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Enregistrement W4389222015 · doi:10.1182/blood-2023-189835

DNA Methylation-Based Classification of Hairy Cell Leukemia and Splenic B Cell Lymphoma

2023· article· en· W4389222015 sur OpenAlexaff
Kyoko Yamaguchi, Salma Abdelbaky, Evgeny Arons, Matt Cross, Yue-Zhong Wu, Christoph Weigel, Mirela Anghelina, Helen Parker, Artur Kibler, Marta Salido, Constance Baer, Manja Meggendorfer, Benjamin H. Durham, Omar Abdel‐Wahab, Seema A. Bhat, Gerard Lozanski, Kerry A. Rogers, Yonghong Wang, Paul S. Meltzer, Sunil Iyengar, Sascha Dietrich, Thorsten Zenz, James S. Blachly, Aurélie Verney, Lucile Baseggio, Alexandra Traverse‐Glehen, Marc Seifert, Ralf Küppers, Richard Burack, Clive S. Zent, Versha Banerji, James B. Johnston, David Oscier, Renata Walewska, Κώστας Σταματόπουλος, Torsten Haferlach, Ana Ferrer, Catherine Thiéblemont, Francesco Forconi, Robert J. Kreitman, Michael R. Grever, Jonathan C. Strefford, Piers Blombery, Christopher C. Oakes

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensUniversity of ManitobaCancerCare Manitoba
Organismes subventionnairesnon disponible
Mots-clésDNA methylationSplenic marginal zone lymphomaBiologyMethylationHairy cell leukemiaSanger sequencingLeukemiaCancer researchEpigeneticsLymphomaMantle cell lymphomaMolecular biologyComputational biologyDNA sequencingGeneticsDNAB cellGeneImmunologyAntibody

Résumé

récupéré en direct d'OpenAlex

Improved classification of rare lymphoid neoplasms would be aided by a deeper understanding of their underlying molecular features and is important for diagnosis, prognosis and therapy. Tumor entities classified within the WHO category of splenic B cell lymphomas and leukemias often exhibit heterogenous, transecting features, and include hairy cell leukemia (HCL), splenic diffuse red pulp lymphoma (SDRPL), splenic marginal zone lymphoma (SMZL), and the newly described WHO entity, splenic B cell lymphoma/leukemia with prominent nucleoli (SBLPN); the latter including patients formerly classified as HCL-variant (HCL-V). Genome-wide epigenetic information provides a tumor cell fingerprint combining cell-of-origin and tumor-specific events. Here we used DNA methylation to perform an unbiased molecular subclassification and to explore novel biological aspects of these patients. Samples from patients with a pathological diagnosis of HCL, HCL-V, SDRPL and SMZL (made prior to the 5 th WHO revision and ICC classifications) were obtained from 19 institutions across 9 countries, totaling 367 patients. Cells were FACS-purified where necessary and DNA was analyzed by 450/850K Illumina DNA methylation arrays. Genetic mutations were assessed by whole-exome or targeted sequencing, IGHV-D-J sequences by Sanger sequencing, and copy number alterations (CNAs) by Illumina arrays. The 1000 most variable CpG methylation sites were used for k-means clustering. Recursive feature elimination/random forest algorithms were used to develop a classifier for DNA methylation-based subgroups with 98% accuracy. Unsupervised clustering of 197 patients diagnosed with HCL, HCL-V or SDRPL revealed 5 distinct DNA methylation (M) subgroups ( Figure 1). Subgroup assignment was stable throughout longitudinal sampling (including pre/post-treatment) and consistent between splenic, bone marrow and PBMC derived cells. A subgroup with universally clonal BRAF-V600E mutations and majority diagnosed as HCL was termed the M-HCL subgroup ( Table 1). Four other groups termed M-SBLPN1-4 contained all HCL-V and SDRPL diagnosed samples and were devoid of BRAF-V600E mutations. M-SBLPN1 comprised MAP2K1 mutations (91%) and was enriched for CREBBP, ARIDIA and TERT-promoter mutations. These patients displayed an HCL-like immunophenotype (64.3% CD25+) with 1/3 diagnosed as HCL. M-SBLPN2 exhibited the highest prevalence of TP53 mutations and concomitant genomic instability. Patients in M-SBLPN1,2 were enriched in unmutated IGHV4-34 rearrangements. M-SBLPN3,4 subgroups displayed an immunophenotype more dissimilar to HCL, mutated IGHV genes, and enrichment of IGLL5, SYK and BIRC3 mutations. M-SBLPN4 contained the most SDRPL samples, suggesting it may represent the SDRPL entity retained by the WHO. We next uncovered that 29/170 SMZL patients displayed DNA methylation patterns mapping to M-SBLPN2-4. These patients were phenotypically and molecularly similar to SBLPN (70% displaying villous morphology and depleted in IGHV1-2*04, NOTCH2, KLF2 mutations), likely representing SMZL patients suggested for reassignment to SBLPN in the updated WHO classification. To elucidate molecular pathways governing the biology of M-SBLPN subgroups, transcription factor motif enrichment analysis in hypomethylated genomic regions revealed selective activation of AP-1 in M-HCL along with ETS in M-SBLPN1,2. Both transcription factors are downstream of MAPK signaling, consistent with activating BRAF and MAP2K1 mutations in these subgroups. However, we observed strong ETS enrichment in the absence of MAP2K1 in M-SBLPN along with mutual exclusivity of MAP2K1 and TP53 mutations, suggesting TP53 mutations are driving ETS activation. Although lymphoid neoplasms rarely exhibit TERT promoter mutations, 83% of M-SBLPN1 patients showed the c.-124C>T mutation commonly observed in other cancers producing an ETS binding site and ectopic TERT activation. ETS activation and gain of an ETS site by mutation implies oncogenesis involves aberrant TERT activation in this subgroup. In summary, we have developed a DNA methylation-based classifier that resolves 4 SBLPN subgroups with distinct molecular features, and reclassifies a subset of SMZL and HCL patients, adding further information to the updated WHO/ICC entities. We reveal distinct biological pathways operating in M-SBLPN subgroups that may aid targeted therapy approaches.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,162
Score d'incertitude au seuil0,992

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
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)0,0000,000

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 Opus0,026
Tête enseignante GPT0,282
Écart entre enseignants0,256 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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
Publié2023
Routes d'admission1
Résumé présentoui

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