2 research outputs found
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Concordance for clonal hematopoiesis is limited in elderly twins.
Although acquisition of leukemia-associated somatic mutations by 1 or more hematopoietic stem cells is inevitable with advancing age, its consequences are highly variable, ranging from clinically silent clonal hematopoiesis (CH) to leukemic progression. To investigate the influence of heritable factors on CH, we performed deep targeted sequencing of blood DNA from 52 monozygotic (MZ) and 27 dizygotic (DZ) twin pairs (aged 70-99 years). Using this highly sensitive approach, we identified CH (variant allele frequency ≥0.5%) in 62% of individuals. We did not observe higher concordance for CH within MZ twin pairs as compared with that within DZ twin pairs, or to that expected by chance. However, we did identify 2 MZ pairs in which both twins harbored identical rare somatic mutations, suggesting a shared cell of origin. Finally, in 3 MZ twin pairs harboring mutations in the same driver genes, serial blood samples taken 4 to 5 years apart showed substantial twin-to-twin variability in clonal trajectories. Our findings propose that the inherited genome does not exert a dominant influence on the behavior of adult CH and provide evidence that CH mutations may be acquired in utero
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RNAmut: robust identification of somatic mutations in acute myeloid leukemia using RNA-sequencing.
Acute myeloid leukemia (AML) is an aggressive malignancy of haematopoietic stem cells driven by a well-defined set of somatic mutations.1,2 Identifying the mutations driving individual cases is important for assigning the patient to a recognized World Health Organisation category, establishing prognostic risk and tailoring post-consolidation therapy.3 As a result, AML research and diagnostic laboratories apply diverse methodologies to detect important mutations and many are introducing next-generation sequencing (NGS) approaches to study extended panels of genes in order to refine genomic classification and prognostic category.1 Besides the implications of these developments on costs, expertise and reliance on commercial providers, they also do not capture gene expression data, which have independent prognostic value that cannot be inferred from somatic mutation profiles. The ability to detect AML gene mutations as well as gene expression profiles from a single assay, could provide a holistic tool that accelerates research, simplifies diagnostic work-up and helps develop integrated algorithms to refine individual patient prognosis. Here, we show that AML RNA sequencing (RNA-seq) data can be used to reliably detect all types of clinically important mutations and develop a bespoke fast and easy-to-use software (RNAmut) for this purpose that can be readily used by teams/laboratories without in-house bioinformatic expertise