungals and grapefruit juice, influence tacrolimus levels, the rs776746 SNP is very important in tacrolimus clearance, with dosing requirements as well as time to therapeutic concentrations explaining up to 45% of the dose and 30% of clearance variability (13). The rs776746-A form is classically referred to as CYP3A5 1, while rs776746-G is referred to as CYP3A5 3. The latter is a noncoding variant that results in a cryptic splicing site, which causes 131 nucleotides of the intronic sequence to be inserted in the mRNA (32). The consequence is the introduction of a premature stop codon that truncatesCYP3A5 and leads to complete lack of CYP3A5 translation in 3 homozygotes (32). The prevalence of this CYP3A5 3 allele is as high as 90% in individuals of European ancestry while 90% of African Americans carry at least one of the common fully functional CYP3A5 1 alleles ( 1/ 1 or 1/ 3). These frequency differences in CYP3A5 3 make it one the most important genetic markers of interindividual and interethnic differences observed in CYP3A-dependent drug responses and clearance (33). There is also evidence of an additional rare variant, CYP3A5 6, which is associated with tacrolimus trough levels. Tables 2–4 review genetic association studies for tacrolimus, cyclosporine and mycophenolic acid regarding 767
768
Variant
rs4343
rs699
rs5186
rs1044240
rs2230199
C4L/S, CNV
rs1024611
109T/C, rs2107538, rs2280788 (a)
rs1799864
rs1799987 (b), rs333 (c)
rs2569190
rs20417
rs1801239, rs7918972
rs1801157
Gene
ACE
AGT
AGTR1
ALCAM
C3
C4
CCL2
CCL5
CCR2
CCR5
CD14
COX2
CUBN
CXCL12
12201365
22574174
19788502
19741468 17217435 17989610
19561149 15458467 12462338b 12201365c 17989610c
15458467 17989610 12201365 12239249
17989610 12462338a
17989610 12462338 12239249
21164027
22176838 19246358
20220571
AR, GS, PS
Proteinuria
GF
AR AR, BOS, GS CAN
AR AR, CAN AR, GS AR, GS, PS AR, SR
AR, CAN AR, CAN, SR AR, GS, PS GF
AR, CAN, SR AR, GS
AR, CAN, SR AR, GS GF
AR, GF, GL, GS
AR, DGF, PNF AR, GF, PS
GS
GF, serCr
GF, serCr
11923700 11923700
GF
GF GF, serCr RHC
Outcome
12548125
12548125 11923700 21659963
PMID
Table 1: Genetic association studies of organ transplantation outcomes
Liver
Kidney
1142R þ 1186D 207
Kidney
Kidney Lung Kidney
Kidney Kidney Liver Liver Kidney
Kidney Kidney Liver Kidney
Kidney Liver
Kidney Liver Kidney
Kidney
603
216D-R 226 436
243D-R 244D 209 207 436
244D 436 207 232
436 209
436 209 232
1969
Kidney Kidney
Kidney
954 þ 1002 1265D-R 1147
Kidney
Kidney
Kidney
Kidney Kidney Heart
Graft
224
224
210
210 224 532
Sample size
American Journal of Transplantation 2014; 14: 764–778
(Continued)
rs1801157 29.0% in dead patients compared to alive 17.1% and PS time (134 vs 98 months wt vs carriers) (p < 0.034)
ESRD: OR ¼ 1.39, elevated proteinuria 1y post (p < 0.015)
rs20417C and GL HR ¼ 2.43 [1.19-4.97], cohort 1; HR ¼ 1.72 [0.99-3.77], cohort 2 (p < 0.051)
NA Earlier AR, BOS and rs2569190TT: HR ¼ 1.65 [1.03–2.64], p ¼ 0.04 NA
# A alleles (D þ R) and AR, RR AR (p ¼ 0.039) NA NA NA
NA NA NA NA
NA NA
NA NA rs1024611GG vs A and GS: 67 14 vs 95 4 months; p ¼ 0.0052
NA
NA NA
NA
rs5186 and preserved long-term GF (p ¼ 0.037)
Increased Cr/t shorter t-to-sustained doubling of Cr, shorter t-to GL (p < 0.007) NA
NA NA RHC and rs4343: HR ¼ 0.58 [0.36-0.95]; p ¼ 0.031
Results
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
rs1126579, rs4674258
rs2228014
rs1799998
rs2814778
rs1800682
rs763110
rs17514136, rs17549193, rs3124952, rs3124953, rs7851696
CXCR2
CXCR4
CYP11B2
DARC
FAS
FASL
FCN2
(GT)n, rs2071746, rs2071747, rs2071748, rs2071749, rs2285112, rs5755720, rs6518952, rs8140669
rs2430561
rs1800871, rs1800872, rs1800896
HMOX1
IFNG
IL10
rs6025
rs2671222
CXCR1
FV
Variant
Gene
Table 1: Continued
15458467 15599305 20622753 15367225
15458467 17989610 15599305 20622753 21659963
18640487
AR, CAN AR, DGF GF, IIF GS
AR, CAN AR, CAN, SR AR, DGF GF, IIF RHC
DGF
AR, GF
BPAR, DGF, PNF, PS
22173060
11858477
RHC
RHC
AR, DGF
GF, serCr
BPAR, GS
AR
21659963
21659963
15327416
11923701
21304904
21452410
AR
BPAR, GS
21304904 21452410
Outcome
PMID
244D 291R þ 206D 218 2298 þ 1901
244D 436 291R þ 206D 218 532
965R
394
1272
532
532
222
224
335D-R
216
216
335D-R
Sample size
Kidney Kidney Kidney Kidney
Kidney Kidney Kidney Kidney Heart
Kidney
Kidney
Kidney
Heart
Heart
Kidney
Kidney
Kidney
Kidney
Kidney
Kidney
Graft
769
(Continued)
NA R ACCACC, ATAATA, GCCATA AR: OR ¼ 1.9 [1.1–3.1], p ¼ 0.016 rs1800871 TT high IIF grade (OR ¼ 3.27 [1.1–9.8]; p ¼ 0.035) NA
CAN (p < 0.008) NA NA NA NA
NA
GA no GF (RR ¼ 2.87 [1.01-8.26]), #dialysis until GF, risk for at least one AR episode (RR ¼ 3.83 [1.38-10.59]). Cr, protein excretion rate (p < 0.05).
NA
NA
RHC and rs1800682AA: HR ¼ 1.84 [1.25–2.69]; p ¼ 0.002
NA
rs1799998TT vs CC worse GF (p ¼ 0.002)
NA
NA
AR and D rs2671222A (OR ¼ 3.56 [1.37-9.27]; p ¼ 0.009)
D-rs1801157A and BPAR: OR ¼ 0.39, [0.20–0.76] and poor GS: HR ¼ 3.01; [1.19–7.60]; p < 0.020
Results
Transplantation Genetics
770
rs1801131, rs1801133
rs1805087
rs1799983
MTHFR
MTR
NOS3
rs12953, rs1131012, rs668
rs11003125, rs1800450, rs1800451, rs5030737, rs7095891, rs7096206
MBL2
PECAM1
rs72550870
rs1800795
IL6
MASP2
rs1801275
IL4R
rs5918
(CA)m(CT)n rs2069762
IL2
ITGB3
rs1143634, rs16944
IL1B
rs4073
rs187238
IL18
IL8
rs3212227
rs1554286, rs1878672, rs2222202, rs3021094, rs3024493, rs3024494, rs3024498
Variant
IL12B
Gene
Table 1: Continued
20220571
19349296
19349296
19349296
22173059 19104434
22173060
17928472
21452410
15458467 15599305 20622753 21659963
15367225
11981437 15458467
20622753
19077897
GS
GS
GS
GS
BPAR, DGF, PNF, PS BOS, GS
BPAR, DGF, PNF, PS
AR
AR
AR, CAN AR, DGF GF, IIF RHC
GS
AR AR, CAN
GF, IIF
AR
GF, IIF
RHC DGF
21659963 18640487
20622753
Outcome
PMID
Kidney Kidney
954 þ 1002
Kidney
Kidney
Kidney Lung
Kidney
Kidney
Kidney
262D-218R
262D-218R
262D-218R
1271 277 D-R
1272
445
216
Kidney Kidney Kidney Heart
Kidney
2298 þ 1901 244D 291R þ 206D 218 532
Heart Kidney
290 244D
Kidney
Kidney
226 þ 148C 218
Kidney
Heart Kidney
Graft
218
532 965R
Sample size
NA
GS: OR ¼ 9.192; [1.09-76.9]; p ¼ 0.0406
NA
NA
(Continued)
NA D-rs7096206 and GS and BOS free PS (p ¼ 0.007)
NA
rs5918TT and AR: OR ¼ 3.4; p ¼ 0.04
NA
NA NA NA NA
NA
Allele 135 and risk of AR (p < 0.03) NA
–
rs187238GG and AR: OR ¼ 3.653; p ¼ 0.015
NA
rs1800896 RHC: HR ¼ 0.49 [0.27–0.90]; p ¼ 0.020 NA
Results
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
American Journal of Transplantation 2014; 14: 764–778
rs5368
rs1131498, rs2205849, rs2229569
rs1800471, rs1800470 (d), rs1800472 (e)
rs3775290, rs3775291, rs3775296
rs10759932, rs1927914
rs1800629 (f), rs1800628 (g), rs3093662 (h) rs361525
rs1042522, rs12951053, rs1614984, rs1625895, rs17884306, rs4968187, rs9894946
rs1005230, rs2010963, rs3556934, rs699947
SELE
SELL
TGFB1
TLR3
TLR4
TNF
TP53
VEGF
h
21659963
RHC
DGF
AR, GS DGF GF, IIF RHC
15367225 18640487f, g, 20622753 21659963 18640487
AR AR AR, CAN, SR AR, DGF
AR
12682890 15458467 17989610 15599305
19741468
AR
DGF GF, IIF GS
18640487d,e 20622753 15367225 19741468
AR, CAN AR, CAN, SR AR, DGF
GS
20220571
15458467 17989610 15599305
AR
Outcome
21452410
PMID
532
Heart
Kidney
Kidney Kidney Kidney Heart
2298 þ 1901 965R 218 532 965R
Liver Kidney Kidney Kidney
Kidney
Kidney
Kidney Kidney Kidney
210D-R 244D 436 291R þ 206D
216D-R
216D-R
965R 218 2298 þ 1901
Kidney Kidney Kidney
Kidney
964 þ 1002
244D 436 291R þ 206D
Kidney
Graft
216
Sample size
NA
NA
NA NA NA R rs1800629AA and AR: OR ¼ 5.0 [3.0–8.3]; D rs1800629GA and DGF OR ¼ 1.6 [1.1–2.6], p < 0.040 GS rate among retransplants: 63.0% AA vs.79.5%G; p ¼ 0.0116 NA NA NA
R rs10759932C vs TT and AR (OR ¼ 0.25 [0.11-0.57]). rs10759932C higher rejection-free PS rates (p < 0.023)
NA
rs1800470 and AR (p ¼ 0.027) NA R rs1800471GG þD rs1800871T and AR: OR ¼ 1.8 [1.1–3.0], p ¼ 0.027 NA NA NA
NA
NA
Results
Summary of the most relevant studies performed to date reporting genes/variants associated with transplantation outcomes. Low case letters a to h in PMID column indicate that, amongst all the variants, these particular studies investigate exclusively those variants specified by the letters. For the rest of the studies, all variants are investigated. In alphabetical order, abbreviations for outcomes are: AR ¼ acute rejection; BOS ¼ bronchiolitis obliterans syndrome; BPAR ¼ biopsy proven acute rejection; CAN ¼ chronic allograft nephropathy; DGF ¼ delayed graft function; GF ¼ graft function; GL ¼ graft loss; GS ¼ graft survival; IIF ¼ infiltration of inflammatory cells; PNF ¼ primary non-function; PS ¼ patient survival; RHC ¼ rejection with hemodynamic compromise; serCr ¼ serum creatinine; SR ¼ subclinic rejection. In sample size and results columns, D ¼ donor and R ¼ recipient. For results, ESRD ¼ end-stage renal disease; NA ¼ No Association
Variant
Gene
Table 1: Continued
Transplantation Genetics
771
772
Variant
rs1045642 (a), rs1128503, rs2032582 (b)
rs3740066, 1446C > G, rs717620 (c)
rs2740574 (d) and rs28371759, rs35599367 (e), rs4987161, rs55785340, rs4986910
rs776746, rs10264272 (f), rs41303343 (g)
Gene
ABCB1
ABCC2
CYP3A4
CYP3A5
104 151 399D-R 301 185
832
18192894 15592326f 15454731 21806386f,g 21903774
19005401
CNI
185
21903774e
CsA
CNI
104
124 151 832
18192894
CsA
185 338
11375290d 15592326 19005401d
19890249c 19494809
MPA þ CNI
104
136 209D-R
18334918 21677300a 18192894
301 832 103 150D
124 151 294
11375290a 15592326a 20061922 21806386 19005401 16753004a,b 17885626
104
Sample size
18192894
PMID
CsA
Tac
CNI
CsA
Primary IST
Table 2: Pharmacogenetic studies on immunosuppressant therapy pharmacokinetics
Kidney
Kidney Kidney Kidney Kidney
Kidney
Kidney
Kidney Kidney and heart Kidney
Kidney
Kidney Kidney
Kidney
Kidney Kidney
Kidney Kidney Kidney Liver
Kidney and heart Kidney Heart
Kidney
Graft
NA NA rs776746AA Tac Co/D than G (p0.0001) Haplotype rs776746AA (CYP3A5)-rs35599367T or CC higher C than rs776746G (CYP3A5)-rs35599367CC (p < 0.03) rs2740574(CYP3A4)AA-rs776746GG and less time to achieve Tac C and higher C/D/Kg (p < 0.001)
NA
NA rs2740574G higher Cl than AA (p < 0.05) rs2740574AA-rs776746 GG (CYP3A5) less time to Tac C and higher C/D/Kg (p < 0.001) rs35599367T D lower than rs35599367CC (p ¼ 0.018). Haplotype rs776746AA (CYP3A5)-rs35599367T or CC higher C than rs776746G (CYP3A5)-rs35599367CC (p < 0.03)
NA
NA NA
NA
NA NA Higher Co/D-Co/weigh in rs1128503CC-rs2032582GGrs1045642CC than TT-TT-TT (p < 0.05) NA NA rs1045642 explains 3.7% of D variation (p ¼ 0.009) rs1128503T and rs2032582T hepatic C greater than CC and GG (p < 0.035) rs1045642TT lower D than C (p < 0.04) NA
Lower oral B: rs2032582GG, rs1128503TT, rs2229109Grs1128503C-rs2032582G-rs1045642C (p < 0.05)
Results
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
American Journal of Transplantation 2014; 14: 764–778
rs17868320, rs6714486, rs72551330, rs2741049
rs7438135, rs7439366, rs7668282
UGT1A9
UGT2B7
18641546 19494809
MPA MPA þ CNI
19890249
19494809
MPA þ CNI
338
332
185
338
185
180
12490779 19890249
178
150D
103 208 150D 209D-R 136
Sample size
15147425
MPA þ CNI
Tac
17885626
16753004 21566507 17885626f 21677300 18334918
Tac
Tac
PMID
Primary IST
Kidney
Kidney
Kidney
Kidney
Kidney
Liver
Kidney Kidney Liver Kidney Kidney
Graft
NA
NA
NA
rs1042597GG higher CsA AUC0–12; rs6714486A and/or rs17868320T lower CsA AUC0–12 (p < 0.05)
Tac patients rs4149117T decrease in MPAG/MPA; Cmax (p < 0.0003)
1 lower C, delay in achieving target C and 3/ 3 C above target (p < 0.003) CYP3AP1 1 vs 3 reduction in C (p < 0.05)
NA
rs776746A explains 35.3% of D variation (p < 0.001) NA rs776746G higher D than AA (p < 0.05) R rs776746G lower Co, Co/D (p < 0.05) rs776746G lower Co/D (p < 0.003)
Results
Relevant studies investigating genes/variants associated with calcineurin inhibitor and mycophenolic acid pharmacokinetics. Similarly to Table 1, letters a to g in PMID column indicate that particular studies investigate exclusively those variants specified by the letters. For Primary IST, CsA ¼ cyclosporine A; CNI ¼ CsA þ Tac; MPA ¼ mycophenolic acid; Tac ¼ tacrolimus. For sample size, D ¼ donor and R ¼ recipient. In results, AUC0–12 ¼ area under the curve from 0 to 12 hours; B ¼ bioavailability; C ¼ concentration; Cl ¼ clearance; Co ¼ trough concentration; D ¼ dose; MPAG/MPA ¼ MPA glucuronide to MPA ratio; NA ¼ No Asociation.
rs1042597, rs17863762
UGT1A8
1/ 3
1C
rs4149117, rs60140950
Variant
SLCO1B3
CYP3AP1
CYP3A7
Gene
Table 2: Continued
Transplantation Genetics
773
774
rs3740066, 1446C > G, rs717620
rs2740574
ABCC2
CYP3A4
PXR
rs11706052 (g), rs4974081 rs1523130, rs2276707,
IMPDH2
1/ 3
rs11770116, rs2228075, rs2288548, rs2288549, rs2288550, rs2288553, rs4731448 and rs2278293 (e), rs2278294 (f)
IMPDH1
CYP3AP1
rs776746
rs1045642 (a), rs1128503 (b), rs2032582 (c), rs717620 (d)
ABCB1
CYP3A5
Variant
Gene
CNI
MPA
MPA
Tac
CNI MPA þ Tac Tac
CsA
CNI MPA þ Tac
CsA
DGF
AR
AR
AR
GL GS AR CAD AR AR, DGF
AR AR, GL AR CAD
AR
CAD AR, DGF
MPA þ Tac Tac
191 456
191 456 178D-R
17851563g 20679962 22453193
178
227D-259R 399D-R 832 252 136 209D-R
124 227D-259R 832 252
338
17851563 20679962e,f
15147425
20505666 15454731 19005401 19762492 18334918 21677300
11375290 20505666 19005401 19762492
19494809
178D-R 252 209D-R
22453193d 19762492a-c 21677300a
147 832
18510642
DGF
227D-259R
294 170
124
Sample size
19005401
20505666
AR
MPA þ CNI
a
20061922 17198259a,c
11375290
PMID
AR, GL
AR
Primary outcome
CNI
CsA
Primary IST
Table 3: Pharmacogenetic studies of transplantation outcomes
Kidney Kidney Kidney
Kidney Kidney
Kidney
Kidney Kidney Kidney Kidney Kidney Kidney
Kidney Kidney Kidney Kidney
Kidney
Kidney Kidney Kidney
Kidney
Kidney
Kidney
Heart Heart
Kidney
Graft
1 earlier AR (p ¼ 0.005)
NA NA Donor 2276707TT higher risk for DGF (p < 0.05)
rs2278293A and rs2278294A lower risk (p < 0.05) rs2278294A lower risk (p ¼ 0.0075)
NA NA NA NA NA NA
NA NA NA NA
NA
NA rs1045642CC and haplotype rs2032582GG-rs1045642CC higher risk for >3A rejection (p ¼ 0.02) D haplotype rs1128503T-rs2032582T-rs1045642T higher risk for AR (p ¼ 0.018) and GL (p ¼ 0.0019) rs1045642T and rs2032582T higher risk for DGF (p < 0.034) Haplotype rs1128503C-rs2032582G-rs1045642T higher risk for AR (p ¼ 0.038) NA D and R rs104564TT higher IF/TA (p ¼ 0.001) NA
NA
Results
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
Relevant pharmacogenetic studies in transplantation outcomes. Letters a to g in PMID column indicate that particular studies investigate exclusively those variants specified by the letters. For Primary IST, CsA ¼ cyclosporine A; CNI ¼ CsA þ Tac; MPA ¼ mycophenolic acid and Tac ¼ tacrolimus. For sample size, D ¼ donor and R ¼ recipient. For primary outcome, AR ¼ acute rejection; CAD ¼ chronic allograft damage; DGF ¼ delayed graft function; GL ¼ graft loss; GS ¼ graft survival. For sample size, D ¼ donor and R ¼ recipient and in results, NA ¼ No Asociation.
rs7438135, rs7439366, rs7668282 UGT2B7
338 19494809 rs6714486, rs17868320 UGT1A9
AR rs3814055, rs1042597, rs7643645 rs17863762 UGT1A8
MPA þ CNI
Sample size Primary outcome Gene
Table 3: Continued
Variant
Primary IST
PMID
Graft
Kidney
Results
Tac patients with 6714486A and/or rs17868320T higher risk of AR (p < 0.05)
Transplantation Genetics
American Journal of Transplantation 2014; 14: 764–778
pharmacokinetics, transplant outcomes and adverse events. Due to the previously mentioned limitations of the candidate gene approach, we only focus on studies with over 100 individuals and appropriate quality control for phenotype and genotype data. Several studies have confirmed the major impact of CYP3A5 on tacrolimus dose requirements and renal clearance; however, similar to genetic association studies on graft outcomes, it has been challenging to robustly replicate additional findings. Apart from CYP3A5, to date, only the associations of ABCB1 rs1045642, rs1128503, rs2032582 and cyclosporine pharmacokinetics and CYP3A4 rs2740574 and rs3559936 with tacrolimus pharmacokinetics appear to be consistent. All of the rigorously designed studies to date conclude that there is a need to dose tacrolimus patients by the CYP3A5 rs776746 genotype. Thervet et al (15), during the first 6 days posttransplantation, studied the effect of guiding tacrolimus dosing based on the number of CYP3A5 functional alleles, on plasma drug concentration. Patients were randomly assigned to either a standard initial dose of tacrolimus (0.2 mg/kg/day) or a genotype-adjusted dose, who received 0.3 mg/kg/day if carrying allele 1 and 0.15 mg/kg/day if 3/ 3. This randomized clinical trial found significantly higher number of patients achieving optimal dosing in the genotype-based dosing group, more rapidly and with fewer dose modifications (15). However, the trial was not designed to investigate hard clinical outcomes such as graft failure—the acid test of a pharmacogenetic test. Passey et al (34) established a dosing algorithm for tacrolimus including clinical, genetic and ethnic information. From a cohort of more than 600 kidney recipients, compared to those with G/G genotype, those with the rs776746 A/G genotype experienced a 69% increase in tacrolimus clearance and A/A genotype a 100% increase in clearance. The final dosing algorithm included CYP3A5 rs776746 genotype, days posttransplantation, age, steroid and calcium channel blocker use as independent predictors of the outcome (34). The incorporation of CYP3A5 genetic information into tacrolimus dosing algorithm will likely be a first major step toward precision genotype-guided dosing in the transplantation setting. Optimal use of pharmacogenetics will require a deeper knowledge of additional genetics factors governing drug disposition, efficacy and toxicity. The application of the genomic advances to this field has the real potential of optimizing dosing strategies for immunosuppressive drugs, avoiding serious adverse and improving patient management after organ transplantation. Ancestry as a genetic risk factor for transplantation outcomes It is also worth noting the large impact that donor and recipient ancestry has in transplantation. Many studies have reported greater risks of rejection and mortality in African Americans when their organs are used as donor 775
776 MPA
MPA
rs5186
rs2740574
rs776746
rs11770116, rs2228075, rs2288548, rs2288549, rs2288550, rs2288553, rs4731448 and rs2278293 (b), rs2278294 (c)
rs11706052, rs4974081
521T > C, rs2306283, rs4149015, rs4149056
rs4149117
98T > C, rs17868320, rs6714486
rs7438135, rs7439366
CYP3A4
CYP3A5
IMPDH1
IMPDH2
SLCO1B1
SLCO1B3
UGT1A9
UGT2B7
Blood pressure
Diarrhea,leukopenia, anemia, infections
456
332 218
18641546 (diarrhea, leukopenia) 21142914
218
20679962 21142914
191
218 456
191
399D-R
209D-R 304
304
233
17851563 (leukopenia)
21142914b,c 20679962 (leukopenia,infection) b,c
17851563 (leukopenia)
15454731
21677300 20526235
20526235
16477235
218
209D-R 304 218
21677300a 20526235 21142914a 21142914
294
Sample size
20061922
PMID
Kidney
Kidney
Kidney
Kidney
Kidney
Kidney Kidney
Kidney
Kidney
Kidney Kidney
Kidney
Liver
Kidney
Kidney Kidney Kidney
Heart
Graft
NA
NA
rs4149056C reduced risk of adverse events (p ¼ 0.002)
NA
NA
NA rs2278294 higher risk of leukopenia (p ¼ 0.0139)
NA
NA rs776746G higher risk of nephrotoxicity (p ¼ 0.01) NA
NA
rs4343DD higher risk of nephrotoxicity (p < 0.0001)
NA
NA NA NA
NA
Results
Pharmacogenetic studies on IST induced adverse events. Letters a to g in PMID column indicate that particular studies investigate exclusively those variants specified by the letters. For Primary IST, CsA ¼ cyclosporine A; CNI ¼ CsA þ Tac; MPA ¼ mycophenolic acid; Tac ¼ tacrolimus. For sample size, D ¼ donor and R ¼ recipient. For sample size, D ¼ donor and R ¼ recipient and in results, NA ¼ No Association.
MPA
Diarrhea,leukopenia, anemia, infections
Leukopenia, infections
Diarrhea,leukopenia, anemia, infections
CsA MPA
Nephrotoxicity
Nephrotoxicity
Nephrotoxicity
Diarrhea,leukopenia, anemia, infections
Diarrhea,leukopenia, anemia, infections
Nephrotoxicity
Adverse event
Tac
Tac
rs5051
AGTR1
CNI
MPA
AGT
rs2273697, rs3740066, 1446C > G, rs717620
ABCC2
MPA
Tac
CsA
rs4343
rs1045642 (a), rs1128503, rs2032582
ABCB1
Primary IST
ACE
Variant
Gene
Table 4: Pharmacogenetics of immunosuppressant therapy–induced adverse events
Almoguera et al
American Journal of Transplantation 2014; 14: 764–778
Transplantation Genetics
grafts or when they are a recipient of a graft (35–37). Callender et al also confirmed lower kidney survival rates among African Americans compared to all other ethnic groups and showed that this ethnic group also harbored the shortest half-life after kidney transplantation: only 5.3 years versus 12.2 years for Asians, 10.2 years for individuals of European descent and 9.0 years for Hispanics (36). In 2010, Li et al (38) published a pivotal study showing that Chinese and African human genomes differed by approximately 5 Mb of unique sequence and over 240 potential genes differing between these sequences. Such ‘‘pangenome’’ data sets illustrate the differences between populations and emphasize the need for further characterization of such genomic differences in the transplantation arena. In addition to the highly polymorphic differences in HLA loci already evident in the different ethnic groups, variants like LoFs or hdCNVs with the potential of acting as incompatibility antigens, or genetic risk factors for postoperative transplant outcomes as well as pharmacogenetic markers, exhibit substantial variability interethnically.
Future Perspectives The current pace of genomic discovery has revolutionized our understanding of the genetic architecture of common diseases (www.genome.gov/gwastudies) (29). In contrast, the pipeline of genomic research in transplantation has been relatively weak. This is surprising because in essence transplantation involves the direct exposure of a foreign donor genome into a recipient, creating unparalleled opportunities to investigate the implications this has on graft function. For example, recipient by donor genomic interactions may play a critical role in graft survival. However, analyses of these types will require large wellcharacterized data sets that have been harmonized for important clinical traits and genotyped using GWAS or preferably second-generation sequencing. While small candidate studies have started the collation of DNA and appropriately phenotyped recipients and donors, very few studies have sufficient numbers to be statistically powered to look at polygenic transplant-related intermediate phenotypes and outcomes. There is a clear need for wider collaborations among all appropriately harmonizable data sets using robust genomic platforms and statistical methods to bolster statistical power among existing studies. With such initiatives, we expect the genetic architecture of transplant rejection ranging from identification of LoF variants, which introduce new epitopes to donors, to genes involved in drug metabolism to change the way transplanted grafts are matched and/or recipients are monitored posttransplant. Furthermore, established genetic techniques such as hypothesis-free analyses and Mendelian randomization can be conducted in this setting to investigate the causal effect of modifiable risk factors on graft function and survival with the potential to discover new therapeutic targets. American Journal of Transplantation 2014; 14: 764–778
Large-scale NIH networks such as The electronic Medical Records and Genomics (eMERGE) network integrating large-scale hospital GWAS biobank resource with EMRs (www.genome.gov/27540473) will allow us a framework to consent, counsel and manage the return of results to physicians and graft recipients in a clinical environment, which will lead to greater patient monitoring and care. As waiting lists for kidney transplantation continue to grow (n ¼ 97 856, September 2013) and with solid organ transplants costing from $200 000 to $850 000 (USD) per patient, efforts such as eMERGE are currently attempting to integrate actionable pharmacogenomics data into patients’ EMRs, and tacrolimus-related dosing is being conducted in at least one eMERGE site thus far, which will likely result in a significant benefit to society in the very near future. Although beyond the scope of this review, a number of protein, RNA and microRNA biomarkers for rejection and graft damage injury are emerging that may aid in the diagnosis of rejection episodes. Specific biomarkers are also under investigation for immune quiescence and minimization of immune-suppressants (reviewed in (39)). We are now entering a period in genomics when tools are becoming available, which will dramatically change our understanding of the molecular mechanisms underpinning graft outcomes, and patient-specific treatments will begin to benefit transplant patient care and to define the future of transplantation.
Disclosure The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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American Journal of Transplantation 2014; 14: 764–778
