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Enregistrement W3217481197 · doi:10.1182/blood-2021-153445

Constrained FL: A Genetically Distinct Subgroup of Follicular Lymphoma with Low Rates of Somatic Hypermutation and a Reduced Propensity for Histologic Transformation

2021· article· en· W3217481197 sur OpenAlexaff
Laura K. Hilton, Kostiantyn Dreval, Shaghayegh Soudi, Susana Ben‐Neriah, Manuela Cruz, Brett Collinge, Krysta M. Coyle, Bruno M. Grande, Gerben Duns, Christopher Rushton, Merrill Boyle, Barbara Meissner, Pedro Farinha, Graham W. Slack, Andrew J. Mungall, Marco A. Marra, Joseph M. Connors, Christian Steidl, David W. Scott, Ryan D. Morin

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensCanada's Michael Smith Genome Sciences CentreSimon Fraser UniversityUniversity of British ColumbiaSpinal Cord Injury BC
Organismes subventionnairesnon disponible
Mots-clésSomatic hypermutationBCL6BiologyFollicular lymphomaGerminal centerSomatic cellGeneticsLymphomaCell of originDiffuse large B-cell lymphomaFluorescence in situ hybridizationCancer researchGeneB cellChromosomeImmunology

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Follicular lymphoma (FL) is an indolent disease that undergoes histological transformation (HT) to aggressive diffuse large B-cell lymphoma (DLBCL) in 8-15% of patients. FLs frequently share genetic features with DLBCL, especially those of the germinal center B-cell-like (GCB) cell-of-origin (COO) and the EZB/C3 genetic subgroup, and approximately 80% of transformed FL (tFL) are classified as GCB. Our current understanding of the genetics of FL and tFL is based on a variety of studies, most of which have sequenced tumors in small case numbers or using targeted approaches such that the potential role of non-coding mutations and aberrant somatic hypermutation (aSHM) in predicting HT have not previously been fully explored. Methods: Whole genome sequencing (WGS) data from 212 FL (including 24 from patients that subsequently underwent HT) and 241 de novo DLBCL were analyzed. Simple somatic mutations (SSMs) were called using an ensemble of somatic variant callers, while structural variants (SVs) were called with Manta and copy number variants (CNVs) with Battenberg and GISTIC. Fluorescence in situ hybridization with break-apart probes (BA-FISH) was used to identify MYC, BCL2, and BCL6 translocations, and with IGH /BCL2 dual-fusion probes (DF-FISH) for a subset of FLs. To compare the genetics of FL and DLBCL, 83 significantly mutated genes (SMGs) were identified with dNdS, MutSigCV, HotMaps, and OncoDriveFML, and non-silent mutations were tabulated for their presence in each genome. For 38 hypermutated regions, we used a region-specific threshold to binarize the data to aSHM/no aSHM. Recurrent missense mutations in FOXO1, MYD88L265P, CREBBP lysine acetyltransferase (KAT) domain, EZH2Y646, MEF2B, and STAT6 were tabulated separately from other mutations in these genes. Using only the FL tumors from patients with no evidence of subsequent transformation and all available de novo DLBCLs, we trained a random forest classifier to separate these two entities using this set of 129 features, including MYC and BCL6 translocations. To validate this classifier, we fit a linear model to the number of FL votes from each discovery case, utilizing the 65 features (including 19 aSHM features) that were adequately sequenced in a validation cohort of 127 tFL. Statistical tests were corrected for multiple comparisons where necessary. Results: This large cohort of FL and DLBCL genomes has enabled the curation of an extensive list of novel and established FL driver genes and the identification of distinguishing genetic features among SMGs and CNVs. Loci that are significantly enriched for mutations in FL vs. DLBCL include the CREBBP KAT domain (OR 11.5, P < 0.0001), RRAGC (OR 9.61, P < 0.001), and ATP6V1B2 (OR 11.17, P < 0.001). Deletions of ARID1A (OR 4.74, P < 0.1), PTEN (OR 3.65, P < 0.01), and TNFRSF14 (OR 3.31, P < 0.01) were among the CNVs significantly enriched in FL. Out of 156 FLs, 24 (15%) were negative for BCL2 translocations by BA-FISH, but 4 (17%) of these had BCL2 translocations detected from WGS data. All 4 of these cryptic events were confirmed using IGH /BCL2 DF-FISH. Using a threshold of 0.7, the linear model separated discovery FL cases into a more DLBCL-like subgroup, termed dFL (n = 107), and a genetically homogeneous subgroup enriched for the FL-associated features, which we describe as constrained FL (cFL, n = 105). This separation is supported by more mutations in dFL vs cFL across several aSHM loci, including the transcription start sites for BCL6, BCL7A, DTX1, and ZFP36L1 (Figure 1), consistent with reduced exposure to the germinal center reaction in cFL. Within the targeted capture validation cohort of tFL, 30 (24%) tumors were classified as cFL and 97 (76%) as dFL. The tFL cohort was significantly depleted for mutations in the CREBBP KAT domain (OR 0.59, P < 0.05), and were significantly less frequently classified as cFL (OR 0.30, P < 0.0001) compared to the discovery FLs. Conclusions: The distinction between cFL and dFL is strongly driven by CREBBP KAT domain mutations and different rates of aSHM genome wide. Given the known early clonal nature of CREBBP mutations in FL and its role in regulating germinal center cycling, we speculate that CREBBP KAT mutations may limit the exposure of FL to the dark zone, reducing the opportunity for aSHM and creating an evolutionary constraint that may limit the opportunity for HT. This classification may serve as a useful biomarker to identify FLs at higher risk of HT. Figure 1 Figure 1. Disclosures Coyle: Allakos, Inc.: Consultancy. Grande: Sage Bionetworks: Current Employment. Slack: Seagen: Consultancy, Honoraria. Steidl: Curis Inc.: Consultancy; Trillium Therapeutics: Research Funding; Epizyme: Research Funding; Bayer: Consultancy; Seattle Genetics: Consultancy; AbbVie: Consultancy; Bristol-Myers Squibb: Research Funding. Scott: Janssen: Consultancy, Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; Incyte: Consultancy; Rich/Genentech: Research Funding; Celgene: Consultancy; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. . Morin: Epizyme: Patents & Royalties; Foundation for Burkitt Lymphoma Research: Membership on an entity's Board of Directors or advisory committees; 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 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 enseignants0,004
Score d'incertitude au seuil0,013

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 ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,016
Tête enseignante GPT0,242
Écart entre enseignants0,225 · 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
Publié2021
Routes d'admission1
Résumé présentoui

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