Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring

Transitioning of renal transplant pathology from allograft to xenograft and tissue engineering pathology: Are we prepared?

Currently, the most feasible and widely practiced option for patients with end-stage organ failure is the transplantation of part of or whole organs, either from deceased or living donors. However, organ shortage has posed and is still posing a big challenge in this field. Newer options being explored are xenografts and engineered/bioengineered tissues/organs. Already small steps have been taken in this direction and sooner or later, these will become a norm in this field. However, these developments will pose different challenges for the diagnosis and management of problems as compared with traditional allografts. The approach to pathologic diagnosis of dysfunction in these settings will likely be significantly different. Thus, there is a need to increase awareness and prepare transplant diagnosticians to meet this future challenge in the field of xenotransplantation/ regenerative medicine. This review will focus on the current status of transplant pathology and how it will be changed in the future with the emerging scenario of routine xenotransplantation.

The role of immunosuppression in long-term graft hepatitis and fibrosis after paediatric liver transplant - comparison of two treatment protocols

Background and aims

We have previously demonstrated high rates of chronic allograft hepatitis and fibrosis in liver transplant patients on long-term cyclosporine monotherapy. We subsequently changed practice to add low-dose prednisolone to maintenance treatment with tacrolimus post-transplant. The aim of the study was to assess the impact of the immunosuppression change on graft histopathology.

Methods

Patients treated in this era (Tac + Pred, 2000-2009, N = 128) were compared to a historical cohort, who had been maintained on a steroid-free, cyclosporine-based regime (CSA-Only, 1985-1996, N = 129). Protocol liver biopsies and laboratory tests were performed five- and ten-years post-transplant in both groups.

Results

Compared to CSA-Only, the Tac + Pred cohort had significantly lower rates of chronic hepatitis (CH) at five (20% vs. 44%, p < 0.001) and ten (15% vs. 67%, p < 0.001) years post-transplant, with similar trends observed in inflammation and fibrosis at five years. The Tac + Pred cohort also had significantly lower hepatic transaminases and IgG levels and was less likely to be autoantibody positive at both time points. However, the degree of graft fibrosis at ten years did not differ significantly between eras (p = 0.356).

Conclusion

Increased immunosuppression effectively reduced chronic allograft hepatitis and fibrosis at five years, suggesting it is an immunologically driven variant of rejection. However, there was no significant reduction in the degree of fibrosis at ten years, indicating a multifactorial origin for long term graft fibrosis.

Next-generation pathology detection of T cell-antigen-presenting cell immune synapses in human liver allografts

BACKGROUND AND AIMS

In otherwise near-normal appearing biopsies by routine light microscopy, next-generation pathology (NGP) detected close pairings (immune pairs; iPAIRs) between lymphocytes and antigen-presenting cells (APCs) that predicted immunosuppression weaning failure in pediatric liver transplant (LTx) recipients (Immunosuppression Withdrawal for Stable Pediatric Liver Transplant Recipients [iWITH], NCT01638559). We hypothesized that NGP-detected iPAIRs enrich for true immune synapses, as determined by nuclear shape metrics, intercellular distances, and supramolecular activation complex (SMAC) formation.

APPROACH AND RESULTS

Intralobular iPAIRs (CD45 high lymphocyte-major histocompatibility complex II + APC pairs; n = 1167, training set) were identified at low resolution from multiplex immunohistochemistry-stained liver biopsy slides from several multicenter LTx immunosuppression titration clinical trials (iWITH; NCT02474199 (Donor Alloantigen Reactive Tregs (darTregs) for Calcineurin Inhibitor (CNI) Reduction (ARTEMIS); Prospective Longitudinal Study of iWITH Screen Failures Secondary to Histopathology). After excluding complex multicellular aggregates, high-resolution imaging was used to examine immune synapse formation ( n = 998). By enriching for close intranuclear lymphocyte-APC distance (mean: 0.713 μm) and lymphocyte nuclear flattening (mean ferret diameter: 2.1), SMAC formation was detected in 29% of iPAIR-engaged versus 9.5% of unpaired lymphocytes. Integration of these morphometrics enhanced NGP detection of immune synapses (ai-iSYN). Using iWITH preweaning biopsies from eligible patients ( n = 53; 18 tolerant, 35 nontolerant; testing set), ai-iSYN accurately predicted (87.3% accuracy vs. 81.4% for iPAIRs; 100% sensitivity, 75% specificity) immunosuppression weaning failure. This confirmed the presence and importance of intralobular immune synapse formation in liver allografts. Stratification of biopsy mRNA expression data by immune synapse quantity yielded the top 20 genes involved in T cell activation and immune synapse formation and stability.

CONCLUSIONS

NGP-detected immune synapses (subpathological rejection) in LTx patients prior to immunosuppression reduction suggests that NGP-detected (allo)immune activity usefulness for titration of immunosuppressive therapy in various settings.

Pre-Implantation Kidney Biopsies in Extended Criteria Donors: From On Call to Expert Pathologist, from Conventional Microscope to Digital Pathology

The number of patients awaiting a kidney transplant is constantly rising but lack of organs leads kidneys from extended criteria donors (ECD) to be used to increase the donor pool. Pre-transplant biopsies are routinely evaluated through the Karpinski-Remuzzi score but consensus on its correlation with graft survival is controversial. This study aims to test a new diagnostic model relying on digital pathology to evaluate pre-transplant biopsies and to correlate it with graft outcomes. Pre-transplant biopsies from 78 ECD utilized as single kidney transplantation were scanned, converted to whole-slide images (WSIs), and reassessed by two expert nephropathologists using the Remuzzi-Karpinski score. The correlation between graft survival at 36 months median follow-up and parameters assigned by either WSI or glass slide score (GSL) by on-call pathologists was evaluated, as well as the agreement between the GSL and the WSIs score. No relation was found between the GSL assessed by on-call pathologists and graft survival (P = 0.413). Conversely, the WSI score assigned by the two nephropathologists strongly correlated with graft loss probability, as confirmed by the ROC curves analysis (DeLong test P = 0.046). Digital pathology allows to share expertise in the transplant urgent setting, ensuring higher accuracy and favoring standardization of the process. Its employment may significantly increase the predictive capability of the pre-transplant biopsy evaluation for ECD, improving the quality of allocation and patient safety.

Adaptation of Imaging Mass Cytometry to Explore the Single Cell Alloimmune Landscape of Liver Transplant Rejection

Rejection continues to be an important cause of graft loss in solid organ transplantation, but deep exploration of intragraft alloimmunity has been limited by the scarcity of clinical biopsy specimens. Emerging single cell immunoprofiling technologies have shown promise in discerning mechanisms of autoimmunity and cancer immunobiology. Within these applications, Imaging Mass Cytometry (IMC) has been shown to enable highly multiplexed, single cell analysis of immune phenotypes within fixed tissue specimens. In this study, an IMC panel of 10 validated markers was developed to explore the feasibility of IMC in characterizing the immune landscape of chronic rejection (CR) in clinical tissue samples obtained from liver transplant recipients. IMC staining was highly specific and comparable to traditional immunohistochemistry. A single cell segmentation analysis pipeline was developed that enabled detailed visualization and quantification of 109,245 discrete cells, including 30,646 immune cells. Dimensionality reduction identified 11 unique immune subpopulations in CR specimens. Most immune subpopulations were increased and spatially related in CR, including two populations of CD45+/CD3+/CD8+ cytotoxic T-cells and a discrete CD68+ macrophage population, which were not observed in liver with no rejection (NR). Modeling via principal component analysis and logistic regression revealed that single cell data can be utilized to construct statistical models with high consistency (Wilcoxon Rank Sum test, p=0.000036). This study highlights the power of IMC to investigate the alloimmune microenvironment at a single cell resolution during clinical rejection episodes. Further validation of IMC has the potential to detect new biomarkers, identify therapeutic targets, and generate patient-specific predictive models of clinical outcomes in solid organ transplantation.

Not only a small liver - The pathologist's perspective in the pediatric liver transplant setting

Pediatric liver transplantation represents a safe and long-lasting treatment option for various disease types, requiring the pathologist’s input. Indeed, an accurate and timely diagnosis is crucial in reporting and grading native liver diseases, evaluating donor liver eligibility and identifying signs of organ injury in the post-transplant follow-up. However, as the procedure is more frequently and widely performed, deceptive and unexplored histopathologic features have emerged with relevant consequences on patient management, particularly when dealing with long-term treatment and weaning of immunosuppression.

In this complex and challenging scenario, this review aims to depict the most relevant histopathologic conditions which could be encountered in pediatric liver transplantation. We will tackle the conditions representing the main indications for transplantation in childhood as well as the complications burdening the post-transplant phases, either immunologically (i.e., rejection) or non-immunologically mediated. Lastly, we hope to provide concise, yet significant, suggestions related to innovative pathology techniques in pediatric liver transplantation.

Advantages of Using a Web-based Digital Platform for Kidney Preimplantation Biopsies

BACKGROUND

In the setting of kidney transplantation, histopathology of kidney biopsies is a key element in the organ assessment and allocation. Despite the broad diffusion of the Remuzzi-Karpinski score on preimplantation kidney biopsies, scientific evidence of its correlation to the transplantation outcome is controversial. The main issues affecting the prognostic value of histopathology are the referral to general on-call pathologists and the semiquantitative feature of the score, which can raise issues of interpretation. Digital pathology has shown very reliable and effective in the oncological diagnosis and treatment; however, the spread of such technologies is lagging behind in the field of transplantation. The aim of our study was to create a digital online platform where whole-slide images (WSI) of preimplantation kidney biopsies could be uploaded and stored.

METHODS

We included 210 kidney biopsies collected between January 2015 and December 2019 from the joint collaboration of the transplantation centers of Padua and Verona. The selected slides, stained with hematoxylin and eosin, were digitized and uploaded on a shared web platform. For each case, the on-call pathologists' Remuzzi grades were obtained from the original report, together with the clinical data and the posttransplantation follow-up.

RESULTS

The storage of WSI of preimplantation kidney biopsies would have several clinical, scientific, and educational advantages. The clinical utility relies on the possibility to consult online expert pathologists and real-time quality checks of diagnosis. From the perspective of follow-up, the archived digitized biopsies can offer a useful comparison to posttransplantation biopsies. In addition, the digital online platform is a precious tool for multidisciplinary meetings aimed both at the clinical discussion and at the design of research projects. Furthermore, this archive of readily available WSI is an important educational resource for the training of professionals.

CONCLUSIONS

Finally, the web platform lays the foundation for the introduction of artificial intelligence in the field of transplantation that would help create new diagnostic algorithms and tools with the final aim of increasing the precision of organ assessment and its predictive value for transplant outcome.

Lymphocyte function based on IFN-γ secretion assay may be a promising indicator for assessing different immune status in renal transplant recipients

BACKGROUND

Immunological monitoring plays a crucial role in organ recipients for allowing tailoring of immunosuppression. However, there is still a paucity of promising indicators for detecting immune status in recipients.

METHODS

We conducted a prospective study to characterize the immune status by detecting dynamically lymphocyte subsets and function (represented by the abilities to secrete IFN-γ) in the first 6 months posttransplant in renal recipients. Participants were classified into an immune stable group, infected group, and rejected group.

RESULTS

In the stable group, our study suggested that the counts and function of CD4+ T, CD8+ T, and NK lymphocytes decreased to their nadir at week 2, and thereafter these indicators were gradually restored. The counts exceeded pre-operative levels, whereas function did not reach the pre-transplant levels by 6 months. We demonstrated that function of lymphocytes was considerably decreased in infected recipients compared with the stable group when infection occurred. By contrast, the function of lymphocytes was obviously increased at the point of rejection. Receiver operating characteristic (ROC) analysis in the combination of subsets and function of lymphocytes presented a superior clinical value with an area under the curve (AUC) of 0.903 in the diagnosis of infected receivers, and IFN-γ+CD8+ T cells% is the highest indicator with the auROC curve of 0.862. Another ROC analysis confirmed that IFN-γ+CD4  T cells% presented a preferable diagnostic value with an area of 0.887 for rejected recipients.

CONCLUSIONS

In conclusion, the ability of lymphocyte subsets secreting IFN-γ may provide a promising assessment of immune status in recipients and allow timely modifying immunosuppression.

Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space

Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor microenvironment (TME). Better understanding of the TME could lead to novel diagnostic, prognostic, and therapeutic discoveries. One way to achieve this involves in vitro tumor models that recapitulate the in vivo TME composition and spatial arrangement. Here, we review the potential of harnessing in vitro tumor models and artificial intelligence to delineate the TME. This includes (i) identification of novel features, (ii) investigation of higher-order relationships, and (iii) analysis and interpretation of multiomics data in a (iv) holistic, objective, reproducible, and efficient manner, which surpasses previous methods of TME analysis. We also discuss limitations of this approach, namely inadequate datasets, indeterminate biological correlations, ethical concerns, and logistical constraints; finally, we speculate on future avenues of research that could overcome these limitations, ultimately translating to improved clinical outcomes.

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.

The Fourth International Workshop on Clinical Transplant Tolerance

The International Workshop on Clinical Transplant Tolerance is a biennial meeting that aims to provide an update on the progress of studies of immunosuppression minimization or withdrawal in solid organ transplantation. The Fourth International Workshop on Clinical Tolerance was held in Pittsburgh, Pennsylvania, September 5-6, 2019. This report is a summary of presentations on the status of clinical trials designed to minimize or withdraw immunosuppressive drugs in kidney, liver, and lung transplantation without subsequent evidence of rejection. All protocols had in common the use of donor or recipient cell therapy combined with organ transplantation. The workshop also included presentations of mechanistic studies designed to improve understanding of the cellular and molecular basis of tolerance and to identify potential predictors/biomarkers of tolerance. Strategies to enhance the safety of hematopoietic cell transplantation and to improve patient selection/risk stratification for clinical trials were also discussed.

Machine learning to predict transplant outcomes: helpful or hype? A national cohort study

An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using -statistics. In predicting rejection, regression (C = 0.601 0.6110.621 ) actually outperformed GB (C = 0.581 0.5910.601 ) and RF (C = 0.569 0.5790.589 ). For all other outcomes, the C-statistics were nearly identical across methods (delayed graft function, 0.717-0.723; death-censored graft failure, 0.637-0.642; all-cause graft failure, 0.633-0.635; and death, 0.705-0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful.

Banff Digital Pathology Working Group: Going digital in transplant pathology

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made.

Precision transplant pathology

PURPOSE OF REVIEW

Transplant pathology contributes substantially to personalized treatment of organ allograft recipients. Rapidly advancing next-generation human leukocyte antigen (HLA) sequencing and pathology are enhancing the abilities to improve donor/recipient matching and allograft monitoring.

RECENT FINDINGS

The present review summarizes the workflow of a prototypical patient through a pathology practice, highlighting histocompatibility assessment and pathologic review of tissues as areas that are evolving to incorporate next-generation technologies while emphasizing critical needs of the field.

SUMMARY

Successful organ transplantation starts with the most precise pratical donor-recipient histocompatibility matching. Next-generation sequencing provides the highest resolution donor-recipient matching and enables eplet mismatch scores and more precise monitoring of donor-specific antibodies (DSAs) that may arise after transplant. Multiplex labeling combined with hand-crafted machine learning is transforming traditional histopathology. The combination of traditional blood/body fluid laboratory tests, eplet and DSA analysis, traditional and next-generation histopathology, and -omics-based platforms enables risk stratification and identification of early subclinical molecular-based changes that precede a decline in allograft function. Needs include software integration of data derived from diverse platforms that can render the most accurate assessment of allograft health and needs for immunosuppression adjustments.

Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

Histological analysis of hepatic tissue specimens is essential for evaluating the pathology of several liver disorders such as chronic liver diseases, hepatocellular carcinomas, liver steatosis, and infectious liver diseases. Manual examination of histological slides on the microscope is a classically used method to study these disorders. However, it is considered time-consuming, limited, and associated with intra- and inter-observer variability. Emerging technologies such as whole slide imaging (WSI), also termed virtual microscopy, have increasingly been used to improve the assessment of histological features with applications in both clinical and research laboratories. WSI enables the acquisition of the tissue morphology/pathology from glass slides and translates it into a digital form comparable to a conventional microscope, but with several advantages such as easy image accessibility and storage, portability, sharing, annotation, qualitative and quantitative image analysis, and use for educational purposes. WSI-generated images simultaneously provide high resolution and a wide field of observation that can cover the entire section, extending any single field of view. In this review, we summarize current knowledge on the application of WSI to histopathological analyses of liver disorders as well as to understand liver biology. We address how WSI may improve the assessment and quantification of multiple histological parameters in the liver, and help diagnose several hepatic conditions with important clinical implications. The WSI technical limitations are also discussed.

The Landscape of Digital Pathology in Transplantation: From the Beginning to the Virtual E-Slide

Background

Digital pathology has progressed over the last two decades, with many clinical and nonclinical applications. Transplantation pathology is a highly specialized field in which the majority of practicing pathologists do not have sufficient expertise to handle critical needs. In this context, digital pathology has proven to be useful as it allows for timely access to expert second-opinion teleconsultation. The aim of this study was to review the experience of the application of digital pathology to the field of transplantation.

Methods

Papers on this topic were retrieved using PubMed as a search engine. Inclusion criteria were the presence of transplantation setting and the use of any type of digital image with or without the use of image analysis tools; the search was restricted to English language papers published in the 25 years until December 31, 2018.

Results

Literature regarding digital transplant pathology is mostly about the digital interpretation of posttransplant biopsies (75 vs. 19), with 15/75 (20%) articles focusing on agreement/reproducibility. Several papers concentrated on the correlation between biopsy features assessed by digital image analysis (DIA) and clinical outcome (45/75, 60%). Whole-slide imaging (WSI) only appeared in recent publications, starting from 2011 (13/75, 17.3%). Papers dealing with preimplantation biopsy are less numerous, the majority (13/19, 68.4%) of which focus on diagnostic agreement between digital microscopy and light microscopy (LM), with WSI technology being used in only a small quota of papers (4/19, 21.1%).

Conclusions

Overall, published studies show good concordance between digital microscopy and LM modalities for diagnosis. DIA has the potential to increase diagnostic reproducibility and facilitate the identification and quantification of histological parameters. Thus, with advancing technology such as faster scanning times, better image resolution, and novel image algorithms, it is likely that WSI will eventually replace LM.