The Risk of Organ Specific Graft-Versus-Host Disease Can Be Predicted by the Multiple Single Nucleotide Polymorphism Based Predictive Models.
Notice bibliographique
Résumé
Abstract Abstract 3056 Background: The pathogenesis of GVHD is not fully understood. Alloreactive T-lymphocytes are believed to be key mediators of GVHD. However, it is not clear if the pathobiology of GHVD is similar in each target organ GVHD. We aimed to identify predictive single nucleotide polymorphisms (SNP) markers associated with the risk of acute or chronic graft versus host disease (GVHD) as well as organ specific GVHD in 394 transplant recipients and donors. Methods: A total of 259 SNPs were genotyped in 53 genes, and evaluated for the risk of acute/chronic GVHD and organ specific GVHD. Predictive models were generated using both clinical factors and genetic SNP markers confirmed by multivariate analyses. Patients were stratified by quartile (25%) according to their risk score, and the risk of overall and organ specific GVHD were compared among the 3 risk groups (low, intermediate and high risk). C-statistic analysis was also performed to compare the stratification power of the predictive model generated using clinical and genetic factors with a model obtained using only clinical factors. Results: Several SNP markers in the cytokine-, apoptosis-, TGF-¥â or PDGF-mediated pathways were identified as predictive markers of acute/chronic GVHD. The risk of acute GVHD was associated with clinical factors such as HLA disparity and patient age. In addition, recipient FAS genotype (rs2234978), EDN1 genotype (rs4714384), and TGFB genotype (rs1800469), and donor TNFRII genotype (rs3397) were also strong predictive markers for acute GVHD. Significant predictive risk factors forchronic GVHD were the source of stem cells, a previous episode of acute GVHD and the donor IL1R1 genotype (rs3917225). Each organ specific GVHD shared common biologic pathways such as cytokine, TGF-¥â or PDGF-mediated pathways. However, different SNP markers were identified as predictive for individual organ-specific GVHD. Multivariate analyses identified several SNP markers may predict the risk of organ specific acute GVHD in combination with clinical factors. For skin acute GVHD, recipient PDGFD (rs10895534), donor NOS2A (rs3730017), TNFRII (rs3397) and TGFB1 (rs1800469) genotypes were predictive together with clinical factors such as HLA disparity. Donor's genotype for TNFRII (rs3397) was predictive not only for overall acute GVHD but also for skin acute GVHD. No clinical factors were identified for the risk of liver or gut acute GVHD, but several SNP markers were found including recipient PDGFRB (rs2302273), IFNGR1 (rs2234711) and donor PTGS1 (rs10306114), NOS1 (rs9658254), IL1R1 (rs2192752) genotypes for liver acute GVHD and recipient IL4 (rs2243248), donor PDGFD (rs1053861), TGFBR1 (rs420549), IL12A (rs2243115) genotypes for gut acute GVHD. In summary, there are no overlapping SNP markers for the risk prediction of organ specific acute GVHD. For organ specific chronic GVHD, 2 clinical risk factors were predictive including source of stem cells and a preceding history of acute GVHD. In addition, several SNP markers were also identified: recipient PDGFC (rs1425486), donor NFKB1 (rs1805034) and NOS2A (rs3730017) for skin chronic GVHD; recipient IL10RB (rs8178561) and PDGFRB (rs22229562), and donor TGFBR1 (rs868) for eye chronic GVHD; recipient IL12RB1 (rs3746190) and donor FCGR2A (rs1801274) for oral chronic GVHD; and donor IL4R (rs2057768), FAS (rs2234767) and TGFB1 (rs1800469) for lung chronic GVHD. Again, In no overlapping SNP markers were observed for organ-specific chronic GVHD risk. Although this predictive model could not stratify patients according to their risk of overall chronic GVHD (p=0.0763), the predictive models per each organ specific chronic GVHD enabled to stratify the patients according to their risks of each organ specific GVHD (p<0.0001 for skin chronic GVHD, p=0.0033 for eye chronic GVHD, p=0.003 for oral chronic GVHD and p=0.0036 for lung chronic GVHD).Predictive models incorporating clinical and genetic factors improved the stratification power by 11.1% compared to models only including clinical factors. Conclusion: Our study suggests that SNP based approaches can predict the risk of organ-specific GVHD. These SNP markers need to be validated in other series. These SNPs may help focus studies into pathobiology and targeted therapy of GVHD in the future. 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 | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».