FC 108: Gene Expression Profiles of Peritubular Capillaritis in Chronic Antibody-Mediated Rejection
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS Chronic antibody-mediated rejection (ABMR) is one of the most significant contributors to late allograft loss. Hallmark characteristics of ABMR include microvascular injury (MVI) lesions such as peritubular capillaritis (ptc) and glomerulitis (g). Diffuse ptc (extent > 50%) was associated with worse graft survival independent from the ptc score. Nevertheless, current ptc thresholds are arbitrarily defined and may not reflect pathophysiological phenotypes accurately enough. The Banff peritubular capillaritis working group has been re-established to study the diagnostic and prognostic relevance of the ptc extent in different scenarios. We hypothesize that the re-assessment of chronic ABMR specimens using Nanostring NCounter based gene expression analysis allows the definition of novel thresholds of ptc extent, reflecting molecular ABMR phenotypes more accurately. METHOD We retrospectively analysed 25 patients with historical diagnosis of ABMR/chronic ABMR and presence of donor specific antibodies (including 44 biopsies). Patients were treated at two different Austrian centres (Medical University of Vienna and Ordensklinikum—Elisabethinnen Linz). Peritubular capillaritis was re-evaluated by an experienced external nephropathologist (M.M.) and included the ptc score as well as the ptc extent (focal ptc: 10–50% capillaries involved, diffuse ptc: >50%). Nanostring nCounter Gene expression analysis was performed with a customized gene set corresponding to the recommendations of molecular ABMR phenotypes in Banff 2017 guidelines (including over 200 genes). To test the correlation of gene expression levels and histological scores, gene expression levels of all 44 patients were divided into quartiles. Gene expressions above the first quartile (ABMRQ>1) were considered as positive values for ROC analysis. RESULTS Ptc was categorized as followed: no ptc in 13, focal ptc in 23 and diffuse ptc in seven biopsies [median ptc extent 25/0–40% (median/IQR)]. ABMR was diagnosed in 27 (67.5%) biopsies, mixed rejection in five (12.5%) and borderline TCMR in three (7.5%). In biopsies with diffuse ptc significant higher gene expressions were found in the ABMR gene set [63/55–83 versus 32/27–52; (median/IQR); P = 0.012], the ABMR exhaust gene set (390/245–609 versus 245/128–358; P = 0.022), the Eculizumab gene set (180/143–339 versus 65/57–133; P = 0.0027) and the TCMR gene set (48/40–75 versus 25/19–35; P = 0.001). Sensitivity analysis revealed improved AUCs for predicting biopsies with ABMR gene expressions over the first quartile with a ptc cutoff of 35% compared to ptc cut-off of 50% [ptc>35: AUC 0.76/0.61–0.90 (95% confidence interval); P = 0.013; ptc>50: AUC: 0.71/0.54–0.88; P = 0.039]. The new ptc>35% cutoff also provided higher AUCs for the prediction of gene expressions over the 25th percentile in all other analysed rejection-associated gene sets. CONCLUSION With the application of gene expression-based Nanostring platform, we were able to identify a new threshold of ptc extent. The newly proposed cut off >35% may reflect molecular phenotypes of ABMR more accurate than the current one and could improve early diagnosis of ABMR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".