PIRCHE Kidney Transplantation – Integration in Today’s Routine


Previous studies showed the beneficial effect of low PIRCHE epitope matching scores in kidney-transplanted patients. In transplantations with high PIRCHE scores, the incidence of de novo donor specific antibodies was heavily increased compared to cases with a low PIRCHE score. This suggests, optimizing for PIRCHE numbers reduces immunological risk after transplantation.

Herein, we want to describe how to use the PIRCHE® matching technology in preparation of a kidney transplantation and show you different tools to stratify risk of your patients.

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Eric Spierings at the University Medical Center Utrecht in the Netherlands invented the concept of PIRCHE. In 2013, his working group published the proof of concept, which showed increased PIRCHE numbers in kidney transplanted patients correlate with the immunogenicity of HLA antibodies.[1] Recently, the working group of Nils Lachmann validated the technology in a large 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 found out, that the PIRCHE score furthermore correlates with graft survival.[3]

The PIRCHE score is calculated between a patient and donor at the time of allocation 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. Integrating this knowledge into the national and international allocation strategies is of course preferable in the future. However, also clinicians benefit from considering the PIRCHE numbers of their patients.

The PIRCHE Technology

The novel PIRCHE® technology forecasts T cell related immune responses against HLA derived peptides after transplantation. In contrast to existing technologies, the indirect pathway of allorecognition is in focus. This takes an important functional aspect of HLA molecules into account: HLAs load peptides into their characteristic binding grooves to present it to specific T cell receptors. Usually, this system allows detecting for instance virus peptides in order to activate and concentrate the defense mechanisms of the immune system.

The same pathway leads to detection of exogenous proteins in the organ transplantation setting, causing production of donor-specific antibodies – an independent risk factor for deterioration of renal allograft function.

The chromosome region encoding for HLA molecules is one of the most polymorphic sections of the whole genome. Transplanting tissue introduces foreign HLA proteins, which will inevitably be processed into smaller fragments by the lysosome. Some of these fragments are unknown to the immune cells and were never detected before, allowing T cell receptors to bind and activate. The lower the number of such presented foreign peptides, the lower the incidence of donor-specific antibodies being developed.

PIRCHE effectively simulates that concept and provides you the number of peptides, which may be detected by T cells.

To enable usage in today’s laboratory workflow, we developed a multiple imputation method to estimate the most probable genotypes of your patient and consider all of these. This allows you to apply epitope matching even though you only have low resolution HLA typings of patients and donors at hand.[4]

PIRCHE in the Process

We apply complex calculations dealing with hundreds of proteins, gigabytes of peptide data, predicting thousands of genotype constellations – just for a single match between one patient and one donor. Still, we fitted all of that into a tidy, easy-to-use web service that generates your results within fractions of a second.

The PIRCHE platform provides a number of modules making use of PIRCHE epitope matching, that go beyond simple patient-donor-matching. We will describe two of them in the following, as they are of high interest in the clinical routine.

Individual Risk Estimation

It is well known, some patients are harder to allocate than others as some combinations of HLA molecules are rarely found in the population. These patients will typically get a worse HLA match leading to increased immunological risk.

Figure 1: The PIRCHE Risk Profile indicates what PIRCHE scores are to be expected by random organ donors appearing in your local population.

With the PIRCHE Risk Profile, we face that issue from an epitope perspective and calculate the individual risk situation of each patient. After copying the HLA data from the lab system into the PIRCHE service, we provide you a detailed analysis of how likely it is, that your patient will be offered an organ with low PIRCHE scores. Moreover, the calculated median – that means 50% of the donors will lead to a lower (i.e. better) PIRCHE score, the other 50% of the donors will result in a higher (i.e. worse) PIRCHE score – gives you a personalized benchmark for your patient (see Figure 1). An offered organ or a willing living donor might not be the best choice, if the PIRCHE score is above the median calculated in the Risk Profile: Chances are good, there is a better donor out there (see Figure 2).

Figure 2: Comparing the donors’ match result with the risk profile suggests going for donor4, as better donors showing up is unlikely. When only donor1 and donor13 are at hand, it might be worth waiting.

Definition of Acceptable Mismatches

The Risk Profile allows you to match a whole population with your patient. However, there are situations, where you want to learn, which mismatches should be avoided and which mismatches are more preferable. Avoiding high-risk mismatches and keeping the number of HLA antibodies low is also in favor for subsequent transplantations, providing more donor options in future.

Figure 3: The Acceptable Mismatch Profile lets you determine individual mismatches’ impact on the PIRCHE score. Where classical HLA matching does not differentiate between antigen mismatches, PIRCHE clearly allows you to discriminate.

The PIRCHE Acceptable Mismatch Profile allows you to evaluate each antigen’s and each allele’s individual PIRCHE score for your patient (see Figure 3 and Figure 4). Mismatches leading to high numbers may be forbidden for donor offers to promote low PIRCHE mismatches in the forthcoming transplantation.

Figure 4: Browsing through the immunogenicity of mismatched alleles reveals the variability within the same antigen group.

Start Today

The PIRCHE web service is available 24/7, worldwide from any computer with Internet access. There is no need to install any software locally. Simply create a free test account and try it out. We are always keen to hear your ideas on how we may collaborate and further improve our service.




[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. Lambeck, 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: A NOVEL TOOL TO IDENTIFY PERMISSIBLE HLA MISMATCHES IN KIDNEY TRANSPLANTATION.“ HLA. 2017;89:373-383.

[4] K. Geneugelijk, J. Wissing, D. Koppenaal, M. Niemann, and E. Spierings, “Computational Approaches to Facilitate Epitope-Based HLA Matching in Solid Organ Transplantation,” Journal of Immunology Research, vol. 2017, Article ID 9130879, 9 pages, 2017.

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