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Kidney transplant recipients carrying the CYP3A5*1 allele have lower tacrolimus troughs, and higher dose requirements compared to those with the CYP3A5*3/*3 genotype. However, data on the effect of CYP3A5 alleles on post-transplant tacrolimus management are lacking. The effect of CYP3A5 metabolism phenotypes on the number of tacrolimus dose adjustments and troughs in the first 6 months post-transplant was evaluated in 78 recipients (64% Caucasians). Time to first therapeutic concentration, percentage of time in therapeutic range (TTR), and estimated glomerular filtration rate (eGFR) were also evaluated. Fifty-five kidney transplant recipients were CYP3A5 poor metabolizers (PM), 17 were intermediate metabolizers (IM), and 6 were extensive metabolizers (EM). Compared to PMs, EMs/IMs had significantly more dose adjustments (6.1 vs. 8.1, p = .015). Overall, 33.82% of trough measurements resulted in a dose change. There was no difference in the number of tacrolimus trough measurements between PMs and EM/IMs. The total daily tacrolimus dose requirements were higher in EMs and IMs compared to PMs (

WHAT IS KNOWN AND OBJECTIVE: Pharmacogenomic biomarkers are now used in many clinical care settings and represent one of the successes of precision medicine. Genetic variants are associated with pharmacokinetic and pharmacodynamic changes leading to medication adverse effects and changes in clinical response. Actionable pharmacogenomic variants are common in transplant recipients and have implications for medications used in transplant, but yet are not broadly incorporated into practice. METHODS: From the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group guidelines, and PharmGKB databases, 12 pharmacogenomic genes with 30 variants were selected and used to create diplotypes and actionable pharmacogenomic phenotypes. A total of 853 kidney allograft recipients who had genomic information available from a genome-wide association study were included. RESULTS: Each recipient had at least one actionable pharmacogenomic diplotype/phenotype, whereas the majority (58%) had three or four actionable diplotypes/phenotypes and 17.4% had five or more among the 12 genes. The participants carried actionable diplotypes/phenotypes for multiple medications, including tacrolimus, azathioprine, clopidogrel, warfarin, simvastatin, voriconazole, antidepressants and proton-pump inhibitors. WHAT IS NEW AND CONCLUSION: Pharmacogenomic variants are common in transplant recipients, and transplant recipients receive medications that have actionable variants. CLINICAL TRIAL: Genomics of Transplantation, clinicaltrials.gov (NCT01714440).

Tacrolimus trough and dose requirements vary dramatically between individuals of European and African American ancestry. These differences are less well described in other populations. We conducted an observational, prospective, multicenter study from which 2595 kidney transplant recipients of European, African, Native American, and Asian ancestry were studied for tacrolimus trough, doses, and genetic determinants of metabolism. We studied the well-known variants and conducted a CYP3A4/5 gene-wide analysis to identify new variants. Daily doses, and dose-normalized troughs were significantly different between the four groups (P

BACKGROUND: Identifying kidney allograft recipients who are predisposed to acute rejection (AR) could allow for optimization of clinical treatment to avoid rejection and prolong graft survival. It has been hypothesized that a part of this predisposition is caused by the inheritance of specific genetic variants. There are many publications reporting a statistically significant association between a genetic variant, usually in the form of a single-nucleotide polymorphism (SNP), and AR. However, there are additional publications reporting a lack of this association when a different cohort of recipients is analyzed for the same single-nucleotide polymorphism. METHODS: In this report, we attempted to validate 75 common genetic variants, which have been previously reported to be associated with AR, using a large kidney allograft recipient cohort of 2390 European Americans and 482 African Americans. RESULTS: Of those variants tested, only 1 variant, rs2910164, which alters the expression of the microRNA MIR146A, was found to exhibit a significant association within the African American cohort. Suggestive variants were found in the genes CTLA and TLR4. CONCLUSIONS: Our results show that most variants previously reported to be associated with AR were not validated in our cohort. This shows the importance of validation when reporting the associations with complex clinical outcomes such as AR. Additional work will need to be done to understand the role of MIR146A in the risk of AR in kidney allograft recipients.

BACKGROUND: The immunosuppressants tacrolimus and mycophenolate are important components to the success of organ transplantation, but are also associated with adverse effects, such as nephrotoxicity, anemia, leukopenia, and new-onset diabetes after transplantation. In this report, we attempted to identify genetic variants which are associated with these adverse outcomes. METHODS: We performed a genome-wide association study, using a genotyping array tailored specifically for transplantation outcomes containing 722 147 single nucleotide polymorphisms, and 2 cohorts of kidney allograft recipients-a discovery cohort and a confirmation cohort-to identify and then confirm genetic variants associated with immunosuppressant pharmacokinetics and adverse outcomes. RESULTS: Several genetic variants were found to be associated with tacrolimus trough concentrations. We did not confirm variants associated with the other phenotypes tested although several suggestive variants were identified. CONCLUSIONS: These results show that adverse effects associated with tacrolimus and mycophenolate are complex, and recipient risk is not determined by a few genetic variants with large effects with but most likely are due to many variants, each with small effect sizes, and clinical factors.

Background: Acute rejection (AR) after kidney transplantation is an important allograft complication. To reduce the risk of post-transplant AR, determination of kidney transplant donor-recipient mismatching focuses on blood type and human leukocyte antigens (HLA), while it remains unclear whether non-HLA genetic mismatching is related to post-transplant complications. Methods: We carried out a genome-wide scan (HLA and non-HLA regions) on AR with a large kidney transplant cohort of 784 living donor-recipient pairs of European ancestry. An AR polygenic risk score (PRS) was constructed with the non-HLA single nucleotide polymorphisms (SNPs) filtered by independence (r2 < 0.2) and P-value (< 1×10−3) criteria. The PRS was validated in an independent cohort of 352 living donor-recipient pairs. Results: By the genome-wide scan, we identified one significant SNP rs6749137 with HR = 2.49 and P-value = 2.15×10−8. 1,307 non-HLA PRS SNPs passed the clumping plus thresholding and the PRS exhibited significant association with the AR in the validation cohort (HR = 1.54, 95% CI = (1.07, 2.22), p = 0.019). Further pathway analysis attributed the PRS genes into 13 categories, and the over-representation test identified 42 significant biological processes, the most significant of which is the cell morphogenesis (GO:0000902), with 4.08 fold of the percentage from homo species reference and FDR-adjusted P-value = 8.6×10−4. Conclusions: Our results show the importance of donor-recipient mismatching in non-HLA regions. Additional work will be needed to understand the role of SNPs included in the PRS and to further improve donor-recipient genetic matching algorithms. Trial registry: Deterioration of Kidney Allograft Function Genomics (NCT00270712) and Genomics of Kidney Transplantation (NCT01714440) are registered on ClinicalTrials.gov. [ABSTRACT FROM AUTHOR]

BackgroundThe probability of liver transplant and death on the waiting list in the United States varies greatly by donation service area (DSA) due to geographic differences in availability of organs and allocation of priority points, making it difficult for providers to predict likely outcomes after listing. We aimed to develop an online calculator to report outcomes by region and patient characteristics.MethodsUsing the Scientific Registry of Transplant Recipients database, we included all prevalent US adults aged 18 years or older waitlisted for liver transplant, examined on 24 days at least 30 days apart over a 2-year period. Outcomes were determined at intervals of 30 to 365 days. Outcomes are reported by transplant program, DSA, region, and the nation for comparison, and can be shown by allocation or by laboratory model for end-stage liver disease (MELD) score (6-14, 15-24, 25-29, 30-34, 35-40), age, and blood type.ResultsOutcomes varied greatly by DSA; for candidates with allocation MELD 25-29, the 25th and 75th percentiles of liver transplant probability were 30% and 67%, respectively, at 90 days. Corresponding percentiles for death or becoming too sick to undergo transplant were 5% and 9%. Outcomes also varied greatly for candidates with and without MELD exception points.ConclusionsThe waitlist outcome calculator highlights ongoing disparities in access to liver transplant and may assist providers in understanding and counseling their patients about likely outcomes on the waiting list.

BACKGROUND: In addition to HLA genetic incompatibility, non-HLA difference between donor and recipients of transplantation leading to allograft rejection are now becoming evident. We aimed to create a unique genome-wide platform to facilitate genomic research studies in transplant-related studies. We designed a genome-wide genotyping tool based on the most recent human genomic reference datasets, and included customization for known and potentially relevant metabolic and pharmacological loci relevant to transplantation. METHODS: We describe here the design and implementation of a customized genome-wide genotyping array, the TxArray, comprising approximately 782,000 markers with tailored content for deeper capture of variants across HLA, KIR, pharmacogenomic, and metabolic loci important in transplantation. To test concordance and genotyping quality, we genotyped 85 HapMap samples on the array, including eight trios. RESULTS: We show low Mendelian error rates and high concordance rates for HapMap samples (average parent-parent-child heritability of 0.997, and concordance of 0.996). We performed genotype imputation across autosomal regions, masking directly genotyped SNPs to assess imputation accuracy and report an accuracy of >0.962 for directly genotyped SNPs. We demonstrate much higher capture of the natural killer cell immunoglobulin-like receptor (KIR) region versus comparable platforms. Overall, we show that the genotyping quality and coverage of the TxArray is very high when compared to reference samples and to other genome-wide genotyping platforms. CONCLUSIONS: We have designed a comprehensive genome-wide genotyping tool which enables accurate association testing and imputation of ungenotyped SNPs, facilitating powerful and cost-effective large-scale genotyping of transplant-related studies.

UnlabelledThe current system granting liver transplant candidates with hepatocellular carcinoma (HCC) additional Model for End-Stage Liver Disease (MELD) points is controversial due to geographic disparity and uncertainty regarding optimal prioritization of candidates. The current national policy assigns a MELD exception score of 22 immediately upon listing of eligible patients with HCC. The aim of this study was to evaluate the potential effects of delays in granting these exception points on transplant rates for HCC and non-HCC patients. We used Scientific Registry of Transplant Recipients data and liver simulated allocation modeling software and modeled (1) a 3-month delay before granting a MELD exception score of 25, (2) a 6-month delay before granting a score of 28, and (3) a 9-month delay before granting a score of 29. Of all candidates waitlisted between January 1 and December 31, 2010 (n = 28,053), 2773 (9.9%) had an HCC MELD exception. For HCC candidates, transplant rates would be 108.7, 65.0, 44.2, and 33.6 per 100 person-years for the current policy and for 3-, 6-, and 9-month delays, respectively. Corresponding rates would be 30.1, 32.5, 33.9, and 34.8 for non-HCC candidates.ConclusionA delay of 6-9 months would eliminate the geographic variability in the discrepancy between HCC and non-HCC transplant rates under current policy and may allow for more equal access to transplant for all candidates.

We performed RNA sequencing (RNAseq) on peripheral blood mononuclear cells (PBMCs) to identify differentially expressed gene transcripts (DEGs) after kidney transplantation and after the start of immunosuppressive drugs. RNAseq is superior to microarray to determine DEGs because its not limited to available probes, has increased sensitivity, and detects alternative and previously unknown transcripts. DEGs were determined in 32 adult kidney recipients, without clinical acute rejection (AR), treated with antibody induction, calcineurin inhibitor, mycophenolate, with and without steroids. Blood was obtained pre-transplant (baseline), week 1, months 3 and 6 post-transplant. PBMCs were isolated, RNA extracted and gene expression measured using RNAseq. Principal components (PCs) were computed using a surrogate variable approach. DEGs post-transplant were identified by controlling false discovery rate (FDR) at < 0.01 with at least a 2 fold change in expression from pre-transplant. The top 5 DEGs with higher levels of transcripts in blood at week 1 were TOMM40L, TMEM205, OLFM4, MMP8, and OSBPL9 compared to baseline. The top 5 DEGs with lower levels at week 1 post-transplant were IL7R, KLRC3, CD3E, CD3D, and KLRC2 (Striking Image) compared to baseline. The top pathways from genes with lower levels at 1 week post-transplant compared to baseline, were T cell receptor signaling and iCOS-iCOSL signaling while the top pathways from genes with higher levels than baseline were axonal guidance signaling and LXR/RXR activation. Gene expression signatures at month 3 were similar to week 1. DEGs at 6 months post-transplant create a different gene signature than week 1 or month 3 post-transplant. RNAseq analysis identified more DEGs with lower than higher levels in blood compared to baseline at week 1 and month 3. The number of DEGs decreased with time post-transplant. Further investigations to determine the specific lymphocyte(s) responsible for differential gene expression may be important in selecting and personalizing immune suppressant drugs and may lead to targeted therapies.