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Enregistrement W3095045701 · doi:10.1182/blood-2020-140793

Genotypic and Phenotypic Spectrum of Dyskeratosis Congenita: Results from the Canadian Inherited Marrow Failure Registry

2020· article· en· W3095045701 sur OpenAlexaffabout
Mohammed Al Nuaimi, Evelyn Elias, Albert Català, Bozana Zlateska, Yeon Jung Lim, Robert J. Klaassen, Geoff D.E. Cuvelier, Conrad V. Fernandez, Meera Rayar, MacGregor Steele, Sharon Abish, Yves Pastore, Vicky R. Breakey, Soumitra Tole, Josée Brossard, Roona Sinha, Mariana Silva, Lisa Goodyear, Jeffrey H. Lipton, Bruno Michon, Catherine Corriveau‐Bourque, Lillian Sung, Yigal Dror, Michaela Cada

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueTelomeres, Telomerase, and Senescence
Établissements canadiensUniversity of AlbertaJaneway Children's Health and Rehabilitation CentreQueen's UniversityCentre hospitalier de l'Université LavalCentre Hospitalier Universitaire de SherbrookeLondon Health Sciences CentreMcMaster Children's HospitalCentre Hospitalier Universitaire Sainte-JustineStollery Children's HospitalIzaak Walton Killam Health CentreAlberta Children's HospitalUniversity Health NetworkCancerCare ManitobaPrincess Margaret Cancer CentreKingston General HospitalUniversity of CalgaryChildren's Hospital of Eastern OntarioMontreal Children's HospitalUniversity of ManitobaBC Children's HospitalSickKids FoundationUniversité de MontréalHospital for Sick Children
Organismes subventionnairesnon disponible
Mots-clésDyskeratosis congenitaMedicineBone marrow failureInternal medicineHazard ratioMyelodysplastic syndromesBone marrowSurvival analysisOncologyTelomereHaematopoiesisBiologyStem cell

Résumé

récupéré en direct d'OpenAlex

Introduction: Dyskeratosis congenita (DC) is an inherited bone marrow failure syndrome caused by mutations in one of 13 telomere-related genes, resulting in disruption of normal telomere maintenance; however, about 30% of patients do not have a molecular diagnosis. DC patients are at increased risk for severe bone marrow failure (SBMF), myelodysplastic syndrome (MDS), acute myeloid leukemia (AML), and solid tumours. Life expectancy is compromised by SBMF, malignancy, pulmonary and liver fibrosis, and GI bleeding. Objectives: Among patients with DC in Canada, aims were to: (1) characterize the genetic profile of DC in Canada, (2) define the spectrum of clinical features of DC, (3) determine the incidence and age when SBMF, MDS, AML or solid tumours develop, (4) identify factors that are associated with higher mortality risk, and (5) describe the causes of death. Methods: Data of patients enrolled in the Canadian Inherited Marrow Failure Registry (CIMFR) and meeting diagnostic criteria for DC between January 1, 2001 and March 1, 2018 were included. The CIMFR is a multicentre registry that captures data on patients with inherited marrow failure syndromes from pediatric tertiary referral centres across all Canadian provinces. We investigated several continuous (e.g. age at diagnosis of SBMF/MDS/AML) and categorical (e.g. mutated gene) variables that are associated with specific outcomes, namely overall survival and development of SBMF. Cox proportional hazard models were used to assess risk of death based on age at diagnosis and presence of SBMF. Kaplan-Meier curves were used to assess overall survival. Results: As of March 1st, 2018, 35 patients with DC were enrolled. The mean age of diagnosis was 10.94 years (0-39.9). The underlying genotypes were: DKC1 (7), TERT (6), TINF2 (5), RTEL1 (3), PARN (2), TERC (2) but remained undetermined in the others (10). Twenty-seven patients were classified as classical DC, 7 had Hoyeraal-Hreidarsson syndrome and 1 patient had Coats plus syndrome. Eight patients (23%) developed SBMF. The mean age of SBMF was 4.22 years (1-8.66). No statistical difference was found between genotypes and progression to SBMF (P=0.1). Modelling death as a function of time varying SBMF status using a cox proportional hazard regression model showed that the presence of SBMF in DC patients was predictive of higher mortality rate (P= 0.009, hazard ratio 5.7, CI 1.54-21.5). None of the patients developed malignancy during childhood (0-18 years). One adult patient developed skin cancer. Eleven patients (31%) received a hematopoietic stem cell transplant (HSCT). The mean age of HSCT was 9.5 years (0.5-37). Ten (29%) patients died, five of whom were recipients of HSCT. Mean age of death was 12.98 years (2-24.6). Extra-hematological complications included gastrointestinal bleeding (50%), pulmonary fibrosis (40%), overwhelming infection (40%), liver fibrosis (20%), cardiomyopathy (10%), hemolytic uremic syndrome (HUS) (10%) and thrombotic microangiopathy (TMA) (10%). Most patients had more than one organ dysfunction. Analysis of survival showed that all patients with TINF2 mutations have died (at median age of 10.8 years, range 2.5-23.25) whereas none died in the TERT group. Patients diagnosed at younger age had lower overall survival compared to patients diagnosed at older ages (P= 0.03, HR: 0.72, CI: 0.57-0.90). All deaths were due to organ dysfunction related to DC. Fifty percent of the patients had concurrent SBMF at the time of death. Conclusion: In this analysis, we characterised the genetic and phenotypic spectrum of DC patients registered in the CIMFR. We found a high mortality rate mainly related to organ dysfunction and SBMF, and described the impact of genotype, earlier age at diagnosis and presence of SBMF in predicting survival. We found that malignancy is an uncommon complication in the pediatric age group. Figure Disclosures Klaassen: Amgen Inc: Consultancy; TranQoL and KIT: Other: creater and owner of Kids ITP tool and TranQoL; Octapharma AG: Speakers Bureau; Baxalta: Speakers Bureau; Biogen Canada Limited: Speakers Bureau; Novo Nordisk Canada Inc: Consultancy; Hoffman-LaRoche Ltd: Consultancy; Agios Pharmaceuticals Inc: Consultancy; Shire Pharma Canada Inc: Consultancy. Pastore:Pfizer: Honoraria. Lipton:BMS: Consultancy, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria; Novartis: Consultancy, Research Funding; Ariad: Consultancy, Research Funding; Pfizer: Consultancy, Honoraria, 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)0,001
score de la tête « metaresearch » (Gemma)0,004
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 enseignants0,027
Score d'incertitude au seuil0,095

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,007
Études des sciences et des technologies0,0020,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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.

Tête enseignante Opus0,019
Tête enseignante GPT0,211
Écart entre enseignants0,192 · 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

Citations0
Publié2020
Routes d'admission2
Résumé présentoui

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