PIRCHE – How to integrate PIRCHE in today’s routine

See our new brochure on how to integrate PIRCHE in today’s routine.

Introduction

Previous studies showed the bene cial effect of low PIRCHE® epitope matching scores in both kidney- transplanted patients and patients who received hematopoietic stem cell transplantation. In kidney transplantations with high PIRCHE-II scores, the incidence of de novo donor specific antibodies was significantly increased compared to cases with a low PIRCHE-II score. This indicates, selecting kidney donors with a lower PIRCHE-II score reduces immunological risk after transplantation. In stem cell transplantations PIRCHE may play a role in development of GvHD, which suggests selecting mismatch donors with low PIRCHE scores. Here we want to describe how the PIRCHE® matching technology can be used in the work-up of a patient for kidney or stem cell transplant and demonstrate the different tools to stratify the risk to your patients.

The PIRCHE algorithm was invented by Eric Spierings at the University Medical Center Utrecht in the Netherlands. In 2013, his group published the proof of concept, which showed that an increased PIRCHE-II score in renal transplant patients correlates with the immunogenicity of donors’ HLA mismatches.(1) Recently, the group of Nils Lachmann validated the technology in a larger single center cohort at the Charité University Hospital in Berlin, Germany.(2) In parallel, the PROCARE team – a consortium of all Dutch kidney transplant centers – independently demonstrated, that the PIRCHE-II score furthermore correlates with graft survival.(3) In the stem cell transplantation setting two smaller cohort studies focused on the impact of PIRCHE on GvHD in HLA-C- and HLA-DPB1-mismatched hematopoietic stem cell transplantations (HSCT). (4, 5) In both studies it was observed, that in patient groups having lower numbers of PIRCHE mismatches there was a lower incidence of GvHD.
These findings are supported by a Dutch multicenter study for 9/10 matched HSCTs. Patient-donor-constellations with low PIRCHE scores had a comparable transplant outcome to fully HLA matched transplantations.(6) A German research group reproduced these findings independently.(7)
The PIRCHE scores are calculated between a patient and donor at the time of donor selection and does not change over time. As it is a pure bioinformatics approach relying on the HLA typing, no additional work at the lab is required. Thus, there are no additional wet test costs involved…

 

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1) H.G. Otten, J.J. Calis, C. Keşmir, A.D. van Zuilen, and E. Spierings, “Predicted indirectly recognizable HLA epitopes presented by HLA-DR correlate with the de novo development of donor-specific HLA IgG antibodies after kidney transplantation.” Human Immunology. 2013; 74, no. 3, 290-296.
2) N. Lachmann, M. Niemann, P. Reinke, K. Budde, D. Schmidt, F. Halleck, A. Pruß, C. Schönemann, E. Spierings, and O. Staeck, „Donor Recipient Matching Based on Predicted Indirectly Recognizable HLA Epitopes Indipendently Predicts the Incidence of De Novo Donor-Specific HLA Antibodies Following Renal Transplantation.“ Am J Transplant. 2017; Jun 14.
3) K. Geneugelijk, M. Niemann, J. Drylewicz, A. D. van Zuilen, I. Joosten, W. A. Allebes, A. van der Meer, L. B. Hilbrands, M. C. Baas, C. E. Hack, F. E. van Reekum, M. Verhaar, E. G. Kamburova, M. L. Bots, M. A. J. Seelen, J.S.- F. Sanders, B. G. Hepkema, A. J. Lam- beck, L. B. Bungener, C. Roozendaal, M. G. J. Tilanus, J. Vanderlocht, C. E. M. Voorter, L. Wieten, E. M. van Duijnhoven, M. Gelens, M. H.- L. Christiaans, F. J. van Ittersum, A. Nurmohamed, N. M. Lardy , W. Swelsen , K. A. van der Pant, N. C. van der Weerd, I. J. M. ten Berge, F. J. Bemelman, A. Hoitsma, P. J. M. van der Boog, J. W. de Fijter, M. G. H. Betjes, S. Heidt, D. L. Roelen, F. H. J. Claas, H. G. Otten, and E. Spierings, „PIRCHE-II is related to graft failure after kidney transplantation“. Front. Immunol. 2018.
4) K.A.Thus, L.Te Boome, J. Kuball, and E. Spierings, “Indirectly recognized HLA-C mismatches and their potential role in transplant outcome.” Frontiers in Immunology. 12th May 2014.
5) K.A. Thus, M.T. Ruizendaal, T.A. de Hoop, E. Borst, H.W. van Deutekom, L. Te Boome, J. Kuball , and E. Spierings, „Refinement of the definition of permissible HLA-DPB1 mismatches with predicted indirectly recognizable HLA-DPB1 epitopes.“ BBMT. November 2014, Volume 20, Issue 11, Pages 1705–1710.
6) K.A. Thus, K. Geneugelijk, H.W. van Deutekom, J. Calis, E. Borst, C. Kesmir, M. Ouds- hoorn, B. van der Holt, E. Meijer, S. Zeerleder, M. R. de Groot, P. von dem Borne, N. Schaap, J. Cornelissen, J. Kuball, and E. Spierings, “Identifying Permissible HLA-Mismatches in Unrelated-Donor Hematopoietic Stem-Cell Transplantation Using Predicted Indirectly Recognizable HLA Epitopes.” Abstract at BMT Tandem 2017, unpublished data.
7) F. Ayuk, M. Bornhäuser, M. Stelljes, T. Zabelina, E.M. Wagner, C. Schmid, M. Christopeit, N. Kröger, W. Bethge, “Predicted Indirectly ReCognizable HLA Epitopes (PIRCHE) are associated with poorer outcome after single mismatch unrelated donor stem cell transplantation: a study of the German Cooperative Transplant Study Group (GCTSG) within the German working group for bone marrow and blood stem cell transplantation (DAG-KBT).” Abstract at EBMT 2017, unpublished data.

Matthias Niemann

Matthias Niemann

Matthias holds a Masters degree in Computer Science with a major in software engineering and a minor in Bioinformatics from Berlin University (Freie Universität). While working at Charité University Hospital in Berlin, he developed a database for kidney transplantation data and worked on various laboratory information systems and research databases. His research at Charité focused on epitope matching models and machine learning. He was instrumental in the implementation methods to increase data quality.
Since fall 2014 he focuses at PIRCHE on further improving the PIRCHE algorithm and investigating the technology's power in new domains.
Matthias Niemann