TY - JOUR
TI - Prediction of acute myeloid leukaemia risk in healthy individuals
AU - Abelson, S.
AU - Collord, G.
AU - Ng, S.W.K.
AU - Weissbrod, O.
AU - Mendelson Cohen, N.
AU - Niemeyer, E.
AU - Barda, N.
AU - Zuzarte, P.C.
AU - Heisler, L.
AU - Sundaravadanam, Y.
AU - Luben, R.
AU - Hayat, S.
AU - Wang, T.T.
AU - Zhao, Z.
AU - Cirlan, I.
AU - Pugh, T.J.
AU - Soave, D.
AU - Ng, K.
AU - Latimer, C.
AU - Hardy, C.
AU - Raine, K.
AU - Jones, D.
AU - Hoult, D.
AU - Britten, A.
AU - McPherson, J.D.
AU - Johansson, M.
AU - Mbabaali, F.
AU - Eagles, J.
AU - Miller, J.K.
AU - Pasternack, D.
AU - Timms, L.
AU - Krzyzanowski, P.
AU - Awadalla, P.
AU - Costa, R.
AU - Segal, E.
AU - Bratman, S.V.
AU - Beer, P.
AU - Behjati, S.
AU - Martincorena, I.
AU - Wang, J.C.Y.
AU - Bowles, K.M.
AU - Quirós, J.R.
AU - Karakatsani, A.
AU - La Vecchia, C.
AU - Trichopoulou, A.
AU - Salamanca-Fernández, E.
AU - Huerta, J.M.
AU - Barricarte, A.
AU - Travis, R.C.
AU - Tumino, R.
AU - Masala, G.
AU - Boeing, H.
AU - Panico, S.
AU - Kaaks, R.
AU - Krämer, A.
AU - Sieri, S.
AU - Riboli, E.
AU - Vineis, P.
AU - Foll, M.
AU - McKay, J.
AU - Polidoro, S.
AU - Sala, N.
AU - Khaw, K.-T.
AU - Vermeulen, R.
AU - Campbell, P.J.
AU - Papaemmanuil, E.
AU - Minden, M.D.
AU - Tanay, A.
AU - Balicer, R.D.
AU - Wareham, N.J.
AU - Gerstung, M.
AU - Dick, J.E.
AU - Brennan, P.
AU - Vassiliou, G.S.
AU - Shlush, L.I.
JO - Nature
PY - 2018
VL - 559
TODO - 7714
SP - 400-404
PB - Nature Publishing Group
SN - 0028-0836
TODO - 10.1038/s41586-018-0317-6
TODO - cancer;  cell;  database;  disease incidence;  health risk;  model validation;  mutation;  prediction;  risk assessment, acute myeloid leukemia;  adult;  aged;  Article;  blood cell;  cancer risk;  cancer survival;  cell expansion;  clinical article;  cohort analysis;  controlled study;  disease free survival;  electronic health record;  female;  gender;  gene frequency;  gene mutation;  gene sequence;  genetic analysis;  genetic variability;  groups by age;  hematopoiesis;  high risk population;  human;  male;  molecular cloning;  prediction;  priority journal;  validation process;  acute myeloid leukemia;  age;  biological model;  disease exacerbation;  genetic predisposition;  genetics;  health;  middle aged;  mutagenesis;  mutation;  pathology;  prevalence;  risk assessment, Adult;  Age Factors;  Aged;  Disease Progression;  Electronic Health Records;  Female;  Genetic Predisposition to Disease;  Health;  Humans;  Leukemia, Myeloid, Acute;  Male;  Middle Aged;  Models, Genetic;  Mutagenesis;  Mutation;  Prevalence;  Risk Assessment
TODO - 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 failure 1 . 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 expansion 2,3 . 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. © 2018 Macmillan Publishers Ltd., part of Springer Nature.
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