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Enregistrement W3017091259 · doi:10.1182/blood.v112.11.477.477

Deletion in Chromosome 17p12 and Gains in Chromosome 9q33.3 by Array Comparative Hybridization Are Associated with R-CHOP Treatment Failure in Patients with Diffuse Large B Cell Lymphoma

2008· article· en· W3017091259 sur OpenAlexaff
Nathalie Johnson, Ronald J. de Leeuw, Julia Chae, Sohrab P. Shah, Wan L. Lam, Thomas Relander, Joseph M. Connors, Douglas E. Horsman, Randy D. Gascoyne

Notice bibliographique

RevueBlood · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésComparative genomic hybridizationDiffuse large B-cell lymphomaCHOPOncologyPMS2Internal medicineBiologyInternational Prognostic IndexLymphomaChromosomeMedicineBioinformaticsCancer researchGeneticsGeneMutationGermline mutation

Résumé

récupéré en direct d'OpenAlex

Abstract Background: The genetic alterations associated with survival in patients with diffuse large B cell lymphomas (DLBCL) treated with combined rituximab-CHOP (R-CHOP) chemo-immunotherapy are not well understood. Methods: 92 patients with DLBCL treated with R-CHOP who had a biopsy available at the time of diagnosis were included in the study. 31 patients were classified as treatment failures defined as progression < 6 months of completing R-CHOP and 61 patients were classified as treatment successes defined as a maintained remission >2 years after diagnosis. We used genome-wide BAC array comparative genomic hybridization (aCGH) to determine the genomic copy number imbalances. The presence of genetic gains and losses were determined using the intersection between visual annotations and a Hidden Markov model algorithm. DLBCL cell of origin (COO) subtype distinctions (GCB vs ABC) were determined using a Bayesian predictor model on gene expression derived from custom Affymetrix arrays (Dave, N Engl J Med, 2006;354:2431). A permutation test was used to identify genetic regions that were significantly different between treatment failures and treatment successes. Functional pathway analysis was performed using Ingenuity software. A novel model based clustering algorithm was applied to the normalized data to determine if any association with outcome correlated with the observed genetic alterations. Results: Lymphoma progressed in 31/92 (34%) patients < 6 months after R-CHOP (median follow-up = 4 y). All 92 patients had successful aCGH and 81 had COO available for this analysis. The International Prognostic Index (IPI) and COO were predictive of outcome (p=0.04, p=0.02, respectively). 451 regions containing 338 genes were associated with treatment failure with a p-value of <10−6. Gains in 9q33.3 were found in 13 patients (14%) and were significantly associated with treatment failure p<10−8. This region contains genes such as HSPA5, a negative regulator of apoptosis and PPP6C, a positive regulator of the cell cycle by targeting IKBe thereby removing inhibition of the NFkB pathway. Deletions in 17p12 were detected in 24 (26%) and were the most statistically significantly associated with treatment failure p<10−9. This region contains tumor necrosis factor (TNF) receptor superfamily member (TNFRSF13B or TACI) which, when deleted or mutated, has been previously shown to lead to activation of the noncanonical NFkB pathway and B cell proliferation. 21 of these 24 patients also had deletion of 17p13 at the TP53 locus (p<10−6). Neither 9p33.3 nor 17p12 deletion was associated with COO distinctions. Using Ingenuity, pathways involving apoptosis and cellular proliferation, specifically those involving P53, MYC and HSPA5 genes, were over-represented in treatment failures (p=2.04 × 10−4). Unsupervised clustering of the aCGH data demonstrated that 60% of cases could be stratified into 4 genetic sub-groups based on the presence of 1q+, 6q−, +7 and the concurrent presence of +3 and +18. Supervised analysis demonstrated that the +3/+18 group and the 6q− group were associated with ABC subtype of DLBCL whereas the +7 and 1q+ groups were associated with the GCB subtype. However, these genetic groups did not correlate with treatment outcome. Conclusions: Some genetic alterations cluster together and can distinguish COO subtypes of DLBCL. Gains on 9q33.3 and deletions of 17p12 are common alterations detected by high resolution aCGH in DLBCL. Most importantly, these alterations involve genes known to be critical in B cell proliferation and apoptosis and alterations at these sites are strongly associated with treatment failure (p values <10−8) in patients treated with R-CHOP.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,214
Écart entre enseignants0,203 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2008
Routes d'admission1
Résumé présentoui

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