Prediction of acute myeloid leukaemia risk in healthy individuals

Sagi Abelson, Grace Collord, Stanley W K Ng, Omer Weissbrod, Netta Mendelson Cohen, Elisabeth Niemeyer, Noam Barda, Philip C Zuzarte, Lawrence Heisler, Yogi Sundaravadanam, Robert Luben, Shabina Hayat, Ting Ting Wang, Zhen Zhao, Iulia Cirlan, Trevor J Pugh, David Soave, Karen Ng, Calli Latimer, Claire HardyKeiran Raine, David Jones, Diana Hoult, Abigail Britten, John D McPherson, Mattias Johansson, Faridah Mbabaali, Jenna Eagles, Jessica K Miller, Danielle Pasternack, Lee Timms, Paul Krzyzanowski, Philip Awadalla, Rui Costa, Eran Segal, Scott V Bratman, Philip Beer, Sam Behjati, Inigo Martincorena, Jean C Y Wang, Kristian M Bowles, J Ramón Quirós, Anna Karakatsani, Carlo La Vecchia, Antonia Trichopoulou, Elena Salamanca-Fernández, José M Huerta, Aurelio Barricarte, Ruth C Travis, Rosario Tumino, Giovanna Masala, Heiner Boeing, Salvatore Panico, Rudolf Kaaks, Alwin Krämer, Sabina Sieri, Elio Riboli, Paolo Vineis, Matthieu Foll, James McKay, Silvia Polidoro, Núria Sala, Kay-Tee Khaw, Roel Vermeulen, Peter J Campbell, Elli Papaemmanuil, Mark D Minden, Amos Tanay, Ran D Balicer, Nicholas J Wareham, Moritz Gerstung, John E Dick, Paul Brennan, George S Vassiliou, Liran I Shlush

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)400-404
Number of pages5
JournalNature
Volume559
Issue number7714
DOIs
Publication statusPublished - Jul 2018

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Acute Myeloid Leukemia
Hematopoietic Stem Cells
Hematopoiesis
Mutation
High-Throughput Nucleotide Sequencing
Electronic Health Records
Cell Aging
Gene Frequency
Genes
Blood Cells
Bone Marrow
Databases

Cite this

Abelson, S., Collord, G., Ng, S. W. K., Weissbrod, O., Mendelson Cohen, N., Niemeyer, E., ... Shlush, L. I. (2018). Prediction of acute myeloid leukaemia risk in healthy individuals. Nature, 559(7714), 400-404. https://doi.org/10.1038/s41586-018-0317-6

Prediction of acute myeloid leukaemia risk in healthy individuals. / 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.

In: Nature, Vol. 559, No. 7714, 07.2018, p. 400-404.

Research output: Contribution to journalArticle

Abelson, S, Collord, G, Ng, SWK, Weissbrod, O, Mendelson Cohen, N, Niemeyer, E, Barda, N, Zuzarte, PC, Heisler, L, Sundaravadanam, Y, Luben, R, Hayat, S, Wang, TT, Zhao, Z, Cirlan, I, Pugh, TJ, Soave, D, Ng, K, Latimer, C, Hardy, C, Raine, K, Jones, D, Hoult, D, Britten, A, McPherson, JD, Johansson, M, Mbabaali, F, Eagles, J, Miller, JK, Pasternack, D, Timms, L, Krzyzanowski, P, Awadalla, P, Costa, R, Segal, E, Bratman, SV, Beer, P, Behjati, S, Martincorena, I, Wang, JCY, Bowles, KM, Quirós, JR, Karakatsani, A, La Vecchia, C, Trichopoulou, A, Salamanca-Fernández, E, Huerta, JM, Barricarte, A, Travis, RC, Tumino, R, Masala, G, Boeing, H, Panico, S, Kaaks, R, Krämer, A, Sieri, S, Riboli, E, Vineis, P, Foll, M, McKay, J, Polidoro, S, Sala, N, Khaw, K-T, Vermeulen, R, Campbell, PJ, Papaemmanuil, E, Minden, MD, Tanay, A, Balicer, RD, Wareham, NJ, Gerstung, M, Dick, JE, Brennan, P, Vassiliou, GS & Shlush, LI 2018, 'Prediction of acute myeloid leukaemia risk in healthy individuals', Nature, vol. 559, no. 7714, pp. 400-404. https://doi.org/10.1038/s41586-018-0317-6
Abelson S, Collord G, Ng SWK, Weissbrod O, Mendelson Cohen N, Niemeyer E et al. Prediction of acute myeloid leukaemia risk in healthy individuals. Nature. 2018 Jul;559(7714):400-404. https://doi.org/10.1038/s41586-018-0317-6
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. / Prediction of acute myeloid leukaemia risk in healthy individuals. In: Nature. 2018 ; Vol. 559, No. 7714. pp. 400-404.
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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.",
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AU - Abelson, Sagi

AU - Collord, Grace

AU - Ng, Stanley W K

AU - Weissbrod, Omer

AU - Mendelson Cohen, Netta

AU - Niemeyer, Elisabeth

AU - Barda, Noam

AU - Zuzarte, Philip C

AU - Heisler, Lawrence

AU - Sundaravadanam, Yogi

AU - Luben, Robert

AU - Hayat, Shabina

AU - Wang, Ting Ting

AU - Zhao, Zhen

AU - Cirlan, Iulia

AU - Pugh, Trevor J

AU - Soave, David

AU - Ng, Karen

AU - Latimer, Calli

AU - Hardy, Claire

AU - Raine, Keiran

AU - Jones, David

AU - Hoult, Diana

AU - Britten, Abigail

AU - McPherson, John D

AU - Johansson, Mattias

AU - Mbabaali, Faridah

AU - Eagles, Jenna

AU - Miller, Jessica K

AU - Pasternack, Danielle

AU - Timms, Lee

AU - Krzyzanowski, Paul

AU - Awadalla, Philip

AU - Costa, Rui

AU - Segal, Eran

AU - Bratman, Scott V

AU - Beer, Philip

AU - Behjati, Sam

AU - Martincorena, Inigo

AU - Wang, Jean C Y

AU - Bowles, Kristian M

AU - Quirós, J Ramón

AU - Karakatsani, Anna

AU - La Vecchia, Carlo

AU - Trichopoulou, Antonia

AU - Salamanca-Fernández, Elena

AU - Huerta, José M

AU - Barricarte, Aurelio

AU - Travis, Ruth C

AU - Tumino, Rosario

AU - Masala, Giovanna

AU - Boeing, Heiner

AU - Panico, Salvatore

AU - Kaaks, Rudolf

AU - Krämer, Alwin

AU - Sieri, Sabina

AU - Riboli, Elio

AU - Vineis, Paolo

AU - Foll, Matthieu

AU - McKay, James

AU - Polidoro, Silvia

AU - Sala, Núria

AU - Khaw, Kay-Tee

AU - Vermeulen, Roel

AU - Campbell, Peter J

AU - Papaemmanuil, Elli

AU - Minden, Mark D

AU - Tanay, Amos

AU - Balicer, Ran D

AU - Wareham, Nicholas J

AU - Gerstung, Moritz

AU - Dick, John E

AU - Brennan, Paul

AU - Vassiliou, George S

AU - Shlush, Liran I

PY - 2018/7

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N2 - 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.

AB - 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.

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DO - 10.1038/s41586-018-0317-6

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