Recurrent somatic mutations are rare in patients with cryptic dyskeratosis congenita.
Martin KirschnerAngela MaurerMarcin W WlodarskiMonica S Ventura FerreiraAnne-Sophie BouillonInsa HalfmeyerWolfgang BlauMichael KreuterMartin RosewichSelim CorbaciogluJoachim BeckMichaela SchwarzJörg BittenbringMarkus P RadsakChristian Matthias WilkSteffen KoschmiederMatthias BegemannIngo KurthMirle SchemionekTim H BrümmendorfFabian BeierPublished in: Leukemia (2018)
Dyskeratosis congenita (DKC) is a paradigmatic telomere disorder characterized by substantial and premature telomere shortening, bone marrow failure, and a dramatically increased risk of developing myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML). DKC can occur as a late-onset, so-called cryptic form, with first manifestation in adults. Somatic MDS-related mutations are found in up to 35% of patients with acquired aplastic anemia (AA), especially in patients with short telomeres. The aim of our study was to investigate whether cryptic DKC is associated with an increased incidence of MDS-related somatic mutations, thereby linking the accelerated telomere shortening with the increased risk of MDS/AML. Samples from 15 adult patients (median age: 42 years, range: 23-60 years) with molecularly confirmed cryptic DKC were screened using next-generation gene panel sequencing to detect MDS-related somatic variants. Only one of the 15 patients (7%) demonstrated a clinically relevant MDS-related somatic variant. This incidence was dramatically lower than formerly described in acquired AA. Based on our data, we conclude that clonal evolution of subclones carrying MDS-related mutations is not the predominant mechanism for MDS/AML initiation in adult cryptic DKC patients.
Keyphrases
- acute myeloid leukemia
- copy number
- end stage renal disease
- late onset
- bone marrow
- chronic kidney disease
- ejection fraction
- allogeneic hematopoietic stem cell transplantation
- newly diagnosed
- early onset
- mesenchymal stem cells
- risk factors
- peritoneal dialysis
- artificial intelligence
- gene expression
- machine learning
- transcription factor
- big data
- single cell
- patient reported outcomes
- patient reported
- data analysis