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.
Original language | English |
---|---|
Pages (from-to) | 400-404 |
Number of pages | 5 |
Journal | Nature |
Volume | 559 |
Issue number | 7714 |
Early online date | 9 Jul 2018 |
DOIs | |
Publication status | Published - 19 Jul 2018 |
Funding
This work was supported by a Quest for Cure grant to L.I.S., J.C.Y.W. and M.D.M. from the Leukemia and Lymphoma Society, and the following grants to L.I.S from: ERC Horizon 2020 MAMLE, Abisch-Frenkel foundation and an American Society of Hematology Scholar Award. Further funding to J.E.D. was provided by the Canada Research Chair Program, Ontario Institute for Cancer Research, the province of Ontario, Canadian Cancer Society, the Canadian Institutes for Health Research and the Ontario Ministry of Health and Long Term Care to UHN, whose views are not expressed here. Work conducted at the Sanger Institute was supported by the Wellcome Trust and UK Medical Research Council. S.A. was personally funded by the Benjamin Pearl fellowship from the McEwen Centre for Regenerative Medicine, G.C. by a Wellcome Trust Clinical PhD Fellowship (WT098051); G.S.V. by a Wellcome Trust Senior Fellowship in Clinical Science (WT095663MA) and a Cancer Research UK Senior Cancer Research Fellowship (C22324/A23015). G.S.V.'s laboratory is also funded by the Kay Kendall Leukaemia Fund and Bloodwise. We thank A. Mitchell and all members of the Dick and Shlush laboratories for comments and T. Hudson for early study planning; G. Barabash for organising the Clalit dataset collaboration. The EPIC study centres were supported by the Hellenic Health Foundation, Regional Government of Asturias, the Regional Government of Murcia (no. 6236), the Spanish Ministry of Health network RTICCC (ISCIII RD12/0036/0018), FEDER funds/European Regional Development Fund (ERDF), “a way to build Europe”, Generalitat de Catalunya, AGAUR 2014SGR726; EPIC Ragusa in Italy-Aire-Onlus Ragusa; Epic Italy-Associazione Italiana per la Ricerca sul Cancro (AIRC) Milan, Italy. S.V.B. and T.J.P. are supported by the Gattuso-Slaight Personalized Cancer Medicine Fund at the Princess Margaret Cancer Centre. Reviewer information Nature thanks R. Levine, P. Van Loo and the other anonymous reviewer(s) for their contribution to the peer review of this work. Author contributions S.W.K.N., O.W., N.M.C. and E.N. contributed equally to the work. S.A. performed error-corrected sequencing, analysed sequencing data, performed statistical analyses, contributed to genetic predictive model derivation and wrote the manuscript. G.C. performed variant calling, statistical analyses, derived genetic predictive models and wrote the manuscript. M.G., S.W.K.N., O.W. and R.C. derived genetic predictive models. N.M.C., E.N. and N.B. derived the clinical prediction model. P.C.Z., Z.Z., I.C., K.N., C.L., C.H., D.H., F.M., J.E., J.K.M., D.P., L.T., P.K., S.V.B. and A.Br. and A.Ba. provided sequencing and technical support and enabled sample acquisition. L.H., Y.S., T.T.W., T.J.P., K.R. and D.J. provided bioinformatics support. R.L., S.H., M.J., K.M.B., A.Kr. and N.J.W. enabled sample acquisition, clinical data curation and/or provided clinical expertise. D.S., J.D.M., P.A., E.S., S.B., P.Be., M.D.M and I.M. contributed to data analysis and interpretation. P.J.C. and E.P. contributed to data interpretation and designed the targeted sequencing assay for the validation cohort. J.C.Y.W. revised the manuscript. J.R.Q., A.Ka., C.L.V., A.T., E.S.-F., J.M.H., R.C.T., R.T., G.M., H.B., S.Pa., R.K., S.S., S.Po., N.J.W., N.S., K.-T.K., M.F., J.M.K., E.R., P.V. and R.V. enabled sample acquisition (EPIC). A.T. and R.D.B. analysed Clalit data and derived the clinical prediction model. M.G. derived predictive genetic models, contributed to sequencing data analysis and manuscript writing. J.E.D. contributed to funding applications, study supervision and manuscript writing. P.Br. supervised sample acquisition from all EPIC centres. G.S.V. and L.I.S. designed and supervised all aspects of the study and wrote the manuscript.
All Science Journal Classification (ASJC) codes
- General