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Record 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 on 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

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversity 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
Fundersnot available
KeywordsDyskeratosis congenitaMedicineBone marrow failureInternal medicineHazard ratioMyelodysplastic syndromesBone marrowSurvival analysisOncologyTelomereHaematopoiesisBiologyStem cell

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.211
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes2
Has abstractyes

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