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Enregistrement W2979810607 · doi:10.1182/blood.v128.22.2919.2919

Targeted Sequencing Reveals Novel Gene Mutations Associated with Transformation and Early Progression in Follicular Lymphoma

2016· article· en· W2979810607 sur OpenAlexaff
Robert Kridel, Fong Chun Chan, Anja Mottok, Merrill Boyle, Pedro Farinha, King Tan, Barbara Meissner, Ali Bashashati, Susana Ben‐Neriah, Eva Giné, Daisuke Ennishi, Andrew J. Mungall, Ryan D. Morin, Nathalie A. Johnson, Laurie H. Sehn, Thomas Tousseyn, Ahmet Doǧan, Joseph M. Connors, David W. Scott, Christian Steidl, Marco A. Marra, Randy D. Gascoyne, Sohrab P. Shah

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensSimon Fraser UniversityCanada's Michael Smith Genome Sciences CentreJewish General HospitalBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésFollicular lymphomaCDKN2AMedicineInternal medicineProgression-free survivalOncologyLymphomaCancerCohortPathologyChemotherapy

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction : Follicular lymphoma (FL) remains an incurable malignancy as patients eventually experience progressive disease. A subset of patients is at risk of early lymphoma-related mortality due to histological transformation (TFL) to aggressive lymphoma (2-3% of patients per year) or early progression after immuno-chemotherapy, each of which leads to shortened survival. Mutations associated with transformation and/or early progression have been reported in the literature (e.g. CDKN2A, TP53, B2M) and the m7-FLIPI clinico-genetic risk model defines a high-risk patient group with poor failure-free survival after first-line treatment. However, the current knowledge of genetic alterations underlying transformation and/or early progression is inadequate to explain the majority of transformed cases and accurately predict early progressive disease. Materials and Methods: We performed targeted capture-based sequencing of 94 genes in a large cohort of fresh-frozen and formalin-fixed and paraffin-embedded patient specimens (402 samples in total). This cohort comprised 277 samples from 159 patients that experienced transformation (including 128 samples at T1 (the time of FL) and 149 samples at T2 (the time of transformation) with 118 paired biopsies) and 125 samples from 125 patients (pre-treatment samples only) presenting with either early progression within 2.5 years after starting immunochemotherapy (n=41) or late or never progression for at least 5 years after starting immunochemotherapy or observation (n=84). Mutations were called using MutationSeq and putative single nucleotide polymorphisms were filtered out using either matching germline samples (n=84) or dbSNP (v147). Bayesian proportion tests were used to compare the prevalence of gene mutations between groups. Results : We first compared T1 (n=128) and T2 (n=149) samples from 159 patients experiencing transformation. Eleven genes were more commonly altered in transformed lymphoma. These included genes that had previously been associated with transformation, such as TP53, B2M, MYC and EBF1, as well as genes that have not yet been implicated as contributing to this process, for example EZH2, CCND3, PIM1 and ITPKB. Moreover, mutations in GNA13, S1PR2 and P2RY8, that have been implicated in dissemination of germinal centre B cells, were enriched in T2 samples. In addition, cell-of-origin classification was available for 108 of the TFL cases with DLBCL histology, 18 and 90 of which were of the ABC and GCB subtype, respectively. Although the number of ABC-TFL cases was small, we observed higher percentages of BCL10 (16% versus 1%, Fisher P=0.004), CD79B (22% versus 4%, Fisher P=0.005) and MYD88 mutations (28% versus 9%, P=0.006) in ABC-TFL than in GCB-TFL, suggesting that B-cell receptor signaling and NF-κB signaling are important contributors to the ABC phenotype in TFL. Next, we assessed the association of gene mutations with patient outcomes contrasting early progressers (n=41) and late or non-progressers (n=84). Eleven genes were mutated more commonly in early progressers than in late progressers, including KMT2C, TP53, BTG1, MKI67, XBP1 and SOCS1. Overall, 32 out of 41 early progressers (78%) had mutations in at least one of the 11 early progression-associated genes, but none of the individual early progression-associated genes were mutated at a frequency > 22%. Thus, early progression appears to be related to relatively infrequent genetic alterations. None of the early progression-associated gene mutations form part of the m7-FLIPI outcome predictor. Furthermore, in our cohort that was enriched for clinical extremes, the m7-FLIPI was similarly associated with early progression when compared to the FLIPI but it was not superior, having higher specificity (89% versus 76%), but lower sensitivity (36% versus 63%). Conclusions: We found novel associations of gene mutations with transformation and showed that TFL is molecularly heterogeneous, with the ABC subtype being characterized by differential gene mutation when compared to the GCB subtype. With regards to progressive disease after immuno-chemotherapy, our approach identified early progression as a distinct clinico-genetic disease category that is imperfectly captured by traditional prognostic tools. Disclosures Connors: Seattle Genetics: Research Funding; Bristol Myers Squib: Research Funding; F Hoffmann-La Roche: Research Funding; NanoString Technologies: Research Funding; Millennium Takeda: Research Funding. Scott:NanoString Technologies: Patents & Royalties: named inventor on a patent for molecular subtyping of DLBCL that has been licensed to NanoString Technologies.

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)0,000
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
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 Opus0,014
Tête enseignante GPT0,243
Écart entre enseignants0,229 · 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
Publié2016
Routes d'admission1
Résumé présentoui

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