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ACUTE MYELOID LEUKEMIA

Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression

Abstract

The prognosis of most patients with AML is poor due to frequent disease relapse. The cause of relapse is thought to be from the persistence of leukemia initiating cells (LIC’s) following treatment. Here we assessed RNA based changes in LICs from matched patient diagnosis and relapse samples using single-cell RNA sequencing. Previous studies on AML progression have focused on genetic changes at the DNA mutation level mostly in bulk AML cells and demonstrated the existence of DNA clonal evolution. Here we identified in LICs that the phenomenon of RNA clonal evolution occurs during AML progression. Despite the presence of vast transcriptional heterogeneity at the single cell level, pathway analysis identified common signaling networks involving metabolism, apoptosis and chemokine signaling that evolved during AML progression and become a signature of relapse samples. A subset of this gene signature was validated at the protein level in LICs by flow cytometry from an independent AML cohort and functional studies were performed to demonstrate co-targeting BCL2 and CXCR4 signaling may help overcome therapeutic challenges with AML heterogeneity. It is hoped this work will facilitate a greater understanding of AML relapse leading to improved prognostic biomarkers and therapeutic strategies to target LIC’s.

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Fig. 1: RNA clonal evolution.
Fig. 2: RNA cluster group biomarkers.
Fig. 3: Metabolic dysregulation.
Fig. 4: Apoptotic dysregulation.
Fig. 5: Cytokine signaling dysregulation.
Fig. 6: Flow cytometry validation study.
Fig. 7: Co-targeting BCL2 and CXCR4 leads to enhanced efficacy against AML cells in vitro and in vivo.

References

  1. 1.

    Estey EH. General approach to, and perspectives on clinical research in, older patients with newly diagnosed acute myeloid leukemia. Semin Hematol. 2006;43:89–95.

    PubMed  Article  Google Scholar 

  2. 2.

    Huntly BJ, Gilliland DG. Leukaemia stem cells and the evolution of cancer-stem-cell research. Nat Rev Cancer. 2005;5:311–21.

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Hanekamp D, Cloos J, Schuurhuis GJ. Leukemic stem cells: identification and clinical application. Int J Hematol. 2017;105:549–57.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Kreso A, Dick JE. Evolution of the cancer stem cell model. Cell Stem Cell. 2014;14:275–91.

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Lechman ER, Gentner B, Ng SW, Schoof EM, van Galen P, Kennedy JA. et al. miR-126 regulates distinct self-renewal outcomes in normal and malignant hematopoietic stem cells. Cancer cell. 2016;29:214–28.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Eppert K, Takenaka K, Lechman ER, Waldron L, Nilsson B, van Galen P. et al. Stem cell gene expression programs influence clinical outcome in human leukemia. Nat Med. 2011;17:1086–93.

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Jordan CT. The leukemic stem cell. Best Pr Res Clin Haematol. 2007;20:13–18.

    CAS  Article  Google Scholar 

  8. 8.

    Metzeler KH, Maharry K, Kohlschmidt J, Volinia S, Mrozek K, Becker H. et al. A stem cell-like gene expression signature associates with inferior outcomes and a distinct microRNA expression profile in adults with primary cytogenetically normal acute myeloid leukemia. Leukemia. 2013;27:2023–31.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Akinduro O, Weber TS, Ang H, Haltalli MLR, Ruivo N, Duarte D. et al. Proliferation dynamics of acute myeloid leukaemia and haematopoietic progenitors competing for bone marrow space. Nat Commun. 2018;9:519.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  10. 10.

    Hira VVV, Van Noorden CJF, Carraway HE, Maciejewski JP, Molenaar RJ. Novel therapeutic strategies to target leukemic cells that hijack compartmentalized continuous hematopoietic stem cell niches. Biochim Biophys Acta Rev Cancer. 2017;1868:183–98.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Aguirre-Ghiso JA. Models, mechanisms and clinical evidence for cancer dormancy. Nat Rev Cancer. 2007;7:834–46.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Saito Y, Uchida N, Tanaka S, Suzuki N, Tomizawa-Murasawa M, Sone A. et al. Induction of cell cycle entry eliminates human leukemia stem cells in a mouse model of AML. Nat Biotechnol. 2010;28:275–80.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  13. 13.

    Essers MA, Trumpp A. Targeting leukemic stem cells by breaking their dormancy. Mol Oncol. 2010;4:443–50.

    PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Lee MC, Lopez-Diaz FJ, Khan SY, Tariq MA, Dayn Y, Vaske CJ. et al. Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing. Proc Natl Acad Sci U.S.A. 2014;111:E4726–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

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

    Article  CAS  Google Scholar 

  16. 16.

    Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND. et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl J Med. 2016;374:2209–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Bolouri H, Farrar JE, Triche T,Jr., 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;24:103–12.

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    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;481:506–10.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Hughes AE, Magrini V, Demeter R, Miller CA, Fulton R, Fulton LL. et al. Clonal architecture of secondary acute myeloid leukemia defined by single-cell sequencing. PLoS Genet. 2014;10:e1004462

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  20. 20.

    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;7:281re282.

    Article  CAS  Google Scholar 

  21. 21.

    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;22:792–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Yan B, Hu Y, Ban KHK, Tiang Z, Ng C, Lee J. et al. Single-cell genomic profiling of acute myeloid leukemia for clinical use: a pilot study. Oncol Lett. 2017;13:1625–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Chen J, Kao YR, Sun D, Todorova TI, Reynolds D, Narayanagari SR. et al. Myelodysplastic syndrome progression to acute myeloid leukemia at the stem cell level. Nat Med. 2019;25:103–10.

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    van Galen P, Hovestadt V, Wadsworth Ii MH, Hughes TK, Griffin GK, Battaglia S. et al. Single-cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity. Cell. 2019;176:1265–81 e1224.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Wang ML, Bailey NG. Acute myeloid leukemia genetics: risk stratification and implications for therapy. Arch Pathol Lab Med. 2015;139:1215–23.

    PubMed  Article  Google Scholar 

  26. 26.

    Baum CM, Weissman IL, Tsukamoto AS, Buckle AM, Peault B. Isolation of a candidate human hematopoietic stem-cell population. Proc Natl Acad Sci U.S.A. 1992;89:2804–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Kersten B, Valkering M, Wouters R, van Amerongen R, Hanekamp D, Kwidama Z. et al. CD45RA, a specific marker for leukaemia stem cell sub-populations in acute myeloid leukaemia. Br J Haematol. 2016;173:219–35.

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Majeti R, Park CY, Weissman IL. Identification of a hierarchy of multipotent hematopoietic progenitors in human cord blood. Cell Stem Cell. 2007;1:635–45.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096–8.

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Majeti R, Becker MW, Tian Q, Lee TL, Yan X, Liu R. et al. Dysregulated gene expression networks in human acute myelogenous leukemia stem cells. Proc Natl Acad Sci U.S.A. 2009;106:3396–401.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Lin Y, Ghazanfar S, Strbenac D, Wang A, Patrick E, Lin DM, et al. Evaluating stably expressed genes in single cells. Gigascience. 2019;8:1–10.

    Article  CAS  Google Scholar 

  32. 32.

    Murga M, Jaco I, Fan Y, Soria R, Martinez-Pastor B, Cuadrado M. et al. Global chromatin compaction limits the strength of the DNA damage response. J Cell Biol. 2007;178:1101–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Liu S, Kharbanda S, Stone RM. OncoHist®, an rh histone 1.3 is cytotoxic acute myeloid leuk cells results altered downstr signal. Blood. 2014;124:3604–4.

    Article  Google Scholar 

  34. 34.

    Su C, Gao G, Schneider S, Helt C, Weiss C, O’Reilly MA. et al. DNA damage induces downregulation of histone gene expression through the G1 checkpoint pathway. EMBO J. 2004;23:1133–43.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Izquierdo-Bouldstridge A, Bustillos A, Bonet-Costa C, Aribau-Miralbes P, Garcia-Gomis D, Dabad M. et al. Histone H1 depletion triggers an interferon response in cancer cells via activation of heterochromatic repeats. Nucleic Acids Res. 2017;45:11622–42.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    Torres CM, Biran A, Burney MJ, Patel H, Henser-Brownhill T, Cohen AS, et al. The linker histone H1.0 generates epigenetic and functional intratumor heterogeneity. Science. 2016;353:1–12.

    Article  CAS  Google Scholar 

  37. 37.

    Liebermann DA, Tront JS, Sha X, Mukherjee K, Mohamed-Hadley A, Hoffman B. Gadd45 stress sensors in malignancy and leukemia. Crit Rev Oncog. 2011;16:129–40.

    PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Chamseddine AN, Cabrero M, Wei Y, Ganan-Gomez I, Colla S, Takahashi K. et al. PDE4 differential expression is a potential prognostic factor and therapeutic target in patients with myelodysplastic syndrome and chronic myelomonocytic leukemia. Clin Lymphoma, Myeloma Leuk. 2016;16:S67–73.

    Article  Google Scholar 

  39. 39.

    Smith PG, Wang F, Wilkinson KN, Savage KJ, Klein U, Neuberg DS. et al. The phosphodiesterase PDE4B limits cAMP-associated PI3K/AKT-dependent apoptosis in diffuse large B-cell lymphoma. Blood. 2005;105:308–16.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. 40.

    Kim DU, Kwak B, Kim SW. Phosphodiesterase 4B is an effective therapeutic target in colorectal cancer. Biochem Biophys Res Commun. 2019;508:825–31.

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74.

    CAS  Article  Google Scholar 

  42. 42.

    Scialdone A, Natarajan KN, Saraiva LR, Proserpio V, Teichmann SA, Stegle O. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods. 2015;85:54–61.

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Kreitz J, Schonfeld C, Seibert M, Stolp V, Alshamleh I, Oellerich T. et al. Metabolic plasticity of acute myeloid leukemia. Cells. 2019;8:8.

    Article  CAS  Google Scholar 

  44. 44.

    Decker T, Bogner C, Oelsner M, Peschel C, Ringshausen I. Antiapoptotic effect of interleukin-2 (IL-2) in B-CLL cells with low and high affinity IL-2 receptors. Ann Hematol. 2010;89:1125–32.

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Lin MT, Juan CY, Chang KJ, Chen WJ, Kuo ML. IL-6 inhibits apoptosis and retains oxidative DNA lesions in human gastric cancer AGS cells through up-regulation of anti-apoptotic gene mcl-1. Carcinogenesis. 2001;22:1947–53.

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Chen Y, Zhang F, Tsai Y, Yang X, Yang L, Duan S. et al. IL-6 signaling promotes DNA repair and prevents apoptosis in CD133+ stem-like cells of lung cancer after radiation. Radiat Oncol. 2015;10:227.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. 47.

    Ahmed NN, Grimes HL, Bellacosa A, Chan TO, Tsichlis PN. Transduction of interleukin-2 antiapoptotic and proliferative signals via Akt protein kinase. Proc Natl Acad Sci U.S.A. 1997;94:3627–32.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Zeng GQ, Yi H, Li XH, Shi HY, Li C, Li MY. et al. Identification of the proteins related to p53-mediated radioresponse in nasopharyngeal carcinoma by proteomic analysis. J Proteom. 2011;74:2723–33.

    CAS  Article  Google Scholar 

  49. 49.

    Swa HL, Blackstock WP, Lim LH, Gunaratne J. Quantitative proteomics profiling of murine mammary gland cells unravels impact of annexin-1 on DNA damage response, cell adhesion, and migration. Mol Cell Proteom: MCP. 2012;11:381–93.

    Article  CAS  Google Scholar 

  50. 50.

    Lim LH, Pervaiz S. Annexin 1: the new face of an old molecule. FASEB J. 2007;21:968–75.

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Guo C, Liu S, Sun MZ. Potential role of Anxa1 in cancer. Future Oncol. 2013;9:1773–93.

    CAS  PubMed  Article  Google Scholar 

  52. 52.

    Zhu F, Wang Y, Zeng S, Fu X, Wang L, Cao J. Involvement of annexin A1 in multidrug resistance of K562/ADR cells identified by the proteomic study. OMICS. 2009;13:467–76.

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Shukla V, Lu R. IRF4 and IRF8: governing the virtues of B Lymphocytes. Front Biol (Beijing). 2014;9:269–82.

    CAS  Article  Google Scholar 

  54. 54.

    Mattei F, Schiavoni G, Sestili P, Spadaro F, Fragale A, Sistigu A. et al. IRF-8 controls melanoma progression by regulating the cross talk between cancer and immune cells within the tumor microenvironment. Neoplasia. 2012;14:1223–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Bi X, Hameed M, Mirani N, Pimenta EM, Anari J, Barnes BJ. Loss of interferon regulatory factor 5 (IRF5) expression in human ductal carcinoma correlates with disease stage and contributes to metastasis. Breast Cancer Res. 2011;13:R111.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Pogosova-Agadjanyan EL, Kopecky KJ, Ostronoff F, Appelbaum FR, Godwin J, Lee H, et al. The prognostic significance of IRF8 transcripts in adult patients with acute myeloid leukemia. PloS ONE. 2013;8:e70812.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Zhao S, Wang J, Qin C. Blockade of CXCL12/CXCR4 signaling inhibits intrahepatic cholangiocarcinoma progression and metastasis via inactivation of canonical Wnt pathway. J Exp Clin Cancer Res. 2014;33:103.

    PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Yu X, Shi W, Zhang Y, Wang X, Sun S, Song Z. et al. CXCL12/CXCR4 axis induced miR-125b promotes invasion and confers 5-fluorouracil resistance through enhancing autophagy in colorectal cancer. Sci Rep. 2017;7:42226.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  59. 59.

    Song ZY, Gao ZH, Chu JH, Han XZ, Qu XJ. Downregulation of the CXCR4/CXCL12 axis blocks the activation of the Wnt/beta-catenin pathway in human colon cancer cells. Biomed Pharmacother. 2015;71:46–52.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  60. 60.

    Lin XL, Xu Q, Tang L, Sun L, Han T, Wang LW, et al. Regorafenib inhibited gastric cancer cells growth and invasion via CXCR4 activated Wnt pathway. PLoS ONE. 2017;12:e0177335.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  61. 61.

    Hu TH, Yao Y, Yu S, Han LL, Wang WJ, Guo H. et al. SDF-1/CXCR4 promotes epithelial-mesenchymal transition and progression of colorectal cancer by activation of the Wnt/beta-catenin signaling pathway. Cancer Lett. 2014;354:417–26.

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Lu Y, Hu B, Guan GF, Chen J, Wang CQ, Ma Q. et al. SDF-1/CXCR4 promotes F5M2 osteosarcoma cell migration by activating the Wnt/beta-catenin signaling pathway. Med Oncol. 2015;32:194

    PubMed  Article  CAS  Google Scholar 

  63. 63.

    Konopleva MY, Jordan CT. Leukemia stem cells and microenvironment: biology and therapeutic targeting. J Clin Oncol. 2011;29:591–9.

    PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Tabe Y, Konopleva M. Role of microenvironment in resistance to therapy in AML. Curr Hematol Malig Rep. 2015;10:96–103.

    PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Cancilla D, Rettig MP, DiPersio JF. Targeting CXCR4 in AML and ALL. Front Oncol. 2020;10:1672.

    PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Pollyea DA, Amaya M, Strati P, Konopleva MY. Venetoclax for AML: changing the treatment paradigm. Blood Adv. 2019;3:4326–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Gupta K, Stefan T, Ignatz-Hoover J, Moreton S, Parizher G, Saunthararajah Y. et al. GSK-3 inhibition sensitizes acute myeloid leukemia cells to 1,25D-mediated differentiation. Cancer Res. 2016;76:2743–53.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Farge T, Saland E, de Toni F, Aroua N, Hosseini M, Perry R. et al. Chemotherapy-resistant human acute myeloid leukemia cells are not enriched for leukemic stem cells but require oxidative metabolism. Cancer Discov. 2017;7:716–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Hattori A, Tsunoda M, Konuma T, Kobayashi M, Nagy T, Glushka J. et al. Cancer progression by reprogrammed BCAA metabolism in myeloid leukaemia. Nature. 2017;545:500–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Bonen A, Campbell SE, Benton CR, Chabowski A, Coort SL, Han XX. et al. Regulation of fatty acid transport by fatty acid translocase/CD36. Proc Nutr Soc. 2004;63:245–9.

    CAS  PubMed  Article  Google Scholar 

  71. 71.

    Schweikhard ES, Ziegler CM. Amino acid secondary transporters: toward a common transport mechanism. Curr Top Membr. 2012;70:1–28.

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Ye H, Adane B, Khan N, Sullivan T, Minhajuddin M, Gasparetto M. et al. Leukemic stem cells evade chemotherapy by metabolic adaptation to an adipose tissue niche. Cell Stem Cell. 2016;19:23–37.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Jones CL, Stevens BM, D’Alessandro A, Reisz JA, Culp-Hill R, Nemkov T. et al. Inhibition of amino acid metabolism selectively targets human leukemia stem cells. Cancer Cell. 2018;34:724–40 e724.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74.

    Tsun ZY, Possemato R. Amino acid management in cancer. Semin Cell Dev Biol. 2015;43:22–32.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Jones CL, Stevens BM, D’Alessandro A, Culp-Hill R, Reisz JA, Pei S. et al. Cysteine depletion targets leukemia stem cells through inhibition of electron transport complex II. Blood. 2019;134:389–94.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Peled A, Klein S, Beider K, Burger JA, Abraham M. Role of CXCL12 and CXCR4 in the pathogenesis of hematological malignancies. Cytokine. 2018;109:11–6.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  77. 77.

    Cho BS, Kim HJ, Konopleva M. Targeting the CXCL12/CXCR4 axis in acute myeloid leukemia: from bench to bedside. Korean J Intern Med. 2017;32:248–57.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Carter BZ, Gronda M, Wang Z, Welsh K, Pinilla C, Andreeff M. et al. Small-molecule XIAP inhibitors derepress downstream effector caspases and induce apoptosis of acute myeloid leukemia cells. Blood. 2005;105:4043–50.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Cassier PA, Castets M, Belhabri A, Vey N. Targeting apoptosis in acute myeloid leukaemia. Br J Cancer. 2017;117:1089–98.

    PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    Phase I study of ABT-199 (GDC-0199) in patients with relapsed/refractory non-Hodgkin lymphoma: responses observed in diffuse large B-cell (DLBCL) and follicular lymphoma (FL) at higher cohort doses. Clin Adv Hematol Oncol. 2014; 12: 18–19.

  81. 81.

    Brinda B, Khan I, Parkin B, Konig H. The rocky road to personalized medicine in acute myeloid leukaemia. J Cell Mol Med. 2018;22:1411–27.

    PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    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;19:216–28.

    CAS  PubMed  Article  Google Scholar 

  83. 83.

    Kayser S, Levis MJ. Advances in targeted therapy for acute myeloid leukaemia. Br J Haematol. 2018;180:484–500.

    PubMed  Article  Google Scholar 

  84. 84.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

The authors are grateful to the patients who contributed to this study and thank the Children’s Oncology Group for providing some of the bone marrow samples used in this study. This study was supported by pilot funding from the Case Comprehensive Cancer Center (NCI P30CA0437093) and the Computational Genomic Epidemiology of Cancer Fellowship (NCI 5T32CA094186-17, L.S.). This research was also supported by the following Shared Resources of the Case Comprehensive Cancer Center (NCI P30CA0437093): Hematopoietic Cell Biorepository and Cellular Therapy, Integrated Genomics, and Cytometry & Imaging Microscopy. This work made use of the High Performance Computing Resources in the Core Facility for Advanced Research Computing at Case Western Reserve University. The authors thank Jose Cancelas, Folashade Otegbeye, Sheela Karunaithi, and Grace Lee for their scientific feedback and Caroline Perry and Kristin Waite for their critical reading and editing of the paper.

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DW conceived of and supervised the project. DB, TS, and KG performed single cell RNA sequencing experiments. LS designed and performed the computational analysis. SF, AS, JB-S, TH, ZJ, RS, and SL contributed to the computational analysis. DW, RS, SPR, RB, AR, XX, and BT designed, optimized, implemented, analyzed and interpreted the flow cytometry data. JM, YS, and ML provided AML samples and assisted in data analysis and interpretation. DW and LS wrote the paper with contributions from RS, ZJ, TL, JB-S, YS, JM, AR, SPR, and DB.

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Correspondence to David N. Wald.

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Stetson, L.C., Balasubramanian, D., Ribeiro, S.P. et al. Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression. Leukemia 35, 2799–2812 (2021). https://doi.org/10.1038/s41375-021-01338-7

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