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Enregistrement W2949669062 · doi:10.1002/hon.99_2629

CONCORDANCE BETWEEN IMMUNOHISTOCHEMISTRY AND GENE EXPRESSION PROFILING SUBTYPING FOR DIFFUSE LARGE B‐CELL LYMPHOMA IN THE PHASE 3 PHOENIX TRIAL

2019· article· en· W2949669062 sur OpenAlexaff
S. Balasubramanian, S. Wang, Chloe’ Major, B. Hodkinson, Michael Schäffer, Laurie H. Sehn, Peter Johnson, Pier Luigi Zinzani, Jodi Carey, G. Liu, Christina Loefgren, Martin Shreeve, Steven Sun, S. H. Zhuang, Jessica Vermeulen, Louis M. Staudt, Anas Younes, Wyndham H. Wilson

Notice bibliographique

RevueHematological Oncology · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensCanadian Centre for Applied Research in Cancer ControlSpinal Cord Injury BC
Organismes subventionnairesnon disponible
Mots-clésSubtypingImmunohistochemistryConcordanceDiffuse large B-cell lymphomaMedicineOncologyInternal medicineLymphomaGerminal centerPathologyB cellImmunologyAntibody

Résumé

récupéré en direct d'OpenAlex

Introduction: Diffuse large B-cell lymphoma (DLBCL) can be classified based on cell-of-origin (COO) into germinal center B-cell–like (GCB), activated B-cell–like (ABC), and unclassified (UNC) subtypes by gene expression profiling (GEP), and GCB and non-GCB subtypes by immunohistochemistry (IHC). In the phase 3 PHOENIX trial (NCT01855750) that enrolled untreated patients (pts) with non-GCB DLBCL by IHC, ibrutinib (ibr) + R-CHOP did not improve event-free survival (EFS) vs placebo (pbo) + R-CHOP in the intent-to-treat (ITT, non-GCB by IHC) or ABC (by GEP) populations; however, an increase in EFS and overall survival with ibr was seen in pts < 60 years (yrs), but not in pts ≥ 60 yrs due to increased toxicity in elderly pts. This work aimed to determine the concordance between IHC and GEP for DLBCL subtyping and outcomes related to subtypes. Methods: Baseline paraffin-embedded, formalin-fixed tissue samples were used to confirm non-GCB DLBCL by Hans-based IHC (Dako pharmDx™ kit) at a central laboratory. Available tumor samples were retrospectively analyzed for ABC subtype by GEP (HTG EdgeSeq DLBCL COO Assay). The concordance was evaluated by comparing non-GCB calls by IHC with ABC + UNC by GEP or GCB calls between IHC and GEP. Survival outcomes were compared between GEP subtypes in each arm and across study arms. Results: In all screened pts, 1111/1336 (83.2%) samples also provided evaluable GEP results; the concordance between GEP and IHC was 80.2% for non-GCB and 61.3% for GCB calls, resulting in an overall concordance of 76.9% (Figure), with 73.7% of non-GCB samples (by IHC) being identified as ABC by GEP. In pts < 60 yrs (n = 506), the concordance for non-GCB, GCB, and overall was 76.1%, 67.6%, and 74.3% respectively. In 747 evaluable samples from 838 enrolled non-GCB pts, 75.9% were ABC by GEP; 17.2% were GCB and 6.8% UNC. In both ITT and age < 60 yrs populations, EFS rate in GCB DLBCL by GEP was higher vs ABC in either arm, although the difference was not statistically significant and even smaller in the ibr arm. When comparing the two arms in the ITT population, EFS was similar between arms regardless of COO. In pts < 60 yrs, EFS was better with the addition of ibr to R-CHOP in ABC pts (HR 0.56 [95% CI, 0.33-0.98]; p = 0.0348; Figure); the difference between arms was not statistically significant in GCB (HR 0.64 [95% CI, 0.22-1.86; p = 0.4119) or UNC (HR 1.12 [95% CI, 0.22-5.97]) subtypes as numbers were small. Keywords: gene expression profile (GEP); ibrutinib; immunohistochemistry (IHC). Disclosures: Balasubramanian, S: Employment Leadership Position: Janssen, Pharmacyclics; Stock Ownership: Pharmacyclics, Johnson & Johnson, Gilead Sciences, Celgene, Vertex, AbbVie. Wang, S: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Major, C: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Hodkinson, B: Employment Leadership Position: Janssen R&D. Schaffer, M: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Sehn, L: Consultant Advisory Role: Celgene, AbbVie, Seattle Genetics, TG Therapeutics, Janssen, Amgen, Roche/Genentech, Gilead Sciences, Lundbeck, Apobiologix, Karyopharm Therapeutics, Kite Pharma, Merck, Takeda Pharmaceuticals, TEVA Pharmaceuticals Industries, TG Therapeutics; Honoraria: Amgen, Apobiologix, AbbVie, Celgene, Gilead Sciences, Janssen-Ortho, Karyopharm Therapeutics, Kite Pharma, Lundbeck, Merck, Roche/Genentech, Seattle Genetics, Takeda Pharmaceuticals, TEVA Pharmaceuticals Industries, TG Therapeutics; Research Funding: Roche/Genentech (Inst). Johnson, P: Consultant Advisory Role: Janssen, Epizyme, Boehringer Ingelheim; Honoraria: Takeda Pharmaceuticals, Bristol-Myers Squibb, Novartis, Celgene, Kite Pharma, Genmab, Incyte, MorphoSys; Research Funding: Janssen, Epizyme; Other Remuneration: Combined use of Fc gamma RIIb (CD32b) and CD20-specific antibodies; WO patent, PCT/GB2011/051572; EU11760819.0. Zinzani, P: Honoraria: Servier, Bristol-Myers Squibb, Gilead, Jansen, Merck Sharp & Dohme, Celltrion, Celgene, Roche; Other Remuneration: Verastem, Servier, Bristol-Myers Squibb, Gilead, Janssen, Merck Sharp & Dohme, Celltrion, Celgene, Roche. Carey, J: Employment Leadership Position: Janssen Research and Development; Stock Ownership: Johnson & Johnson. Liu, G: Employment Leadership Position: Janssen. Loefgren, C: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Shreeve, M: Employment Leadership Position: Janssen; Stock Ownership: Johnson & Johnson, Pfizer. Sun, S: Employment Leadership Position: Johnson & Johnson; Stock Ownership: Johnson & Johnson. Zhuang, S: Employment Leadership Position: Janssen Research & Development; Stock Ownership: Johnson & Johnson. Vermeulen, J: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Staudt, L: Other Remuneration: Patents and patents pending regarding gene expression profiling in lymphoma that have been licensed by Nanostring and for which I receive royalties. Younes, A: Consultant Advisory Role: BMS, Incyte, Janssen, Genentech and Merck; Honoraria: Merck, Roche, Takeda Pharmaceuticals, Janssen, AbbVie; Research Funding: Janssen (Inst), Curis (Inst), Pharmacyclics (Inst), Roche (Inst), AstraZeneca (Inst), Genentech (Inst).

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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,515
Score d'incertitude au seuil0,461

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
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)0,0000,000

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 Opus0,034
Tête enseignante GPT0,361
Écart entre enseignants0,327 · 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

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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