Gene Expression Model of Survival and Transformation in Follicular Lymphoma (FL): A Study by the LLMPP.
Notice bibliographique
Résumé
Abstract Background: FL is a common NHL that has a broad spectrum of clinical outcomes. Over time some pts will transform to an aggressive histology (Tly) associated with inferior survival. In 2004, the LLMPP constructed a model that was predictive of overall survival (OS) based on the gene expression profiles (GEP) of 191 specimens taken from pts with untreated FL. The genes associated with survival were derived from the non-neoplastic immune response (IR) cells. However the risk of developing Tly was not addressed in this study. Thus we re-analyzed the GEP with updated clinical data. Our goal was to validate our previous model with extended follow-up and to create a model that would predict the risk of developing TLy. Methods: 170 of 191 previously untreated FL pts had updated clinical information but only 142 had transformation outcome. Transformation was defined as biopsy proven DLBCL or clinically based on the presence of at least one of the following: hypercalcemia, a sudden rise in LDH >twice baseline, unusual extranodal growth or rapid discordant nodal growth. Raw CEL files from Affymetrix U133A arrays were pre-processed and normalized using Bioconductor’s GCRMA package. Models were developed using SignS package (http://signs/bioinfo.cnio.es/), with 10 times cross-validation. All gene lists produced in these analyses were then re-tested for association with outcome using Bioconductor’s Globaltest package. Over Representation Analysis of signature components was performed using Dchip. Results: The median OS of these patients was 8 yrs. A new 7-component survival model (85 genes) was developed that was significantly associated with survival (p= 2.9×10−13). In Globaltest, these gene lists were associated with survival at a level of (p=2.6×10−5). The previous model using IR-1 and IR-2 signatures was associated with survival at a level of p=2.6×10−4. Although there is little overlap between the 2 models, the new model confirms the importance of IR genes and extracellular matrix genes as being prognostically important. Interestingly, one component containing 10 genes on chromosome 6q was associated with a superior survival (p<1×107). 27% developed Tly over a median follow-up time of 11.2 yrs (69% biopsy proven). Our transformation model included 53 genes divided into 3 components (p=0.001). The Globaltest analysis for association of these genes with transformation was significant (p=0.018). 54 genes overlapped between the survival genes and transformation genes that were present in >1 cross validation run. These were significantly enriched in genes important in immune response like T cell and macrophage activation. Conclusion: Our survival model is stable and confirms the importance of key genes involved in the immune response and lymph node remodeling. It also introduces new genes that are potentially important for survival. Our transformation model may shed light on the mechanisms involved in the progression of FL to DLBCL but it is less stable and less reliable than our survival model at predicting outcome.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 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 | 0,001 |
| 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 ».