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Enregistrement W4389233810 · doi:10.1182/blood-2023-182390

A CD5 Gene Signature Identifies Diffuse Large B-Cell Lymphomas Sensitive to Brutonʼs Tyrosine Kinase Inhibition

2023· article· en· W4389233810 sur OpenAlexaff
Alan Cooper, Sravya Tumuluru, Kyle Kissick, Girish Venkataraman, Joo Y. Song, Andrew Lytle, Gerben Duns, Jovian Yu, Nikita Kotlov, Alexander Bagaev, Brendan P. Hodkinson, Srimathi Srinivasan, Sonali M. Smith, David W. Scott, Christian Steidl, James Godfrey, Justin Kline

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensSpinal Cord Injury BCUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésDiffuse large B-cell lymphomabreakpoint cluster regionCancer researchChronic lymphocytic leukemiaPopulationBiologySurrogate endpointInternal medicineLymphomaImmunologyGeneticsMedicineGeneLeukemia

Résumé

récupéré en direct d'OpenAlex

Introduction: Diffuse large B-cell lymphomas (DLBCLs) with a non-germinal center B cell-like (non-GCB) cell-of-origin are frequently driven by genetic alterations that culminate in constitutive B-cell receptor (BCR) signaling, which has inspired the exploration of Bruton's tyrosine kinase inhibitors (BTKi) in these lymphomas. However, the phase III PHOENIX study that randomized untreated, non-GCB DLBCL patients to R-CHOP plus placebo or ibrutinib failed to meet its primary endpoint of event-free survival (Younes et al. 2019), which suggests that cell-of-origin alone is an insufficient biomarker to predict BTKi sensitivity in DLBCL. More recently, a DLBCL genetic classifier termed LymphGen has identified distinct subtypes (MCD and N1) of non-GCB DLBCL that benefit from the addition of BTKi to R-CHOP (Wilson et al. 2021). However, genetic classifiers are complex and difficult to implement in routine clinical settings and may fail to capture all DLBCLs that benefit from BTKi. Therefore, we sought to identify a straightforward biomarker of BTKi responsiveness in DLBCL with greater precision than cell-of-origin but with broader inclusivity than current genomic platforms, such as LymphGen. We hypothesized that CD5 - a surrogate marker of BCR activation - may effectively identify BCR-driven, non-GCB DLBCLs that are sensitive to BTKi therapy, and evaluated the extent to which CD5 protein expression and a transcriptionally defined CD5 gene signature could accurately identify BCR-activated DLBCLs with potential susceptibility to BTKi-based therapies. Methods: CD5 immunohistochemistry (IHC) was performed on a cohort of 406 diagnostic DLBCL samples, which were considered CD5+ if >=30% of lymphoma cells exhibited unequivocal membranous staining. A majority of DLBCL samples had available RNA-sequencing and targeted mutational sequencing data. A comparison of differentially expressed genes between CD5+ and CD5- DLBCLs was performed in order to construct a 60-gene CD5 signature (CD5sig), which was applied to large genomic DLBCL datasets, including pre-treatment biopsies from patients enrolled on PHOENIX (n = 584) to evaluate the utility of the CD5sig in identifying DLBCLs that benefitted from the addition of ibrutinib to R-CHOP. Results: Twenty-six of 406 DLBCLs were identified as CD5+ by IHC (6% of all DLBCLs; 12% of non-GCB DLBCLs). CD5 IHC+ DLBCLs were majority non-GCB cell-of-origin and were associated with inferior progression-free survival (PFS) to R-CHOP (50% 3-year PFS), compared with CD5 IHC- DLBCLs, consistent with previous reports. Gene set enrichment analysis revealed that CD5 IHC+ DLBCLs exhibited transcriptional features of BCR activation, and mutational analysis demonstrated that CD5 IHC+ DLBCLs were enriched for CD79B BCR-activating mutations known to correlate with BTKi sensitivity. Many CD5 IHC+ DLBCLs, however, lacked canonical BCR-activating mutations or were classified as “Other” by LymphGen. A CD5 gene signature (CD5sig; Figure 1A) was developed that recapitulated these findings in independent DLBCL datasets (NCI, Duke), where ~13% of non-GCB DLBCLs were classified as CD5sig+. Together, these results suggest that CD5 signature expression captures DLBCLs with both a genetic and non-genetic basis for BCR dependence. Supporting this notion, CD5sig+ DLBCL patients (< 60 years) derived a selective and striking event-free and overall survival advantage from the addition of ibrutinib to R-CHOP in the PHOENIX study ( Figure 1B), independent of LymphGen classification. Conclusions: We demonstrate that CD5 IHC and a novel CD5 gene signature identify high-risk, BCR-driven DLBCLs. Importantly, the CD5 signature also identifies DLBCL patients with a selective survival advantage to BTK inhibitor-based therapy, independent of LymphGen classification. In conclusion, the CD5 signature expands upon LymphGen classification as a biomarker of BTKi response by accurately identifying DLBCLs with both genetic and non-genetic bases for BTKi response. The CD5 signature and/or CD5 IHC should be prospectively evaluated in BTKi-based clinical trials for non-GCB DLBCLs.

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 candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,080
Score d'incertitude au seuil1,000

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)0,0000,002

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 Opus0,013
Tête enseignante GPT0,271
Écart entre enseignants0,258 · 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.

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

Citations2
Publié2023
Routes d'admission1
Résumé présentoui

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