Article | Published:

Functional genomic landscape of acute myeloid leukaemia

Naturevolume 562pages526531 (2018) | Download Citation

Abstract

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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Data availability

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. 13 and Extended Data Figs. 19 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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References

  1. 1.

    Jemal, A., Siegel, R., Xu, J. & Ward, E. Cancer statistics, 2010. CA Cancer J. Clin. 60, 277–300 (2010).

  2. 2.

    SEER. Cancer stat facts: leukemia — acute myeloid leukemia (AML). National Cancer Institute https://seer.cancer.gov/statfacts/html/amyl.html (2018).

  3. 3.

    Papaemmanuil, E. et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209–2221 (2016).

  4. 4.

    Arber, D. A. et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 127, 2391–2405 (2016).

  5. 5.

    Döhner, H. et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 129, 424–447 (2017).

  6. 6.

    The Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  7. 7.

    Byrd, J. C. et al. Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461). Blood 100, 4325–4336 (2002).

  8. 8.

    Patel, J. P. et al. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N. Engl. J. Med. 366, 1079–1089 (2012).

  9. 9.

    Haferlach, T. et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia 28, 241–247 (2014).

  10. 10.

    Lundberg, P. et al. Clonal evolution and clinical correlates of somatic mutations in myeloproliferative neoplasms. Blood 123, 2220–2228 (2014).

  11. 11.

    Deininger, M. W. N., Tyner, J. W. & Solary, E. Turning the tide in myelodysplastic/myeloproliferative neoplasms. Nat. Rev. Cancer 17, 425–440 (2017).

  12. 12.

    Busque, L. et al. Recurrent somatic TET2 mutations in normal elderly individuals with clonal hematopoiesis. Nat. Genet. 44, 1179–1181 (2012).

  13. 13.

    Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).

  14. 14.

    Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).

  15. 15.

    Xie, M. et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat. Med. 20, 1472–1478 (2014).

  16. 16.

    Huang, M. E. et al. Use of all-trans retinoic acid in the treatment of acute promyelocytic leukemia. Blood 72, 567–572 (1988).

  17. 17.

    Shen, Z. X. et al. Use of arsenic trioxide (As2O3) in the treatment of acute promyelocytic leukemia (APL): II. Clinical efficacy and pharmacokinetics in relapsed patients. Blood 89, 3354–3360 (1997).

  18. 18.

    Nakao, M. et al. Internal tandem duplication of the FLT3 gene found in acute myeloid leukemia. Leukemia 10, 1911–1918 (1996).

  19. 19.

    Tse, K. F., Mukherjee, G. & Small, D. Constitutive activation of FLT3 stimulates multiple intracellular signal transducers and results in transformation. Leukemia 14, 1766–1776 (2000).

  20. 20.

    Yamamoto, Y. et al. Activating mutation of D835 within the activation loop of FLT3 in human hematologic malignancies. Blood 97, 2434–2439 (2001).

  21. 21.

    Yokota, S. et al. Internal tandem duplication of the FLT3 gene is preferentially seen in acute myeloid leukemia and myelodysplastic syndrome among various hematological malignancies. A study on a large series of patients and cell lines. Leukemia 11, 1605–1609 (1997).

  22. 22.

    Knapper, S. et al. A phase 2 trial of the FLT3 inhibitor lestaurtinib (CEP701) as first-line treatment for older patients with acute myeloid leukemia not considered fit for intensive chemotherapy. Blood 108, 3262–3270 (2006).

  23. 23.

    O’Farrell, A. M. et al. An innovative phase I clinical study demonstrates inhibition of FLT3 phosphorylation by SU11248 in acute myeloid leukemia patients. Clin. Cancer Res. 9, 5465–5476 (2003).

  24. 24.

    Smith, B. D. et al. Single-agent CEP-701, a novel FLT3 inhibitor, shows biologic and clinical activity in patients with relapsed or refractory acute myeloid leukemia. Blood 103, 3669–3676 (2004).

  25. 25.

    DeAngelo, D. J. et al. Phase 1 clinical results with tandutinib (MLN518), a novel FLT3 antagonist, in patients with acute myelogenous leukemia or high-risk myelodysplastic syndrome: safety, pharmacokinetics, and pharmacodynamics. Blood 108, 3674–3681 (2006).

  26. 26.

    Stone, R. M. et al. Midostaurin plus chemotherapy for acute myeloid leukemia with a FLT3 mutation. N. Engl. J. Med. 377, 454–464 (2017).

  27. 27.

    Mardis, E. R. et al. Recurring mutations found by sequencing an acute myeloid leukemia genome. N. Engl. J. Med. 361, 1058–1066 (2009).

  28. 28.

    Wang, F. et al. Targeted inhibition of mutant IDH2 in leukemia cells induces cellular differentiation. Science 340, 622–626 (2013).

  29. 29.

    Rohle, D. et al. An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells. Science 340, 626–630 (2013).

  30. 30.

    Fiskus, W. et al. Combined epigenetic therapy with the histone methyltransferase EZH2 inhibitor 3-deazaneplanocin A and the histone deacetylase inhibitor panobinostat against human AML cells. Blood 114, 2733–2743 (2009).

  31. 31.

    Schenk, T. et al. Inhibition of the LSD1 (KDM1A) demethylase reactivates the all-trans-retinoic acid differentiation pathway in acute myeloid leukemia. Nat. Med. 18, 605–611 (2012).

  32. 32.

    Daigle, S. R. et al. Selective killing of mixed lineage leukemia cells by a potent small-molecule DOT1L inhibitor. Cancer Cell 20, 53–65 (2011).

  33. 33.

    Itzykson, R. et al. Impact of TET2 mutations on response rate to azacitidine in myelodysplastic syndromes and low blast count acute myeloid leukemias. Leukemia 25, 1147–1152 (2011).

  34. 34.

    Welch, J. S. et al. TP53 and decitabine in acute myeloid leukemia and myelodysplastic syndromes. N. Engl. J. Med. 375, 2023–2036 (2016).

  35. 35.

    Konopleva, M. et al. Efficacy and biological correlates of response in a phase II study of venetoclax monotherapy in patients with acute myelogenous leukemia. Cancer Discov. 6, 1106–1117 (2016).

  36. 36.

    DiNardo, C. D. et al. Safety and preliminary efficacy of venetoclax with decitabine or azacitidine in elderly patients with previously untreated acute myeloid leukaemia: a non-randomised, open-label, phase 1b study. Lancet Oncol. 19, 216–228 (2018).

  37. 37.

    Tyner, J. W. et al. Kinase pathway dependence in primary human leukemias determined by rapid inhibitor screening. Cancer Res. 73, 285–296 (2013).

  38. 38.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

  39. 39.

    Puissant, A. et al. SYK is a critical regulator of FLT3 in acute myeloid leukemia. Cancer Cell 25, 226–242 (2014).

  40. 40.

    Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B 58, 267–288 (1996).

  41. 41.

    Canisius, S., Martens, J. W. M. & Wessels, L. F. A. A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome Biol. 17, 261 (2016).

  42. 42.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  43. 43.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  44. 44.

    Huntley, M. A. et al. ReportingTools: an automated result processing and presentation toolkit for high-throughput genomic analyses. Bioinformatics 29, 3220–3221 (2013).

  45. 45.

    Buffalo, V. qrqc: Quick Read Quality Control. R package version 1.22.0 http://github.com/vsbuffalo/qrqc (2012).

  46. 46.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

  47. 47.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

  48. 48.

    McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).

  49. 49.

    Memorial Sloan Kettering. vcf2maf. version 1.6.6 https://github.com/mskcc/vcf2maf/ (2016).

  50. 50.

    Koboldt, D. Release note for Varscan version 2.4.1. https://github.com/dkoboldt/varscan/blob/master/VarScan.v2.4.1.description.txt (2015).

  51. 51.

    Memorial Sloan Kettering. maf2vcf. version 1.6.6 https://github.com/mskcc/vcf2maf/ (2016).

  52. 52.

    Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).

  53. 53.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  54. 54.

    Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).

  55. 55.

    Costello, M. et al. Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation. Nucleic Acids Res. 41, e67 (2013).

  56. 56.

    Kottaridis, P. D. et al. The presence of a FLT3 internal tandem duplication in patients with acute myeloid leukemia (AML) adds important prognostic information to cytogenetic risk group and response to the first cycle of chemotherapy: analysis of 854 patients from the United Kingdom Medical Research Council AML 10 and 12 trials. Blood 98, 1752–1759 (2001).

  57. 57.

    Döhner, K. et al. Mutant nucleophosmin (NPM1) predicts favorable prognosis in younger adults with acute myeloid leukemia and normal cytogenetics: interaction with other gene mutations. Blood 106, 3740–3746 (2005).

  58. 58.

    Falini, B., Nicoletti, I., Martelli, M. F. & Mecucci, C. Acute myeloid leukemia carrying cytoplasmic/mutated nucleophosmin (NPMc+ AML): biologic and clinical features. Blood 109, 874–885 (2007).

  59. 59.

    Huang, Q. et al. A rapid, one step assay for simultaneous detection of FLT3/ITD and NPM1 mutations in AML with normal cytogenetics. Br. J. Haematol. 142, 489–492 (2008).

  60. 60.

    Wouters, B. J. et al. Double CEBPA mutations, but not single CEBPA mutations, define a subgroup of acute myeloid leukemia with a distinctive gene expression profile that is uniquely associated with a favorable outcome. Blood 113, 3088–3091 (2009).

  61. 61.

    Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

  62. 62.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

  63. 63.

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

  64. 64.

    Hansen, K. D., Irizarry, R. A. & Wu, Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13, 204–216 (2012).

  65. 65.

    Kim, D. & Salzberg, S. L. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 12, R72 (2011).

  66. 66.

    Langfelder, P. & Horvath, S. Fast R functions for robust correlations and hierarchical clustering. J. Stat. Softw. 46, 1–17 (2012).

  67. 67.

    Langfelder, P., Luo, R., Oldham, M. C. & Horvath, S. Is my network module preserved and reproducible? PLOS Comput. Biol. 7, e1001057 (2011).

  68. 68.

    Parsana, P. et al. Addressing confounding artifacts in reconstruction of gene co-expression networks. Preprint at https://www.biorxiv.org/content/early/2017/10/13/202903 (2017).

  69. 69.

    Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).

  70. 70.

    The International HapMap Consortium. The International HapMap Project. Nature 426, 789–796 (2003).

  71. 71.

    Zheng, X. & Weir, B. S. Eigenanalysis of SNP data with an identity by descent interpretation. Theor. Popul. Biol. 107, 65–76 (2016).

  72. 72.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

  73. 73.

    Slovak, M. L., Theisen, A. & Shaffer, L. G. in The Principles of Clinical Cytogenetics (eds Gersen, S. L. & Keagle, M. B.) 23–49 (Springer, New York, 2013).

  74. 74.

    Kurtz, S. E. et al. Molecularly targeted drug combinations demonstrate selective effectiveness for myeloid- and lymphoid-derived hematologic malignancies. Proc. Natl Acad. Sci. USA 114, E7554–E7563 (2017).

  75. 75.

    Davis, M. I. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046–1051 (2011).

  76. 76.

    Blucher, A. S., Choonoo, G., Kulesz-Martin, M., Wu, G. & McWeeney, S. K. Evidence-based precision oncology with the cancer targetome. Trends Pharmacol. Sci. 38, 1085–1099 (2017).

  77. 77.

    Gu, Z., Eils, R. & Schlesner, M. Complex heat maps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

  78. 78.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  79. 79.

    Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, e161 (2007).

  80. 80.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

  81. 81.

    Parker, H. S., Corrada Bravo, H. & Leek, J. T. Removing batch effects for prediction problems with frozen surrogate variable analysis. PeerJ 2, e561 (2014).

  82. 82.

    Fraley, C. & Raftery, A. E. Enhanced model-based clustering, density estimation, and discriminant analysis software: MCLUST. J. Classif. 20, 263–286 (2003).

  83. 83.

    Pison, G., Struyf, A. & Rousseeuw, P. J. Displaying a clustering with CLUSPLOT. Comput. Stat. Data Anal. 30, 381–392 (1999).

  84. 84.

    Wei, T. et al. corrplot: Visualization of a Correlation Matrix. R package version 0.84 https://github.com/taiyun/corrplot (2017).

  85. 85.

    Friedman, J., Hastie, T. & Tibshirani, R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J. Stat. Softw. 33, 1–22 (2010).

  86. 86.

    Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).

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Acknowledgements

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.

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Nature thanks P. Campbell and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Affiliations

  1. Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR, USA

    • Jeffrey W. Tyner
    • , Samantha L. Savage
    • , Anna Reister Schultz
    • , Melissa Abel
    • , Jade Bryant
    • , Amanda d’Almeida
    • , David K. Edwards V
    • , Jason MacManiman
    • , Jacqueline Martinez
    • , Angela Rofelty
    •  & Haijiao Zhang
  2. Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA

    • Jeffrey W. Tyner
    • , Cristina E. Tognon
    • , Daniel Bottomly
    • , Beth Wilmot
    • , Stephen E. Kurtz
    • , Samantha L. Savage
    • , Nicola Long
    • , Anna Reister Schultz
    • , Elie Traer
    • , Melissa Abel
    • , Anupriya Agarwal
    • , Aurora Blucher
    • , Uma Borate
    • , Jade Bryant
    • , Russell Burke
    • , Amy Carlos
    • , Richie Carpenter
    • , Joseph Carroll
    • , Bill H. Chang
    • , Cody Coblentz
    • , Amanda d’Almeida
    • , Rachel Cook
    • , Alexey Danilov
    • , Kim-Hien T. Dao
    • , Michie Degnin
    • , Deirdre Devine
    • , James Dibb
    • , David K. Edwards V
    • , Christopher A. Eide
    • , Isabel English
    • , Jason Glover
    • , Rachel Henson
    • , Hibery Ho
    • , Abdusebur Jemal
    • , Kara Johnson
    • , Ryan Johnson
    • , Brian Junio
    • , Andy Kaempf
    • , Jessica Leonard
    • , Chenwei Lin
    • , Selina Qiuying Liu
    • , Pierrette Lo
    • , Marc M. Loriaux
    • , Samuel Luty
    • , Tara Macey
    • , Jason MacManiman
    • , Jacqueline Martinez
    • , Motomi Mori
    • , Ceilidh Nichols
    • , Jill Peters
    • , Angela Rofelty
    • , Robert Searles
    • , Erik Segerdell
    • , Rebecca L. Smith
    • , Stephen E. Spurgeon
    • , Tyler Sweeney
    • , Aashis Thapa
    • , Corinne Visser
    • , Jake Wagner
    • , Kevin Watanabe-Smith
    • , Kristen Werth
    • , Joelle Wolf
    • , Libbey White
    • , Haijiao Zhang
    • , Shannon K. McWeeney
    •  & Brian J. Druker
  3. Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA

    • Cristina E. Tognon
    • , Stephen E. Kurtz
    • , Nicola Long
    • , Elie Traer
    • , Uma Borate
    • , Russell Burke
    • , Richie Carpenter
    • , Cody Coblentz
    • , Rachel Cook
    • , Alexey Danilov
    • , Kim-Hien T. Dao
    • , Michie Degnin
    • , Deirdre Devine
    • , James Dibb
    • , Christopher A. Eide
    • , Isabel English
    • , Hibery Ho
    • , Kara Johnson
    • , Ryan Johnson
    • , Brian Junio
    • , Jessica Leonard
    • , Selina Qiuying Liu
    • , Pierrette Lo
    • , Samuel Luty
    • , Tara Macey
    • , Ceilidh Nichols
    • , Jill Peters
    • , Rebecca L. Smith
    • , Stephen E. Spurgeon
    • , Tyler Sweeney
    • , Aashis Thapa
    • , Corinne Visser
    • , Jake Wagner
    • , Kevin Watanabe-Smith
    • , Kristen Werth
    •  & Brian J. Druker
  4. Howard Hughes Medical Institute, Portland, OR, USA

    • Cristina E. Tognon
    • , Christopher A. Eide
    •  & Brian J. Druker
  5. Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA

    • Daniel Bottomly
    • , Beth Wilmot
    • , Aurora Blucher
    • , Justin Ramsdill
    • , Robert Schuff
    • , Erik Segerdell
    • , Libbey White
    • , Amy Yates
    •  & Shannon K. McWeeney
  6. Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR, USA

    • Beth Wilmot
    • , Justin Ramsdill
    • , Robert Schuff
    • , Amy Yates
    •  & Shannon K. McWeeney
  7. Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA

    • Anupriya Agarwal
  8. Integrated Genomics Laboratories, Oregon Health & Science University, Portland, OR, USA

    • Amy Carlos
    • , Rachel Henson
    • , Chenwei Lin
    •  & Robert Searles
  9. Technology Transfer & Business Development, Oregon Health & Science University, Portland, OR, USA

    • Joseph Carroll
  10. Division of Hematology and Oncology, Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA

    • Bill H. Chang
    • , Jason Glover
    • , Abdusebur Jemal
    •  & Joelle Wolf
  11. Biostatistics Shared Resource, Oregon Health & Science University, Portland, OR, USA

    • Andy Kaempf
    •  & Motomi Mori
  12. Dapartment of Pathology, Oregon Health & Science University, Portland, OR, USA

    • Marc M. Loriaux
  13. Oregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA

    • Motomi Mori
  14. High-Throughput Screening Services Laboratory, Oregon State University, Corvalis, OR, USA

    • Dylan Nelson
  15. Department of Medicine, Division of Hematology and Oncology, University of Florida, Gainesville, FL, USA

    • Christopher R. Cogle
    •  & Leylah Drusbosky
  16. Department of Internal Medicine/Hematology Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    • Robert H. Collins
  17. Molecular Therapeutics Program, Fox Chase Cancer Center, Philadelphia, PA, USA

    • Denise C. Connolly
  18. Fox Chase Cancer Center Biosample Repository Facility, Philadelphia, PA, USA

    • Denise C. Connolly
    •  & Ryan M. Winters
  19. Division of Hematology & Hematologic Malignancies, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA

    • Michael W. Deininger
  20. National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    • Christopher S. Hourigan
  21. Division of Hematology, University of Colorado, Denver, CO, USA

    • Craig T. Jordan
    •  & Daniel A. Pollyea
  22. Bone Marrow Transplant Program, Fox Chase Cancer Center, Philadelphia, PA, USA

    • Patricia Kropf
  23. Division of Hematologic Malignancies & Cellular Therapeutics, University of Kansas, Kansas City, KS, USA

    • Tara L. Lin
  24. Clinical Research Services, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL, USA

    • Micaela E. Martinez
    •  & Rachel R. Pallapati
  25. Department of Medicine—Hematology, Stanford University, Stanford, CA, USA

    • Bruno C. Medeiros
  26. Department of Hematology, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL, USA

    • Ronan T. Swords
    •  & Justin M. Watts
  27. Department of Toxicology, Pharmacology and Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA

    • Scott J. Weir
  28. Department of Medicine, Division of Medical Oncology, University of Kansas Medical Center, Kansas City, KS, USA

    • Scott J. Weir
  29. Blood Cell Development and Function Program, Fox Chase Cancer Center, Philadelphia, PA, USA

    • David L. Wiest

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Contributions

J.W.T., C.E.T., D.B., B.W., S.E.K., S.L.S., N.L., A.R.S., E.T., S.K.M. and B.J.D. contributed equally to this work. J.W.T., C.E.T., S.K.M., A.A. and B.J.D. provided project oversight for experimental design, data management, data analysis and interpretation, methods development and development of the Vizome platform; B.W. conceived the Vizome platform, provided oversight for development of the platform and helped to provide project oversight for experimental design, workflow development, data processing, management, analysis and dissemination, and methods development; E.T. acquired patient samples, and structured, collected and analysed clinical data; S.L.S., A.R.S., M.A., I.E. and A.R. helped with processing of patient samples, ex vivo drug screening, DNA/RNA extractions and submissions of sequencing samples; N.L., R.Ca., J.D. and C.V. curated and annotated the clinical data of patients; S.E.K. provided variant confirmation and analysed data; D.B. led the workflow development for pre-processing and analysis of RNA sequencing, exome sequencing and ex vivo drug screening data, multivariate modelling and integration and helped with clinical data curation and integration, methods development and the back-end development of the Vizome platform, including the integration of Hitwalker; L.W. wrote all of the software for the Vizome platform, developed the platform as well as novel visualizations for data integration and display; J.R., R.Sc. and A.Y. provided clinical data integration from OHSU Research Data Warehouse; P.K. helped with the recruitment of patients and collection of samples for analysis; C.R.C. and R.T.S. were co-investigators for the repository protocol, and edited and provided feedback on the manuscript; L.D., C.T.J. and D.A.P. collected samples for the repository protocol and provided feedback on the manuscript; M.E.M. and R.R.P. consented patients and collected samples for the repository protocol and aided with clinical annotation; D.N. helped with the creation of drug-screening replicate plates; C.A.E., K.W.-S. and H.Z. helped with data analysis; D.K.E. analysed and curated data and developed analytical processes; A.K. and M.M. helped with the development of the ex vivo drug screening processing workflow; S.J.W. enabled, facilitated and mentored basic, translational and clinical research activities arising from data generated by Beat AML at the University of Kansas Cancer Center site, participated in the Beat AML Symposia to share research and create new research projects; A.C., R.H., C.L. and R.Se. helped with the creation of exome-sequencing and RNA-sequencing libraries and with sequencing and data processing; D.D., C.N. and J.P. helped with genomic isolation, curation of samples and entry of patient clinical annotations; E.S. helped with the whole-exome sequencing and RNA-sequencing data processing, data management and developed the data dissemination workflows; D.L.W. served as a liaison for sample acquisition; M.W.D. was a local principal investigator for the repository protocol, consented patients, collected samples for the repository protocol, aided with clinical annotation, and edited and provided feedback on the manuscript; R.M.W. served as a manager for their repository protocol, consented patients and collected samples for the repository, aided with clinical annotation, and edited and provided feedback on the manuscript; M.M.L. helped with patient sample acquisition, IRB protocol development and maintenance, clinical data structure, collection and analysis; U.M., B.H.C., R.Co., A.D., K.-H.T.D., J.L. and S.E.S. helped with the acquisition of patient samples; A.d’A., J.B., R.B., C.C., M.D., J.G., H.H., A.J., K.J., R.J., S.Q.L., S.L., J.Mac., J.Mar., R.L.S., T.S., A.T., J.Wa. and J.Wo. helped with patient sample processing and ex vivo drug screening; C.S.H. was a principal investigator for their local protocol; consented patients and collected samples for the repository protocol, aided with clinical annotation, and edited and provided feedback on the manuscript; J.M.W. was a principal investigator for their local repository protocol, and edited and provided feedback on the manuscript; B.C.M. was a principal investigator for their local repository protocol, consented patients and collected samples for the repository protocol, aided with clinical annotation, and edited and provided feedback on the manuscript; D.C.C., T.L.L. and R.H.C. were principal investigators for their local repository protocol, consented patients and collected samples for the repository protocol, aided with clinical annotation, and edited and provided feedback on the manuscript; T.M. provided regulatory oversight; B.J. and K.W. provided regulatory oversight, and helped with the curation and entry of clinical annotations of patients; A.B. provided targetome overlay; J.C. and P.L. helped with technology transfer and project development.

Competing interests

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.

Corresponding authors

Correspondence to Shannon K. McWeeney or Brian J. Druker.

Extended data figures and tables

  1. 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. Source Data

  2. 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. Source Data

  3. 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. Source Data

  4. 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. Source Data

  5. 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. Source Data

  6. 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. Source Data

  7. 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). Source Data

  8. 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. Source Data

  9. 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. Source Data

Supplementary information

  1. Supplementary Information

    This file contains a guide to Supplementary Tables 1 – 24.

  2. Reporting Summary

  3. Supplementary Tables

    This file contains Supplementary Tables 1- 24 – refer to Supplementary Information document for full guide.

Source data

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DOI

https://doi.org/10.1038/s41586-018-0623-z

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