Abstract 2872: Acquisition of drug resistance mutations during chemotherapy treatment in pediatric acute lymphoblastic leukemia
Notice bibliographique
Résumé
Abstract Acute lymphoblastic leukemia (ALL) is a leading cause of cancer-associated death in children. To study the mechanisms of drug resistance in ALL, we performed whole-genome sequencing of diagnosis-relapse-germline trios from 103 Chinese patients and ultra-deep sequencing of 208 serial bone marrow samples from 17 of them. Relapse-specific somatic alterations were enriched in 12 genes (NR3C1, NR3C2, TP53, NT5C2, FPGS, CREBBP, MSH2, MSH6, PMS2, WHSC1, PRPS1, and PRPS2), which were predominantly involved in response to thiopurines, glucocorticoids, methotrexate, and other drugs. Four lines of evidence indicate that these resistance mutations frequently developed during treatment, rather than pre-existing at diagnosis. First, two novel, relapse-specific mutational signatures (novel signatures 1 and 2), most likely caused by chemotherapeutic regimens, were detected in 15% and 14% of relapsed cases, respectively. Drug resistance mutations frequently appeared at novel signature-associated trinucleotide contexts, indicating that chemotherapy may directly cause drug resistance mutations in ALL. The signatures were validated in NCI TARGET relapsed ALL samples, 2% and 23% of which harbored novel signatures 1 and 2, respectively. The varying signature prevalence between cohorts may reflect treatment differences. The novel signatures were not detected in >2,000 adult cancers from the PCAWG study. Novel signature 1 induced C>G transversions, particularly at GCC and TCT trinucleotides, and showed transcription-strand bias indicating guanine adducts. Novel signature 2 favored C>T and C>G mutations at CCG, and correlated with relapse-specific dinucleotide variants and structural variants, indicating an agent causing multiple mutation types. The drugs inducing these novel signatures are being explored in vitro. Second, mathematical modeling using growth curves of drug-resistant ALL indicated that drug resistance mutations occur, in some cases, long after diagnosis, during active treatment. Third, some patients acquired multiple drug resistance mutations sequentially through successive relapses, a finding inconsistent with their pre-existence at diagnosis. Indeed, 20% of relapses had multiple drug resistance mutations targeting different drug classes. Fourth, most relapsed ALLs derived from a subclone detected at diagnosis, which then evolved additional mutations, including drug resistance mutations, not detectable at diagnosis using 2000X targeted sequencing. Drug resistance mutations were often subclonal at relapse, suggesting later appearance. Together these data indicate that fully drug-resistant clones may not necessarily pre-exist at diagnosis in ALL, but may be acquired later during treatment. Thus, early intensive or targeted treatment strategies in slow responders may forestall the subsequent development of drug resistance mutations. Citation Format: Benshang Li, Samuel W. Brady, Xiaotu Ma, Shuhong Shen, Yingchi Zhang, Yongjin Li, Yu Liu, Ningling Wang, Diane Flasch, Matthew Myers, Heather Mulder, Lixia Ding, Yanling Lu, Liqing Tian, Kohei Hagiwara, Ke Xu, Edgar Sioson, Tianyi Wang, Liu Yang, Jie Zhao, Hui Zhang, Ying Shao, Hongye Sun, Lele Sun, Jiaoyang Cai, Ting-Nien Lin, Lijuan Du, Fan Yang, Michael Rusch, Michael Edmonson, John Easton, Xiaofan Zhu, Jingliao Zhang, Cheng Cheng, Benjamin Raphael, Jingyan Tang, James Downing, Bin-Bing Zhou, Ching-Hon Pui, Jun Yang, Jinghui Zhang. Acquisition of drug resistance mutations during chemotherapy treatment in pediatric acute lymphoblastic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2872.
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 | 0,000 | 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 ».