Optimizing Detection of Kidney Transplant Injury by Assessment of Donor-Derived Cell-Free DNA via Massively Multiplex PCR
Tara K Sigdel, Felipe Acosta Archila, Tudor Constantin, Sarah A Prins, Juliane Liberto, Izabella Damm, Parhom Towfighi, Samantha Navarro, Eser Kirkizlar, Zachary P Demko, Allison Ryan, Styrmir Sigurjonsson, Reuben D Sarwal, Szu-Chuan Hseish, Chitranon Chan-On, Bernhard Zimmermann, Paul R Billings, Solomon Moshkevich, Minnie M Sarwal, Tara K Sigdel, Felipe Acosta Archila, Tudor Constantin, Sarah A Prins, Juliane Liberto, Izabella Damm, Parhom Towfighi, Samantha Navarro, Eser Kirkizlar, Zachary P Demko, Allison Ryan, Styrmir Sigurjonsson, Reuben D Sarwal, Szu-Chuan Hseish, Chitranon Chan-On, Bernhard Zimmermann, Paul R Billings, Solomon Moshkevich, Minnie M Sarwal
Abstract
Standard noninvasive methods for detecting renal allograft rejection and injury have poor sensitivity and specificity. Plasma donor-derived cell-free DNA (dd-cfDNA) has been reported to accurately detect allograft rejection and injury in transplant recipients and shown to discriminate rejection from stable organ function in kidney transplant recipients. This study used a novel single nucleotide polymorphism (SNP)-based massively multiplexed PCR (mmPCR) methodology to measure dd-cfDNA in various types of renal transplant recipients for the detection of allograft rejection/injury without prior knowledge of donor genotypes. A total of 300 plasma samples (217 biopsy-matched: 38 with active rejection (AR), 72 borderline rejection (BL), 82 with stable allografts (STA), and 25 with other injury (OI)) were collected from 193 unique renal transplant patients; dd- cfDNA was processed by mmPCR targeting 13,392 SNPs. Median dd-cfDNA was significantly higher in samples with biopsy-proven AR (2.3%) versus BL (0.6%), OI (0.7%), and STA (0.4%) (p < 0.0001 all comparisons). The SNP-based dd-cfDNA assay discriminated active from non-rejection status with an area under the curve (AUC) of 0.87, 88.7% sensitivity (95% CI, 77.7⁻99.8%) and 72.6% specificity (95% CI, 65.4⁻79.8%) at a prespecified cutoff (>1% dd-cfDNA). Of 13 patients with AR findings at a routine protocol biopsy six-months post transplantation, 12 (92%) were detected positive by dd-cfDNA. This SNP-based dd-cfDNA assay detected allograft rejection with superior performance compared with the current standard of care. These data support the feasibility of using this assay to detect disease prior to renal failure and optimize patient management in the case of allograft injury.
Keywords: cfDNA; kidney transplantation; rejection.
Conflict of interest statement
T.K.S., J.L., I.D., P.T., R.D.S., C.C.-O., S.-C.H, and M.M.S. declare no conflicts. F.A.A., T.C., S.A.P., S.N., E.K., Z.P.D., A.R., S.S., B.Z., P.R.B., and S.M. are or were employees of Natera, Inc. with stock/options to own stock in the company.
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References
- Jacquemont L., Soulillou J.P., Degauque N. Blood biomarkers of kidney transplant rejection, an endless search? Expert Rev. Mol. Diagn. 2017;17:687–697. doi: 10.1080/14737159.2017.1337512.
- Humar A., Kerr S., Hassoun A., Granger D., Suhr B., Matas A. The association between acute rejection and chronic rejection in kidney transplantation. Transplant. Proc. 1999;31:1302–1303. doi: 10.1016/S0041-1345(98)02006-5.
- Nankivell B.J., Alexander S.I. Rejection of the kidney allograft. N. Engl. J. Med. 2010;363:1451–1462. doi: 10.1056/NEJMra0902927.
- Sigdel T.K., Sarwal M.M. The proteogenomic path towards biomarker discovery. Pediatr. Transplant. 2008;12:737–747. doi: 10.1111/j.1399-3046.2008.01018.x.
- Lo D.J., Kaplan B., Kirk A.D. Biomarkers for kidney transplant rejection. Nat. Rev. Nephrol. 2014;10:215–225. doi: 10.1038/nrneph.2013.281.
- Nasr M., Sigdel T., Sarwal M. Advances in diagnostics for transplant rejection. Expert Rev. Mol. Diagn. 2016;16:1121–1132. doi: 10.1080/14737159.2016.1239530.
- Yang J.Y., Sarwal M.M. Transplant genetics and genomics. Nat. Rev. Genet. 2017;18:309–326. doi: 10.1038/nrg.2017.12.
- Loupy A., Haas M., Solez K., Racusen L., Glotz D., Seron D., Nankivell B.J., Colvin R.B., Afrouzian M., Akalin E., et al. The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology. Am. J. Transplant. 2017;17:28–41. doi: 10.1111/ajt.14107.
- Snyder T.M., Khush K.K., Valantine H.A., Quake S.R. Universal noninvasive detection of solid organ transplant rejection. Proc. Natl. Acad. Sci. USA. 2011;108:6229–6234. doi: 10.1073/pnas.1013924108.
- Beck J., Bierau S., Balzer S., Andag R., Kanzow P., Schmitz J., Gaedcke J., Moerer O., Slotta J.E., Walson P., et al. Digital droplet PCR for rapid quantification of donor DNA in the circulation of transplant recipients as a potential universal biomarker of graft injury. Clin. Chem. 2013;59:1732–1741. doi: 10.1373/clinchem.2013.210328.
- De Vlaminck I., Valantine H.A., Snyder T.M., Strehl C., Cohen G., Luikart H., Neff N.F., Okamoto J., Bernstein D., Weisshaar D., et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci. Transl. Med. 2014;6:241ra77. doi: 10.1126/scitranslmed.3007803.
- Sigdel T.K., Vitalone M.J., Tran T.Q., Dai H., Hsieh S.C., Salvatierra O., Sarwal M.M. A rapid noninvasive assay for the detection of renal transplant injury. Transplantation. 2013;96:97–101. doi: 10.1097/TP.0b013e318295ee5a.
- Knight S.R., Thorne A., Faro M.L.L. Donor-specific Cell-Free DNA as a Biomarker in Solid Organ Transplantation. A Systematic Review. Transplantation. 2018 doi: 10.1097/TP.0000000000002482.
- Bloom R.D., Bromberg J.S., Poggio E.D., Bunnapradist S., Langone A.J., Sood P., Matas A.J., Mehta S., Mannon R.B., Sharfuddin A., et al. Cell-Free DNA and Active Rejection in Kidney Allografts. J. Am. Soc. Nephrol. 2017;28:2221–2232. doi: 10.1681/ASN.2016091034.
- Dar P., Curnow K.J., Gross S.J., Hall M.P., Stosic M., Demko Z., Zimmermann B., Hill M., Sigurjonsson S., Ryan A., et al. Clinical experience and follow-up with large scale single-nucleotide polymorphism-based noninvasive prenatal aneuploidy testing. Am. J. Obstet. Gynecol. 2014;211:e1–e17. doi: 10.1016/j.ajog.2014.08.006.
- Ryan A., Hunkapiller N., Banjevic M., Vankayalapati N., Fong N., Jinnett K.N., Demko Z., Zimmermann B., Sigurjonsson S., Gross S.J., et al. Validation of an Enhanced Version of a Single-Nucleotide Polymorphism-Based Noninvasive Prenatal Test for Detection of Fetal Aneuploidies. Fetal Diagn. Ther. 2016;40:219–223. doi: 10.1159/000442931.
- Pergament E., Cuckle H., Zimmermann B., Banjevic M., Sigurjonsson S., Ryan A., Hall M.P., Dodd M., Lacroute P., Stosic M., et al. Single-nucleotide polymorphism-based noninvasive prenatal screening in a high-risk and low-risk cohort. Pt 1Obstet. Gynecol. 2014;124:210–218. doi: 10.1097/AOG.0000000000000363.
- Haas M., Loupy A., Lefaucheur C., Roufosse C., Glotz D., Seron D., Nankivell B.J., Halloran P.F., Colvin R.B., Akalin E., et al. The Banff 2017 Kidney Meeting Report: Revised diagnostic criteria for chronic active T cell-mediated rejection, antibody-mediated rejection, and prospects for integrative endpoints for next-generation clinical trials. Am. J. Transplant. 2018;18:293–307. doi: 10.1111/ajt.14625.
- Chen J., Xie W., Wang H., Jin J., Wu J., He Q. C4d as a significant predictor for humoral rejection in renal allografts. Clin. Transplant. 2005;19:785–791.
- Crespo M., Pascual M., Tolkoff-Rubin N., Mauiyyedi S., Collins A.B., Fitzpatrick D., Farrell M.L., Williams W.W., Delmonico F.L., Cosimi A.B., et al. Acute humoral rejection in renal allograft recipients: I. Incidence, serology and clinical characteristics. Transplantation. 2001;71:652–658. doi: 10.1097/00007890-200103150-00013.
- Abbosh C., Birkbak N.J., Wilson G.A., Jamal-Hanjani M., Constantin T., Salari R., Le Quesne J., Moore D.A., Veeriah S., Rosenthal R., et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature. 2017;545:446–451. doi: 10.1038/nature22364.
- Zimmermann B., Hill M., Gemelos G., Demko Z., Banjevic M., Baner J., Ryan A., Sigurjonsson S., Chopra N., Dodd M., et al. Noninvasive prenatal aneuploidy testing of chromosomes 13, 18, 21, X, and Y, using targeted sequencing of polymorphic loci. Prenat. Diagn. 2012;32:1233–1241. doi: 10.1002/pd.3993.
- Holm S. A Simple Sequentially Rejective Multiple Test Procedure. Scand. J. Stat. 1979;6:65–70.
- Dunn O.J. Multiple Comparisons Using Rank Sums. Technometrics. 1964;6:241–252. doi: 10.1080/00401706.1964.10490181.
- Levey A.S., Bosch J.P., Lewis J.B., Greene T., Rogers N., Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: A new prediction equation. Modification of Diet in Renal Disease Study Group. Ann. Intern. Med. 1999;130:461–470. doi: 10.7326/0003-4819-130-6-199903160-00002.
- Schwartz G.J., Munoz A., Schneider M.F., Mak R.H., Kaskel F., Warady B.A., Furth S.L. New equations to estimate GFR in children with CKD. J. Am. Soc. Nephrol. 2009;20:629–637. doi: 10.1681/ASN.2008030287.
- National Institute of Diabetes and Digestive and Kidney Diseases Chronic Kidney Disease Tests & Diagnosis. [(accessed on 31 August 2018)]; Available online: .
- Efron B., Tibshirani R. Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Stat. Sci. 1986;1:54–75. doi: 10.1214/ss/1177013815.
- Laird N.M., Ware J.H. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974. doi: 10.2307/2529876.
- Kenward M.G., Roger J.H. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics. 1997;53:983–997. doi: 10.2307/2533558.
Source: PubMed