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Clonal Events in Normal Aging Hematopoiesis

2015· article· en· W2593273640 on OpenAlexaff
Lambert Busque

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMyeloidBiologyPopulationHaematopoiesisSomatic evolution in cancerBone marrow failureBone marrowImmunologyGeneticsInternal medicineStem cellCancerMedicine

Abstract

fetched live from OpenAlex

Chronological aging of the hematopoietic compartment is associated with decreased bone marrow cellularity, reduced lymphopoiesis, increased anemia, a myeloid proliferation bias and an increased incidence of myeloid cancers. Beerman et al. proposed that this age-related myeloid lineage favoritism may be explained by clonal expansion of intrinsically myeloid-biased hematopoietic stem cells with robust self-renewal potential(1). This age-associated clonal expansion was initially suspected by X-chromosome inactivation (XCI) studies performed in the normal aging population, which documented a skewed XCI pattern in a significant proportion of women over 60 year-old(2). More recently, genome wide approaches led several groups to document au augmented prevalence of acquired clonal copy number changes (3,4,5) or clonal somatic mutations with increasing age (6,7,8,9). The most frequently mutated genes are the same as those documented in myeloid cancers, such as TET2, DNMT3A, ASXL1, PPM1D, GNAS, TP53, JAK2 and SF3B1 among others. The prevalence of these age-associated mutations may reach > 10% of older individuals, and is associated with an 11-12 fold increased relative risk of developing hematological malignancies. However, the actual problematic is to define the prognostic significance of these clonal mutations in the aging population. Steensma et al. proposed to consider these mutations as «Clonal Hematopoiesis of Indeterminate Potential (CHIP)»(10). The goal of our research group is to define the oncogenic penetrance of CHIP by applying a precision medicine approach in a large prospective cohort (n=4000) of aging individuals comprised of related and unrelated subjects. The variables under investigation include, clonality by XCI in women, deep sequencing (NGS) of myeloid cancer associated genes, epigenetic markers (5hmC, 5mC), telomere length, blood counts, heritability and outcome. PRELIMINARY RESULTS. XCI analyses Acquired skewing of XCI predominantly affects the myeloid lineage with a prevalence of 41.4% for PMN and is age dependent (r=0.15, P<10-4), in contrast to T cells 22.5%. These results support the idea of an age-associated clonal myeloid expansion. NGS of myeloid gene panel. We documented a prevalence of 17.9% of mutated individuals. Mutations were mainly documented in TET2 and DNMT3A which accounted for 90% of all identified mutations. Other significantly mutated genes included JAK2, ASXL1, CBL, TP53 and KRAS. Double mutations were identified in 2.5% of individuals (14% of the mutated individuals) and half of them had concomitant mutation in TET2 and DNMT3A. Age and XCI skewing was similar between subjects with mutation in TET2 or DNMT3A, but slightly higher in double mutants. Epigenetic markers. Subjects with mutation in TET2 had a significant reduction in 5hmC level that correlated with Variable Allele Frequency (VAF) of the mutation. No specific global epigenetic phenotype was documented in the DNMT3A mutation subgroup. We also documented an age-associated reduction in 5hmC that was independent of acquired mutation in the TET2 gene. Taken together these results indicate that age-associated clonal mutations involves predominantly two genes (TET2 and DNMT3A), suggesting that alteration of epigenetic maintenance is a central to the initiation of clonal dominance. Completion of investigation of the aging cohort and prospective follow-up will help characterize the link between aging hematopoiesis and the development of myeloid cancers. 1. Beerman I, Maloney WJ, Weissmann IL, et al. Stem cells and the aging hematopoietic system. Curr Opin Immunol. 2010;22(4):500-506. 2. Busque L, Mio R, Mattioli J, et al. Non-random X-inactivation patterns in normal females: lyonization ratios vary with age. Blood. 1996;88(1):59-65. 3. Forsberg LA, Rasi C, Razzaghian HR, et al. Age-related somatic structural changes in the nuclear genome of human blood cells. AJHG, 2012;90:217-228. 3. Laurie CC, Laurie CA, Rice K, et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nat Genet. 2012;44(6):642-650. 4. Jacobs KB, Yeager M, Zhou W, et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nat Genet. 2012;44(6):651-658. 5. Busque L, Patel JP, Figueroa ME, et al. Recurrent somatic TET2 mutation in normal elderly individuals with clonal hematopoiesis. Nat Genet. 2012;444(11):1179-1181. 6. Xie M, Lu C, Wang J, et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat Med. 2014;20(12):1472-1478. 7. Genovese G, Kähler AK, Handsaker RE, et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med. 2014;371(26):2477-2487. 8. Jaiswal S, Fontanillas P, Flannick J, et al. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med. 2014;371(26):2488-2498. 9.Steensma DP, Bejar R, Jaiswal S, et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood. 2015;126(1):9-16 Disclosures Busque: Pfizer: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.308
Teacher spread0.276 · 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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Citations2
Published2015
Admission routes1
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