1932
0

Annual Review of Cancer Biology

Volume 9, 2025

Insights into Leukemia from Single-Cell Genomics

  • Nurefsan Sariipek1,2,3 and Peter van Galen1,2,3
  • 1Department of Medicine and Ludwig Center, Harvard Medical School, Boston, Massachusetts, USA 2Division of Hematology, Brigham and Women's Hospital, Boston, Massachusetts, USA; email: [email protected] 3Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
  • Vol. 9:267-284 (Volume publication date April 2025)
  • First published as a Review in Advance on December 11, 2024
  • Copyright © 2025 by the author(s).
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Abstract

Despite its relatively recent emergence, single-cell sequencing has cemented its place in scientific research. It has grown exponentially in less than two decades since its start, with broad impact in the biological sciences. The blood represents an attractive system for early development and application of single-cell technologies. As a result, single-cell analyses in blood and leukemia have led the way in describing how cellular heterogeneity affects cancer progression. In this review, we discuss the technological and conceptual advances brought by single-cell genomics, ranging from genetic evolution and differentiation states that mediate drug resistance to the complex interactions required for immunotherapy responses. These high-resolution insights are starting to enter clinical assessment.

Keyword(s): cellular heterogeneity, drug resistance, immunotherapy, leukemia, leukemia stem cells, single-cell sequencing
Loading

Article metrics loading...

/content/journals/10.1146/annurev-cancerbio-062024-060443
2025-04-11
2025-06-19
Loading full text...

Full text loading...

/deliver/fulltext/cancerbio/9/1/annurev-cancerbio-062024-060443.html?itemId=/content/journals/10.1146/annurev-cancerbio-062024-060443&mimeType=html&fmt=ahah

Literature Cited

  1. Acosta J, Ssozi D, van Galen P. 2021.. Single-cell RNA sequencing to disentangle the blood system. . Arterioscler. Thromb. Vasc. Biol. 41:(3):1012–18
     [Crossref] [Google Scholar]
  2. Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, et al. 2017.. SCENIC: single-cell regulatory network inference and clustering. . Nat. Methods 14:(11):1083–86
     [Crossref] [Google Scholar]
  3. Ainciburu M, Morgan DM, DePasquale EAK, Love JC, Prósper F, van Galen P. 2022.. WAT3R: recovery of T-cell receptor variable regions from 3′ single-cell RNA-sequencing. . Bioinformatics 38:(14):3645–47
     [Crossref] [Google Scholar]
  4. Albertí-Servera L, Demeyer S, Govaerts I, Swings T, De Bie J, et al. 2021.. Single-cell DNA amplicon sequencing reveals clonal heterogeneity and evolution in T-cell acute lymphoblastic leukemia. . Blood 137:(6):801–11
     [Crossref] [Google Scholar]
  5. Aminov S, Giricz O, Melnekoff DT, Sica RA, Polishchuk V, et al. 2024.. Immunotherapy-resistant acute lymphoblastic leukemia cells exhibit reduced CD19 and CD22 expression and BTK pathway dependency. . J. Clin. Investig. 134:(8):e175199
     [Crossref] [Google Scholar]
  6. Andorsky DJ, Yamada RE, Said J, Pinkus GS, Betting DJ, Timmerman JM. 2011.. Programmed death ligand 1 is expressed by non-Hodgkin lymphomas and inhibits the activity of tumor-associated T cells. . Clin. Cancer Res. 17:(13):4232–44
     [Crossref] [Google Scholar]
  7. Atallah E, Sweet K. 2021.. Treatment-free remission: the new goal in CML therapy. . Curr. Hematol. Malig. Rep. 16:(5):433–39
     [Crossref] [Google Scholar]
  8. Baccin C, Al-Sabah J, Velten L, Helbling PM, Grünschläger F, et al. 2020.. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. . Nat. Cell Biol. 22:(1):38–48
     [Crossref] [Google Scholar]
  9. Bai Z, Woodhouse S, Zhao Z, Arya R, Govek K, et al. 2022.. Single-cell antigen-specific landscape of CAR T infusion product identifies determinants of CD19-positive relapse in patients with ALL. . Sci. Adv. 8:(23):eabj2820
     [Crossref] [Google Scholar]
  10. Bandura DR, Baranov VI, Ornatsky OI, Antonov A, Kinach R, et al. 2009.. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. . Anal. Chem. 81:(16):6813–22
     [Crossref] [Google Scholar]
  11. Baryawno N, Przybylski D, Kowalczyk MS, Kfoury Y, Severe N, et al. 2019.. A cellular taxonomy of the bone marrow stroma in homeostasis and leukemia. . Cell 177:(7):1915–32.e16
     [Crossref] [Google Scholar]
  12. Becker AJ, McCulloch EA, Till JE. 1963.. Cytological demonstration of the clonal nature of spleen colonies derived from transplanted mouse marrow cells. . Nature 197::452–54
     [Crossref] [Google Scholar]
  13. Belizaire R, Wong WJ, Robinette ML, Ebert BL. 2023.. Clonal haematopoiesis and dysregulation of the immune system. . Nat. Rev. Immunol. 23:(9):595–610
     [Crossref] [Google Scholar]
  14. Bendall SC, Davis KL, Amir E-AD, Tadmor MD, Simonds EF, et al. 2014.. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. . Cell 157:(3):714–25
     [Crossref] [Google Scholar]
  15. Beneyto-Calabuig S, Merbach AK, Kniffka J-A, Antes M, Szu-Tu C, et al. 2023.. Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia. . Cell Stem Cell 30:(5):706–21.e8
     [Crossref] [Google Scholar]
  16. Borcherding N, Bormann NL, Kraus G. 2020.. scRepertoire: an R-based toolkit for single-cell immune receptor analysis. . F1000Research 9::47
     [Crossref] [Google Scholar]
  17. Bottomly D, Long N, Schultz AR, Kurtz SE, Tognon CE, et al. 2022.. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. . Cancer Cell 40:(8):850–64.e9
     [Crossref] [Google Scholar]
  18. Braun TP, Eide CA, Druker BJ. 2020.. Response and resistance to BCR-ABL1-targeted therapies. . Cancer Cell 37:(4):530–42
     [Crossref] [Google Scholar]
  19. Browaeys R, Saelens W, Saeys Y. 2020.. NicheNet: modeling intercellular communication by linking ligands to target genes. . Nat. Methods 17:(2):159–62
     [Crossref] [Google Scholar]
  20. Brück O, Blom S, Dufva O, Turkki R, Chheda H, et al. 2018.. Immune cell contexture in the bone marrow tumor microenvironment impacts therapy response in CML. . Leukemia 32:(7):1643–56
     [Crossref] [Google Scholar]
  21. Brunner AM, Esteve J, Porkka K, Knapper S, Traer E, et al. 2024.. Phase Ib study of sabatolimab (MBG453), a novel immunotherapy targeting TIM-3 antibody, in combination with decitabine or azacitidine in high- or very high-risk myelodysplastic syndromes. . Am. J. Hematol. 99:(2):E32–36
     [Crossref] [Google Scholar]
  22. Brusa D, Serra S, Coscia M, Rossi D, D'Arena G, et al. 2013.. The PD-1/PD-L1 axis contributes to T-cell dysfunction in chronic lymphocytic leukemia. . Haematologica 98:(6):953–63
     [Crossref] [Google Scholar]
  23. Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, et al. 2015.. Single-cell chromatin accessibility reveals principles of regulatory variation. . Nature 523:(7561):486–90
     [Crossref] [Google Scholar]
  24. Busarello E, Biancon G, Lauria F, Ibnat Z, Ramirez C, et al. 2024.. Interpreting single-cell messages in normal and aberrant hematopoiesis with the Cell Marker Accordion. . bioRxiv 2024.03.08.584053. https://doi.org/10.1101/2024.03.08.584053
  25. Chen GM, Chen C, Das RK, Gao P, Chen C-H, et al. 2021.. Integrative bulk and single-cell profiling of premanufacture T-cell populations reveals factors mediating long-term persistence of CAR T-cell therapy. . Cancer Discov. 11:(9):2186–99
     [Crossref] [Google Scholar]
  26. Civin CI, Loken MR. 1987.. Cell surface antigens on human marrow cells: dissection of hematopoietic development using monoclonal antibodies and multiparameter flow cytometry. . Int. J. Cell Cloning 5:(4):267–88
     [Crossref] [Google Scholar]
  27. Clarkson B, Ohkita T, Ota K, Fried J. 1967.. Studies of cellular proliferation in human leukemia. I. Estimation of growth rates of leukemic and normal hematopoietic cells in two adults with acute leukemia given single injections of tritiated thymidine. . J. Clin. Investig. 46:(4):506–29
     [Crossref] [Google Scholar]
  28. Coifman RR, Lafon S, Lee AB, Maggioni M, Nadler B, et al. 2005.. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. . PNAS 102:(21):7426–31
     [Crossref] [Google Scholar]
  29. Ctortecka C, Mechtler K. 2021.. The rise of single-cell proteomics. . Anal. Sci. Adv. 2:(3–4):84–94
     [Crossref] [Google Scholar]
  30. De Falco A, Caruso F, Su X-D, Iavarone A, Ceccarelli M. 2023.. A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data. . Nat. Commun. 14:(1):1074
     [Crossref] [Google Scholar]
  31. DePasquale EAK, Ssozi D, Ainciburu M, Good J, Noel J, et al. 2022.. Single-cell multiomics reveals clonal T-cell expansions and exhaustion in blastic plasmacytoid dendritic cell neoplasm. . Front. Immunol. 13::809414
     [Crossref] [Google Scholar]
  32. Dick JE. 2008.. Stem cell concepts renew cancer research. . Blood 112:(13):4793–807
     [Crossref] [Google Scholar]
  33. Ding J, Adiconis X, Simmons SK, Kowalczyk MS, Hession CC, et al. 2020.. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. . Nat. Biotechnol. 38:(6):737–46
     [Crossref] [Google Scholar]
  34. Ding W, LaPlant BR, Call TG, Parikh SA, Leis JF, et al. 2017.. Pembrolizumab in patients with CLL and Richter transformation or with relapsed CLL. . Blood 129:(26):3419–27
     [Crossref] [Google Scholar]
  35. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, et al. 2013.. STAR: ultrafast universal RNA-seq aligner. . Bioinformatics 29:(1):15–21
     [Crossref] [Google Scholar]
  36. Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. 2019.. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit receptor–ligand complexes. . Nat. Protoc. 15::1484–506
     [Crossref] [Google Scholar]
  37. Eide CA, Kurtz SE, Kaempf A, Long N, Joshi SK, et al. 2023.. Clinical correlates of venetoclax-based combination sensitivities to augment acute myeloid leukemia therapy. . Blood Cancer Discov. 4:(6):452–67
     [Crossref] [Google Scholar]
  38. Eppert K, Takenaka K, Lechman ER, Waldron L, Nilsson B, et al. 2011.. Stem cell gene expression programs influence clinical outcome in human leukemia. . Nat. Med. 17:(9):1086–93
     [Crossref] [Google Scholar]
  39. Fabbri G, Dalla-Favera R. 2016.. The molecular pathogenesis of chronic lymphocytic leukaemia. . Nat. Rev. Cancer 16:(3):145–62
     [Crossref] [Google Scholar]
  40. Fabbri G, Khiabanian H, Holmes AB, Wang J, Messina M, et al. 2013.. Genetic lesions associated with chronic lymphocytic leukemia transformation to Richter syndrome. . J. Exp. Med. 210:(11):2273–88
     [Crossref] [Google Scholar]
  41. Farge T, Saland E, de Toni F, Aroua N, Hosseini M, et al. 2017.. Chemotherapy resistant human acute myeloid leukemia cells are not enriched for leukemic stem cells but require oxidative metabolism. . Cancer Discov. 7:(7):716–35
     [Crossref] [Google Scholar]
  42. Finak G, McDavid A, Yajima M, Deng J, Gersuk V, et al. 2015.. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. . Genome Biol. 16::278
     [Crossref] [Google Scholar]
  43. Flygt H, Söderlund S, Stentoft J, Richter J, Koskenvesa P, et al. 2021.. Long-term tolerability and efficacy after initial PegIFN-α addition to dasatinib in CML-CP: five-year follow-up of the NordCML007 study. . Eur. J. Haematol. 107:(6):617–23
     [Crossref] [Google Scholar]
  44. Fortmann SD, Frey BF, Hanumanthu VS, Liu S, Goldsborough A, et al. 2023.. Fixation before dissociation using a deep eutectic solvent preserves in vivo states and phospho-signaling in single-cell sequencing. . bioRxiv 2023.02.13.528370. https://doi.org/10.1101/2023.02.13.528370
  45. Furth J, Kahn MC, Breedis C. 1937.. The transmission of leukemia of mice with a single cell. . Am. J. Cancer 31:(2):276–82
     [Google Scholar]
  46. Gao T, Soldatov R, Sarkar H, Kurkiewicz A, Biederstedt E, et al. 2023.. Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. . Nat. Biotechnol. 41:(3):417–26
     [Crossref] [Google Scholar]
  47. Gawad C, Koh W, Quake SR. 2014.. Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. . PNAS 111:(50):17947–52
     [Crossref] [Google Scholar]
  48. Genovese G, Kähler AK, Handsaker RE, Lindberg J, Rose SA, et al. 2014.. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. . N. Engl. J. Med. 371:(26):2477–87
     [Crossref] [Google Scholar]
  49. Giustacchini A, Thongjuea S, Barkas N, Woll PS, Povinelli BJ, et al. 2017.. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. . Nat. Med. 23:(6):692–702
     [Crossref] [Google Scholar]
  50. Griffin GK, Booth CAG, Togami K, Chung SS, Ssozi D, et al. 2023.. Ultraviolet radiation shapes dendritic cell leukaemia transformation in the skin. . Nature 618::834–41
     [Crossref] [Google Scholar]
  51. Griffin JD, Löwenberg B. 1986.. Clonogenic cells in acute myeloblastic leukemia. . Blood 68:(6):1185–95
     [Crossref] [Google Scholar]
  52. Gulati GS, Sikandar SS, Wesche DJ, Manjunath A, Bharadwaj A, et al. 2020.. Single-cell transcriptional diversity is a hallmark of developmental potential. . Science 367:(6476):405–11
     [Crossref] [Google Scholar]
  53. Haghverdi L, Buettner F, Theis FJ. 2015.. Diffusion maps for high-dimensional single-cell analysis of differentiation data. . Bioinformatics 31:(18):2989–98
     [Crossref] [Google Scholar]
  54. Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, et al. 2021.. Integrated analysis of multimodal single-cell data. . Cell 184:(13):3573–87.e29
     [Crossref] [Google Scholar]
  55. Hasin Y, Seldin M, Lusis A. 2017.. Multi-omics approaches to disease. . Genome Biol. 18:(1):83
     [Crossref] [Google Scholar]
  56. Heaton H, Talman AM, Knights A, Imaz M, Gaffney DJ, et al. 2020.. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. . Nat. Methods 17:(6):615–20
     [Crossref] [Google Scholar]
  57. Hjorth-Hansen H, Stentoft J, Richter J, Koskenvesa P, Höglund M, et al. 2016.. Safety and efficacy of the combination of pegylated interferon-α2b and dasatinib in newly diagnosed chronic-phase chronic myeloid leukemia patients. . Leukemia 30:(9):1853–60
     [Crossref] [Google Scholar]
  58. Ho T-C, LaMere M, Stevens BM, Ashton JM, Myers JR, et al. 2016.. Evolution of acute myelogenous leukemia stem cell properties after treatment and progression. . Blood 128:(13):1671–78
     [Crossref] [Google Scholar]
  59. Huang X, Li Y, Zhang J, Yan L, Zhao H, et al. 2024.. Single-cell systems pharmacology identifies development-driven drug response and combination therapy in B cell acute lymphoblastic leukemia. . Cancer Cell 42:(4):552–67.e6
     [Crossref] [Google Scholar]
  60. Hume DA, Millard SM, Pettit AR. 2023.. Macrophage heterogeneity in the single-cell era: facts and artifacts. . Blood 142:(16):1339–47
     [Crossref] [Google Scholar]
  61. Huuhtanen J, Adnan-Awad S, Theodoropoulos J, Forstén S, Warfvinge R, et al. 2024.. Single-cell analysis of immune recognition in chronic myeloid leukemia patients following tyrosine kinase inhibitor discontinuation. . Leukemia 38:(1):109–25
     [Crossref] [Google Scholar]
  62. Huuhtanen J, Ilander M, Yadav B, Dufva OM, Lähteenmäki H, et al. 2022.. IFN-α with dasatinib broadens the immune repertoire in patients with chronic-phase chronic myeloid leukemia. . J. Clin. Investig. 132:(17):e152585
     [Crossref] [Google Scholar]
  63. Iacobucci I, Witkowski MT, Mullighan CG. 2023.. Single-cell analysis of acute lymphoblastic and lineage-ambiguous leukemia: approaches and molecular insights. . Blood 141:(4):356–68
     [Crossref] [Google Scholar]
  64. Ichikawa Y, Pluznik DH, Sachs L. 1966.. In vitro control of the development of macrophage and granulocyte colonies. . PNAS 56:(2):488–95
     [Crossref] [Google Scholar]
  65. Ilander M, Olsson-Strömberg U, Schlums H, Guilhot J, Brück O, et al. 2017.. Increased proportion of mature NK cells is associated with successful imatinib discontinuation in chronic myeloid leukemia. . Leukemia 31:(5):1108–16
     [Crossref] [Google Scholar]
  66. Ioannou N, Hagner PR, Stokes M, Gandhi AK, Apollonio B, et al. 2021.. Triggering interferon signaling in T cells with avadomide sensitizes CLL to anti-PD-L1/PD-1 immunotherapy. . Blood 137:(2):216–31
     [Crossref] [Google Scholar]
  67. Irani YD, Hughes A, Clarson J, Kok CH, Shanmuganathan N, et al. 2020.. Successful treatment-free remission in chronic myeloid leukaemia and its association with reduced immune suppressors and increased natural killer cells. . Br. J. Haematol. 191:(3):433–41
     [Crossref] [Google Scholar]
  68. Irani YD, Kok CH, Clarson J, Shanmuganathan N, Branford S, et al. 2023.. Association of TIM-3 checkpoint receptor expression on T cells with treatment-free remission in chronic myeloid leukemia. . Blood Adv. 7:(11):2364–74
     [Crossref] [Google Scholar]
  69. Izzo F, Myers RM, Ganesan S, Mekerishvili L, Kottapalli S, et al. 2024.. Mapping genotypes to chromatin accessibility profiles in single cells. . Nature 629:(8014):1149–57
     [Crossref] [Google Scholar]
  70. Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV, et al. 2014.. Age-related clonal hematopoiesis associated with adverse outcomes. . N. Engl. J. Med. 371:(26):2488–98
     [Crossref] [Google Scholar]
  71. Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, et al. 2021.. Inference and analysis of cell-cell communication using CellChat. . Nat. Commun. 12::1088
     [Crossref] [Google Scholar]
  72. Kikushige Y, Miyamoto T. 2013.. TIM-3 as a novel therapeutic target for eradicating acute myelogenous leukemia stem cells. . Int. J. Hematol. 98:(6):627–33
     [Crossref] [Google Scholar]
  73. Kreso A, Dick JE. 2014.. Evolution of the cancer stem cell model. . Cell Stem Cell 14:(3):275–91
     [Crossref] [Google Scholar]
  74. Krishnan V, Schmidt F, Nawaz Z, Venkatesh PN, Lee KL, et al. 2023.. A single-cell atlas identifies pretreatment features of primary imatinib resistance in chronic myeloid leukemia. . Blood 141:(22):2738–55
     [Google Scholar]
  75. Lapidot T, Sirard C, Vormoor J, Murdoch B, Hoang T, et al. 1994.. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. . Nature 367:(6464):645–48
     [Crossref] [Google Scholar]
  76. Laurenti E, Göttgens B. 2018.. From haematopoietic stem cells to complex differentiation landscapes. . Nature 553:(7689):418–26
     [Crossref] [Google Scholar]
  77. Levine JH, Simonds EF, Bendall SC, Davis KL, Amir E-AD, et al. 2015.. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. . Cell 162:(1):184–97
     [Crossref] [Google Scholar]
  78. Liao Y-M, Hsu S-H, Chiou S-S. 2024.. Harnessing the transcriptional signatures of CAR-T-cells and leukemia/lymphoma using single-cell sequencing technologies. . Int. J. Mol. Sci. 25:(4):2416
     [Crossref] [Google Scholar]
  79. Longo SK, Guo MG, Ji AL, Khavari PA. 2021.. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. . Nat. Rev. Genet. 22:(10):627–44
     [Crossref] [Google Scholar]
  80. Luecken MD, Theis FJ. 2019.. Current best practices in single-cell RNA-seq analysis: a tutorial. . Mol. Syst. Biol. 15:(6):e8746
     [Crossref] [Google Scholar]
  81. Lun ATL, Bach K, Marioni JC. 2016.. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. . Genome Biol. 17::75
     [Crossref] [Google Scholar]
  82. Ma J, Pettit N, Talburt J, Wang S, Weissman SM, Yang MQ. 2022.. Integrating single-cell transcriptome and network analysis to characterize the therapeutic response of chronic myeloid leukemia. . Int. J. Mol. Sci. 23:(22):14335
     [Crossref] [Google Scholar]
  83. Maaten L, Hinton GE. 2008.. Visualizing data using t-SNE. . J. Mach. Learn. Res. 9::2579–605
     [Google Scholar]
  84. Matsumoto H, Kiryu H, Furusawa C, Ko MSH, Ko SBH, et al. 2017.. SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. . Bioinformatics 33:(15):2314–21
     [Crossref] [Google Scholar]
  85. Maximow AA. 1909.. Der Lymphozyt als gemeinsame Stammzelle der verschiedenen Blutelemente in der embryonalen Entwicklung und im postfetalen Leben der Säugetiere. . Folia Haematol. 8::125–34
     [Google Scholar]
  86. McCarthy DJ, Rostom R, Huang Y, Kunz DJ, Danecek P, et al. 2020.. Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes. . Nat. Methods 17:(4):414–21
     [Crossref] [Google Scholar]
  87. McInnes L, Healy J, Melville J. 2018.. UMAP: uniform manifold approximation and projection for dimension reduction. . arXiv:1802.03426 [stat.ML]
  88. Meyers S, Alberti-Servera L, Gielen O, Erard M, Swings T, et al. 2022.. Monitoring of leukemia clones in B-cell acute lymphoblastic leukemia at diagnosis and during treatment by single-cell DNA amplicon sequencing. . HemaSphere 6:(4):e700
     [Crossref] [Google Scholar]
  89. Middleton D, Diler AS, Meenagh A, Sleator C, Gourraud PA. 2009.. Killer immunoglobulin-like receptors (KIR2DL2 and/or KIR2DS2) in presence of their ligand (HLA-C1 group) protect against chronic myeloid leukaemia. . Tissue Antigens 73:(6):553–60
     [Crossref] [Google Scholar]
  90. Milano F, Emerson RO, Salit R, Guthrie KA, Thur LA, et al. 2020.. Impact of T cell repertoire diversity on mortality following cord blood transplantation. . Front. Oncol. 10::583349
     [Crossref] [Google Scholar]
  91. Miles LA, Bowman RL, Merlinsky TR, Csete IS, Ooi AT, et al. 2020.. Single-cell mutation analysis of clonal evolution in myeloid malignancies. . Nature 587:(7834):477–82
     [Crossref] [Google Scholar]
  92. Miller TE, Lareau CA, Verga JA, DePasquale EAK, Liu V, et al. 2022.. Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations. . Nat. Biotechnol. 40:(7):1030–34
     [Crossref] [Google Scholar]
  93. Moerman T, Aibar Santos S, Bravo González-Blas C, Simm J, Moreau Y, et al. 2019.. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. . Bioinformatics 35:(12):2159–61
     [Crossref] [Google Scholar]
  94. Nam AS, Kim K-T, Chaligne R, Izzo F, Ang C, et al. 2019.. Somatic mutations and cell identity linked by Genotyping of Transcriptomes. . Nature 571:(7765):355–60
     [Crossref] [Google Scholar]
  95. Nie Y, Lu W, Chen D, Tu H, Guo Z, et al. 2020.. Mechanisms underlying CD19-positive ALL relapse after anti-CD19 CAR T cell therapy and associated strategies. . Biomarker Res. 8::18
     [Crossref] [Google Scholar]
  96. O'Brien SG, Guilhot F, Larson RA, Gathmann I, Baccarani M, et al. 2003.. Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. . N. Engl. J. Med. 348:(11):994–1004
     [Crossref] [Google Scholar]
  97. Pagliuca S, Gurnari C, Hong S, Zhao R, Kongkiatkamon S, et al. 2021.. Clinical and basic implications of dynamic T cell receptor clonotyping in hematopoietic cell transplantation. . JCI Insight 6:(13):e149080
     [Crossref] [Google Scholar]
  98. Papalexi E, Satija R. 2018.. Single-cell RNA sequencing to explore immune cell heterogeneity. . Nat. Rev. Immunol. 18:(1):35–45
     [Crossref] [Google Scholar]
  99. Parry EM, Lemvigh CK, Deng S, Dangle N, Ruthen N, et al. 2023.. ZNF683 marks a CD8+ T cell population associated with anti-tumor immunity following anti-PD-1 therapy for Richter syndrome. . Cancer Cell 41:(10):1803–16.e8
     [Crossref] [Google Scholar]
  100. Pearson K. 1901.. LIII. On lines and planes of closest fit to systems of points in space. . Lond. Edinburgh Dublin Philos. Mag. J. Sci. 2:(11):559–72
     [Crossref] [Google Scholar]
  101. Pei S, Pollyea DA, Gustafson A, Stevens BM, Minhajuddin M, et al. 2020.. Monocytic subclones confer resistance to venetoclax-based therapy in patients with acute myeloid leukemia. . Cancer Discov. 10:(4):536–51
     [Crossref] [Google Scholar]
  102. Penter L, Borji M, Nagler A, Lyu H, Lu WS, et al. 2024.. Integrative genotyping of cancer and immune phenotypes by long-read sequencing. . Nat. Commun. 15:(1):32
     [Crossref] [Google Scholar]
  103. Penter L, Gohil SH, Lareau C, Ludwig LS, Parry EM, et al. 2021.. Longitudinal single-cell dynamics of chromatin accessibility and mitochondrial mutations in chronic lymphocytic leukemia mirror disease history. . Cancer Discov. 11:(12):3048–63
     [Crossref] [Google Scholar]
  104. Puente XS, Pinyol M, Quesada V, Conde L, Ordóñez GR, et al. 2011.. Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia. . Nature 475:(7354):101–5
     [Crossref] [Google Scholar]
  105. Purroy N, Tong YE, Lemvigh CK, Cieri N, Li S, et al. 2022.. Single-cell analysis reveals immune dysfunction from the earliest stages of CLL that can be reversed by ibrutinib. . Blood 139:(14):2252–56
     [Crossref] [Google Scholar]
  106. Qiu X, Mao Q, Tang Y, Wang L, Chawla R, et al. 2017.. Reversed graph embedding resolves complex single-cell trajectories. . Nat. Methods 14:(10):979–82
     [Crossref] [Google Scholar]
  107. Quesada V, Conde L, Villamor N, Ordóñez GR, Jares P, et al. 2011.. Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. . Nat. Genet. 44:(1):47–52
     [Crossref] [Google Scholar]
  108. Rabilloud T, Potier D, Pankaew S, Nozais M, Loosveld M, Payet-Bornet D. 2021.. Single-cell profiling identifies pre-existing CD19-negative subclones in a B-ALL patient with CD19-negative relapse after CAR-T therapy. . Nat. Commun. 12:(1):865
     [Crossref] [Google Scholar]
  109. Rea D, Henry G, Khaznadar Z, Etienne G, Guilhot F, et al. 2017.. Natural killer-cell counts are associated with molecular relapse-free survival after imatinib discontinuation in chronic myeloid leukemia: the IMMUNOSTIM study. . Haematologica 102:(8):1368–77
     [Crossref] [Google Scholar]
  110. Rendeiro AF, Krausgruber T, Fortelny N, Zhao F, Penz T, et al. 2020.. Chromatin mapping and single-cell immune profiling define the temporal dynamics of ibrutinib response in CLL. . Nat. Commun. 11:(1):577
     [Crossref] [Google Scholar]
  111. Rendeiro AF, Schmidl C, Strefford JC, Walewska R, Davis Z, et al. 2016.. Chromatin accessibility maps of chronic lymphocytic leukaemia identify subtype-specific epigenome signatures and transcription regulatory networks. . Nat. Commun. 7::11938
     [Crossref] [Google Scholar]
  112. Rezaei M, Tan J, Zeng C, Li Y, Ganjalikhani-Hakemi M. 2021.. TIM-3 in leukemia; immune response and beyond. . Front. Oncol. 11::753677
     [Crossref] [Google Scholar]
  113. Safina KR, van Galen P. 2024.. New frameworks for hematopoiesis derived from single-cell genomics. . Blood 144:(10):1039–47
     [Crossref] [Google Scholar]
  114. Sango J, Carcamo S, Sirenko M, Maiti A, Mansour H, et al. 2024.. RAS-mutant leukaemia stem cells drive clinical resistance to venetoclax. . Nature 636::24150
     [Crossref] [Google Scholar]
  115. Sariipek N, Safina KR, Cutler C, Ho VT, Koreth J, et al. 2023.. Post-transplant T cell clonotype diversity is associated with survival in patients with TP53-mutated acute myeloid leukemia. . Blood 142:(Suppl. 1):2176
     [Crossref] [Google Scholar]
  116. Schoof EM, Furtwängler B, Üresin N, Rapin N, Savickas S, et al. 2021.. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies. . Nat. Commun. 12:(1):3341
     [Crossref] [Google Scholar]
  117. Schwenger E, Steidl U. 2021.. An evolutionary approach to clonally complex hematologic disorders. . Blood Cancer Discov. 2:(3):201–15
     [Crossref] [Google Scholar]
  118. Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E, et al. 2020.. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. . Nat. Med. 26:(5):792–802
     [Crossref] [Google Scholar]
  119. Spangrude GJ, Heimfeld S, Weissman IL. 1988.. Purification and characterization of mouse hematopoietic stem cells. . Science 241:(4861):58–62
     [Crossref] [Google Scholar]
  120. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, et al. 2017.. Simultaneous epitope and transcriptome measurement in single cells. . Nat. Methods 14:(9):865–68
     [Crossref] [Google Scholar]
  121. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. 2020.. Multi-omics data integration, interpretation, and its application. . Bioinform. Biol. Insights 14::1177932219899051
     [Crossref] [Google Scholar]
  122. Takao S, Morell V, Brown FC, Koche R, Kentsis A. 2022.. Epigenetic mechanisms controlling human leukemia stem cells and therapy resistance. . bioRxiv 2022.09.22.509005. https://doi.org/10.1101/2022.09.22.509005
  123. Talati C, Pinilla-Ibarz J. 2018.. Resistance in chronic myeloid leukemia: definitions and novel therapeutic agents. . Curr. Opin. Hematol. 25:(2):154–61
     [Crossref] [Google Scholar]
  124. Tameni A, Toffalori C, Vago L. 2024.. Tricking the trickster: precision medicine approaches to counteract leukemia immune escape after transplant. . Blood 143:(26):2710–21
     [Crossref] [Google Scholar]
  125. Tickle T, Tirosh I, Georgescu C, Brown M, Haas B. 2019.. inferCNV of the Trinity CTAT Project. . https://github.com/broadinstitute/inferCNV
  126. Till JE, McCulloch EA. 1961.. A direct measurement of the radiation sensitivity of normal mouse bone marrow cells. . Radiat. Res. 14::213–22
     [Crossref] [Google Scholar]
  127. Trumpp A, Haas S. 2022.. Cancer stem cells: the adventurous journey from hematopoietic to leukemic stem cells. . Cell 185:(8):1266–70
     [Crossref] [Google Scholar]
  128. Tu AA, Gierahn TM, Monian B, Morgan DM, Mehta NK, et al. 2019.. TCR sequencing paired with massively parallel 3′ RNA-seq reveals clonotypic T cell signatures. . Nat. Immunol. 20:(12):1692–99
     [Crossref] [Google Scholar]
  129. van Galen P, Hovestadt V, Wadsworth MH II, Hughes TK, Griffin GK, et al. 2019.. Single-cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity. . Cell 176:(6):1265–81.e24
     [Crossref] [Google Scholar]
  130. van Leeuwenhoek A. 1695.. Arcana Naturae Detecta. Delft, Neth:.: Henrik van Kroonevelt
     [Google Scholar]
  131. Vickovic S, Eraslan G, Salmén F, Klughammer J, Stenbeck L, et al. 2019.. High-definition spatial transcriptomics for in situ tissue profiling. . Nat. Methods 16:(10):987–90
     [Crossref] [Google Scholar]
  132. Waclawiczek A, Leppä A-M, Renders S, Stumpf K, Reyneri C, et al. 2023.. Combinatorial BCL2 family expression in acute myeloid leukemia stem cells predicts clinical response to Azacitidine/Venetoclax. . Cancer Discov. 13:(6):1408–27
     [Crossref] [Google Scholar]
  133. Wang JCY, Dick JE. 2005.. Cancer stem cells: lessons from leukemia. . Trends Cell Biol. 15:(9):494–501
     [Crossref] [Google Scholar]
  134. Wang Z, Chen J, Wang M, Zhang L, Yu L. 2021.. One stone, two birds: the roles of Tim-3 in acute myeloid leukemia. . Front. Immunol. 12::618710
     [Crossref] [Google Scholar]
  135. Warfvinge R, Geironson L, Sommarin MNE, Lang S, Karlsson C, et al. 2017.. Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML. . Blood 129:(17):2384–94
     [Crossref] [Google Scholar]
  136. Wei AH, Esteve J, Porkka K, Knapper S, Vey N, et al. 2020.. Sabatolimab (MBG453) dose selection and dose-response analysis in myelodysplastic syndrome (MDS)/acute myeloid leukemia (AML): population pharmacokinetics (PK) modeling and evaluation of clinical efficacy/safety by dose. . Blood 136::40–42
     [Crossref] [Google Scholar]
  137. Weng C, Yu F, Yang D, Poeschla M, Liggett LA, et al. 2024.. Deciphering cell states and genealogies of human haematopoiesis. . Nature 627:(8003):389–98
     [Crossref] [Google Scholar]
  138. Xie M, Lu C, Wang J, McLellan MD, Johnson KJ, et al. 2014.. Age-related mutations associated with clonal hematopoietic expansion and malignancies. . Nat. Med. 20:(12):1472–78
     [Crossref] [Google Scholar]
  139. Xu C, Su Z. 2015.. Identification of cell types from single-cell transcriptomes using a novel clustering method. . Bioinformatics 31:(12):1974–80
     [Crossref] [Google Scholar]
  140. Yew PY, Alachkar H, Yamaguchi R, Kiyotani K, Fang H, et al. 2015.. Quantitative characterization of T-cell repertoire in allogeneic hematopoietic stem cell transplant recipients. . Bone Marrow Transplant. 50:(9):1227–34
     [Crossref] [Google Scholar]
  141. Yin Q, Sivina M, Robins H, Yusko E, Vignali M, et al. 2017.. Ibrutinib therapy increases T cell repertoire diversity in patients with chronic lymphocytic leukemia. . J. Immunol. 198:(4):1740–47
     [Crossref] [Google Scholar]
  142. Zeiser R, Vago L. 2019.. Mechanisms of immune escape after allogeneic hematopoietic cell transplantation. . Blood 133:(12):1290–97
     [Crossref] [Google Scholar]
  143. Zeng AGX, Bansal S, Jin L, Mitchell A, Chen WC, et al. 2022.. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. . Nat. Med. 28:(6):1212–23
     [Crossref] [Google Scholar]
  144. Zeng AGX, Iacobucci I, Shah S, Mitchell A, Wong G, et al. 2023.. Precise single-cell transcriptomic mapping of normal and leukemic cell states reveals unconventional lineage priming in acute myeloid leukemia. . bioRxiv 2023.12.26.573390. https://doi.org/10.1101/2023.12.26.573390
  145. Zhang W, Yang B, Weng L, Li J, Bai J, et al. 2020.. Single cell sequencing reveals cell populations that predict primary resistance to imatinib in chronic myeloid leukemia. . Aging 12:(24):25337–55
     [Crossref] [Google Scholar]
  146. Zhao Z, Condomines M, van der Stegen SJC, Perna F, Kloss CC, et al. 2015.. Structural design of engineered costimulation determines tumor rejection kinetics and persistence of CAR T cells. . Cancer Cell 28:(4):415–28
     [Crossref] [Google Scholar]
  147. Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, et al. 2017.. Massively parallel digital transcriptional profiling of single cells. . Nat. Commun. 8::14049
     [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-cancerbio-062024-060443
Loading
Insights into Leukemia from Single-Cell Genomics
Annual Review of Cancer Biology 9, 267 (2025); https://doi.org/10.1146/annurev-cancerbio-062024-060443
/content/journals/10.1146/annurev-cancerbio-062024-060443
/content/journals/10.1146/annurev-cancerbio-062024-060443
Loading

Data & Media loading...

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error