MicroRNA Are Useful Biomarkers for Prediction of Response to Therapy and Survival of Patients with Diffuse Large B-Cell Lymphoma.
Notice bibliographique
Résumé
Abstract Abstract 624 Background: Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with a variable clinical outcome even in the current era of rituximab containing anthracycline-based chemotherapy (RCHOP). We have previously reported a 6 gene model which is predictive for outcome in DLBCL (Lossos et al NEJM 2004, 350:1829 and Malumbres et al Blood 2008, 111:5509). Recently, short non-protein coding microRNAs (miRNA) that regulate gene expression by targeting the 3'UTR region of mRNA have been identified and are postulated to play an important role in oncogenesis. We have previously demonstrated that miRNAs exhibit specific expressions at different lymphocyte differentiation stages as well as distinct expression in gene expression-defined DLBCL subtypes (Malumbres et al Blood 2009; 113:3754). Herein, we evaluated whether specific miRNAs differentially expressed across DLBCL cell lines were prognostic biomarkers for prediction of outcome of DLBCL patients (pts). Patients and Methods: RNA was extracted from paraffin-embedded archived specimens (Chen et al Diagn Mol Pathol 2007, 16:61), from pts with newly diagnosed de novo DLBCL treated with R-CHOP at 4 different institutions. Expression of 11 miRNAs, including miR-146a, miR-146b-5p, miR-222, miR-500, miR-574-3p, miR-363, miR-155, and miR-21 that are differentially expressed between the ABC and GCB DLBCL subtypes, as well as expression of miR-18a, miR-140-3p, and miR-181a which has been reported to be variably expressed across DLBCL tumors were analyzed by ABI real-time PCR assays. Expression of the genes comprising the 6 gene survival prediction model were measured as reported previously. Expression was correlated to progression-free survival (PFS) and overall survival (OS). Predictive value of miRNAs was evaluated both as a continuous variable as well as a in a dichotomous model with pts grouped based on miRNA median expression. Results: The study group consisted of 176 pts with a median age 59 years (y) (range 16-92) of which 84 pts (48%) were > 60 y. 54 pts (30%) had an ECOG PS ≥2 and 86 (49%) presented with stage III or IV. Distribution according to the IPI was: 0-1 factor (n=77); 2 factors (n=50); 3 factors (n=30); and ≥4 factors (n=19). There were 41 deaths during a median follow-up of 2.6 years (range 0.04-8.1y). OS correlated with expression of miR-18a as a continuous variable, with higher expression correlating with inferior OS (p=0.038) but was not independent of the IPI in a multivariate model. PFS correlated with expression of miR-181a as a continuous variable (p=0.026; higher expression associated with longer PFS) and with expression of miR-222 as a categorical variable (miR-222cat; p=0.004, lower expression associated with longer PFS). In a multivariable model including IPI, both miR-222cat and miR-181a were IPI independent (p=0.01 and p=0.003, respectively). The mortality-prediction score calculated from the 6-gene model predicted both the OS (p=0.007) and PFS (p=0.004) and was IPI-independent. A multivariate Cox regression analysis that included IPI scores, mortality predictor scores and expression of miR-18a, miR-181a and miR-222cat revealed that all factors except miR-222cat were independent predictors of OS and all factors except miR-18a were independent predictors of PFS. Conclusions: We confirm that in RCHOP treated pts with DLBCL the 6-gene model predicts both OS and PFS independent of the IPI. Expression of miRNAs miR-18a, miR-181a and miR-222 is associated with response to therapy and outcome of pts with DLBCL, independent of the IPI and the mortality-prediction score calculated from the 6-gene model. While the expression of miR-181a and miR-222 may reflect the cell of origin of DLBCL tumors, the expression of miR-18a does not. Further studies are warranted to evaluate the precise role of these miRNAs in B-cell biology and DLBCL pathogenesis and prognosis. Disclosures: Advani: Seattle Genetics, Inc.: Research Funding.
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 | 0,001 |
| 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 ».