The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here we report initial findings from the Beat AML programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response. Collectively, we have generated a dataset—accessible through the Beat AML data viewer (Vizome)—that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML.
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All raw and processed sequencing data, along with relevant clinical annotations, have been submitted to dbGaP and Genomic Data Commons. The dbGaP study ID is 30641 and accession ID is phs001657.v1.p1. The raw data for clinical annotations, variant calls, gene expression counts and drug sensitivity that underlie Figs. 1–3 and Extended Data Figs. 1–9 are provided as Source Data. In addition, all data can be accessed and queried through our online, interactive user interface, Vizome, at http://www.vizome.org/.
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We thank all of our patients at all sites for donating precious time and tissue. DNA and RNA quality assessments, library creation and short read sequencing assays were performed by the OHSU Massively Parallel Sequencing Shared Resource. S. Sheela, C. Lai, K. Lindblad and K. Oetjen helped with study coordination at NIH. B. Sawicki and C. Cline helped with study coordination at the University of Florida. S. Ravencroft helped with patient sample shipping and data entry and K. Schorno provided project management and support of activities at the University of Kansas Cancer Center. J. Taw helped with patient sample shipping and S. Patel helped with data entry at Stanford University. Funding for this project was provided, in part, by a Therapy Acceleration Grant to B.J.D. and J.W.T. from The Leukemia & Lymphoma Society and by support provided by the Knight Cancer Research Institute (Oregon Health & Science University, OHSU). Supported by grants from the National Cancer Institute (1U01CA217862, 1U54CA224019, 1U01CA214116, 3P30CA069533-18S5) and NIH/NCATS CTSA UL1TR002369 (S.K.M., B.W.). A.S.B. was supported by the National Library of Medicine Informatics Training Grant (T15LM007088). J.W.T. received grants from the V Foundation for Cancer Research, the Gabrielle’s Angel Foundation for Cancer Research, and the National Cancer Institute (1R01CA183947). C.R.C. received a Scholar in Clinical Research Award from The Leukemia & Lymphoma Society (2400-13), was distinguished with a Pierre Chagnon Professorship in Stem Cell Biology and Blood & Marrow Transplant and a UF Research Foundation Professorship. This work was supported in part by the Intramural Research Program of the National Heart, Lung, and Blood Institute of the National Institutes of Health.
Nature thanks P. Campbell and the other anonymous reviewer(s) for their contribution to the peer review of this work.
J.W.T. receives research support from Agios, Aptose, Array, AstraZeneca, Constellation, Genentech, Gilead, Incyte, Janssen, Seattle Genetics, Syros and Takeda, and is a co-founder of Vivid Biosciences. M.W.D. serves on the advisory boards of and/or as a consultant for Novartis, Incyte and BMS, and receives research funding from BMS and Gilead. C.S.H. receives research funding from Merck. T.L.L. consults for Jazz Pharmaceuticals and receives research funding from Tolero, Gilead, Precient, Ono, Bio-Path, Mateon, Genentech/Roche, Trovagene, Abbvie, Pfizer, Celgene, Imago, Astellas, Karyopharm, Seattle Genetics and Incyte. D.A.P. receives research funding from Pfizer and Agios and served on the advisory boards of Pfizer, Celyad, Agios, Celgene, AbbVie, Argenx, Takeda and Servier. B.J.D. serves on the advisory boards of Gilead, Aptose, and Blueprint Medicines and is a principal investigator or coinvestigator on Novartis and BMS clinical trials. The Oregon Health & Science University (on behalf of B.J.D.) has contracts with these companies to pay for patient costs, nurse and data manager salaries and institutional overhead. B.J.D. does not derive salary, nor does his laboratory receive funds from these contracts. The authors certify that all compounds tested in this study were chosen without input from any of the industry partners.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Genomic landscape of the Beat AML cohort.
In total, 622 specimens from 531 patients were used for whole-exome sequencing. Automated and manual curation steps (described in the Methods, Supplementary Information and at http://vizome.org/additional_figures_BeatAML.html) were used to obtain a final set of high-confidence variants (annotated in Supplementary Table 7) and the earliest sample for each individual patient was used in this analysis. Clinical cytogenetics and gene fusion calls from RNA sequencing were used to curate recurrent gene rearrangements (Supplementary Information). The mutational profile for each patient is shown for frequency-ranked mutational events (top) and frequency-ranked gene rearrangements (bottom). The mosaic plot is annotated with clinical features of each case, such as diagnosis or relapse and de novo or transformed disease states, and the first bar chart on the right summarizes the cohort frequencies of mutational and gene rearrangement events. The last bar chart on the right summarizes the frequency of significant drug–mutation associations for the given gene across the cohort with drug sensitivity displayed in red and drug resistance displayed in blue. Eleven genes that have not previously been reported to be somatically mutated in cancer were observed with mutations at approximately 1% cohort frequency: CUB and Sushi multiple domains 2 (CSMD2), NAC alpha domain containing (NACAD), teneurin transmembrane protein 2 (TENM2), aggrecan (ACAN), ADAM metallopeptidase with thrombospondin type 1 motif 7 (ADAMTS7), immunoglobulin-like and fibronectin type III domain containing 1 (IGFN1), neurobeachin-like 2 (NBEAL2), poly(U) binding splicing factor 60 (PUF60), zinc-finger protein 687 (ZNF687), cadherin EGF LAG seven-pass G-type receptor 2 (CELSR2) and glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B). For the number of samples used to correlate each drug with mutations, see Supplementary Table 17.
Extended Data Fig. 2 Transcriptomic landscape of the Beat AML cohort.
In total, 451 specimens from 411 patients with AML were used for RNA-sequencing analyses. The 2,000 genes with the greatest differential expression across these patients with AML are displayed as a heat map. The heat map is annotated with disease type, ELN risk stratification groups, and genetic and cytogenetic features of disease as indicated in the key.
Extended Data Fig. 3 Functional drug sensitivity landscape of the Beat AML cohort.
In total, 409 specimens from 363 patients with AML were subjected to an ex vivo drug sensitivity assay, in which freshly isolated mononuclear cells from blood or bone marrow of patient specimens were incubated with graded concentrations of 122 small-molecule inhibitors (seven dose points in addition to the no drug control). Probit curve fits were used to compute drug-response metrics, and the z score of area under the dose–response curve is plotted for each individual patient specimen against each drug. Drug sensitivity (blue) and resistance (red) are annotated by a colour gradient, with grey indicating no drug data available. The heat map is annotated at the top and bottom with major clinical, cytogenetic and genetic features of disease as indicated in the key.
Extended Data Fig. 4 Drug response in de novo versus transformed AML cases.
The average inhibitor response AUCs for all cases that were de novo (n = 288) versus all cases that transformed from a background of myelodysplastic syndromes (n = 111) were compared for every inhibitor that had at least three cases with evaluable data in each group. The middle point represents the average difference in AUC between the two groups with the bars representing the 95% confidence interval. For the sample size and statistical results of each drug–sample group correlation, see Supplementary Table 20.
Extended Data Fig. 5 Pairwise drug sensitivity correlations and association with drug family.
To understand patterns of small-molecule sensitivity against prior annotations of the gene and pathway targets of each drug, drugs were placed into drug families according to target genes and/or pathways and the Pearson’s correlation value of each drug was plotted onto a clustered heat map, showing drugs with similar or dissimilar patterns of sensitivity across the patient cohort. Annotations based on prior knowledge of the drug families to which each drug could be assigned are shown to the right of the heat map with alternating black and grey boxes and labels used to aid in tracking. Descriptions of each drug family as well as the number of samples used to calculate each pairwise drug correlation are found in Supplementary Tables 11, 21.
Extended Data Fig. 6 Binary drug response calls and correlation with clinical subsets.
a, For the intersect of every specimen with evaluable response data for each inhibitor, we created a threshold for binary sensitive or resistant calls based on whether the individual specimen response fell within the most sensitive 20% of all specimens tested against that drug. A matrix plot showing the unsupervised clustering of the binary calls can be found at http://vizome.org/additional_figures_BeatAML.html. The binary drug-resistance calls for each specimen were combined into a single value, representing the proportion of drugs to which an individual specimen was sensitive (left) or resistant (right). Specimens were divided into ‘Favourable’ and ‘Adverse’ groups based on ELN 2017 criteria to determine whether overall drug sensitivity or resistance correlated with prognostic features of disease (n = 233 patients). b, The binary drug-resistance calls for each specimen as in a. Specimens were divided into diagnostic groups based on WHO 2016 categories to determine whether overall drug sensitivity or resistance correlated with cytogenetic or morphologic features of disease (n = 340 patients). a, b, The top and bottom points of the box plots show 1.5 times the interquartile range (IQR) from the upper and lower lines; the top, middle and bottom lines indicate the 75th, median and 25th percentile of the data with the notches extending 1.58 × IQR/(√(n)). Specific sample sizes of each group are reported in Supplementary Table 22.
Extended Data Fig. 7 Integration of genetic events with drug sensitivity.
a, Circos plot showing AML rearrangements in the centre, mutational events in the next concentric ring, and gene expression events in the outer ring. The size and width indicates frequency of the event and the FDR-corrected Q value of association with drug sensitivity is colour-coded (sensitivity (red); resistance (blue)). For each gene, tests involving expression were two-sided Student’s t-tests (linear model) of the differences between sensitive and resistant samples. For mutational events, the average difference in AUC between mutant and wild-type samples was determined using two-sided Student’s t-tests from a linear model as shown in Fig. 2a. FDR was computed using the Benjamini–Hochberg method over all the drugs. The number of samples used to correlate each mutational event with drug sensitivity is reported in Supplementary Table 17. b, As in Fig. 2a, the average difference in AUC drug response between mutant and wild-type cases was determined using a two-sided Student’s t-test from a linear model fit (plotted on the x axis and the FDR-corrected Q value is plotted on the y axis). This analysis shows only FLT3-ITD-negative cases. FDR was computed using the Benjamini–Hochberg method over all the drugs. The number of samples used to correlate each mutational event with drug sensitivity is reported in Supplementary Table 17. Expanded and interactive plots are available in our online data browser (http://www.vizome.org/ and http://vizome.org/additional_figures_BeatAML.html).
Extended Data Fig. 8 Integration of drug sensitivity with genetic events.
Correlation between drug sensitivity and mutational events. The average difference in AUC drug response between mutant and wild-type cases was determined using a two-sided Student’s t-test from a linear model fit. FDR was computed using the Benjamini–Hochberg method over all the drugs. The degree of significance is represented on the y axis (sensitivity (red); resistance (blue)). The number of samples used to correlate each mutational event with drug sensitivity is reported in Supplementary Table 17.
Extended Data Fig. 9 Functional drug sensitivity landscape of the Beat AML cohort.
a, Co-occurrences with regard to WGCNA gene expression clusters and/or mutational events (coefficients) were detected by multivariate modelling with respect to ibrutinib response (resistance (blue); sensitivity (red)) and the degree of correlation is shown in the stacked bar plot (top). All coefficients that appear in 25% of the bootstrapped sample sets are shown as segments of the circle. Segment width (the coloured ring) corresponds to the percentage of bootstrapped samples in which that coefficient appears (quantified above the dotted line). The variables appear in descending order clockwise starting at 12 o’clock. Each link indicates pairwise co-occurrence of mutational events and gene expression clusters (width represents frequency of the co-occurrence). The largest co-occurrence for each coefficient is quantified. b, The capacity of differential gene expression to distinguish the 20% most ibrutinib-sensitive (n = 46) from 20% most resistant (n = 44) specimens is shown on a principal component plot (n = 239 patient samples were tested for ibrutinib sensitivity and RNA sequencing). For the number of samples used to correlate each drug with gene expression and perform LASSO regression, see Supplementary Table 17.
This file contains a guide to Supplementary Tables 1 – 24.
This file contains Supplementary Tables 1- 24 – refer to Supplementary Information document for full guide.
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Tyner, J.W., Tognon, C.E., Bottomly, D. et al. Functional genomic landscape of acute myeloid leukaemia. Nature 562, 526–531 (2018). https://doi.org/10.1038/s41586-018-0623-z
- Whole Exome Sequencing
- Fms-related Tyrosine Kinase (FLT3)
- BCL6 Corepressor (BCOR)
- Weighted Gene Correlation Network Analysis (WGCNA)
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