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Enregistrement W2985949593 · doi:10.1182/blood-2019-127399

Recurrent Patterns of Clonal Evolution in Relapsed-Refractory DLBCL Following Treatment with R-CHOP

2019· article· en· W2985949593 sur OpenAlexaff
Christopher Rushton, Sarah E. Arthur, Miguel Alcaide, Matthew C. Cheung, Nicole Thomas, Laura K. Hilton, Neil R. Michaud, Scott R. Daigle, Jordan Davidson, Kevin Bushell, Stephen Yu, Michael D. Jain, Lois E. Shepherd, Michael Crump, Koren K. Mann, John Kuruvilla, Sarit Assouline, Kirstie L.S. Cleary, Mark S. Cragg, David W. Scott, Nathalie A. Johnson, Ryan D. Morin

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensJewish General HospitalSpinal Cord Injury BCMcGill UniversityQueen's UniversitySimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésDiffuse large B-cell lymphomaOncologyLymphomaInternal medicineExome sequencingFollicular lymphomaExomeMedicineCancer researchMutationBiologyGeneGenetics

Résumé

récupéré en direct d'OpenAlex

Introduction Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and is characterized by its genetic and clinical heterogeneity. Patients can develop DLBCL de novo or as a transformation from other lymphoid malignancies, most commonly follicular lymphoma. For patients with relapsed/refractory DLBCL (rrDLBCL), prognosis is extremely poor with 2-year overall survival of 20-40%. While numerous treatments are under investigation to improve patient outcomes, the success of these treatments has been limited as the genetic mechanisms underpinning treatment resistance are largely unknown. Identifying genomic alterations which contribute to relapse may improve salvage therapy for patients with rrDLBCL or allow patients to be stratified prior to frontline treatment. Methods To identify genomic alterations which contribute to R-CHOP resistance, we previously collected samples from patients enrolled in four clinical trials exploring candidate salvage therapies for patients with rrDLBCL as well as a retrospective rrDLBCL cohort, totalling 193 cases (133 de-novo DLBCL, 60 transformed). Plasma samples were collected from each patient upon relapse along with diagnostic tissue biopsies where available. A combination of exome sequencing and target-panel sequencing of lymphoma associated genes was performed on circulating tumour DNA and tissue biopsies (if available). Mutations implicated in R-CHOP resistance were identified through two complimentary strategies. First, the mutation frequency of recurrently mutated genes across de novo rrDLBCL samples was compared to a cohort of unrelated diagnostic DLBCL cases (n=1691) to identify genes enriched for mutations. Second, the genomic landscape and tumor clonal structure was compared prior to and following R-CHOP to identify mutations in each patient that underwent clonal expansion following therapy. Anti-CD20 antibody binding affinity of MS4A1 mutants was evaluated using flow cytometry on transfected CHO-S cells. Results We have identified five genes enriched for mutations in our rrDLBCL cohort relative to diagnostic DLBCL: KMT2D (Mutated in 49%, Q=0.0385), TP53 (47%, Q=1.07x10-9), FOXO1 (11%, Q=0.0727), NFKBIE (11%, Q=0.0385), and MS4A1 (8%, Q=0.0522). Consistent with its characterization as a poor prognostic marker, mutations in TP53 were typically present at diagnosis and remained stable following R-CHOP therapy for both de novo and transformed DLBCL (23/27 cases, 85%). Recurrent mutations affecting Arg248 of TP53 (6.8%, Q=0.0413) were also clonally stable and have previously been associated with poor overall survival across several cancer types. The histone methyltransferase KMT2D is dominated by nonsense and frameshift mutations which were stable or underwent clonal expansion following R-CHOP (17/19, 89%). Recurrent missense mutations in MS4A1 targeted the small loop and adjacent transmembrane domains of CD20, including several patients with a Tyr86 mutation. Transfected cells carrying Tyr86Cys or Leu66Arg mutations were not bound by rituximab or other anti-CD20 antibodies including obinituzumab and ofatumumab. Subclonal populations containing MS4A1 mutations underwent clonal expansion (6 cases) or were stable (1 case) following treatment, including one case with multiple MS4A1 mutations in distinct subclonal populations which both underwent clonal expansion. In another unique case, a series of ctDNA samples were available prior to and following R-CHOP and salvage therapy, where we again observed convergent evolution of two mutually exclusive clonal subpopulations containing MS4A1 mutations. The first subpopulation underwent clonal expansion following frontline therapy but was extinguished following salvage therapy, while the other subpopulation underwent clonal expansion following salvage therapy and harboured a transmembrane domain mutation. Conclusion Mutations in TP53 and truncating mutations in KMT2D are generally present prior to treatment and will be investigated as biomarkers of treatment failure. Additional mutations are not always present at diagnosis, but their emergence can be detected in ctDNA and relapsed tissue, specifically mutations in MS4A1. As mutations in MS4A1 attenuate rituximab binding and are recurrently associated with clonal expansion, they likely impart a selective advantage and lead to resistance against anti-CD20 antibodies. Disclosures Michaud: Epizyme: Employment. Daigle:Epizyme: Employment. Jain:Kite/Gilead: Consultancy. Kuruvilla:Roche: Honoraria; Astra Zeneca: Honoraria; Novartis: Honoraria; Merck: Honoraria; Karyopharm: Honoraria; Gilead: Honoraria; Celgene: Honoraria; BMS: Honoraria; Amgen: Honoraria; Seattle Genetics: Consultancy; Roche: Consultancy; Merck: Consultancy; Karyopharm: Consultancy; Gilead: Consultancy; Janssen: Research Funding; Roche: Research Funding; BMS: Consultancy; Abbvie: Consultancy; Seattle Genetics: Honoraria; Janssen: Honoraria. Assouline:Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria, Speakers Bureau. Scott:NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Celgene: Consultancy; Roche/Genentech: Research Funding; Janssen: Consultancy, Research Funding. Johnson:BD Biosciences: Other: Provided a significant proportion of the antibodies used in this project free of cost.; Merck: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Abbvie: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding; Seattle Genetics: Honoraria; Lundbeck: Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, 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 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
Bibliométrie0,0010,000
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 Opus0,010
Tête enseignante GPT0,242
Écart entre enseignants0,233 · 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

Citations3
Publié2019
Routes d'admission1
Résumé présentoui

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