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Enregistrement W2979315058 · doi:10.1182/blood.v126.23.692.692

Genomic Landscape of Relapsed Acute Lymphoblastic Leukemia

2015· article· en· W2979315058 sur OpenAlexaff
Esmé Waanders, Stephanie M. Dobson, Xiaotu Ma, Debbie Payne-Turner, Guangchun Song, Yiping Fan, Zhaohui Gu, Ying Shao, Michael Rusch, Kelly McCastlain, Ilaria Iacobucci, Kathryn G. Roberts, Shann-Ching Chen, Pankaj Gupta, Ji Wen, Gang Wu, Jing Ma, Colin Bailey, Matthew Lear, Scott R. Olsen, Deanna Naeve, Geoffrey Neale, John Easton, Sima Jeha, Ching‐Hon Pui, James R. Downing, William E. Evans, Mary V. Relling, Jun J. Yang, John E. Dick, Jinghui Zhang, Charles G. Mullighan

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMedicineMyeloid leukemiaInternal medicineSNP arrayOncologyExome sequencingLeukemiaSingle-nucleotide polymorphismBiologyGeneticsGeneGenotypeMutation

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Despite risk stratification according to presenting clinical and genetic features, 10-25% of children with acute lymphoblastic leukemia (ALL) relapse, which is associated with a poor prognosis. Here, we sought to provide a comprehensive overview of the genetic alterations associated with relapse in ALL. Methods: We studied 93 children (27 female, 43 male) diagnosed with ALL (62 B-progenitor, 25 T-lineage) between 1987 and 2008 and treated on total therapy studies XI-XVI who experienced relapse and/or a second tumor. Age at diagnosis ranged from 3 months to 18 years. Median time to relapse was 3 years (range 3 months to 10 years). Seventy patients had a single relapse, 15 cases had 2 relapses, and 8 cases developed a second tumor of different lineage (B-cell lymphoma, chronic myeloid leukemia (n=1 each) and acute myeloid leukemia (n=6)). Diagnosis, relapse and matched normal samples (n=299) were studied using Affymetrix SNP 6.0 microarrays and whole genome or whole exome sequencing. Results: We found 2692 copy number aberrations (CNAs) with a median of 9 (range 0-109) in the diagnosis samples (n=91) compared to a median of 10 (range 0-112) in the relapse samples (n=89) and 12 (range 0-70) in subsequent samples (n=20). The number of CNAs did not differ significantly between diagnosis, relapse or subsequent samples. We identified a 7286 non-silent single nucleotide variants (SNVs) and small insertions or deletions (indels) in 5002 genes, 1392 of which were recurrent. The median number of variants was 12 (range 0-70) at diagnosis (n=91), 21 (range 0-858) at relapse (n=91; P=0.0029 v. diagnosis) and 60 (range 10-650) in subsequent samples (n=20; P<0.0001 v. diagnosis). A subset of samples revealed very high (variant range 658-1703; 7 cases, 9 samples: all relapse or subsequent samples), or high mutation burden (variant range 104-290; 12 cases, 16 samples: 2 diagnosis, 10 relapse, and 4 subsequent samples). Genes encoding mediators of DNA repair were affected in all cases with very high mutation burden, compared to 7 of the high burden cases and 18 out of 72 other cases (most commonly genes TP53, MSH2, and MUTYH; P<0.0001). The most frequently mutated genes were NOTCH1 (n=33), NRAS (n=24), CREBBP (n=20) and KRAS (n=16). Of the recurrently altered genes, only 87 genes were known to be affected in cancer (Cancer Gene Census, COSMIC database), of which 59 were affected in leukemia and lymphoma tissues, indicating that we have identified 1306 novel recurrently affected genes, most commonly C13orf40 and MKI67. Mutations in epigenetic regulators were particularly frequent, with genes mutated in at least 3 cases altered in over 60% of the cohort (e.g. CREBBP, EP300, MLL2, MLL3, KDM6A/B, CTCF, SETD2, TET2/3, and EZH2). Clonal evolution analyses showed multiple patterns of evolution, with relapses sharing either few or many variants with the diagnosis sample in a frequency that reflects both predominant clones and minor subclones propagating relapse. Variants in NOTCH1, NRAS, and CREBBP were preserved from a major clone at diagnosis in 4, 6, and 5 cases respectively, but acquired at relapse or grown out from a minor subclone at diagnosis in 3, 5, and 8 cases respectively. In contrast, variants in USH2A (n=4), FOXA1 (n=3), and purine/pyrimidine synthesis pathway genes NT5C2 (n=3), PRPS1 (n=3) and NT5C1B (n=1) were exclusively found in relapse samples. Notably, the NT5C2 mutations, which are thought to confer resistance to thiopurines, were subclonal at relapse in the majority of cases. We identified 13 cases (10 B-lineage, 3 T-lineage) in which the diagnosis and relapse were fully discordant for all CNAs and sequence mutations, only 4 of which showed a prolonged remission time (>5 years). This suggests that these patients developed a second primary malignancy and may be predisposed to leukemia development. Indeed, one case revealed focal amplifications on chromosome 1q21.1 encompassing the neuroblastoma breakpoint family genes, which are implicated in cancer development. Comprehensive germline analyses are underway. Conclusion: This study has provided detailed insight into the genetic basis of relapse, implicating multiple new genes and pathways involved in treatment resistance, demonstrating multiple patterns of clonal evolution, and revealing an unexpectedly high frequency of genetically discordant second malignancy in relapse in ALL. Disclosures Evans: Prometheus Labs: Patents & Royalties: Royalties from licensing TPMT genotyping. Mullighan:Amgen: Honoraria, Speakers Bureau; Cancer Science Institute: Membership on an entity's Board of Directors or advisory committees; Incyte: Consultancy, Honoraria; Loxo Oncology: Research Funding.

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: Observationnel · Signal consensuel: Observationnel
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
Bibliométrie0,0010,001
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 Opus0,018
Tête enseignante GPT0,265
Écart entre enseignants0,247 · 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'étudeObservationnel
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

Citations2
Publié2015
Routes d'admission1
Résumé présentoui

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