Targeted_sequencing_of_genes_recurrently_mutated_in_AML___part2

Genomic libraries will be generated from total genomic DNA derived from 4000 samples with Acute Myeloid Leukaemia. Libraries will be enriched for a selected panel of genes using a bespoke pulldown protocol. 64 Samples will be individually barcoded and subjected to up to one lanes of Illumina HiSeq. Paired reads will be mapped to build 37 of the human reference genome to facilitate the characterisation of known gene mutations in cancer as well as the validation of potentially novel variants identified by prior exome sequencing.

Type: Cancer Genomics
Archiver: EGA European Genome-Phenome Archive

2 Datasets 3 Publications

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001000747	Genomic libraries will be generated from total genomic DNA derived from 4000 samples with Acute Myeloid Leukaemia. Libraries will be enriched for a selected panel of genes using a bespoke pulldown protocol. 64 Samples will be individually barcoded and subjected to up to one lanes of Illumina HiSeq. Paired reads will be mapped to build 37 of the human reference genome to facilitate the characterisation of known gene mutations in cancer as well as the validation of potentially novel variants identified by prior exome sequencing.	Illumina HiSeq 2000	2734
EGAD00001003703	The incidence of acute myeloid leukemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 60. Only 10-15% of cases evolve from a pre-existing myeloproliferative or myelodysplastic disorder; the remaining cases arise de novo without a detectable prodrome and are diagnosed upon development of bone marrow failure. Analysis of diagnostic blood samples has demonstrated that de novo AML is preceded by the accumulation of somatic mutations in pre-leukemic hematopoietic stem and progenitor cells (preL-HSPCs) that subsequently undergo clonal expansion. If individuals in this pre-leukemic phase could be identified, methods for determination of risk and monitoring for progression to overt AML could be developed. However recurrent AML mutations also accumulate during aging in healthy individuals who never develop AML, referred to as age related clonal hematopoiesis (ARCH). To distinguish individuals with preL-HSPCs at high risk of developing AML from those with ARCH, we undertook deep targeted sequencing of genes recurrently mutated in AML in blood samples from 133 individuals in the European Prospective Investigation into Cancer and Nutrition (EPIC) study taken on average 6 years before they developed AML (pre-AML group), together with 683 matched healthy individuals (Control group). Pre-AML cases displayed accelerated age-correlated accumulation of somatic mutations.The identity, number and variant allele frequency (VAF) of mutations differed between the two groups, and were incorporated into a computational model of AML risk prediction that accurately distinguished pre-AML cases from controls on average 7 years prior to AML development. Our findings provide proof of concept that early prediction of AML development is feasible in high-risk populations, paving the way for early disease detection, monitoring, and potentially prevention.	Illumina HiSeq 2000 Illumina HiSeq 2500	628

Publications	Citations
Prediction of acute myeloid leukaemia risk in healthy individuals. 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 KT, Vermeulen R, Campbell PJ, Papaemmanuil E, Minden MD, Tanay A, Balicer RD, Wareham NJ, Gerstung M, Dick JE, Brennan P, Vassiliou GS, Shlush LI. Nature 559: 2018 400-404	330
Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing. Abelson S, Zeng AGX, Nofech-Mozes I, Wang TT, Ng SWK, Minden MD, Pugh TJ, Awadalla P, Shlush LI, Murphy T, Chan SM, Dick JE, Bratman SV. Sci Adv 6: 2020 eabe3722	5
Unified classification and risk-stratification in Acute Myeloid Leukemia. Tazi Y, Arango-Ossa JE, Zhou Y, Bernard E, Thomas I, Gilkes A, Freeman S, Pradat Y, Johnson SJ, Hills R, Dillon R, Levine MF, Leongamornlert D, Butler A, Ganser A, Bullinger L, Döhner K, Ottmann O, Adams R, Döhner H, Campbell PJ, Burnett AK, Dennis M, Russell NH, Devlin SM, Huntly BJP, Papaemmanuil E. Nat Commun 13: 2022 4622	14