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Evaluating Prospera alongside MMDx: Lessons learned from the comparison of two objective molecular platforms

By Philip F Halloran
MD; Director at the Alberta Transplant Applied Genomics Centre at the University of Alberta

Renal transplant is a highly effective treatment for the end-stage kidney disease patient, and the goal is to achieve excellent function for the life of the recipient.. However, with the exception of identical twins, and despite advances in immunosuppression,1 allograft rejection threatens the long-term viability of the transplant. In addition, all transplants sustain injury at the time of donation – wounding that must be healed – and are at risk of recurrent or de novo diseases.

The biopsy is an essential tool to diagnosing rejection and assessing the parenchyma. Accurate interpretation of kidney allograft biopsies is the key to management of the renal transplant that is experiencing problems e.g. dysfunction or proteinuria. Until recently, biopsy assessment has relied on histology, in transplantation and many other areas of medicine. But histology has limitations, and new molecular methods are being introduced to improve assessments. For transplant biopsies, we developed a system for biopsy assessment based on genome-wide mRNA measurements and ensembles of machine-learning algorithms – the Molecular Microscope® Diagnostic System (MMDx).2–4 MMDx is a more precise way to analyze biopsy specimens and can be used to calibrate other platforms.19

However, an important priority for clinicians is to minimize unnecessary needle biopsies by measuring signals in body fluids that could indicate the need for a biopsy. Among these approaches, the most promising is measurement of donor-derived cell-free DNA (“dd-cfDNA”) in blood (plasma). Measurements of Prospera dd-cfDNA levels correlate strongly with active antibody-mediated rejection (ABMR) and T cell-mediated rejection (TCMR) as detected by MMDx .5 Our results suggest a strategy that uses both of these objective molecular methods to reduce unnecessary biopsies and to maximize the utility of biopsies to make the best management decisions.

Limitations of histology for guiding the management of renal transplants

Histology is and will remain an important part of biopsy assessment. The MMDx project arose from the Banff project, and many clinicians and pathologists have contributed to both. Both are part of the quest for the true disease processes in transplant patients and how they should be managed.

Histology requires human observers to recognize patterns, and as such is subject to the limitations of such systems.6 Histology is associated with limited interobserver agreement rate on scoring of lesions and in diagnoses2–5 . In the context of T-cell-mediated rejection (TCMR), pathologists viewing the same kidney biopsy sample agree on diagnosis roughly 50% of the time.6 In cases at the boundaries - “suspicious for acute rejection” - the frequency is even lower.7 Moreover, the actual disease states in the transplant population change with time and advances in immunosuppressive management – “data drift” so that the significance of specific lesions in 2022 may be different from 1991 when the Banff algorithms were drafted.

The relationships between histology and MMDx have been discussed previously,4,7,8 and we have outlined why MMDx is more likely to be correct in table 1 of Jeff Reeves paper in AJT 2019 (8), plus our recent experience with dd-cfDNA:

  • MMDx assesses many more features selected from genome-wide microarray measurements – measured objectively on a continuous scale with high reproducibility.

  • In predicting a phenotype with a well-defined goal standard such as survival, MMDx outperforms histology.9–14

  • MMDx outputs are continuous rather than semiquantitative or binary which is especially important when a result is near the boundary.

  • MMDx has trained some classifiers on histology features and therefore incorporates the lessons learned from histology in the reference set.

  • We have extensive molecular studies in laboratory animals that we rely on to interpret the genes assessed in MMDx.

  • MMDx findings have been used to update the Banff classification: recognition of C4d-negative ABMR and DSA-negative ABMR.

  • MMDx can assess recent injury,15 which is very difficult to estimate in routine histology.

  • Most importantly, dd-cfDNA measurements - a valid objective estimate of the probability of rejection – has been shown to correlate better with MMDx than with histology.5,16

Changing understanding of Donor-Specific Antibodies (DSA) alongside dd-cfDNA with Prospera

We have been studying this in the Trifecta Study and will be presenting more on the relative performance of dd-cfDNA with Prospera vs. DSA at ATC2022. The Banff classification system relies on the identification of DSA for antibody-mediated rejection (ABMR) diagnosis.9,12 However, we began observing large numbers of patients with molecular ABMR but no DSA,2,17 and histology has also recognized DSA-negative ABMR.18,19, 5, 20 . The explanations for DSA-negative ABMR are not clear, but the absence of DSA increases the concerns with using histology-DSA criteria alone.21

MMDx can improve rejection identification

While we acknowledge that histology can provide substantial insights, transplant medicine needs to explore molecular assessment for kidney transplant patients.13 By leveraging artificial intelligence and data from a reference set of a thousand kidney biopsies from many centers, the MMDx offers a way to harness the power of precision measurements and machine learning to assess transplant biopsies in an objective, reproducible manner. In addition to its high level of accuracy and its ability to eliminate interobserver disagreement, MMDx offers unique value, by confirming many histologic diagnoses (giving confidence for management decisions), by revealing errors, and by resolving in challenging cases deemed suspicious or borderline by histopathologists.14 It can also offer recalibration of histology and mechanistic insights.

MMDx correlates with clinical outcomes better than Banff on histology alone

The consensus algorithms in systems such as Banff typically require revisiting on a regular basis.15 The changing consensus criteria in the Banff system have in certain cases negatively impacted prognostic performance.16 Specifically, the removal of the “suspicious” category in 2017 increased ABMR diagnoses and led to a decline in the correlation between Banff predictions and clinical outcomes.

Comparative data have shown that MMDx can provide unambiguous diagnoses in cases where histology results are ambiguous.14 Studies looking at the agreement in diagnosis of renal transplant rejection between MMDx and histology have shown that considerable disagreement about 37% of the time14 , many being clear discrepancies with implications for therapy. Discrepancies were increased in some scenarios e.g. histology TCMR.

Combined with histology, MMDx improves our ability to identify kidney allograft rejection

MMDx offers quantitative, actionable data that can be used to track the efficacy of treatments and the success of immunosuppressive drugs.17

While dd-cfDNA can be used to detect kidney transplant rejection, studies that have demonstrated the ability of cell-free DNA to detect rejection have used Banff as “truth” for rejection.18 However, given the limitations of Banff, there are significant opportunities to compare dd-cfDNA utilizing Prospera to MMDx for a more precise and consistent evaluation of performance capabilities. Given our belief that MMDx is a more accurate estimate of rejection, previous studies may have underestimated the relationship of dd-cfDNA to rejection, and the relationship of both to DSA. This was the motivation for Trifecta.

The Trifecta study is a large, multi-center study that examines the relationships among three objective molecular tests measured centrally: MMDx, cell-free DNA with Prospera, and DSA. This study currently has over 600 biopsy-matched samples from over 20 US and international sites, and we have already reported on the first 300.5 This first paper shows that cfDNA with Prospera correlates very strongly with antibody mediated rejection and strongly with cellular rejection detected using MMDx as well as histology.19 These results suggest that cfDNA with Prospera is a more powerful predictor of rejection when using molecular rejection than had previously been shown with histology.

Trifecta presents an example of how MMDx can be used to help calibrate existing and new technologies: biopsy MMDx, biopsy histology, local DSA, central DSA measured by One Lambda, as well as % dd-cfDNA13 and soon we are launching a Trifecta heart study to continue this process.



1Halloran PF. Immunosuppressive drugs for kidney transplantation. The New England journal of medicine. 2004;351(26):2715-2729. doi:10.1056/NEJMRA033540

2Reeve J, Böhmig GA, Eskandary F, et al. Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes. JCI Insight. 2017;2(12). doi:10.1172/JCI.INSIGHT.94197

3Halloran PF, Famulski KS, Reeve J. Molecular assessment of disease states in kidney transplant biopsy samples. Nature reviews Nephrology. 2016;12(9):534-548. doi:10.1038/NRNEPH.2016.85

4Reeve J, Böhmig GA, Eskandary F, et al. Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers. American Journal of Transplantation. 2019;19(10):2719-2731. doi:10.1111/AJT.15351

5Halloran PF, Reeve J, Madill-Thomsen KS, Demko Z, Prewett A, Billings P. The Trifecta Study: Comparing plasma levels of donor-derived-cell-free DNA with molecular phenotype of transplant biopsies. Journal of the American Society of Nephrology : JASN. 2022;33(2):387-400. doi:10.1681/ASN.2021091191

6Topol EJ. Deep medicine : how artificial intelligence can make healthcare human again. :378.

7Madill-Thomsen K, Perkowska-Ptasińska A, Böhmig GA, et al. Discrepancy analysis comparing molecular and histology diagnoses in kidney transplant biopsies. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2020;20(5):1341-1350. doi:10.1111/AJT.15752

8Halloran PF, Madill-Thomsen KS. The Molecular Microscope® Diagnostic System meets eminence-based medicine: A clinician’s perspective. American Journal of Transplantation. 2020;20(10):2964-2965. doi:10.1111/AJT.15940

9Einecke G, Reeve J, Sis B, et al. A molecular classifier for predicting future graft loss in late kidney transplant biopsies. Journal of Clinical Investigation. 2010;120(6):1862-1872. doi:10.1172/JCI41789

10Halloran PF, Pereira AB, Chang J, et al. Microarray diagnosis of antibody-mediated rejection in kidney transplant biopsies: An international prospective study (INTERCOM). American Journal of Transplantation. 2013;13(11):2865-2874. doi:10.1111/AJT.12465

11Famulski KS, Reeve J, de Freitas DG, Kreepala C, Chang J, Halloran PF. Kidney transplants with progressing chronic diseases express high levels of acute kidney injury transcripts. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2013;13(3):634-644. doi:10.1111/AJT.12080

12Halloran PF, Matas A, Kasiske BL, Madill-Thomsen KS, Mackova M, Famulski KS. Molecular phenotype of kidney transplant indication biopsies with inflammation in scarred areas. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2019;19(5):1356-1370. doi:10.1111/AJT.15178


Predictors of graft survival at the time of kidney transplant indication biopsy - - ATC Abstracts. Accessed February 17, 2022.

14Halloran PF, Chang J, Famulski K, et al. Disappearance of T cell-mediated rejection despite continued antibody-mediated rejection in the late kidney transplant recipients. Journal of the American Society of Nephrology : JASN. 2015;26(7):1711-1720. doi:10.1681/ASN.2014060588

15Famulski KS, de Freitas DG, Kreepala C, et al. Molecular phenotypes of acute kidney injury in kidney transplants. Journal of the American Society of Nephrology : JASN. 2012;23(5):948-958. doi:10.1681/ASN.2011090887

16Gupta GMIKLKAWRDMKLKPLMBCMDKDHP. Correlation of donor-derived cell-free DNA with histology and molecular diagnoses of kidney transplant biopsies. Transplantation. Published online 2021.

17Halloran PF, Famulski KS, Chang J. A probabilistic approach to histologic diagnosis of antibody-mediated rejection in kidney transplant biopsies. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2017;17(1):129-139. doi:10.1111/AJT.13934

18Senev A, Callemeyn J, Lerut E, Emonds MP, Naesens M. Histological picture of ABMR without HLA-DSA: Temporal dynamics of effector mechanisms are relevant in disease reclassification. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2019;19(3):954-955. doi:10.1111/AJT.15234

19Callemeyn J, Lerut E, de Loor H, et al. Transcriptional changes in kidney allografts with histology of antibody-mediated rejection without Anti-HLA donor-specific antibodies. Journal of the American Society of Nephrology. 2020;31(9):2168-2183. doi:10.1681/ASN.2020030306/-/DCSUPPLEMENTAL

20Loupy A, Haas M, Roufosse C, et al. The Banff 2019 Kidney Meeting Report (I): Updates on and clarification of criteria for T cell- and antibody-mediated rejection. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2020;20(9):2318-2331. doi:10.1111/AJT.15898

21Callemeyn J, Ameye H, Lerut E, et al. Revisiting the changes in the Banff classification for antibody-mediated rejection after kidney transplantation. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2021;21(7):2413-2423. doi:10.1111/AJT.16474

Prospera has been developed and its performance characteristics determined by the CLIA-certified laboratory performing the test. The test has not been cleared or approved by the US Food and Drug Administration (FDA). CAP accredited, ISO 13485 certified, and CLIA certified. © 2021 Natera, Inc. All Rights Reserved.