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Enregistrement W2557976939 · doi:10.1182/blood.v120.21.2648.2648

Genetic Abnormalities in Follicular Lymphoma and Transformed Follicular Lymphoma.

2012· article· en· W2557976939 sur OpenAlexaff
Alyssa Bouska, Timothy W. McKeithan, Karen Deffenbacher, Cynthia M. Lachel, George W. Wright, Javeed Iqbal, Lynette M. Smith, Zhongfeng Liu, Can Küçük, Francesco Bertoni, Andrea Rinaldi, Jude Fitzgibbon, Kai Fu, Dennis D. Weisenburger, Timothy C. Greiner, Randy D. Gascoyne, Andreas Rosenwald, Elı́as Campo, Lisa M. Rimsza, Jan Delabie, Elaine S. Jaffe, Louis M. Staudt, Wing-Chung Chan

Notice bibliographique

RevueBlood · 2012
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésFollicular lymphomaLymphomaPTENBiologyComparative genomic hybridizationChromosomal translocationSNP arrayDiffuse large B-cell lymphomaCopy number analysisCancer researchUnivariate analysisChromosomeChromosome abnormalityGene expression profilingGeneticsOncologyKaryotypeGeneInternal medicineMedicineCopy-number variationSingle-nucleotide polymorphismImmunologyGene expressionMultivariate analysisGenomePI3K/AKT/mTOR pathway

Résumé

récupéré en direct d'OpenAlex

Abstract Abstract 2648 Follicular lymphoma (FL) is an indolent lymphoma and the second most common type of non-Hodgkin lymphoma in the Western world. It is characterized by the t(14;18) chromosomal translocation, which is present in up to 90% of cases. About 40% of FL cases eventually transform into a more aggressive lymphoma (tFL), most commonly diffuse large B-cell lymphoma (DLBCL). To identify the secondary chromosomal abnormalities that contribute to the development of FL, and to its transformation, we undertook a large study using the Affymetrix 250k NspI SNP array to identify copy number abnormalities (CNAs) in 198 FL and 79 tFL samples, 75% of which have concurrent gene expression profiling studies using Affymetrix U133+2 or A+B arrays for correlative analysis. There were 22 recurrent chromosomal abnormalities that were present in over 10% of FL cases, including gains of 7, 12, 18, 21, X, 1q, 2q, 5p, 6p, 8q, 17q and loss of 6q. We also identified 20 smaller CNAs that occurred in over 5% of FL cases, the most frequent being loss of chromosome 1p36.33-p36.31 including TNFRSF14, loss of chromosome 10q23.1-q25.1 encompassing several possible cancer-related genes such as PTEN, gain of chromosome 2p16.1-p15 including REL, and gain of chromosome 8q24.13-q24.3 including MYC. Univariate Cox regression models were used to analyze the CNA regions that occurred in at least 10 FL cases as predictors of overall survival. Four recurrent CNAs were predictive of survival in univariate analysis below the p=0.05 significance level, and two were found to be borderline significant. A gain of X or the p arm of X was predictive of poor survival. Additionally, two losses on 6q (6q13–15 and 6q23.3–24.1) were associated with poor survival. The 6q23.3–24.1 loss contains TNFAIP3, which encodes a negative regulator of the NF-kB pathway, and is a frequent site of homozygous loss. Additionally, a gain of chromosome 8 that includes the MYC gene, and a loss of chromosome 9 that includes CDKN2A, were borderline predictors of poor survival. Patients with FLs that have 7 or more abnormalities had worse survival than those with fewer abnormalities. We also compared the CNAs found in tFL samples to FL samples and identified 26 abnormalities that were at least 5 times more frequent in tFL and present in at least 5% of tFLs. A gain of 3q27.3-q28 containing 5 genes including BCL6 and LPP, for example, was found in 11% of tFL case, but only 2% of FL cases. We also found differences in the deletion of Beta-2 Microglobulin (B2M) between FL and tFL. The B2M locus is deleted in 8% of FLs, but in 21% of tFLs. B2M, a subunit of the MHC class I molecule, is known to be repressed by mechanisms such as mutation and deletion in de novo DLBCL, as a way for the tumor to evade immune surveillance. HLA-A- B, and/or -C were deleted in 5% of FLs and almost 9% of tFLs. CD58, which plays a role in T- and NK-cell immune responses, was deleted in 3% of FLs and 11% of tFLs. Overall, 19% of FLs and 37% of tFLs had an abnormality in CD58, B2M, and/or HLA class I, indicating that evasion of immune surveillance is important in transformation to a more aggressive disease. We also compared CNAs from tFL cases to those found in de novo GCB-DLBCL cases and identified several that differed markedly between the 2 diseases, such as a gain of chromosome 21 which was present in 21% of tFL cases but only 3% of DLBCL cases. In conclusion, FL, tFL, and de novo GCB-DLBCL share common CNAs, but the prevalence of the individual lesions differ among the 3 entities. Functional validation of potential candidate genes will determine important pathways in the development and progression of FL, and identify possible targets for therapeutic intervention. 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 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 enseignants0,003
Score d'incertitude au seuil0,010

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)0,0030,000

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 GPT0,231
Écart entre enseignants0,220 · 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

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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