The Impact of Age and Genomics on Drug Sensitivity in 1,076 Children and Adults with B-Cell Acute Lymphoblastic Leukemia
Notice bibliographique
Résumé
While the cure rate of pediatric acute lymphoblastic leukemia (ALL) now exceeds ~90% with contemporary combination chemotherapy, the prognosis for adults with ALL remains significantly inferior with long-term overall survival ranging from 50% to 70%. Recent studies have uncovered marked differences in ALL genomics between children and adults, with some high-risk subtypes becoming more prevalent with age. However, the underlying biology of age-related disparities in ALL is not fully understood, especially with regard to differences in leukemia sensitivity to chemotherapy. To address this knowledge gap, we performed ex vivo drug sensitivity profiling (i.e., pharmacotyping) of 21 anti-leukemia agents on primary B-ALL diagnostic samples from 767 pediatric (age, 0-18 years) and 309 adult (19-84 years) patients. Drug sensitivity was measured as LC 50: the concentration of drug required to kill 50% of the leukemia cells (PMID: 36604538). A total of 7,975 unique LC 50 values were experimentally determined. RNA-seq was used for subtype classification and gene expression analysis. Among 21 drugs, seven showed significant differences in overall LC 50 between children and adults ( P<0.05 after Bonferroni correction): children displayed higher sensitivity to asparaginase, prednisolone, mercaptopurine, daunorubicin, and inotuzumab, while adults showed higher sensitivity to dasatinib and nelarabine. In multivariate models adjusting for 23 ALL molecular subtypes, only mercaptopurine remained significantly associated with age ( P=1.5×10 -5), suggesting that age-related differences in drug sensitivity can be primarily attributed to the variation in ALL subtypes between children and adults. For mercaptopurine, within KMT2A, CRLF2, and DUX4 subtypes, pediatric samples consistently showed a lower LC 50 than adults carrying the same genomic abnormality ( P=0.032, 0.0045, and 0.02, respectively). To explore intra-subtype heterogeneity, we performed unsupervised clustering using gene expression data for each of these three subtypes. Remarkably, within each of these subtypes, we identified two clusters with distinct transcriptomic profiles that were also largely segregated by age group, i.e., an adult-dominated cluster (C-a) and a pediatric-dominated cluster (C-p). In the KMT2A subtype, cases in C-a exhibited an over-representation of the KMT2A:: AFF1 fusion, and resistance to mercaptopurine ( P=0.029), prednisolone ( P=0.0039), vincristine ( P=0.046) and cytarabine ( P=0.0037). Within CRLF2 ALL, cases in C-a were associated with the presence of BCR:: ABL1-like signature and IGH:: CRLF2 rearrangements, and were more resistant to mercaptopurine ( P=0.0073) and prednisolone ( P=0.00036) compared to those in C-p. For DUX4 ALL, C-a was characterized by an under-representation of ERG deletions and resistance to mercaptopurine ( P=0.0056) and prednisolone ( P=0.0031), compared to C-p within DUX4. To explore the clinical relevance of this heterogeneity, we analyzed the in vivo treatment response of KMT2A (N=35), CRLF2 (N=59) and DUX4 (N=118) B-ALL enrolled in six frontline ALL trials. Compared to cases in C-p (usually drug-sensitive), those in C-a (usually drug-resistant) consistently had significantly poorer initial treatment responses as measured by persistent end-of-induction minimal residual disease (≥0.01%) in KMT2A (58% vs 9%; P=0.0063), CRLF2 (74% vs 41%; P=0.030), and DUX4 (66% vs 40%; P=0.0058) ALL. In conclusion, these studies have revealed important new insights into the pharmacogenomic basis of age-related differences in B-ALL treatment response. These results indicate that both inter- and intra-subtype heterogeneity contribute to inferior prognosis in adults with ALL, but also point to therapeutic opportunities to improve their outcomes.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,001 | 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 ».