The Molecular Microscope (MMDXR ) interpretation of thoracic and abdominal allograft biopsies: Putting things in perspective for the clinician

Caveats in Interpretation of Molecular Diagnostics in Heart Allografts

Histologic separation of injury, T cell-mediated rejection, or antibody-mediated rejection in allograft heart biopsies is difficult. A critical review of publications was performed to evaluate the caveats of using molecular diagnostics (MDX) to distinguish between these entities. Typically, only 1 to 2 fragments of unknown histologic appearance are evaluated. Archetype and molecular classifier analyses use gene lists derived from histologic labels and associated reproducibility issues influence the accuracy of the derived MDX classes. Archetypes A1, A2, and A3 archetypes created by bioinformatics were renamed no rejection, T cell-mediated rejection, and antibody-mediated rejection despite as little as 40% concordance with histologic diagnoses and overlapping archetype scores. Additional archetypes S4 and minor injury were created using arbitrary cutoffs based on visual examination of principal component analysis plots. Therapeutic implications of the numerous discrepancies with histology remain unexplored. Many MDX-derived observations are ambiguous and open to alternate logical explanations. Better molecular methods and more rigorous validation studies are needed to advance the field. Ideally, these methods should analyze all available biopsy fragments to minimize sampling issues. It is also desirable to incorporate spatial transcriptomics into the workflow, so that gene expression data can be directly compared with the underlying histology lesions.

The Utility of Spatial Transcriptomics for Solid Organ Transplantation

Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing-based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.

Utility of Banff Human Organ Transplant Gene Panel in Human Kidney Transplant Biopsies

BACKGROUND

Microarray transcript analysis of human renal transplantation biopsies has successfully identified the many patterns of graft rejection. To evaluate an alternative, this report tests whether gene expression from the Banff Human Organ Transplant (B-HOT) probe set panel, derived from validated microarrays, can identify the relevant allograft diagnoses directly from archival human renal transplant formalin-fixed paraffin-embedded biopsies. To test this hypothesis, principal components (PCs) of gene expressions were used to identify allograft diagnoses, to classify diagnoses, and to determine whether the PC data were rich enough to identify diagnostic subtypes by clustering, which are all needed if the B-HOT panel can substitute for microarrays.

METHODS

RNA was isolated from routine, archival formalin-fixed paraffin-embedded tissue renal biopsy cores with both rejection and nonrejection diagnoses. The B-HOT panel expression of 770 genes was analyzed by PCs, which were then tested to determine their ability to identify diagnoses.

RESULTS

PCs of microarray gene sets identified the Banff categories of renal allograft diagnoses, modeled well the aggregate diagnoses, showing a similar correspondence with the pathologic diagnoses as microarrays. Clustering of the PCs identified diagnostic subtypes including non-chronic antibody-mediated rejection with high endothelial expression. PCs of cell types and pathways identified new mechanistic patterns including differential expression of B and plasma cells.

CONCLUSIONS

Using PCs of gene expression from the B-Hot panel confirms the utility of the B-HOT panel to identify allograft diagnoses and is similar to microarrays. The B-HOT panel will accelerate and expand transcript analysis and will be useful for longitudinal and outcome studies.

Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning

Background

Biopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods.

Methods

Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR.

Results

The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively.

Conclusions

This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-based diagnostic model. It costs less than 10 dollars per test.

The MMDx® diagnostic system: A critical re-appraisal of its knowledge gaps and a call for rigorous validation studies

Transcriptomics generates pathogenetic insights not obtainable by histology, but translation of these insights into diagnostic tests is not a trivial task. This opinion-piece critically appraises declarative MMDx statements, such as the infallibility of machine learning algorithms, measurements of gene expression with >99% precision, and "unambiguous reclassifications" of contentious biopsies such as those with borderline change, polyomavirus nephropathy, chronic active T-cell or mixed rejection, isolated intimal arteritis, and renal medullary pathology. It is shown that molecular diagnoses that do not agree with histology cannot be attributed primarily to pathology reading errors. Neither can all molecular calls derived from arbitrary binary thresholds be automatically accepted as the ground truth. Important other sources of discrepancies between clinico-pathologic and molecular calls include: (a) organ being studied, (b) disease definition, (c) clinical histologic, and gene expression heterogeneity within the same diagnostic label, (d) size and composition of comparator groups, (e) molecular noise, (f) variability in output of different machine learning algorithms, and (g) the nonavailability of a molecular classifier for chronic active TCMR. Carefully designed clinical trials are needed to determine which of the proposed indications of MMDx provide incremental value over existing standard of care protocols.