Deciphering the Clinical Benefit of Pola-R-CHP versus R-CHOP in Different Genetic Subtypes Beyond Cell of Origin in the POLARIX Study
Notice bibliographique
Résumé
Introduction: In the POLARIX study, polatuzumab vedotin in combination with rituximab plus cyclophosphamide, doxorubicin, and prednisone (Pola-R-CHP) demonstrated prolonged progression-free survival (PFS) vs rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) in patients (pts) with previously untreated diffuse large B-cell lymphoma (DLBCL; NCT03274492; Tilly et al. N Engl J Med 2022); the PFS benefit was sustained with longer follow-up (Herrera et al. Blood 2022). Exploratory analyses demonstrated a trend towards greater PFS improvement in activated B cell-like (ABC) DLBCL with Pola-R-CHP vs R-CHOP (Tilly et al. N Engl J Med 2022). Although not yet available for daily practice, more granular molecular subtypes of DLBCL beyond cell of origin (COO) classification have demonstrated poor prognosis with R-CHOP, including EZH2 mutations/ BCL2 translocations (EZB), MYD88/ CD79B-mutated (MCD; as defined by LymphGen; Wright et al. Cancer Cell 2020), and dark zone gene expression signature (DZsig; Alduaij et al. Blood 2023). Here, in a post hoc exploratory analysis, we investigate the prevalence and clinical outcomes of molecularly defined subtypes of DLBCL in pts treated with Pola-R-CHP vs R-CHOP in POLARIX. Methods: All genetic analyses were performed on baseline tumor biopsies. Using mutations derived from whole exome sequencing (WES), pts were allocated to different genetic subtypes, as defined by the LymphGen classifier. DZsig status was determined from global gene expression patterns (GEP) as measured by RNAseq, as previously described (Ennishi et al. J Clin Oncol 2019). COO was determined by NanoString. Hazard ratios (HR) were adjusted for International Prognostic Index score (2 vs 3-5) and age (≤60 vs >60 years). Due to the exploratory nature of the subtype analyses, all statistics are descriptive. Results: WES data were available for 594 pts (Pola-R-CHP, n=292; R-CHOP, n=302), and baseline clinical characteristics were balanced between treatment arms. The observed 2-year PFS rates with EZB and MCD subtypes were numerically higher but not statistically significant in the Pola-R-CHP vs R-CHOP arm ( Table): EZB, 83% (95% confidence interval [CI]: 76-92) vs 75% (95% CI: 65-86), HR 0.61, 95% CI: 0.33-1.13; MCD, 85% (95% CI: 73-98) vs 73% (95% CI: 63-90), HR 0.64, 95% CI: 0.24-1.70. Lower 2-year PFS rates were observed in pts with the BCL6 fusions and NOTCH2 mutations (BN2) subtype treated with Pola-R-CHP vs R-CHOP (HR 1.82, 95% CI: 0.46-7.18; Table). In the subgroup of pts with an undetermined genetic subtype, 2-year PFS estimates were numerically higher with Pola-R-CHP vs R-CHOP (74% vs 62%, respectively; HR 0.70, 95% CI: 0.46-1.07). GEP data were available for 665 pts (Pola-R-CHP, n=331; R-CHOP, n=334); 108 (16.2%) were DZsig+ (Pola-R-CHP, n=52; R-CHOP, n=56). Of those who were DZsig+, 103 (95.4%) had germinal center B cell-like (GCB) DLBCL, 3 (2.8%) had ABC DLBCL, and 2 (1.9%) had an unknown COO. In pts who were DZsig+ and had FISH results, 54/86 (62.8%) had BCL2 translocations, 41/92 (44.6%) had MYC translocations, and 6/40 (15%) had BCL6 translocations (BCL6 FISH was only done in pts with a MYC translocation), resulting in 23/90 (25.6%) pts with double/triple-hit DLBCL. Pts who were DZsig+ were predominantly within the EZB genetic subtype (60.3%). In the R-CHOP arm, pts who were DZsig+ had shorter PFS vs pts who were DZsig− (2-yr PFS: 62% [95% CI: 51-77] vs 73% [95% CI: 68-79], respectively; HR 1.61, 95% CI: 1.02-2.56; Figure). In the Pola-R-CHP arm, no significant difference in PFS was observed between pts who were DZsig+ vs DZsig− (2-yr PFS: 77% [95% CI: 66-89] vs 79% [95% CI: 74-84]; HR 0.97, 95% CI: 0.54-1.75). Improved PFS was demonstrated in pts who were DZsig+ and treated with Pola-R-CHP vs R-CHOP (HR 0.47, 95% CI: 0.24-0.95). Conclusions: In this exploratory biomarker analysis, we recapitulated that pts with molecularly defined DLBCL subtypes, including EZB and MCD by LymphGen and DZsig+ by RNAseq, have poor outcomes with R-CHOP therapy. In pts with the EZB and MCD subtypes, Pola-R-CHP appeared to improve 2-year PFS compared with R-CHOP. Pts with GCB DLBCL who were DZsig+ significantly benefited from Pola-R-CHP vs R-CHOP. In summary, our data indicate that molecular analysis beyond COO leads to identification of distinct molecular subsets of DLBCL that may respond favorably to Pola-R-CHP; future prospective validation is required.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».