Early Prediction of Moderate-Severe Chronic GvHD By Immunity Related Transcriptome
Notice bibliographique
Résumé
Abstract Introduction:Graft versus host disease (GvHD) is the most common and debilitating complication of allogeneic hematopoietic cell transplantation (HCT). Among the two clinically significant forms, moderate-severe chronic GvHD (cGvHD) is more difficult to treat than grade 2-4 acute GvHD (aGvHD). Chronic GvHD rarely shows a sustained complete response to immunosuppressive drugs and non-responders either die or suffer long-term. However, if an early and accurate prediction of ensuing cGvHD is posiible, then the high risk patients can be identified and treated preemptively. In the present investigation, we retrospectively analyzed the transcriptome of immunity related genes at one month post-transplant with the aim to identify a potential early and accurate predictor of clinically significant cGvHD. Methods:A cohort of 73 HLA matched first allogeneic HCT recipients with moderate-severe cGvHD (n = 31) and no chronic GvHD (n = 42) were included in the study. All patients received similar myeloablative conditioning regimens along with 4.5 mg/kg antithymocyte globulin as GvHD prophylaxis. The diagnosis of cGvHD was based on National Institutes of Health consensus criteria and median day of diagnosis in our cohort was 126 days. Total RNA was extracted from cryopreserved peripheral blood mononuclear cells (PBMNCs) at one month post-transplant. Gene expression CodeSet profiling of 594 immunity related genes including 15 internal reference genes was performed using Nano string Technology Immunology panel. Differential gene expression and receiver operating characteristic (ROC) analyses was performed using R statistical packages and GraphPad PRISM software. Benjamini Hochberg procedure was used to calculate the P value and the false discovery rate (FDR). Differentially expressed genes with a Benjamini Hochberg P value (BHP) of <0.05 and FDR of <0.01 were considered significant. Cumulative incidence of moderate-severe chronic GvHD was calculated using Fine-Gray competing risk analysis. Results:A gene panel consisting of immunity related 12 genes was identified at one month post-transplant whose transcriptome profile was significantly different between patients that developed moderate-severe cGvHD compared to the patients who did not develop cGvHD. The identified gene panel included highly upregulated T cell activation and proliferation markers like CD3D (BHP=0.005), CD3E (BHP=0.01), CCR7 (BHP=0.005), CD5 (BHP=0.006); TNF receptor superfamily markers like B- and T-lymphocyte attenuator-BTLA (BHP=0.004), CD27 (BHP=0.004), CD40LG (BHP=0.005); and Costimulatory signaling molecules like Inducible T-cell Co-stimulator ICOS (BHP=0.001), CD28 (BHP=0.006) in patients with cGVHD. A gene score of >7 in this panel of 12 genes was able to differentiate patients with high risk of developing mod-severe cGvHD with a sensitivity of 79% and specificity of 88% (AUC = 0.88, P <0.0001) (Figure 1). Conclusions:The cGvHD gene panel consisting of 12 genes from multiple immune regulatory pathways identified in the present study provided an early prediction of moderate-severe cGvHD (one month) with high sensitivity and specificity. The robust and rapid technique of nanostring based transcriptome profiling has the potential to be implemented in the clinic. Differentiating patients at high risk of mod-severe cGvHD can help develop new strategies for preemptive therapy and pave the way towards precision medicine leading to improved outcomes of HCT. Disclosures No relevant conflicts of interest to declare.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 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 ».