RITUXIMAB‐CONTAINING COMBINED MODALITY THERAPY IN LIMITED STAGE FOLLICULAR LYMPHOMA: MATURE FOLLOW UP AND DERIVATION OF A NOVEL PROGNOSTIC SCORE FROM THE TROG99.03 TRIAL
Notice bibliographique
Résumé
Introduction: The TROG99.03 represents the only randomised phase III trial of combined modality therapy (CMT) in limited-stage follicular lymphoma ‘LSFL’, reporting a prolonged progression-free survival (PFS) in the CMT arm (MacManus, JCO, 2018). Here, we report extended follow up of this study providing mature data of patients treated with a rituximab-containing CMT regimen and the development of a new gene-expression based prognostic score. Methods: Patients with LSFL, grade 1–3a were randomised (1:1) to either involved-field radiotherapy alone (IFRT) (30–36Gy) or to CMT consisting of identical IFRT followed by 6 cycles of CVP. Reflecting evolving clinical practice, from 2006 onwards (i.e., ‘modern-era’), PET staging was increasingly utilised, and rituximab added to the CMT arm. Digital multiplex gene expression by Nanostring was performed on diagnostic biopsies based on genes previously identified to differentiate LSFL from advanced stage FL ‘ASFL’ (AM Staiger, Blood, 2020). Results: 150 patients were recruited between 2000 and 2012 with 31/75 patients in each arm recruited in the ‘modern-era’. At median follow-up 11.3 years, PFS remained superior for CMT compared to RT (HR 0.6; p = 0.043). Although no significant difference in OS was observed (HR 0.45, p = 0.11), compared with IFRT, patients in the CMT arm experienced fewer composite (deaths and histological transformation ‘HT’) events (HR 0.25; p = 0.045). With additional follow up no new non-malignant late toxicities were observed and incidence of secondary malignancies were similar between both arms (11 IFRT vs. 10 CMT, p = 0.99). Patients treated with a rituximab regimen (i.e., IFRT+R-CVP) had a markedly superior PFS compared to those treated without rituximab (i.e., IFRT, or IFRT+CVP), 8 year PFS rates 81% versus 52%, HR 0.42 p = 0.013 (Figure 1). Amongst PET staged patients the difference between R-CVP/IFRT versus IFRT increased (HR 0.35 p =0.027) suggesting this effect was not due to stage migration. No clinical factors were significantly associated with PFS on multivariate analysis. Nor were prognostic associations found for expression level of any individual genes. However, by penalised Cox regression an 8-gene Lasso-weighted prognosticator was identified, termed the ‘Bio-LSFL-score’. Genes (CACNA2D2, CD69, GZMB, IL7R, MYCT1, SLP1, TNFRSF14, TNFRSF25) reflected both B-cells and the microenvironment. The Bio-LSFL-score was highly significant for PFS (HR 0.25, p < 0.0001) with 100% patients in the high-risk group relapsing before 8 years. Conclusions: Particularly when incorporating rituximab, LSFL patients demonstrated significant increases in PFS and reduction in the rates of death and/or HT when treated with CMT compared with IFRT alone. A novel gene expression prognosticator was identified which in this trial cohort showed ‘ASFL-like’ behaviour by identifying LSFL patients unlikely to experience durable remissions. Keywords: diagnostic and prognostic biomarkers, indolent non-Hodgkin lymphoma, radiation therapy Conflicts of interests pertinent to the abstract C. Cheah Consultant or advisory role: Roche, Janssen, Gilead, Astra Zeneca, Lilly, TG Therapeutics, Beigene, Novartis, Menarini, Daizai, Abbvie, Genmab, BMS Research funding: BMS, Roche, Abbvie, MSD, Lilly J. F. Seymour Consultant or advisory role: Abbvie, Astra Zeneca, Celgene/BMS, Genentech, Genor Bio, Gilead, Janssen, Morphosys, Roche, Sunesis, TG Therapeutics Research funding: Celgene/BMS M. K. Gandhi Research funding: Beigene, Janssen
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 | 0,001 |
| 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 ».