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Nature. 2018 Jul;559(7714):400-404. doi: 10.1038/s41586-018-0317-6. Epub 2018 Jul 9.

Prediction of acute myeloid leukaemia risk in healthy individuals.

Author information

1
Princess Margaret Cancer Centre, University Health Network (UHN), Toronto, Ontario, Canada.
2
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.
3
Department of Paediatrics, University of Cambridge, Cambridge, UK.
4
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
5
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
6
Department of Immunology, Weizmann Institute of Science, Rehovot, Israel.
7
Clalit Research Institute, Tel Aviv, Israel.
8
Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
9
Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK.
10
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
11
MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
12
International Agency for Research on Cancer, World Health Organization, Lyon, France.
13
European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Genome Campus, Hinxton, UK.
14
Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
15
Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
16
Division of Medical Oncology and Hematology, University Health Network, Toronto, Ontario, Canada.
17
Department of Molecular Haematology, Norwich Medical School, The University of East Anglia, Norwich, UK.
18
Department of Haematology, Norfolk and Norwich University Hospitals NHS Trust, Norwich, UK.
19
Public Health Directorate, Asturias, Spain.
20
Hellenic Health Foundation, Athens, Greece.
21
2nd Pulmonary Medicine Department, School of Medicine, National and Kapodistrian University of Athens, "ATTIKON" University Hospital, Haidari, Athens, Greece.
22
Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.
23
Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs.GRANADA, Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain.
24
CIBER Epidemiology and Public Health CIBERESP, Madrid, Spain.
25
Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain.
26
Navarra Public Health Institute, Pamplona, Spain.
27
Navarra Institute for Health Research, Pamplona, Spain.
28
Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
29
Cancer Registry and Histopathology Department, Civic-M. P. Arezzo Hospital, Azienda Sanitaria Provinciale, Ragusa, Italy.
30
Cancer Risk Factors and Life-Style Epidemiology Unit, Cancer Research and Prevention Institute - ISPO, Florence, Italy.
31
Department of Epidemiology, German Institute of Human Nutrition (DIfE), Potsdam-Rehbrücke, Germany.
32
Dipartimento Di Medicina Clinica E Chirurgia, Federico II University, Naples, Italy.
33
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
34
Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center (DKFZ) and Department of Internal Medicine V, University of Heidelberg, Heidelberg, Germany.
35
Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy.
36
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
37
Italian Institute for Genomic Medicine, Torino, Italy.
38
Unit of Nutrition and Cancer, Cancer Epidemiology Research Program and Translational Research Laboratory, Catalan Institute of Oncology, ICO-IDIBELL, Barcelona, Spain.
39
University of Cambridge, Cambridge, UK.
40
Division of Environmental Epidemiology and Veterinary Public Health, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
41
Department of Haematology, University of Cambridge, Cambridge, UK.
42
Center for Molecular Oncology and Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
43
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK. moritz.gerstung@ebi.ac.uk.
44
European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Genome Campus, Hinxton, UK. moritz.gerstung@ebi.ac.uk.
45
Princess Margaret Cancer Centre, University Health Network (UHN), Toronto, Ontario, Canada. John.Dick@uhnresearch.ca.
46
Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. John.Dick@uhnresearch.ca.
47
International Agency for Research on Cancer, World Health Organization, Lyon, France. BrennanP@iarc.fr.
48
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK. gsv20@sanger.ac.uk.
49
Department of Haematology, University of Cambridge, Cambridge, UK. gsv20@sanger.ac.uk.
50
Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. gsv20@sanger.ac.uk.
51
Princess Margaret Cancer Centre, University Health Network (UHN), Toronto, Ontario, Canada. liranshlush3@gmail.com.
52
Department of Immunology, Weizmann Institute of Science, Rehovot, Israel. liranshlush3@gmail.com.
53
Division of Hematology, Rambam Healthcare Campus, Haifa, Israel. liranshlush3@gmail.com.

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.

PMID:
29988082
DOI:
10.1038/s41586-018-0317-6

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