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Enregistrement W3096329976 · doi:10.1182/blood-2020-137125

Distinct Genetic Pathways Define Leukemia Predisposition Versus Adaptive Clonal Hematopoiesis in Shwachman-Diamond Syndrome

2020· article· en· W3096329976 sur OpenAlexaff
Alyssa L. Kennedy, Kasiani C. Myers, James R. Bowman, Christopher J. Gibson, Gwen M. Muscato, Robert H. Klein, Kaitlyn Ballotti, Nicholas D. Camarda, Elissa Furutani, Chad E. Harris, Shanshan Liu, Ashley Galvin, Maggie Malsch, David C. Dale, John M. Gansner, Taizo A. Nakano, Alison A. Bertuch, Adrianna Vlachos, Jeff H. Lipton, Paul Castillo, James A. Connelly, John Edwards, Nobuko Hijiya, Richard Ho, Inga Hofmann, James N. Huang, Sioḃán Keel, Adam J. Lamble, Bonnie Lau, Kelly Walkovich, Maxim Norkin, Wendy Stock, Steffen Boettcher, Christian Brendel, Elliot Stieglitz, Mark D. Fleming, Stella M. Davies, Edie Weller, Chris Bahl, Scott L. Carter, Akiko Shimamura, R. Coleman Lindsley

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueBlood disorders and treatments
Établissements canadiensPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésGermline mutationBiologyMutationGeneticsExome sequencingMyeloidGermlineBone marrow failureCancer researchGeneHaematopoiesisStem cell

Résumé

récupéré en direct d'OpenAlex

Background: Shwachman-Diamond Syndrome (SDS) is a bone marrow failure disorder caused by impaired removal of EIF6 from the nascent 60S ribosome subunit, resulting in defective ribosome assembly. SDS patients have a high risk of myeloid neoplasms (MN) and the prognosis of those that develop MN is poor. Knowledge of the kinetics and functional consequences of somatic mutation acquisition in SDS may offer insight into mechanism of transformation and the potential for therapuetic intervention. Methods: We performed whole exome sequencing of 45 samples from 30 patients, and validated recurrent somatically mutated genes using targeted sequencing with error suppression in prospectively collected samples from 110 patients in the North American SDS Registry. We correlated mutation status with clinical outcome and performed functional studies to understand the consequence of somatic mutations in SDS. Results: We detected somatic mutations in 74 of 98 (76%) patients with germline biallelic SBDS mutations (median 2 mutations/patient, range 0-21). We found no mutations in patients with SDS-like disease; those who have clinical features of SDS without disease defining mutations. Of the 83 patients with SDS without a MN diagnosis, 60 (72%) had detectable clonal hematopoiesis (CH), 40 of whom had more than one mutation (median 3, range 1-21). The most frequently mutated genes were EIF6 (60/98, 61%),TP53 (44/98, 45%), PRPF8 (12/98, 12%), and CSNK1A1 (6/98, 6%). Among SDS patients with TP53 mutated CH, 90.9% (30 of 33) had concurrent EIF6 mutations. To determine whether EIF6 and TP53 mutations occur in the same or different clones, we performed single cell DNA sequencing. Among the 47 clones identified with either EIF6 or TP53 mutations, 24 had a sole EIF6 mutation, and 21 had a sole TP53 mutation, showing that these mutations arise in separate clones. To study the functional consequences of EIF6 missense mutations, we cloned 7 patient-derived mutations and generated cell lines expressing wild-type or mutant EIF6 cDNA. We found six mutants (I13N, R67W, G69S, P73R, A194T, G196R) reduced levels of EIF6 protein compared with wild type EIF6, despite comparable abundance of mRNA. The most common recurrent mutation, N106S, was found in 20% of patients and, by contrast to others listed above, did not change protein expression. This mutation is located at the EIF6/60S protein interface and disrupted the interaction of N106S-EIF6 with the 60S subunit as measured by polysome profiling followed by western blotting. To compare the effects of EIF6 versus TP53 somatic mutations in context of SDS deficient translation, we measured ribosome maturation and translation in SDS cells containing shRNAs targeting EIF6 or TP53. EIF6 knockdown ameliorated the SDS defect, reflected by improved ribosome joining (normalization of the 80:60s ratio) and enhanced protein translation (increased O-propargyl-puromycin incorporation), whereas TP53 knockdown had no effect. Knockdown of EIF6 in SDS deficient cells decreased p53 pathway activation as demonstrated by decreased CDKN1A expression. TP53 mutations were significantly associated with MN diagnosis (p=0.023), but were also common in SDS CH and typically stable over time. To identify the characteristics associated with transformation, we analyzed exomes from 7 patients with TP53 mutated myeloid malignancy for allelic imbalances at the TP53 locus and found that all 7 had biallelic alteration of TP53. Using single cell DNA sequencing from serial samples, we observed that TP53 LOH can precede transformation by several years and can distinguish pre-leukemic clones from indolent clones with monoallelic TP53 alterations. Somatic EIF6 mutations were not found in the leukemic clones. These results suggest early detection of TP53 LOH may distinguish clones with leukemic potential. Conclusions: In SDS, impairment of ribosome maturation drives selection of clones with somatic EIF6 or TP53 mutations. EIF6 mutations promote competitive fitness by rescuing the SDS ribosome defect and decreasing p53 pathway activation, and do not contribute to malignant transformation. TP53 mutations decrease checkpoint activation without affecting ribosome assembly. These results provide genetic evidence that germline SBDS deficiency causes a global, disease-specific HSC fitness constraint that drives parallel development of somatic CH and provides a mechanistic rationale for clinical surveillance. Disclosures Dale: Emendo BioTherapeutics: Consultancy; X4 Pharmaceuticals: Research Funding; X4 Pharmaceuticals: Honoraria. Gansner:Alnylam Pharmaceuticals: Current Employment, Current equity holder in private company. Edwards:Jazz Pharmaceuticals: Consultancy, Honoraria. Fleming:DISC Medicine: Consultancy, Membership on an entity's Board of Directors or advisory committees. Lindsley:MedImmune: Research Funding; Takeda Pharmaceuticals: Consultancy; Bluebird Bio: Consultancy; Jazz Pharmaceuticals: Consultancy, 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)0,001
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 seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
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 Opus0,015
Tête enseignante GPT0,208
Écart entre enseignants0,193 · 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

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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