Prediction of acute myeloid leukaemia risk in healthy individuals

• Published in: Nature (London) 2018-07, Vol.559 (7714), p.400-404
• Main Authors: Abelson, Sagi, Collord, Grace, Ng, Stanley W. K., Weissbrod, Omer, Mendelson Cohen, Netta, Niemeyer, Elisabeth, Barda, Noam, Zuzarte, Philip C., Heisler, Lawrence, Sundaravadanam, Yogi, Luben, Robert, Hayat, Shabina, Wang, Ting Ting, Zhao, Zhen, Cirlan, Iulia, Pugh, Trevor J., Soave, David, Ng, Karen, Latimer, Calli, Hardy, Claire, Raine, Keiran, Jones, David, Hoult, Diana, Britten, Abigail, McPherson, John D., Johansson, Mattias, Mbabaali, Faridah, Eagles, Jenna, Miller, Jessica K., Pasternack, Danielle, Timms, Lee, Krzyzanowski, Paul, Awadalla, Philip, Costa, Rui, Segal, Eran, Bratman, Scott V., Beer, Philip, Behjati, Sam, Martincorena, Inigo, Wang, Jean C. Y., Bowles, Kristian M., Quirós, J. Ramón, Karakatsani, Anna, La Vecchia, Carlo, Trichopoulou, Antonia, Salamanca-Fernández, Elena, Huerta, José M., Barricarte, Aurelio, Travis, Ruth C., Tumino, Rosario, Masala, Giovanna, Boeing, Heiner, Panico, Salvatore, Kaaks, Rudolf, Krämer, Alwin, Sieri, Sabina, Riboli, Elio, Vineis, Paolo, Foll, Matthieu, McKay, James, Polidoro, Silvia, Sala, Núria, Khaw, Kay-Tee, Vermeulen, Roel, Campbell, Peter J., Papaemmanuil, Elli, Minden, Mark D., Tanay, Amos, Balicer, Ran D., Wareham, Nicholas J., Gerstung, Moritz, Dick, John E., Brennan, Paul, Vassiliou, George S., Shlush, Liran I.
• Format: Article
• Language: English
• Subjects: 49/23; 631/67/68; 631/67/71; 692/499; 692/53/2423; 692/699/67/1990/283/1897; Acute myelocytic leukemia; Acute myeloid leukemia; Adult; Age; Age Factors; Aged; Aging; Arches; Bioinformatics; Blood cells; Bone marrow; Cancer; Cells (biology); Cloning; Disease Progression; Electronic Health Records; Electronic medical records; Electronic records; Female; Gene frequency; Gene sequencing; Genes; Genetic Predisposition to Disease; Genetic transformation; Genomes; Health; Health aspects; Health care; Hematology; Hematopoiesis; Hematopoietic stem cells; Humanities and Social Sciences; Humans; Letter; Leukemia; Leukemia, Myeloid, Acute - epidemiology; Leukemia, Myeloid, Acute - genetics; Leukemia, Myeloid, Acute - pathology; Male; Mathematical models; Medical records; Medical schools; Middle Aged; Models, Genetic; Mortality; multidisciplinary; Mutagenesis; Mutation; Patient outcomes; Peripheral blood; Prediction models; Predictive control; Prevalence; Progenitor cells; Recurrence (Disease); Risk; Risk Assessment; Risk factors; Science; Science (multidisciplinary); Stem cells
• ISSN: 0028-0836; 1476-4687
• DOI: 10.1038/s41586-018-0317-6

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. Individuals who are at high risk of developing acute myeloid leukaemia can be identified years before diagnosis using genetic information from blood samples.