CONCORDANCE BETWEEN IMMUNOHISTOCHEMISTRY AND GENE EXPRESSION PROFILING SUBTYPING FOR DIFFUSE LARGE B‐CELL LYMPHOMA IN THE PHASE 3 PHOENIX TRIAL
Notice bibliographique
Résumé
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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 tête enseignante, 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 ».