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Copy Number Variants Underlying Inherited Bone Marrow Failure Syndromes

2015· article· en· W2563965053 on OpenAlexaffabout
Nicolas Waespe, Santhosh Dhanraj, Manju Wahala, Tom Enbar, Bozana Zlateska, Hongbing Li, Robert J. Klaassen, Conrad V. Fernandez, Rochelle Yanofsky, John K. Wu, Yves Pastore, Mariana Silva, Jeffrey H. Lipton, Josée Brossard, Bruno Michon, Sharon Abish, MacGregor Steele, Roona Sinha, Mark Belletrutti, Vicky R. Breakey, Lawrence Jardine, Lisa Goodyear, Liat Kofler, Ibrahim Ghemlas, Michaela Cada, Lillian Sung, Mary Shago, Stephen W. Scherer, Yigal Dror

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsJaneway Children's Health and Rehabilitation CentreLondon Health Sciences CentreMcMaster UniversityUniversity of AlbertaCentre hospitalier universitaire de QuébecCentre Hospitalier Universitaire de SherbrookeCentre Hospitalier Universitaire Sainte-JustineCancerCare ManitobaPopulation Health Research InstituteChildren's Hospital of Eastern OntarioBC Children's HospitalRoyal University HospitalMontreal Children's HospitalUniversity of TorontoSickKids FoundationPrincess Margaret Cancer CentreKingston General HospitalUniversity of ManitobaAlberta Children's HospitalIzaak Walton Killam Health CentreHospital for Sick Children
Fundersnot available
KeywordsCopy-number variationExact testSNP arrayGeneticsBiologyGenotypeGenotype-phenotype distinctionGenetic testingBone marrow failureSNPCopy number analysisSingle-nucleotide polymorphismBioinformaticsMedicineGeneGenomeInternal medicineHaematopoiesis

Abstract

fetched live from OpenAlex

Abstract Background. Inherited bone marrow failure syndromes (IBMFSs) comprise a genetically heterogeneous group of diseases with hematopoietic failure and varying degrees of physical malformations. The diagnosis of an IBMFS and categorizing the specific syndrome critically impact on clinical care; however, these are commonly challenging and rely on genetic testing. Since over 80 genes have been associated with IBMFSs and might be affected by different types of DNA aberrations, the best strategy to establish a diagnosis in a timely and cost effective manner is unknown. The aims of this study were to evaluate the role of genome-wide copy number variant (CNV) analysis in unraveling causal genetic alterations in IBMFS patients with unknown genotype and determine whether correlation exists between large CNVs and more severe phenotype. Methods. Patients from the Canadian Inherited Marrow Failure Registry (CIMFR) who were genetically investigated were included in this analysis. Genetic and clinical data were extracted and analyzed. Mann-Whitney test and Fisher's exact test were used to assess statistical significance. Results. Among 328 patients from the CIMFR who underwent molecular investigation, a causal genotype was identified in 185 cases (56.4%). 69 patients had genome-wide CNV analysis by SNP/CGH arrays, among which ten (14.5%) had positive results. In four out of ten cases who were genotyped by SNP/CGH array, genome-wide CNV analysis was critical for establishing the diagnosis. Among 308 patients who were tested for nucleotide-level mutations by either targeted gene analysis or next generation sequencing panels, casual mutations were found in 169 (54.9%). Three patients had compound heterozygosity for a CNV and nucleotide-level mutation. To determine whether large deletions are correlated with more severe phenotype we included nine additional patients with causal CNVs whose genotype was identified by MLPA (n=1), targeted FISH (n=1), DNA-qPCR analysis (n=1), Southern blotting (n=1) or metaphase cytogenetics (n=5). The causal CNVs among patients in our cohort ranged from 0.02 to 145.5 Mb in size. The most common disease associated with causal CNVs was Diamond-Blackfan anemia (four patients). Patients with CNVs tended to have significantly more non-hematological organ system involvement (p=0.03), developmental delay (mean=56% vs. 28%, p=0.03) and short stature (mean=67% vs. 40%, p=0.04) than patients with nucleotide-level mutations. The difference remained significant when we compared all patients with mutations that are predicted to result in truncation or lack of protein from the respective allele (large CNV, nonsense, and indel/ frameshift) to patients with mutations that are predicted to be hypomorphic or affect function (splicing, indel/ inframe and missense). There was no correlation between CNVs and the severity of the hematological disease. Conclusions. Most patients with IBMFSs have nucleotide-level mutations. However, a significant proportion of patients without such mutations have large CNVs that are not efficiently detected by current nucleotide-level testing methods. Therefore, genome-wide CNV analysis should be considered in IBMFS cases, where nucleotide-level sequencing does not reveal the causal mutation. Patients with IBMFSs and large CNVs had more non-hematological organ system involvement, a higher prevalence of developmental delay and short stature. This might be related to an additional impact of the CNVs on other genes close to the affected IBMFS gene or the severe damaging effect of the CNVs. Disclosures Lipton: Teva: Consultancy, Research Funding; Ariad: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis Pharmaceuticals: Consultancy, 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)0.000
metaresearch head score (Gemma)0.001
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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.331
Teacher spread0.262 · 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
Published2015
Admission routes2
Has abstractyes

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