Acute myeloid leukemia

Data mining for mutation-specific targets in acute myeloid leukemia

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Abstract

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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References

  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. https://doi.org/10.1038/nrdp.2016.10.

  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.

  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. https://doi.org/10.1182/blood-2016-03-643544.

  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. https://doi.org/10.1111/ejh.12450.

  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.

  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.

  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. https://doi.org/10.1038/nature13127.

  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. https://doi.org/10.1186/gm432.

  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.

  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. https://doi.org/10.1093/nar/gks1111.

  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. https://doi.org/10.1038/sdata.2014.35.

  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.

  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.

  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.

  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.

  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.

  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. https://doi.org/10.1038/ncomms15580.

  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.

  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.

  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.

  21. 21.

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

  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.

  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. https://doi.org/10.1371/journal.pone.0039148.

  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. https://doi.org/10.1038/nm.4439.

  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. https://doi.org/10.1093/nar/gkx338.

  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. https://doi.org/10.1093/nar/gkv1101.

  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. https://doi.org/10.1093/database/bar026.

  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. https://doi.org/10.1126/scisignal.2004088.

  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. https://doi.org/10.1093/nar/gks1021.

  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. https://doi.org/10.1016/j.cell.2011.01.004.

  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. https://doi.org/10.1038/ni.2370.

  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.

  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.

  35. 35.

    Sahoo D, Dill DL, Tibshirani R, Plevritis SK. Extracting binary signals from microarray time-course data. Nucleic Acids Res. 2007. https://doi.org/10.1093/nar/gkm284.

  36. 36.

    Brown AS, Kong SW, Kohane IS, Patel CJ. ksRepo: a generalized platform for computational drug repositioning. BMC Bioinformatics. 2016;17. https://doi.org/10.1186/s12859-016-0931-y.

  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.

  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.

  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. https://doi.org/10.1126/scitranslmed.3003215.

  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.

  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. https://doi.org/10.1093/jamia/ocw022.

  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. https://doi.org/10.1182/blood-2008-02-134411.

  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. https://doi.org/10.1182/blood-2008-09-179895.

  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. https://doi.org/10.1182/blood-2007-09-113027.

  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. https://doi.org/10.1093/nar/gku1290.

  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. https://doi.org/10.1186/s12859-018-2034-4.

  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.

  49. 49.

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

  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. https://doi.org/10.1093/nar/gkp1038.

  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.

  52. 52.

    Jacunski A, Dixon SJ, Tatonetti NP. Connectivity homology enables inter-species network models of synthetic lethality. PLoS Comput Biol. 2015;11. https://doi.org/10.1371/journal.pcbi.1004506.

  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. https://doi.org/10.1126/scisignal.2001083.

  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.

  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. https://doi.org/10.1186/s13062-015-0086-1.

  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. https://doi.org/10.1186/s12864-016-2375-1.

  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. https://doi.org/10.1038/s41467-018-04647-1.

  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. https://doi.org/10.1016/S1470-2045(18)30010-X.

  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. https://doi.org/10.1038/nm.3788.

  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. https://doi.org/10.1158/2159-8290.CD-16-0313.

  61. 61.

    Appelbaum FR, Bernstein ID. Gemtuzumab ozogamicin for acute myeloid leukemia. Blood. 2017. https://doi.org/10.1182/blood-2017-09-797712.

  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. https://doi.org/10.1200/JCO.2015.64.0060.

  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. https://doi.org/10.1038/bcj.2014.39.

  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. https://doi.org/10.1182/blood-2013-08-523548.

  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. https://doi.org/10.3324/haematol.2011.043786.

  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. https://doi.org/10.1038/s41586-018-0251-7.

  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. https://doi.org/10.1200/JCO.2006.06.2265.

  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. https://doi.org/10.1016/S1535-6108(02)00096-X.

  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. https://doi.org/10.1038/nature22993.

  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. https://doi.org/10.1038/nature10738.

  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. https://doi.org/10.1126/scitranslmed.aaa0763.

  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. https://doi.org/10.1016/j.ccr.2014.01.031.

  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. https://doi.org/10.1038/nm.4125.

  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. https://doi.org/10.1073/pnas.1324297111.

  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. https://doi.org/10.1038/nature13038.

  76. 76.

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

  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. https://doi.org/10.1038/nbt.2642.

  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. https://doi.org/10.1038/nmeth.1315.

  79. 79.

    Svensson V, Vento-Tormo R, Teichmann SA. Exponential scaling of single-cell RNA-seq in the past decade. Nat Protoc. 2018. https://doi.org/10.1038/nprot.2017.149.

  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. https://doi.org/10.1038/ncomms14049.

  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. https://doi.org/10.1038/s41556-018-0121-4.

  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. https://doi.org/10.1182/blood-2016-05-716480.

  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. https://doi.org/10.1038/nm.4336.

  84. 84.

    Povinelli BJ, Rodriguez-Meira A, Mead AJ. Single cell analysis of normal and leukemic hematopoiesis. Mol Aspects Med. 2018. https://doi.org/10.1016/j.mam.2017.08.006.

  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. https://doi.org/10.1182/blood-2016-04-711820.

  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. https://doi.org/10.1038/nmeth.3035.

  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. https://doi.org/10.1038/nbt.3383.

  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. https://doi.org/10.1038/nature15740.

  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. https://doi.org/10.1038/nature14590.

  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. https://doi.org/10.1016/j.cell.2018.03.074.

  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. https://doi.org/10.1038/nmeth.4380.

  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. https://doi.org/10.1038/ng.3646.

  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. https://doi.org/10.1038/nbt.3129.

  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. https://doi.org/10.1038/nmeth.3370.

  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. https://doi.org/10.1038/nmeth.3961.

  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.

  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. https://doi.org/10.1038/nm.4103.

  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. https://doi.org/10.1038/s41588-018-0091-2.

  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.

  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. https://doi.org/10.1186/s12918-015-0243-2.

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Acknowledgements

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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