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Enregistrement W2765240381 · doi:10.1182/blood.v128.22.4096.4096

Molecular Subgroups of Peripheral T-Cell Lymphoma Evolve By Distinct Genetic Pathways

2016· article· en· W2765240381 sur OpenAlexaff
Tayla B. Heavican, Jiayu Yu, Alyssa Bouska, Timothy C. Greiner, Cynthia M. Lachel, Chao Wang, Bhavana J. Davé, Catalina Amador, Kai Fu, Julie M. Vose, Dennis D. Weisenburger, Randy D. Gascoyne, Sylvia Hartmann, Martin Bjerregård Pedersen, Ryan A. Wilcox, Bin Tean Teh, Soon Thye Lim, Choon Kiat Ong, Masao Seto, Françoise Berger, Andreas Rosenwald, German Ott, Elı́as Campo, Lisa M. Rimsza, Elaine S. Jaffe, Rita M. Braziel, Francesco d’Amore, Giorgio Inghirami, Francesco Bertoni, Louis M. Staudt, Timothy W. McKeithan, Stefano Pileri, Wing C. Chan, Javeed Iqbal

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésPeripheral T-cell lymphomaSNP arrayLymphomaSNPComparative genomic hybridizationBiologyCopy-number variationSingle-nucleotide polymorphismCancer researchGeneGeneticsMedicineOncologyChromosomeT cellGenotypeImmunology

Résumé

récupéré en direct d'OpenAlex

Abstract Peripheral T-cell lymphoma (PTCL) is a group of clinically and pathologically heterogeneous non-Hodgkin lymphomas (NHL). Using gene expression profiling (GEP), we have defined molecular classifiers for PTCL subtypes reflecting their pathobiology and oncogenic pathways (Iqbal et al. 2014). We have also shown associations of specific mutations with the molecular subgroups (Wang et al. 2015). Although genomic information is increasing, the pathogenetic mechanisms of PTCLs remain largely unknown. Therefore, we analyzed copy number variation (CNV) and GEP to identify unique genetic abnormalities in the defined PTCL molecular subgroups. CNV data were generated on fresh frozen or formalin-fixed paraffin-embedded genomic DNA (n=114) on 3 Affymetrix platforms (SNP 6.0, 250K SNP, and OncoScan). Two published cohorts (PTCL-NOS, Hartmann et al. 2010; ALCL, Boi et al. 2013) were included for validation. The gene expression analysis, morphological review and clinical characteristics of these cases have been included in previous studies (Iqbal et al. 2010, 2014). Angioimmunoblastic T-cell lymphoma (AITL) represents 20% of all PTCL cases. The most recurrent CNV in AITL was chromosome (chr) 5 gain (39%), followed by chr 21 gain (21%). Interestingly, chr 21 gain co-occurred with chr 5 gain (p=0.003). No recurrent losses (≥20%) were identified among these cases. Molecularly re-classified AITL cases from morphologically classified PTCL-NOS cases showed concordant results with bonafide AITL cases. Of the commonly mutated genes, DNMT3A, IDH2, RHOA and TET2, only IDH2R172Kshowed a significant association (p=0.012) with chr 5 gain. GEP showed enrichment of gene signatures associated with oxidative phosphorylation (PGC-1α target genes) in cases with chr 5 gain. PTCL, not otherwise specified (PTCL-NOS) is the most common PTCL subtype and cannot be further sub-classified using conventional approaches; however, we have identified 2 molecular subgroups within PTCL-NOS, the GATA3 and TBX21 subgroups which are related to 2 distinct T-helper subsets (Iqbal et al. 2014), by employing GEP. Consistent with earlier observations (Hartmann et al. 2010), PTCL-NOS showed remarkably varied CNVs with nearly 50% of cases showing high CNV frequencies. When correlated with molecular subgroups, distinctive CNVs were observed in the molecular GATA3 and TBX21 subgroups. The GATA3 subgroup displayed a large assortment of CNVs. Complete or partial gain of chr 7 (57%) was the most recurrent gain in these cases. Losses affecting 17p, 10q and 9p21, encompassing tumor suppressors such as TP53 (57%), PTEN (43%) and CDKN2A (43%), were frequent in the GATA3 subgroup. The TBX21 subgroup had significantly fewer CNVs, as none were recurring (≥20%); but gains of 5p or 11p were observed in 14%. Additionally, PTCL-NOS cases with ≥10% abnormal genome had significantly poorer overall survival (p=0.012) compared to those with fewer abnormalities. This finding validates the GEP molecularly defined subgroups, as the GATA3 subgroup displayed more CNVs and has been associated with a worse prognosis compared to the TBX21 subgroup (Iqbal et al. 2014). We were able to distinguish CNVs characteristic of the different entities, including the co-occurrence of chr 5 and 21 gains specific in AITL. Gain of 1q (complete or partial) was identified in the GATA3 subgroup of PTCL-NOS and anaplastic lymphoma kinase (ALK) (-) ALCL with equal frequencies (~ 36%), but only 16% in ALK(+) ALCL. Complete or partial gain of chr 7 was also observed in ALCL, but at a considerably lower frequency than in the GATA3 subgroup. Additionally, gain of chr 18 or regions of 17q, and loss of 5q or regions on both arms of chr 9, were more frequent in the GATA3 subgroup compared to other entities. The TBX21 subgroup was primarily differentiated from the GATA3 subgroup by presence of fewer CNVs. Our analysis provides a framework for future investigations into the molecular pathogenesis of PTCL, and highlights potential candidate oncogenes and tumor suppressors deregulated by copy number aberrations. Comparative analysis revealed that certain chromosomal abnormalities are entity-specific. AITL cases with IDH2R172K also had trisomy 5 suggesting that these oncogenic events cooperate in malignant transformation. Thus, the complexity of PTCL is finally becoming clearer with the integration of high resolution molecular techniques for global genomic analysis. 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 enseignants0,001
Score d'incertitude au seuil0,005

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 savante0,0010,000
Science ouverte0,0000,001
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 Opus0,007
Tête enseignante GPT0,201
Écart entre enseignants0,194 · 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

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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