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Acute myeloid leukemia

Data mining for mutation-specific targets in acute myeloid leukemia


Three mutation-specific targeted therapies have recently been approved by the FDA for the treatment of acute myeloid leukemia (AML): midostaurin for FLT3 mutations, enasidenib for relapsed or refractory cases with IDH2 mutations, and ivosidenib for cases with an IDH1 mutation. Together, these agents offer a mutation-directed treatment approach for up to 45% of de novo adult AML cases, a welcome deluge after a prolonged drought. At the same time, a number of computational tools have recently been developed that promise to further accelerate progress in mutation-specific therapy for AML and other cancers. Technical advances together with comprehensively annotated AML tissue banks have resulted in the availability of large and complex data sets for exploration by the end-user, including (i) microarray gene expression, (ii) exome sequencing, (iii) deep sequencing data of sub-clone heterogeneity, (iv) RNA sequencing of gene expression (bulk and single cell), (v) DNA methylation and chromatin, (vi) and germline quantitative trait loci. Yet few clinicians or experimental hematologists have the time or the training to access or analyze these repositories. This review summarizes the data sets and bioinformatic tools currently available to further the discovery of mutation-specific targets with an emphasis on web-based applications that are open, accessible, user-friendly, and do not require coding experience to navigate. We show examples of how available data can be mined to identify potential targets using synthetic lethality, drug repurposing, epigenetic sub-grouping, and proteomic networks while also highlighting strengths and limitations and the need for superior models for validation.

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Fig. 2


  1. 1.

    Khwaja A, Bjorkholm M, Gale RE, Levine RL, Jordan CT, Ehninger G, et al. Acute myeloid leukaemia. Nat Rev Dis Prim. 2016;2.

  2. 2.

    Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson G, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368:2059–74.

    Google Scholar 

  3. 3.

    Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016.

    CAS  Google Scholar 

  4. 4.

    Li H-Y, Deng D-H, Huang Y, Ye F-H, Huang L-L, Xiao Q, et al. Favorable prognosis of biallelic CEBPA gene mutations in acute myeloid leukemia patients: a meta-analysis. Eur J Haematol. 2015.

    CAS  PubMed  Google Scholar 

  5. 5.

    Valk PJ, Verhaak RG, Beijen MA, Erpelinck CAJ, van Doorn-Khosrovani BWW, Boer JM, et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med. 2004;350:1617–28.

    CAS  PubMed  Google Scholar 

  6. 6.

    Figueroa ME, Lugthart S, Li Y, Erpelinck-Verschueren C, Deng X, Christos PJ, et al. dna methylation signatures identify biologically distinct subtypes in acute myeloid leukemia. Cancer Cell. 2010;17:13–27.

    CAS  PubMed Central  PubMed  Google Scholar 

  7. 7.

    MacArthur DG, Manolio TA, Dimmock DP, Rehm HL, Shendure J, Abecasis GR, et al. Guidelines for investigating causality of sequence variants in human disease. Nature. 2014.

    CAS  PubMed Central  PubMed  Google Scholar 

  8. 8.

    O’Rawe J, Jiang T, Sun G, Wu Y, Wang W, Hu J, et al. Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 2013.

    CAS  PubMed Central  PubMed  Google Scholar 

  9. 9.

    Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–7.

    CAS  PubMed Central  PubMed  Google Scholar 

  10. 10.

    Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41.

    PubMed Central  PubMed  Google Scholar 

  11. 11.

    Cowley GS, Weir BA, Vazquez F, Tamayo P, Scott JA, Rusin S, et al. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci Data. 2014;1.

  12. 12.

    Rees MG, Seashore-Ludlow B, Cheah JH, Adams DJ, Price EV, Gill S, et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat Chem Biol. 2016;12:109–16.

    CAS  Google Scholar 

  13. 13.

    Seashore-Ludlow B, Rees MG, Cheah JH, Coko M, Price EV, Coletti ME, et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 2015;5:1210–23.

    CAS  PubMed Central  PubMed  Google Scholar 

  14. 14.

    Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell. 2013;154:1151–61.

    CAS  PubMed Central  PubMed  Google Scholar 

  15. 15.

    Chang K, Creighton CJ, Davis C, Donehower L, Drummond J, Wheeler D, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45:1113–20.

    CAS  Google Scholar 

  16. 16.

    Jerby-Arnon L, Pfetzer N, Waldman YY, McGarry L, James D, Shanks E, et al. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell. 2014;158:1199–209.

    CAS  Google Scholar 

  17. 17.

    Sinha S, Thomas D, Chan S, Gao Y, Brunen D, Torabi D, et al. Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data. Nat Commun. 2017;8.

  18. 18.

    Gerstung M, Papaemmanuil E, Martincorena I, Bullinger L, Gaidzik VI, Paschka P, et al. Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet. 2017;49:332–40.

    CAS  PubMed Central  PubMed  Google Scholar 

  19. 19.

    Dugas M, Schoch C, Schnittger S, Haferlach T, Danhauser-Riedl S, Hiddemann W, et al. A comprehensive leukemia database: integration of cytogenetics, molecular genetics and microarray data with clinical information, cytomorphology and immunophenotyping. Leukemia. 2001;15:1805–10.

    CAS  Google Scholar 

  20. 20.

    Parkinson H, Kapushesky M, Shojatalab M, Abeygunawardena N, Coulson R, Farne A, et al. ArrayExpress—a public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 2007;35:D747–50.

    CAS  Google Scholar 

  21. 21.

    Edgar R. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–10.

    CAS  PubMed Central  PubMed  Google Scholar 

  22. 22.

    Verhaak RGW, Wouters BJ, Erpelinck CAJ, Abbas S, Beverloo HB, Lugthart S, et al. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling. Haematologica. 2009;94:131–4.

    Google Scholar 

  23. 23.

    Hebestreit K, Gröttrup S, Emden D, Veerkamp J, Ruckert C, Klein HU, et al. Leukemia gene atlas—a public platform for integrative exploration of genome-wide molecular data. PLoS One. 2012;7.

    CAS  PubMed Central  PubMed  Google Scholar 

  24. 24.

    Bolouri H, Farrar JE, Triche T, Ries RE, Lim EL, Alonzo TA, et al. The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions. Nat Med. 2018.

    PubMed Central  PubMed  Google Scholar 

  25. 25.

    Lemieux S, Sargeant T, Laperrière D, Ismail H, Boucher G, Rozendaal M, et al. MiSTIC, an integrated platform for the analysis of heterogeneity in large tumour transcriptome datasets. Nucleic Acids Res. 2017;45.

    CAS  PubMed Central  PubMed  Google Scholar 

  26. 26.

    Bagger FO, Sasivarevic D, Sohi SH, Laursen LG, Pundhir S, Sønderby CK, et al. BloodSpot: a database of gene expression profiles and transcriptional programs for healthy and malignant haematopoiesis. Nucleic Acids Res. 2016.

    PubMed Central  PubMed  Google Scholar 

  27. 27.

    Behrens K, Maul K, Tekin N, Kriebitzsch N, Indenbirken D, Prassolov V, et al. RUNX1 cooperates with FLT3-ITD to induce leukemia. J Exp Med. 2017;214:737–52.

  28. 28.

    Zhang J, Baran J, Cros A, Guberman JM, Haider S, Hsu J, et al. International Cancer Genome Consortium data portal—a one-stop shop for cancer genomics data. Database. 2011.

    PubMed Central  PubMed  Google Scholar 

  29. 29.

    Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013.

    PubMed Central  PubMed  Google Scholar 

  30. 30.

    Bagger FO, Rapin N, Theilgaard-Mönch K, Kaczkowski B, Thoren LA, Jendholm J, et al. HemaExplorer: a database of mRNA expression profiles in normal and malignant haematopoiesis. Nucleic Acids Res. 2013.

    PubMed Central  PubMed  Google Scholar 

  31. 31.

    Novershtern N, Subramanian A, Lawton LN, Mak RH, Haining WN, McConkey ME, et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell. 2011.

    CAS  PubMed Central  PubMed  Google Scholar 

  32. 32.

    Miller JC, Brown BD, Shay T, Gautier EL, Jojic V, Cohain A, et al. Deciphering the transcriptional network of the dendritic cell lineage. Nat Immunol. 2012.

    CAS  PubMed Central  PubMed  Google Scholar 

  33. 33.

    Lee S-I, Celik S, Logsdon BA, Lundberg SM, Martins TJ, Oehler VG, et al. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nat Commun. 2018;9:42.

    PubMed Central  PubMed  Google Scholar 

  34. 34.

    Sinha S, Thomas D, Yu L, Gentles AJ, Jung N, Corces-Zimmerman MR, et al. Mutant WT1 is associated with DNA hypermethylation of PRC2 targets in AML and responds to EZH2 inhibition. Blood. 2015;125:316–26.

    CAS  PubMed Central  PubMed  Google Scholar 

  35. 35.

    Sahoo D, Dill DL, Tibshirani R, Plevritis SK. Extracting binary signals from microarray time-course data. Nucleic Acids Res. 2007.

    CAS  PubMed Central  PubMed  Google Scholar 

  36. 36.

    Brown AS, Kong SW, Kohane IS, Patel CJ. ksRepo: a generalized platform for computational drug repositioning. BMC Bioinformatics. 2016;17.

  37. 37.

    Davis AP, Grondin CJ, Johnson RJ, Sciaky D, King BL, McMorran R, et al. The Comparative Toxicogenomics Database: Update 2017. Nucleic Acids Res. 2017;45:D972–8.

    CAS  PubMed  Google Scholar 

  38. 38.

    Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27:29–34.

    CAS  PubMed Central  PubMed  Google Scholar 

  39. 39.

    Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med. 2011;3.

  40. 40.

    Rubio-Perez C, Tamborero D, Schroeder MP, Antolín AA, Deu-Pons J, Perez-Llamas C, et al. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell. 2015;27:382–96.

    CAS  Google Scholar 

  41. 41.

    Hintzsche J, Kim J, Yadav V, Amato C, Robinson SE, Seelenfreund E, et al. IMPACT: a whole-exome sequencing analysis pipeline for integrating molecular profiles with actionable therapeutics in clinical samples. J Am Med Informatics Assoc. 2016.

    PubMed Central  PubMed  Google Scholar 

  42. 42.

    Metzeler KH, Hummel M, Bloomfield CD, Spiekermann K, Braess J, Sauerland MC, et al. An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood. 2008.

    CAS  PubMed Central  PubMed  Google Scholar 

  43. 43.

    Wouters BJ, Löwenberg B, Erpelinck-Verschueren CAJ, Van Putten WLJ, Valk PJM, Delwel R. 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. 2009.

    CAS  PubMed Central  PubMed  Google Scholar 

  44. 44.

    Tomasson MH, Xiang Z, Walgren R, Zhao Y, Kasai Y, Miner T, et al. Somatic mutations and germline sequence variants in the expressed tyrosine kinase genes of patients with de novo acute myeloid leukemia. Blood. 2008.

    CAS  PubMed Central  PubMed  Google Scholar 

  45. 45.

    Logsdon BA, Gentles AJ, Miller CP, Blau CA, Becker PS, Lee SI. Sparse expression bases in cancer reveal tumor drivers. Nucleic Acids Res. 2015.

    CAS  PubMed Central  PubMed  Google Scholar 

  46. 46.

    Chebouba L, Miannay B, Boughaci D, Guziolowski C. Discriminate the response of acute myeloid leukemia patients to treatment by using proteomics data and answer set programming. BMC Bioinformatics. 2018;19.

  47. 47.

    Shnaps O, Perry E, Silverbush D, Sharan R. Inference of personalized drug targets via network propagation. Pac Symp Biocomput. 2016;21:156–67.

  48. 48.

    Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, Rajan D, et al. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am J Hum Genet. 2009;84:524–33.

    CAS  PubMed Central  PubMed  Google Scholar 

  49. 49.

    Stark C. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006;34:D535–9.

    CAS  PubMed  Google Scholar 

  50. 50.

    Gilsdorf M, Horn T, Arziman Z, Pelz O, Kiner E, Boutros M. GenomeRNAi: a database for cell-based RNAi phenotypes. 2009 update. Nucleic Acids Res. 2009;38.

    PubMed Central  PubMed  Google Scholar 

  51. 51.

    Deshpande R, Asiedu MK, Klebig M, Sutor S, Kuzmin E, Nelson J, et al. A comparative genomic approach for identifying synthetic lethal interactions in human cancer. Cancer Res. 2013;73:6128–36.

    CAS  Google Scholar 

  52. 52.

    Jacunski A, Dixon SJ, Tatonetti NP. Connectivity homology enables inter-species network models of synthetic lethality. PLoS Comput Biol. 2015;11.

    PubMed Central  PubMed  Google Scholar 

  53. 53.

    Astsaturov I, Ratushny V, Sukhanova A, Einarson MB, Bagnyukova T, Zhou Y, et al. Synthetic lethal screen of an EGFR-centered network to improve targeted therapies. Sci Signal. 2010;3.

    PubMed Central  PubMed  Google Scholar 

  54. 54.

    Megchelenbrink W, Katzir R, Lu X, Ruppin E, Notebaart RA. Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival. Proc Natl Acad Sci USA. 2015;112:12217–22.

    CAS  Google Scholar 

  55. 55.

    Srihari S, Singla J, Wong L, Ragan MA. Inferring synthetic lethal interactions from mutual exclusivity of genetic events in cancer. Biol Direct. 2015;10.

  56. 56.

    Wappett M, Dulak A, Yang ZR, Al-Watban A, Bradford JR, Dry JR. Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs. BMC Genomics. 2016;17.

  57. 57.

    Lee JS, Das A, Jerby-Arnon L, Arafeh R, Auslander N, Davidson M, et al. Harnessing synthetic lethality to predict the response to cancer treatment. Nat Commun. 2018.

  58. 58.

    DiNardo CD, Pratz KW, Letai A, Jonas BA, Wei AH, Thirman M, 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. 2018.

    CAS  Google Scholar 

  59. 59.

    Chan SM, Thomas D, Corces-Zimmerman MR, Xavy S, Rastogi S, Hong WJ, et al. Isocitrate dehydrogenase 1 and 2 mutations induce BCL-2 dependence in acute myeloid leukemia. Nat Med. 2015.

    CAS  PubMed Central  PubMed  Google Scholar 

  60. 60.

    Konopleva M, Pollyea DA, Potluri J, Chyla B, Hogdal L, Busman T, et al. Efficacy and biological correlates of response in a phase II study of venetoclax monotherapy in patients with acute myelogenous leukemia. Cancer Discov. 2016.

    CAS  PubMed Central  PubMed  Google Scholar 

  61. 61.

    Appelbaum FR, Bernstein ID. Gemtuzumab ozogamicin for acute myeloid leukemia. Blood. 2017.

    CAS  Google Scholar 

  62. 62.

    Amadori S, Suciu S, Selleslag D, Aversa F, Gaidano G, Musso M, et al. Gemtuzumab ozogamicin versus best supportive care in older patients with newly diagnosed acute myeloid leukemia unsuitable for intensive chemotherapy: results of the randomized phase III EORTC-GIMEMA AML-19 trial. J Clin Oncol. 2016.

    Google Scholar 

  63. 63.

    Ehninger A, Kramer M, Röllig C, Thiede C, Bornhäuser M, Von Bonin M, et al. Distribution and levels of cell surface expression of CD33 and CD123 in acute myeloid leukemia. Blood Cancer J. 2014.

    CAS  PubMed Central  PubMed  Google Scholar 

  64. 64.

    Krupka C, Kufer P, Kischel R, Zugmaier G, Bögeholz J, Köhnke T, et al. CD33 target validation and sustained depletion of AML blasts in long-term cultures by the bispecific T-cell-engaging antibody AMG 330. Blood. 2014.

    Google Scholar 

  65. 65.

    de Propris MS, Raponi S, Diverio D, Milani ML, Meloni G, Falini B, et al. High CD33 expression levels in acute myeloid leukemia cells carrying the nucleophosmin (NPM1) mutation. Haematologica. 2011.

    CAS  PubMed Central  PubMed  Google Scholar 

  66. 66.

    Intlekofer AM, Shih AH, Wang B, Nazir A, Rustenburg AS, Albanese SK, et al. Acquired resistance to IDH inhibition through trans or cis dimer-interface mutations. Nature. 2018.

    CAS  PubMed Central  PubMed  Google Scholar 

  67. 67.

    Heinrich MC, Corless CL, Blanke CD, Demetri GD, Joensuu H, Roberts PJ, et al. Molecular correlates of imatinib resistance in gastrointestinal stromal tumors. J Clin Oncol. 2006.

    CAS  Google Scholar 

  68. 68.

    Shah NP, Nicoll JM, Nagar B, Gorre ME, Paquette RL, Kuriyan J, et al. Multiple BCR-ABL kinase domain mutations confer polyclonal resistance to the tyrosine kinase inhibitor imatinib (STI571) in chronic phase and blast crisis chronic myeloid leukemia. Cancer Cell. 2002.

    CAS  Google Scholar 

  69. 69.

    Shlush LI, Mitchell A, Heisler L, Abelson S, Ng SWK, Trotman-Grant A, et al. Tracing the origins of relapse in acute myeloid leukaemia to stem cells. Nature. 2017.

    CAS  Google Scholar 

  70. 70.

    Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS, et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature. 2012.

    CAS  PubMed Central  PubMed  Google Scholar 

  71. 71.

    Paguirigan AL, Smith J, Meshinchi S, Carroll M, Maley C, Radich JP. Single-cell genotyping demonstrates complex clonal diversity in acute myeloid leukemia. Sci Transl Med. 2015.

    PubMed Central  PubMed  Google Scholar 

  72. 72.

    Klco JM, Spencer DH, Miller CA, Griffith M, Lamprecht TL, O’Laughlin M, et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell. 2014.

    CAS  PubMed Central  PubMed  Google Scholar 

  73. 73.

    Li S, Garrett-Bakelman FE, Chung SS, Sanders MA, Hricik T, Rapaport F, et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nat Med. 2016.

    CAS  PubMed Central  PubMed  Google Scholar 

  74. 74.

    Corces-Zimmerman MR, Hong W-J, Weissman IL, Medeiros BC, Majeti R. Preleukemic mutations in human acute myeloid leukemia affect epigenetic regulators and persist in remission. Proc Natl Acad Sci USA. 2014.

    CAS  Google Scholar 

  75. 75.

    Shlush LI, Zandi S, Mitchell A, Chen WC, Brandwein JM, Gupta V, et al. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature. 2014.

    CAS  PubMed Central  PubMed  Google Scholar 

  76. 76.

    Thomas D, Majeti R. Biology and relevance of human acute myeloid leukemia stem cells. Blood. 2017;129:1577–85.

    CAS  PubMed Central  PubMed  Google Scholar 

  77. 77.

    Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ, Sexton DW, et al. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol. 2013.

    CAS  PubMed  Google Scholar 

  78. 78.

    Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009.

    CAS  PubMed  Google Scholar 

  79. 79.

    Svensson V, Vento-Tormo R, Teichmann SA. Exponential scaling of single-cell RNA-seq in the past decade. Nat Protoc. 2018.

    CAS  PubMed  Google Scholar 

  80. 80.

    Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017.

  81. 81.

    Giladi A, Paul F, Herzog Y, Lubling Y, Weiner A, Yofe I, et al. Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis. Nat Cell Biol. 2018.

    CAS  Google Scholar 

  82. 82.

    Nestorowa S, Hamey FK, Pijuan Sala B, Diamanti E, Shepherd M, Laurenti E, et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood. 2016.

    CAS  PubMed Central  PubMed  Google Scholar 

  83. 83.

    Giustacchini A, Thongjuea S, Barkas N, Woll PS, Povinelli BJ, Booth CAG, et al. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. Nat Med. 2017.

    CAS  Google Scholar 

  84. 84.

    Povinelli BJ, Rodriguez-Meira A, Mead AJ. Single cell analysis of normal and leukemic hematopoiesis. Mol Aspects Med. 2018.

    CAS  Google Scholar 

  85. 85.

    Smith CC, Paguirigan A, Jeschke GR, Lin KC, Massi E, Tarver T, et al. Heterogeneous resistance to quizartinib in acute myeloid leukemia revealed by single-cell analysis. Blood. 2017.

    CAS  PubMed Central  PubMed  Google Scholar 

  86. 86.

    Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods. 2014.

    CAS  PubMed Central  PubMed  Google Scholar 

  87. 87.

    Rotem A, Ram O, Shoresh N, Sperling RA, Goren A, Weitz DA, et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol. 2015.

    CAS  PubMed Central  PubMed  Google Scholar 

  88. 88.

    Jin W, Tang Q, Wan M, Cui K, Zhang Y, Ren G, et al. Genome-wide detection of DNase i hypersensitive sites in single cells and FFPE tissue samples. Nature. 2015.

    CAS  PubMed Central  PubMed  Google Scholar 

  89. 89.

    Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015.

    CAS  PubMed Central  PubMed  Google Scholar 

  90. 90.

    Buenrostro JD, Corces MR, Lareau CA, Wu B, Schep AN, Aryee MJ, et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell. 2018.

    PubMed Central  PubMed  Google Scholar 

  91. 91.

    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017.

    CAS  PubMed Central  PubMed  Google Scholar 

  92. 92.

    Corces MR, Buenrostro JD, Wu B, Greenside PG, Chan SM, Koenig JL, et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat Genet. 2016.

    CAS  PubMed Central  PubMed  Google Scholar 

  93. 93.

    Dey SS, Kester L, Spanjaard B, Bienko M, Van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol. 2015.

    CAS  PubMed Central  PubMed  Google Scholar 

  94. 94.

    Macaulay IC, Haerty W, Kumar P, Li YI, Hu TX, Teng MJ, et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat Methods. 2015.

    CAS  Google Scholar 

  95. 95.

    Cheow LF, Courtois ET, Tan Y, Viswanathan R, Xing Q, Tan RZ, et al. Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat Methods. 2016.

    CAS  Google Scholar 

  96. 96.

    Haynes WA, Vallania F, Liu C, Bongen E, Tomczak A, Andres-Terrè M, et al. Empowering multi-cohort gene expression analysis to increase reproducibility. Pac Symp Biocomput. 2016;22:144–53.

    PubMed Central  PubMed  Google Scholar 

  97. 97.

    Reinisch A, Thomas D, Corces MR, Zhang X, Gratzinger D, Hong WJ, et al. A humanized bone marrow ossicle xenotransplantation model enables improved engraftment of healthy and leukemic human hematopoietic cells. Nat Med. 2016.

    CAS  PubMed Central  PubMed  Google Scholar 

  98. 98.

    Zhang W, Bojorquez-Gomez A, Velez DO, Xu G, Sanchez KS, Shen JP, et al. A global transcriptional network connecting noncoding mutations to changes in tumor gene expression. Nat Genet. 2018.

    CAS  PubMed Central  PubMed  Google Scholar 

  99. 99.

    Zong N, Kim H, Ngo V, Harismendy O. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Bioinformatics. 2017;33:2337–44.

    PubMed Central  PubMed  Google Scholar 

  100. 100.

    Yu H, Choo S, Park J, Jung J, Kang Y, Lee D. Prediction of drugs having opposite effects on disease genes in a directed network. BMC Syst Biol. 2016;10.

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The authors thank Amy Fan for her critical review of this manuscript. BB is supported by NIH training grant 5T32CA9302-40. DT was funded by a Pathway-to-Independence K99 National Institutes of Health, National Cancer Institute Grant Number 5K99CA207731-02. Supported by NIH R01-CA188055 (to RM) and the Ludwig Institute for Cancer Research (to RM). RM is a Leukemia & Lymphoma Society Scholar. Due to citation limits, we apologize for not referencing all notable studies.

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Correspondence to Ravindra Majeti or Daniel Thomas.

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Benard, B., Gentles, A.J., Köhnke, T. et al. Data mining for mutation-specific targets in acute myeloid leukemia. Leukemia 33, 826–843 (2019).

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