Abstract LB-341: Evolving functional heterogeneity in B-acute lymphoblastic leukemia
Notice bibliographique
Résumé
Abstract Current cancer therapies are directed at molecular markers or dominant pathways present in the bulk of neoplastic cells. However, recent studies have identified many genetically distinct subclones co-existing within a single neoplasm. In over 50% of patients with relapsed acute lymphoblastic leukemia (ALL), the genetic clones present at relapse are not the dominant clone present at diagnosis, but have evolved from a minor or ancestral clone (Mullighan et al., Science, 2008). Previous work has shown that this subclonal diversity in B-ALL exists at the level of the leukemia-initiating cells (L-IC) capable of generating patient derived xenografts (Notta et al., Nature, 2011). In order to investigate the functional consequences of genetic clonal evolution during disease progression, we performed in-depth genomic and functional analysis of 14 paired diagnosis/relapse samples from adult and pediatric B-ALL patients of varying cytogenetics. Patient samples were subjected to whole exome sequencing (WES), SNP analysis and RNA sequencing. Diagnosis-specific, relapse-specific, and shared variants at both clonal and subclonal frequencies were identified. Limiting dilution analysis by transplantation of CD19+ leukemic blasts into 870 immune-deficient mice (xenografts) identified no significant trend in enrichment in L-IC frequency between paired patient samples with a median frequency of 1 in 2691. Despite similar frequencies of L-IC, functional differences within identically sourced patient xenografts were observed, including increased leukemic dissemination of relapse cells to distal sites such as the central nervous system (CNS), differences in engraftment levels and differences in immunophenotypes. Targeted-sequencing and copy number analysis of the xenografts, in comparison to the patient sample from which they were derived, has uncovered clonal variation and the unequivocal identification of minor subclones ancestral to the relapse in xenografts transplanted with the diagnostic sample from 8 patients. Some of these subclones are rare and were not captured through standard WES analysis of the patient samples, highlighting the value of xenografting to functionally identify and viably isolate subclones for further study. Interrogation of the therapeutic responses of the ‘relapse-like’ diagnosis subclones in secondary xenografts displayed differential resistance to standard chemotherapeutic agents (vincristine and L-asparaginase) pre-existing in the patient diagnosis samples prior to treatment. Furthermore, investigation of different sites of leukemic infiltration in the xenografts provided evidence of distinct clonal selection in the CNS, a known site of disease relapse, in comparison to the bone marrow. Using this data we can begin to draw the evolutionary paths to relapse. We have shown evidence that minor subclones at diagnosis, ancestral to the relapsing clone, possess functional advantages over other diagnostic clones. Overall, this work provides a substantial advance in connecting genetic diversity to functional consequences, thereby furthering our understanding of the heterogeneity identified in B-ALL and its contributions to therapy failure and disease recurrence. Citation Format: Stephanie M. Dobson, Robert Vanner, Esmé Waanders, Olga I. Gan, Jessica McLeod, Ildiko Grandal, Debbie Payne-Turner, Michael Edmonson, Zhaohui Gu, Xioatu Ma, Yiping Fan, Pankaj Gupta, Sagi Abelson, Michael Rusch, Ying Shao, Scott Olsen, Geoffrey Neale, John Easton, Cynthia J. Guidos, Jayne S. Danska, Jinghui Zhang, Mark D. Minden, Charles G. Mullighan, John E. Dick. Evolving functional heterogeneity in B-acute lymphoblastic leukemia. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-341.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,002 | 0,001 |
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 ».