FC 108: Gene Expression Profiles of Peritubular Capillaritis in Chronic Antibody-Mediated Rejection
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».