Journal Article DKFZ-2018-01117

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png
Prediction of acute myeloid leukaemia risk in healthy individuals.

 ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;

2018
Nature Publ. Group London [u.a.]

Nature <London> 559(7714), 400 - 404 () [10.1038/s41586-018-0317-6]
 GO

This record in other databases:  

Please use a persistent id in citations: doi:

Abstract: The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4-8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention.

Classification:

Contributing Institute(s):
  1. C020 Epidemiologie von Krebs (C020)
  2. KKE Molekulare Hämatologie/Onkologie (G330)
Research Program(s):
  1. 319H - Addenda (POF3-319H) (POF3-319H)

Appears in the scientific report 2018
Database coverage:
Medline ; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Life Sciences ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; IF >= 40 ; JCR ; NCBI Molecular Biology Database ; NationallizenzNationallizenz ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection ; Zoological Record
Click to display QR Code for this record

The record appears in these collections:
Document types > Articles > Journal Article
Institute Collections > C020
Public records
Publications database

 Record created 2018-08-10, last modified 2024-02-29



Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)