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Somatic Mutations in TET2, but Not SF3B1, Are Prevalent during Normal Aging Hematopoiesis in Human

2014· article· en· W2300062471 on OpenAlexaff
Manuel Buscarlet, Lambert Busque, Guylaine Lépine, Ross L. Levine

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsPopulationMyelodysplastic syndromesBiologyCancerEpigeneticsMyeloidSomatic cellGeneticsMedicineBioinformaticsOncologyGeneCancer researchImmunologyBone marrow

Abstract

fetched live from OpenAlex

Abstract BACKGROUND. Somatic mutations acquired in the course of a lifetime contribute to the aging process and to the development of age-associated diseases including cancers. Our capacity to identify such events in the normal aging population prior to the appearance of malignancy is of utmost clinical importance to devise prevention or early intervention strategies. This is particularly relevant to myeloid cancers such as acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) which incidence increases dramatically with age. Recently, next-generation sequencing (NGS) efforts have helped to establish lists of recurrently mutated genes in these cancers. With these lists of candidate genes in hand, it is now possible to look for the presence of low frequency pre-malignant somatic lesions in the aging population. The contribution of these mutations to cancer development can then be assessed in longitudinal studies. We have previously shown (Nature Genet, 2012; 44:179), that somatic mutations in the epigenetic regulator gene TET2 occur in the normal aging population and is associated with clonal derivation of cells. However, little is known about the occurrence of mutation in other categories of frequently mutated gene in MDS such as the splicing factor SF3B1, which is the second mostly frequently mutated gene after TET2. SUBJECTS AND METHODS. We have selected 217 individuals based on age (>70 years) and clonality status from a well characterized cohort of normal aging individuals (n=4000). This cohort is comprised of women mostly aged greater than 60 years and without any known hematological disorder (medical history reviewed and normal complete blood counts at enrollment). We obtained blood cells and buccal epithelial cells from each subject. Blood cells were separated into polymorphonuclear (PMN) cells and mononuclear cells using standard procedures. T cells (CD3+) were further isolated from mononuclear cells. DNA and RNA were then isolated from all cell populations. X-chromosome inactivation (XCI) patterns at the HUMARA locus was determined in PMN, T-cells to assess the clonality of myeloid derived cells. MUTATIONAL ANALYSIS. We have developed a custom next generation sequencing approach using Ion AmpliSeq libraries on an Ion Torrent PGM sequencer. Extensive experimental validation and algorithm optimisation were performed to ensure the quality of the method, control experiments with positive and negative controls were done to demonstrate the specificity, while dilution curves were run to validate the sensitivity of mutation detection down to 5% variant allele frequency (VAF) at 500x mean coverage. The final validated design is composed of 65 amplicons spanning 9.78kb covering TET2 at 100% and 53 amplicons spanning 8.84kb covering SF3B1at 95.61% including all known hot spots. RESULTS. 14/207 subject had acquired (present in PMN, not in T-cells or epithelial cells) mutation in the TET2 gene in line with our previous results. The VAF varied between 6 and 47%, all mutation were further validated by Sanger sequencing. In contrast to TET2, no subject had mutation in SF3B1gene. CONCLUSION. Mutation in the SF3B1gene does not occur at a significant frequency in the aging population suggesting that alteration of this gene is not an early initiating event in the pathogenesis of MDS. This further suggest that in contrast to the extended number of genes mutated in MDS, only a limited number are likely to be found and implicated in the pre-leukemic phase. It is possible that epigenetic alterations, and not dysfunction of the spliceosome, could be a driving force underlying the pathogenesis of MDS. Complete exome sequencing and iterative studies will help decipher the sequence of events leading to age-associated myeloid cancer. Disclosures No relevant conflicts of interest to declare.

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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)0.000
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.017
GPT teacher head0.285
Teacher spread0.269 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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