MétaCan
Menu
← Back to cohort
Record W2922326480 · doi:10.1182/blood-2018-99-120168

The Clonal Hematopoietic Spectrum of Down Syndrome and ML-DS

2018· article· en· W2922326480 on OpenAlexaff
R. Spencer Tong, Winghing Wong, David R O’Leary, Kathleen S. Jay, Dorothy K. Grange, Jason N. Berman, Johann Hitzler, Todd E. Druley

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHospital for Sick ChildrenIzaak Walton Killam Health Centre
Fundersnot available
KeywordsGATA1MyeloidHaematopoiesisBiologyEpigeneticsMedicineOncologyCancer researchImmunologyGeneticsBioinformaticsGeneStem cell

Abstract

fetched live from OpenAlex

Abstract Introduction: Children with Down syndrome (DS) have a 150-fold higher risk of developing myeloid leukemia (ML-DS) (Wechsler J, Nat Gen 2002). Ten percent (10%) of DS children are also predisposed to developing a preleukemic self-limiting condition, transient myeloproliferative disorder (TMD). While the majority of these children achieve complete remission, 20-30% of TMD patients will eventually develop ML-DS (Gamis AS, Blood 2011). Unique to ML-DS and TMD is the association with mutations in the X-linked hematopoietic transcription factor, GATA1 (Alford KA, Blood 2011), which are pathognomonic, but perhaps not sufficient, for ML-DS. Along with GATA1 mutations, ML-DS has a distinct mutational landscape implicating the cohesin complex, epigenetic regulators, signal transducers and the RAS pathway (Yoshida K, Nat Gen 2013). While these findings have informed our understanding of ML-DS etiology, ~30% of healthy DS neonates - without overt TMD or ML-DS - also harbor GATA1 mutations (Roberts I, Blood 2013), suggesting that ML-DS arises from clonal outgrowth of these progenitors that acquire specific additional mutations. Because the spectrum of physiologic clonal hematopoiesis is unknown in DS, we sought to characterize clonal mutations in healthy DS children as compared to a cohort of ML-DS cases using our highly sensitive and specific error-corrected sequencing (ECS) methodology. A precise understanding of their altered hematopoietic development may inform risk stratification, therapeutic selection and outcomes. Methods: The Druley lab has developed a custom error-corrected sequencing (ECS) panel targeting 80 genes frequently mutated in both pediatric and adult myeloid malignancies (Young AL, Nat Comm 2016) that is validated to identify clonal mutations as rare as 0.0001 variant allele fraction (VAF), 100X below the error-rate of next-generation sequencing (Wong TN, Nature 2015; Young AL, Leukemia 2015). Using ECS, we surveyed 102 DS children (47 ML-DS enrolled from a current Phase III ML-DS study; 55 healthy DS enrolled at St. Louis Children's Hospital). Results: We identified 294 total clonal variants in 72/102 children (60.0%, healthy DS; 83.0%, ML-DS cases) at 0.0002-0.82 VAF. On average, we found 1.8 clonal variants per healthy DS control and 4.1 clonal variants per ML-DS case. Consistent with Yoshida et al., we find a spectrum of recurrent mutations in ML-DS cases: GATA1 (53.2%), EZH2 (25.5%), RAD21 (14.9%), and STAG2 (10.6%) etc. Surprisingly, we also identified frequent mutations in FAT1 (14.9%) and SETD2 (10.6%), genes previously unassociated with ML-DS. Moreover, we report a different set of recurrent mutations in healthy DS children: FAT1 (12.7%), BCOR (12.7%), TET2 (10.9%), SETD2 (7.3%), and TRIM24 (5.5%), and TP53 (5.5%) etc. Discussion: Earlier ML-DS studies could only detect more common mutations >0.02 VAF due to the error-rate of standard next-generation sequencing (NGS). Using ECS, we present the first characterization of the unique clonal hematopoietic spectrum in children with DS and ML-DS. These results reveal that clonal hematopoiesis is common amongst healthy DS children without hematologic conditions, and that children with ML-DS have a higher clonal mutation burden than those without. Notably, we identified two novel genes recurrently mutated in both ML-DS cases and healthy DS controls: FAT1 and SETD2. We suspect that these mutations were not identified in other ML-DS studies as the median VAFs of our detected FAT1 and SETD2 mutations were 0.001 and 0.0008, respectively. Mutations in FAT1 and SETD2 have been shown to lead to dysregulated Wnt signaling in numerous cancers (Morris LG, Nat Gen 2013; Yuan H, J Clin Invest 2017). We and others have previously shown that Wnt signaling is crucial in specifying a primitive or definitive hematopoietic program (Sturgeon CM, Nat Biotech 2014; Creamer JP, Blood 2017). We speculate that these mutant FAT1 and SETD2 clones, like mutant GATA1 clones, may inhibit the definitive hematopoietic potential in DS children. Lastly, we note a reduced incidence of GATA1 mutations in our healthy DS cohort and attribute this to an older average age (8.3 years), as compared to Roberts et al. studying neonates. Other groups have speculated that somatic mutations in GATA1 (with a DS background) may only occur during a restricted developmental window (<4 years) as TMD clones cycle into quiescence afterwards (Zhe L, Nat Gen 2005; Hasle H, Leukemia 2007). Disclosures No relevant conflicts of interest to declare.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.011
GPT teacher head0.267
Teacher spread0.257 · 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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicAcute Myeloid Leukemia Research→French-language works237,207→