The Copy Number Landscape of Relapsed and Refractory Diffuse Large B-Cell Lymphoma
Notice bibliographique
Résumé
Introduction Patients diagnosed with diffuse large B-cell lymphoma (DLBCL) are treated with standard frontline immunochemotherapy (R-CHOP). However, for cases where R-CHOP fails (relapsed-refractory DLBCL, rrDLBCL), prognosis is extremely poor, with 2-year overall survival of 20-40%. The successful development of new therapies may be hampered by our limited understanding of the genetic and molecular mechanisms underpinning treatment resistance. For example, recent data from our group has highlighted novel mutations that emerge following treatment with R-CHOP. The contribution of copy-number variations (CNVs) towards treatment resistance has not yet been thoroughly explored. A more complete characterization of these genetic alterations may lead to new prognostic biomarkers or treatment strategies. Methods We analyzed exome sequencing data from 59 rrDLBCL cases derived from either tissue biopsies or liquid biopsies collected after relapse, including both unpublished and previously published cases (Schmitz et al. (2018) NEJM 378:1396-1407 and Morin et al. (2016) Clin Can Res 22(9)). We separately performed low-pass whole-genome sequencing (lpWGS, 0.1-1x coverage) on 45 rrDLBCL liquid biopsies with ctDNA levels insufficient for exome-based analysis, for a total of 104 cases with copy-number information. We identified CNVs from exome and lpWGS data using Sequenza and ichorCNA, respectively. Next, we identified significant peaks of recurrent gains and losses using GISTIC2. Comparison of these peaks to CNVs in a previously published diagnostic DLBCL cohort (Schmitz et al. (2018) NEJM 378:1396-1407) enabled the identification of events that were significantly more prevalent in rrDLBCL. Results Overall, the landscape of CNVs in rrDLBCL is reminiscent of diagnostic DLBCL, with recurrent amplifications of chromosome 7 (43/104, 41.3%) and 18q (42/104, 40.4%) and recurrent deletions of 6q (25/104, 24.0%) and 17p13 (39/104, 37.5%). We identified nine regions enriched for recurrent amplifications or deletions among rrDLBCLs. These include deletions of 17p13.1 (20.4% in diagnostic biopsies vs 41.3% of rrDLBCLs, q=8.53x10-5) and recurrent amplifications of 8q24 (18.5% vs 42.3%, q=5.72x10-7) and 7p22 (27.2% vs 57.9%, q=6.29x10-8). Many of these peaks represent focal events that are exceedingly rare in diagnostic DLBCL and do not contain established lymphoma-associated genes, including amplifications affecting 700kb of 6p11.2 (2.03% vs 7.69%, q=0.0178) and 500kb of 19p13.3 (6.7% vs 31.7%, q=9.99x10-10). Notably, the 6p11.2 amplifications were associated with inferior progression-free survival following R-CHOP (p=0.02), with most tumors harboring this alteration relapsing within 12 months. We also identified a novel, recurrent deletion affecting a 20mb region of 5q (2.78% vs 10.6%, q=0.00604) which was significantly deleted in rrDLBCL. For tumors with additional samples collected prior to R-CHOP and following salvage therapy, deletions of 5q appeared to emerge following frontline therapy and persisted after subsequent treatments, suggesting they may contribute to treatment resistance. Discussion The 17p13.1 deletion enriched in rrDLBCL encompasses TP53, which is a common target of somatic point mutations in rrDLBCL and associated with inferior treatment outcomes. The amplification of 8q24 and 7p22 include MYC and GNA12/CARD11, respectively, although these large events encompass numerous additional genes which may be the target of such events. Curiously, the focal 6p11.2 amplification only overlaps a handful of genes including miR_598, which has been predicted to target CD27 and CD38 and whose expression is upregulated in B-cell cell lines (Lawrie et al. (2008) Leukemia 22:1440-2446). Further investigation and validation of these events and their corresponding targets will provide insight into the biology of rrDLBCL and may reveal novel therapeutic targets. Disclosures Michaud: Epizyme: Current Employment. Daigle:Epizyme: Current Employment. Jain:Kite/Gilead: Consultancy; Novartis: Consultancy. Kuruvilla:Merck: Consultancy, Honoraria; Bristol-Myers Squibb Company: Consultancy; Celgene Corporation: Honoraria; AstraZeneca Pharmaceuticals LP: Honoraria, Research Funding; AbbVie: Consultancy; Gilead: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; Janssen: Honoraria, Research Funding; Amgen: Honoraria; Antengene: Honoraria; Novartis: Honoraria; Pfizer: Honoraria; TG Therapeutics: Honoraria. Crump:Servier: Consultancy; Roche: Consultancy; Kite/Gilead: Consultancy. Assouline:BeiGene: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Takeda: Research Funding; Pfizer: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria, Speakers Bureau; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding. Steidl:Juno Therapeutics: Consultancy; Seattle Genetics: Consultancy; Roche: Consultancy; Bristol-Myers Squibb: Research Funding; AbbVie: Consultancy; Bayer: Consultancy; Curis Inc: Consultancy. Johnson:AbbVie: Research Funding; Roche/Genentech, Merck: Honoraria; Roche/Genentech, Merck, Bristol-Myers Squibb, AbbVie: Consultancy. Scott:NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; Janssen: Consultancy, Research Funding; Roche/Genentech: Research Funding; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Celgene: Consultancy; Abbvie: Consultancy; AstraZeneca: Consultancy. Morin:Celgene: Consultancy.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».