Complement detection in kidney biopsies - utility and challenges.

PURPOSE OF REVIEW: This review discusses the important role of staining for components of the complement cascade in both native and transplant kidney biopsies. The use of complement staining as a marker of prognosis, disease activity, and as a potential future tool in identifying patients who may benefit from complement-targeted therapies is discussed. RECENT FINDINGS: While staining for C3, C1q and C4d can yield valuable information about complement activation in kidney biopsies, to adequately assess complement activation and potential therapeutic targets, expanded staining panels looking at multiple split products and complement regulatory proteins are needed. Recent progress has been made in identifying markers of disease severity in C3 glomerulonephritis and IgA nephropathy, such as Factor H-related Protein-5, which may serve as future tissue biomarkers. In the transplant setting, the limitation of relying on C4d staining to identify antibody mediated rejection is giving way to molecular diagnostics, including The Banff Human Organ Transplant (B-HOT) panel, which includes numerous complement complement-related transcripts, with the classical, lectin, alternative, and common pathways. SUMMARY: Staining for complement components in kidney biopsies to understand how complement is activated in individual cases may help to identify patients who may benefit from complement-targeted therapies.

Defining an abnormal p53 immunohistochemical stain in Barrett's oesophagus-related dysplasia: a single-positive crypt is a sensitive and specific marker of dysplasia.

AIMS: p53 is an independent risk stratification marker in Barrett's oesophagus (BE), but no universally accepted definition exists for abnormal p53 staining. Herein, we assess p53 stains in two cohorts to: (1) define abnormal p53 staining in BE-related dysplasia (BERD) and (2) assess the specificity and sensitivity of this cut-point for the diagnosis of dysplasia. METHODS: Cohort 1 (n = 313) included (1) dysplastic BE biopsies, (2) prior non-dysplastic BE (NDBE) biopsies from the same patients and (3) NDBE biopsies from patients who never progressed to dysplasia. Cohort 2 (n = 191) consisted of BE biopsies in which p53 staining aided in diagnosing dysplasia. Automated p53 staining quantification was performed on cohort 1. A semiquantitative p53 analysis, performed on both cohorts, included: (1) number of strongly positive glands, (2) strong glandular surface staining, (3) percentage of strongly positive glands and (4) null phenotype. RESULTS: NDBE biopsies from cohort 1 patients who progressed to dysplasia were more likely to show p53 positivity than non-progressors (16.9 versus 0.6%) (P = 0.0001). The optimal quantitative cut-point for distinguishing dysplastic from never-dysplasia biopsies was 10 strongly positive cells. By semiquantitative analysis, a single strongly p53-positive gland distinguished dysplastic from never-dysplasia BE (sensitivity 98.6%, specificity 99.4%). The semiquantitative and quantitative analyses correlated (P = 0.0001). In cohort 2, the sensitivity and specificity for BERD of ≥ 1 strongly positive p53 gland were 86.0 and 88.6%. CONCLUSIONS: A single strongly positive p53 gland is sensitive and specific for BERD. Automated p53 analysis may reduce subjectivity associated with the diagnosis of BERD.

Banff Human Organ Transplant Transcripts Correlate with Renal Allograft Pathology and Outcome: Importance of Capillaritis and Subpathologic Rejection.

BACKGROUND: To seek insights into the pathogenesis of chronic active antibody-mediated rejection (CAMR), we performed mRNA analysis and correlated transcripts with pathologic component scores and graft outcomes. METHODS: We utilized the NanoString nCounter platform and the Banff Human Organ Transplant gene panel to quantify transcripts on 326 archived renal allograft biopsy samples. This system allowed correlation of transcripts with Banff pathology scores from the same tissue block and correlation with long-term outcomes. RESULTS: The only pathology score that correlated with AMR pathways in CAMR was peritubular capillaritis (ptc). C4d, cg, g, v, i, t, or ci scores did not correlate. DSA-negative CAMR had lower AMR pathway scores than DSA-positive CAMR. Transcript analysis in non-CAMR biopsies yielded evidence of increased risk of later CAMR. Among 108 patients without histologic CAMR, 23 developed overt biopsy-documented CAMR within 5 years and as a group had higher AMR pathway scores (P=3.4 × 10-5). Random forest analysis correlated 3-year graft loss with elevated damage, innate immunity, and macrophage pathway scores in CAMR and TCMR. Graft failure in CAMR was associated with TCMR transcripts but not with AMR transcripts, and graft failure in TCMR was associated with AMR transcripts but not with TCMR transcripts. CONCLUSIONS: Peritubular capillary inflammation and DSA are the primary drivers of AMR transcript elevation. Transcripts revealed subpathological evidence of AMR, which often preceded histologic CAMR and subpathological evidence of TCMR that predicted graft loss in CAMR.

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.