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Enregistrement W2607148153 · doi:10.1182/blood.v128.22.605.605

Linking Subclonal Genetic Diversity with Functional Heterogeneity Identifies Diagnosis Subclones Destined to Relapse

2016· article· en· W2607148153 sur OpenAlexaff
Stephanie M. Dobson, Esmé Waanders, Robert J. Vanner, Olga I. Gan, Jessica McLeod, Ildiko Grandal, Debbie Payne-Turner, Michael N. Edmonson, Zhaohui Gu, Xiaotu Ma, Yiping Fan, Sagi Abelson, Pankaj Gupta, Michael Rusch, Ying Shao, Lei Shi, Stanley Pounds, Scott R. Olsen, Geoffrey Neale, John Easton, Cynthia J. Guidos, Jayne S. Danska, Jinghui Zhang, Mark D. Minden, Charles G. Mullighan, John E. Dick

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensHospital for Sick ChildrenPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésBiologyclone (Java method)Somatic evolution in cancerSNP arrayDiseaseLeukemiaGeneticsGenetic heterogeneityExome sequencingCancer researchCancerImmunologyMutationOncologyPhenotypeMedicineInternal medicineGenotypeGeneSingle-nucleotide polymorphism

Résumé

récupéré en direct d'OpenAlex

Abstract Despite significant advances in the treatment of B cell acute lymphoblastic leukemia (B-ALL), mortality rates following disease relapse remain high. Recent studies have identified many genetically distinct subclones co-existing within a single neoplasm. In over 50% of patients with relapsed 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; Anderson et al., Nature, 2011). 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 (PDX) (Notta et al., Nature, 2011). However, little is known of the functional properties of relapse driving subclones and how they contribute to disease recurrence. 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, SNP analysis and RNA sequencing. Diagnosis-specific, relapse-specific, and shared variants at both clonal and subclonal frequencies were detected. Limiting dilution analysis by transplantation of CD19+ leukemic blasts into 870 immune-deficient mice (PDX) 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 PDX 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, uncovered clonal variation and the unequivocal identification of minor subclones ancestral to the relapse in xenografts transplanted with the diagnostic sample. In 8 of the 14 patient samples, PDX at varying cell doses allowed for the selection and isolation of rare relapse driving subclones present at diagnosis ('relapse-like' diagnosis clones). In 2 of these 8 samples, as well as in 5 other patient samples, relapse specific variants were identified in the PDX that were not detected in the patient diagnosis genomic analysis at our level of detection. In secondary xenografts, comparison of the therapeutic responses of the identified 'relapse-like' diagnosis subclones against more representative diagnosis subclones displayed differential resistance to standard chemotherapeutic agents (vincristine, L-asparaginase and dexamethasone). This indicates that genetic subclones possessing varying therapeutic responses preexisted in the patient diagnostic sample. In addition to therapeutic differences, variations in cell migration were detected. This may contribute to the therapeutic evasion of the relapse driving subclones. Interestingly, 'relapse-like' diagnosis cells also displayed phenotypic plasticity generating CD19-CD33+ cells from CD19+ cells in 2 patient samples upon treatment with dexamethasone. This is suggestive that relapse driving subclones may arise from primitive cells with multilineage potential upon steroid challenge. 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 draw an evolutionary path to relapse for these patients samples. We have shown evidence that minor subclones at diagnosis, ancestral to the relapsing clone, possess functional advantages and unique properties over other diagnostic subclones prior to treatment exposure. 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. Disclosures No relevant conflicts of interest to declare.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,026
Tête enseignante GPT0,251
Écart entre enseignants0,225 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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