Cytogenetic Subgroups Drive Risk Stratification and Response to Chemotherapy and Blinatumomab in Children and Young Adults with Relapsed B-ALL: A Children's Oncology Group Study
Notice bibliographique
Résumé
Background At initial diagnosis, sentinel cytogenetic (CG) abnormalities differ based on demographic/clinical features and are predictive of relapse risk in B-lymphoblastic leukemia (B-ALL). The relevance of CG at 1st relapse is not well defined. Children's Oncology Group (COG) AALL1331 enrolled non-BCR-ABL1+, non-Down syndrome 1st relapse B-ALL patients (pts) 1-30 years (yr) old. After a common chemotherapy induction (Block 1), pts were risk assigned as: High risk (HR): Early bone marrow (BM) relapse <36 months from diagnosis or isolated extramedullary (IEM) relapse <18 months; Intermediate risk (IR): Late BM relapse (>36 months) with minimal residual disease (MRD) ≥0.1% at end Block-1 (EB-1); Low risk (LR): Late BM relapse with MRD <0.1% at EB-1; Treatment failure (TF): M3 BM or failure to clear central nervous system at EB-1. The primary objective of the trial for HR/IR pts was to randomly compare post-reinduction therapy with 2 blocks of chemotherapy vs. blinatumomab, followed by allogeneic transplant. We previously reported the superiority of blinatumomab for HR/IR pts (Brown, ASH 2019). We hypothesized that CG would correlate with clinical features at relapse and response to therapy, enhancing relapse risk stratification. Methods CG data (karyotype and FISH) at relapse were centrally reviewed. CG groups were defined as: favorable (fav) (double trisomies (DT) or ETV6-RUNX1), unfavorable (unfav) (hypodiploid, KMT2A-rearranged (KMT2A-R) or iAMP21), and neutral (all others). Of 662 eligible pts, 130 IEM relapses were excluded, since CG data were not available due to lack of BM involvement. Of 532 BM relapses, CG data were available for 394 (74%); this group was used for associations of CG with clinical features and response to Block 1. Of 188 BM relapses risk assigned HR/IR and randomized, CG data were available for 147 (78%; 72 Arm A chemotherapy, 75 Arm B blinatumomab); this group was used for associations of CG with response to randomized post-reinduction therapy. CG distributions at initial diagnosis were based on data from over 16,000 pts enrolled on COG upfront trials from 1996-2014. Statistical comparisons used Pearson's chi-squared, Fisher's exact, Wilcoxon rank sum, or logrank test as appropriate. All p values are two-sided. Results As expected, unfav CG were more common at relapse compared to diagnosis (17% vs 7% p<0.001), and the opposite for fav CG (22% vs 42% p<0.001). Also expected was the younger age at relapse for the fav (median 8 yr) and KMT2A-R (median 2 yr) CG vs. others (median 12 yr, p=0.002). WBC at relapse was higher in the KMT2A-R (median 11k/uL), but similar in all others (median 5k/uL, p=0.041). Time from diagnosis to relapse differed by CG group, with late BM relapses comprising 79% of fav vs. 61% of neutral vs. 48% of unfav (p<0.001). EB-1 MRD-negative rates were highest for fav (53%), intermediate for neutral (38%) and lowest for unfav (25%, p=0.002). Interestingly, in fav CG group, 62% of ETV6-RUNX1 vs. 40% of DT were EB-1 MRD-negative (p=0.043), suggesting that as for newly diagnosed pts, MRD clearance during relapse reinduction may be slower for DT than for ETV6-RUNX1. EB-1 risk assignments differed significantly by CG group, with rates for fav/neutral/unfav CG of 57%/35%/27% for LR, 16%/31%/34% for HR, and 3%/7%/7% for TF (p<0.01). After 1st course of randomized therapy for HR/IR pts, the rate of EB-2 MRD negativity was (as previously reported) much higher for arm B (75%) vs arm A (25%, p<0.001). Interestingly, EB-2 MRD clearance rates did not vary significantly by CG group for either randomized arm. There was, however, an influence of CG group on survival for arm B, but not for arm A. The 2 yr DFS/OS for fav CG on arm B was 77%/93%, vs. 49%/65% for neutral and 49%/49% for unfav (p=0.069 for DFS, p=0.035 for OS, univariate analysis). For arm A, DFS/OS was similar and consistently poor in all three groups (44%/52% fav vs. 49%/61% neutral and 39%/61% unfav, p=0.92 for DFS, p=0.62 for OS) (Table 1). Conclusions Pts with fav CG B-ALL relapse later and are more likely to achieve MRD negativity after reinduction chemotherapy, resulting in higher rates of LR risk assignment. Interestingly, while the superior MRD clearance with blinatumomab vs. chemotherapy is seen in all CG groups, this only translated into higher rates of DFS/OS in the fav CG group. Thus, the influence of CG on outcomes after 1st relapse may differ based on whether chemotherapy or immunotherapy is used as post-reinduction consolidation. Disclosures Gore: Amgen, Novartis, Roche: Membership on an entity's Board of Directors or advisory committees. Hunger:Novartis: Consultancy; Amgen: Current equity holder in publicly-traded company, Honoraria. Loh:Medisix Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pfizer: Other: Institutional Research Funding. Brown:Jazz: Honoraria; Servier: Honoraria; Janssen: Consultancy; Novartis: Consultancy.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».