Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia
Open accessAbstract
Recent advances in single-cell transcriptomics are ideally placed to unravel intratumoral heterogeneity and selective resistance of cancer stem cell (SC) subpopulations to molecularly targeted cancer therapies. However, current single-cell RNA-sequencing approaches lack the sensitivity required to reliably detect somatic mutations. We developed a method that combines high-sensitivity mutation detection with whole-transcriptome analysis of the same single cell. We applied this technique to analyze more than 2,000 SCs from patients with chronic myeloid leukemia (CML) throughout the disease course, revealing heterogeneity of CML-SCs, including the identification of a subgroup of CML-SCs with a distinct molecular signature that selectively persisted during prolonged therapy. Analysis of nonleukemic SCs from patients with CML also provided new insights into cell-extrinsic disruption of hematopoiesis in CML associated with clinical outcome. Furthermore, we used this single-cell approach to identify a blast-crisis-specific SC population, which was also present in a subclone of CML-SCs during the chronic phase in a patient who subsequently developed blast crisis. This approach, which might be broadly applied to any malignancy, illustrates how single-cell analysis can identify subpopulations of therapy-resistant SCs that are not apparent through cell-population analysis.
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HSPCs WT for all mutations analyzed were present in 10 of 14 TP53-sAML patients.
“HSPCs WT for all mutations analyzed were present in 10 of 14 patients (Extended Data Fig. 2b–o), providing a valuable population of cells for intrapatient comparison with mutation-positive cells17.”
From Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution (2023) · cites this paper as [17]
The supplied passages are an IRIS repository page, with no results section or page number from the paper. Searches for the 10-of-14 wild-type HSPC finding and TP53-sAML returned only repository text, so our indexed copy does not establish the claim. (LLM, conf=0.75)
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“Full-length transcriptome sequencing allows the detection of splice variants and alternative transcripts, as well as genetic alterations in the transcribed fraction, such as single-nucleotide variants 19,43 or fusion transcripts 44 .”
BackgroundA general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing
Petti, A. A., Williams, S. R., Miller, C. A., Fiddes, I. T., Srivatsan, S. N., Chen, D. Y., Fronick, C. C., Fulton, R. S., Church, D. M., & Ley, T. J. (2019) · Nature Communications · doi:10.1038/s41467-019-11591-1 · Cited by 215 · see citing works
Understanding tumor ecosystems by single-cell sequencing: promises and limitations
Ren, X., Kang, B., & Zhang, Z. (2018) · Genome biology · doi:10.1186/s13059-018-1593-z · Cited by 214 · see citing works
Single-cell transcriptomics in cancer: computational challenges and opportunities
Fan, J., Slowikowski, K., & Zhang, F. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0422-0 · Cited by 191 · see citing works
Psaila, B., Wang, G., Rodriguez-Meira, A., Li, R., Heuston, E. F., Murphy, L., Yee, D., Hitchcock, I. S., Sousos, N., O’Sullivan, J., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.04.008 · Cited by 185 · see citing works
“Molecular Cell 78, 477–49 This is an open access article und vides an unprecedented opportunity to identify cancer-cell-specific targets (Giustacchini et al., 2017; Nam et al., 2019; Rodriguez-Meira et al., 2019).”
BackgroundSelf-citationChronic Myeloid Leukemia: A Model Disease of the Past, Present and Future
Minciacchi, V. R., Kumar, R., & Krause, D. S. (2021) · Cells · doi:10.3390/cells10010117 · Cited by 184 · see citing works
“The identification of the different LSC subgroups may allow predictions on which patients will develop resistance or progress to BC [22].”
BackgroundChronic myeloid leukemia stem cells
Houshmand, M., Simonetti, G., Circosta, P., Gaidano, V., Cignetti, A., Martinelli, G., Saglio, G. N., & Gale, R. P. (2019) · Leukemia · doi:10.1038/s41375-019-0490-0 · Cited by 177 · see citing works
Mitochondrial metabolism as a potential therapeutic target in myeloid leukaemia
de Beauchamp, L., Himonas, E., & Helgason, G. V. (2021) · Leukemia · doi:10.1038/s41375-021-01416-w · Cited by 168 · see citing works
“As AML LSCs are relatively quiescent and metabolically inactive, this could explain why they might not require such high levels of metabolic intermediates and therefore, why they do not rely on glycolysis [23, 65, 93].”
BackgroundRodriguez-Meira, A., Norfo, R., Wen, S., Chédeville, A. L., Rahman, H., O’Sullivan, J., Wang, G., Louka, E., Kretzschmar, W. W., Paterson, A., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01480-1 · Cited by 158 · see citing works
Self-citationA niche-dependent myeloid transcriptome signature defines dormant myeloma cells
Khoo, W. H., Ledergor, G., Weiner, A., Roden, D. L., Terry, R. L., McDonald, M. M., Chai, R. C. C., Veirman, K. D., Owen, K. L., Opperman, K. S., et al. (2019) · Blood · doi:10.1182/blood.2018880930 · Cited by 138 · see citing works
Velten, L., Story, B. A., Hernández-Malmierca, P., Raffel, S., Leonce, D. R., Milbank, J. H., Paulsen, M. S., Demir, A., Szu‐Tu, C., Frömel, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21650-1 · Cited by 135 · see citing works
Abarrategi, A., Mian, S. A., Passaro, D., Rouault‐Pierre, K., Grey, W., & Bonnet, D. A. (2018) · The Journal of Experimental Medicine · doi:10.1084/jem.20172139 · Cited by 126 · see citing works
“Chronic myeloid leukemia (CML) harboring the translocation t(9:22), which generates fusion protein BCRABL1, is the classical example of a stem cell disease where the LSCs originate from CD34+CD38− HSCs (Nowell and Hungerford, 1961; Rowley, 1973; Heisterkamp et al., 1983; Giustacchini et al., 2017).”
BackgroundDeciphering Metabolic Heterogeneity by Single-Cell Analysis
Evers, T. M. J., Hochane, M., Tans, S. J., Heeren, R. M. A., Semrau, S., Nemes, P., & Mashaghi, A. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b02410 · Cited by 121 · see citing works
BCR-ABL Independent Mechanisms of Resistance in Chronic Myeloid Leukemia
Loscocco, F., Visani, G., Galimberti, S., Curti, A., & Isidori, A. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00939 · Cited by 120 · see citing works
“CML-LSCs were also analyzed by combining high sensitivity mutation detection with whole-transcriptome analysis of the same single cell (9).”
BackgroundMethodInfluentialCross, N. C. P., Ernst, T., Branford, S., Cayuela, J.-M., Deininger, M., Fabarius, A., Kim, D. D. H., Machova Polakova, K., Radich, J. P., Hehlmann, R., et al. (2023) · Leukemia · doi:10.1038/s41375-023-02048-y · Cited by 120 · see citing works
“This technology has the potential to measure MRD over a wide dynamic range and provide information on the cellular identity of residual BCR::ABL1 positive cells [79], but is probably many years away from routine clinical use.”
Amarante-Mendes, G. P., Rana, A., Datoguia, T. S., Hamerschlak, N., & Brumatti, G. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14010215 · Cited by 111 · see citing works
“Acquired heterogeneity helps some LSCs to survive TKI treatment, thus serving as a reservoir of leukemic cells that can eventually cause relapse after therapy discontinuation [68].”
BackgroundIdentification of two distinct pathways of human myelopoiesis
Drissen, R., Thongjuea, S., Theilgaard‐Mönch, K., & Nerlov, C. (2019) · Science Immunology · doi:10.1126/sciimmunol.aau7148 · Cited by 98 · see citing works
“We selected 4731 predicted variable genes based on a simple noise model using the Lowess model of the average gene expression and the coefficient of variation (CV) (36).”
MethodSelf-citationSingle-cell metabolomics by mass spectrometry: Advances, challenges, and future applications
Ali, A., Abouleila, Y., Shimizu, Y., Hiyama, E., Emara, S., Mashaghi, A., & Hankemeier, T. (2019) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2019.02.033 · Cited by 102 · see citing works
Single-cell RNA sequencing in cancer: Applications, advances, and emerging challenges
Sun, G., Li, Z., Rong, D., Zhang, H., Shi, X., Yang, W., Zheng, W., Sun, G., Wu, F., Cao, H., et al. (2021) · Molecular Therapy — Oncolytics · doi:10.1016/j.omto.2021.04.001 · Cited by 104 · see citing works
Role of inflammation in the biology of myeloproliferative neoplasms
Koschmieder, S., & Chatain, N. (2020) · Blood Reviews · doi:10.1016/j.blre.2020.100711 · Cited by 91 · see citing works
SC2disease: a manually curated database of single-cell transcriptome for human diseases
Zhao, T., Lyu, S., Lu, G., Juan, L., Zeng, X., Wei, Z., Hao, J., & Peng, J. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa838 · Cited by 89 · see citing works
“Many researchers have identified specificity of gene expression in diseases using scRNA-seq, such as identifying differentially expressed genes of multiple neuronal cell subgroups in Alzheimer ’s disease (6), characterizing molecular signatures of cancer stem cell subpopulations in different stages of chronic myeloid…”
BackgroundWang, H., Gong, P., Chen, T., Gao, S., Wu, Z., Wang, X., Li, J., Marjani, S. L., Costa, J. C., Weissman, S. M., et al. (2021) · Advanced Science · doi:10.1002/advs.202004320 · Cited by 89 · see citing works
Single-cell transcriptome atlas of lung adenocarcinoma featured with ground glass nodules
Lu, T., Yang, X., Shi, Y., Zhao, M., Bi, G., Liang, J., Chen, Z., Huang, Y., Jiang, W., Lin, Z., et al. (2020) · Cell Discovery · doi:10.1038/s41421-020-00200-x · Cited by 86 · see citing works
Spatially resolved transcriptomics and its applications in cancer
Maniatis, S., Petrescu, J., & Phatnani, H. P. (2021) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.12.002 · Cited by 80 · see citing works
Demaree, B., Delley, C. L., Vasudevan, H. N., Peretz, C. A. C., Ruff, D., Smith, C. C., & Abate, A. R. (2021) · Nature Communications · doi:10.1038/s41467-021-21810-3 · Cited by 86 · see citing works
Roy, A., Wang, G., Iskander, D., O’Byrne, S., Elliott, N., O’Sullivan, J., Buck, G., Heuston, E. F., Wen, W. X., Rodríguez-Meira, A., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109698 · Cited by 82 · see citing works
Self-citationNew insights into hematopoietic differentiation landscapes from single-cell RNA sequencing
Watcham, S., Kuciński, I., & Göttgens, B. (2019) · Blood · doi:10.1182/blood-2018-08-835355 · Cited by 77 · see citing works
Hepatocellular senescence induces multi-organ senescence and dysfunction via TGFβ
Kiourtis, C., Terradas-Terradas, M., Gee, L., May, S., Γεωργακοπούλου, Α., Collins, A., O'Sullivan, E. D., Baird, D., Hassan, M., Shaw, R., et al. (2024) · Nature Cell Biology · doi:10.1038/s41556-024-01543-3 · Cited by 85 · see citing works
Mechanisms of Resistance and Implications for Treatment Strategies in Chronic Myeloid Leukaemia
Poudel, G., Tolland, M., Hughes, T. P., & Pagani, I. S. (2022) · Cancers · doi:10.3390/cancers14143300 · Cited by 75 · see citing works
“Studies demonstrated that the treatment of CML-LSCs with the SMO inhibitor PF-04449913 promoted differentiation and increased their sensitivity to TKI [93,94].”
BackgroundInfluentialPanomics for Precision Medicine
Sandhu, C., Qureshi, A. P., & Emili, A. (2017) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2017.11.001 · Cited by 76 · see citing works
Defining Cell Identity with Single‐Cell Omics
Mincarelli, L., Lister, A., Lipscombe, J., & Macaulay, I. C. (2018) · PROTEOMICS · doi:10.1002/pmic.201700312 · Cited by 75 · see citing works
Issa, G. C., Kantarjian, H. M., González, G. N., Borthakur, G. M., Tang, G., Wierda, W. G., Sasaki, K., Short, N. J., Ravandi, F., Kadia, T. M., et al. (2017) · Blood · doi:10.1182/blood-2017-07-792143 · Cited by 74 · see citing works
Ko, T. K., Javed, A., Lee, K. L., Pathiraja, T. N., Liu, X., Malik, S., Soh, S. X., Heng, X. T., Takahashi, N., Tan, J. H. J., et al. (2020) · Blood · doi:10.1182/blood.2020004834 · Cited by 72 · see citing works
Transitioning single-cell genomics into the clinic
Lim, J. W. Y., Chin, V. T., Fairfax, K. A., Moutinho, C., Suan, D., Ji, H. P., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00613-w · Cited by 72 · see citing works
Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing
Gupta, R. K., & Kuźnicki, J. (2020) · Cells · doi:10.3390/cells9081751 · Cited by 70 · see citing works
Wu, Z., Gao, S., Gao, Q., Patel, B. A., Groarke, E. M., Feng, X., Manley, A. L., Li, H., Cardona, D. O., Kajigaya, S., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101160 · Cited by 71 · see citing works
Mutation accumulation in cancer genes relates to nonoptimal outcome in chronic myeloid leukemia
Adnan Awad, S., Kankainen, M., Ojala, T., Koskenvesa, P., Eldfors, S., Ghimire, B., Kumar, A., Kytölä, S., Kamel, M. M., Heckman, C. A., et al. (2020) · Blood Advances · doi:10.1182/bloodadvances.2019000943 · Cited by 67 · see citing works
“R177K at diagnosis progressed into BP at 7 months.(43) We also detected variants in epigenetic modifiers.”
BackgroundSelf-citationCardelino: computational integration of somatic clonal substructure and single-cell transcriptomes
McCarthy, D. J., Rostom, R., Huang, Y., Kunz, D. J., Danecek, P., Bonder, M. J., Hagai, T., Lyu, R., Wang, W., Gaffney, D. J., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0766-3 · Cited by 69 · see citing works
Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology
Valdés‐Mora, F., Handler, K., Law, A. M. K., Salomon, R., Oakes, S. R., Ormandy, C. J., & Gallego‐Ortega, D. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.02582 · Cited by 68 · see citing works
“Thus, in the last 5 years, studies on single-cell transcriptome of tumors of different cancer types have emerged, this includes (Table 2): glioma (150, 151, 157), melanoma (112), colorectal cancer (159), hepatocellular carcinoma (113, 152), renal carcinoma (153), nonsmall-cell lung cancer (NSCLC) (115, 116, 154), brea…”
BackgroundCancer biology deciphered by single-cell transcriptomic sequencing
Li, Y., Jin, J., & Bai, F. (2021) · Protein & Cell · doi:10.1007/s13238-021-00868-1 · Cited by 67 · see citing works
“In chronic myeloid leukemia (CML), researchers identified distinct molecular signatures of CSCs and revealed their heterogeneity (Giustacchini et al., 2017).”
BackgroundIntegration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods
Ryu, Y., Han, G. H., Jung, E., & Hwang, D. (2023) · Molecules and Cells · doi:10.14348/molcells.2023.0009 · Cited by 68 · see citing works
“These approaches have been also used to remove batch effects when integrating multiple scRNA-seq datasets in early studies (Giustacchini et al., 2017; Kadoki et al., 2017; Smillie et al., 2019; Young et al., 2018).”
Immune-Based Therapies in Acute Leukemia
Witkowski, M. T., Lasry, A., Carroll, W. L., & Aifantis, I. (2019) · Trends in cancer · doi:10.1016/j.trecan.2019.07.009 · Cited by 63 · see citing works
“For example, single cell analysis of HSCs from chronic myeloid leukemia patients allowed the identification of quiescent BCR-ABL1+ stem cells that failed to respond to tyrosine-kinase inhibition therapy, as well as identification of cell extrinsic signaling events affecting non-leukemic BCR-ABL1− stem cell function [1…”
BackgroundMapping genotypes to chromatin accessibility profiles in single cells
Izzo, F., Myers, R. M., Ganesan, S., Mekerishvili, L., Kottapalli, S. V., Prieto, T., Eton, E. O., Botella, T., Dunbar, A. J., Bowman, R. L., et al. (2024) · Nature · doi:10.1038/s41586-024-07388-y · Cited by 64 · see citing works
“To resolve admixed WT and mutated cells in primary human samples, single-cell multi-omics methods can link genotypes with transcriptional 15–19 or cell-surface protein 20,21 profiles.”
Awad, S. A., Dufva, O., Ianevski, A., Ghimire, B. R., Koski, J., Maliniemi, P., Thomson, D. W., SCHREIBER, A. W., Heckman, C. A., Koskenvesa, P., et al. (2020) · Leukemia · doi:10.1038/s41375-020-01011-5 · Cited by 61 · see citing works
Self-citationVries, N. L. D., Mahfouz, A. E., Koning, F., & de Miranda, N. F. C. C. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01254 · Cited by 58 · see citing works
“Thereafter, scRNA-seq has been used to study the microenvironment of several cancer types including prostate cancer (46), breast cancer (47), glioma (48–50), renal cancer (51), lung cancer (52), melanoma (53– 56), colorectal cancer (57–59), pancreatic ductal adenocarcinoma (60), liver cancer (61), head and neck cancer…”
MethodCancer stem cells: Recent insights and therapies
Zhou, H., Tan, L., Liu, B., & Guan, X. (2023) · Biochemical Pharmacology · doi:10.1016/j.bcp.2023.115441 · Cited by 60 · see citing works
Zhang, Y., Jiang, S., He, F., Tian, Y., Hu, H., Gao, L., Zhang, L., Chen, A., Hu, Y., Fan, L., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03031-7 · Cited by 58 · see citing works
“A straight-forward approach is leveraging genomic mutations (point mutation, fusion gene, or chromosomal copy number variation) detected in full-length and 5’-end scRNA-seq data to annotate tumor cells [9, 22, 36–39].”
MethodDecoding Cancer Biology One Cell at a Time
Castro, L. N. G., Tirosh, I., & Suvà, M. L. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1376 · Cited by 59 · see citing works
“, 2020 ( 71 ) High-grade serous ovarian cancer ascites 10,906 (22)”
MethodTranscriptomic classes of BCR-ABL1 lymphoblastic leukemia
Kim, J. C., Chan-Seng-Yue, M., Ge, S., Zeng, A. G. X., Ng, K., Gan, O., Garcia-Prat, L., Flores-Figueroa, E., Woo, T., Zhang, A., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01429-4 · Cited by 55 · see citing works
Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data
Rashid, S., Shah, S. P., Bar‐Joseph, Z., & Pandya, R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz095 · Cited by 58 · see citing works
Li, X., Wang, Y., Deng, S., Zhu, G., Wang, C., Johnson, N. A., Zhang, Z., Tirado, C. R., Xu, Y., Metang, L. A., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.06.010 · Cited by 56 · see citing works
Epigenetic regulation of cancer stemness
Galassi, C., Manic, G., Esteller, M., Galluzzi, L., & Vitale, I. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02340-6 · Cited by 57 · see citing works
“213 These approaches also demonstrated that therapy directly promotes transcriptional reprogramming and chromatin-mediated plasticity, resulting in DTP cell-dependent residual disease in lung 214 and breast cancer 215 patients.”
Johnson, R. W., & Suva, L. J. (2017) · Calcified Tissue International · doi:10.1007/s00223-017-0362-4 · Cited by 52 · see citing works
“Such fundamental questions must be answered and perhaps the answers reside in the still murky area of cancer stem cells and stem cell division [27] that may be resolved by transcriptomic or other ‘‘omic’’ profiling [27, 112] at the single cell level.”
Endothelial stem and progenitor cells (stem cells): (2017 Grover Conference Series)
Yöder, M. C. (2017) · Pulmonary Circulation · doi:10.1177/2045893217743950 · Cited by 52 · see citing works
Deciphering Human Tumor Biology by Single-Cell Expression Profiling
Tirosh, I., & Suvà, M. L. (2018) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-030518-055609 · Cited by 50 · see citing works
Stem cell persistence in CML is mediated by extrinsically activated JAK1-STAT3 signaling
Kuepper, M. K., Bütow, M., Herrmann, O., Ziemons, J., Chatain, N., Maurer, A., Kirschner, M., Maié, T., Costa, I. G., Eschweiler, J., et al. (2019) · Leukemia · doi:10.1038/s41375-019-0427-7 · Cited by 51 · see citing works
Single cell analysis of normal and leukemic hematopoiesis
Povinelli, B. J., Rodríguez-Meira, A., & Mead, A. J. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.08.006 · Cited by 47 · see citing works
“Using single cell approaches to analyse non-clonally involved cells can also be informative for prognosis; we recently demonstrated the potential prognostic utility of scRNA-seq in patients with CML through analysis of inflammatory signatures in the non-leukemic HSC compartment [105].”
BackgroundMethodInfluentialSelf-citationCarrelha, J., Mazzi, S., Winroth, A., Hagemann-Jensen, M., Ziegenhain, C., Högstrand, K., Seki, M., Brennan, M. S., Lehander, M., Wu, B., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01845-6 · Cited by 49 · see citing works
“Differential gene expression analysis was performed with a combination of the Wilcoxon test for differences in expression levels and Fisher’s exact test for expression frequencies, as previously described 67 .”
Self-citationHuuhtanen, J., Awad, S. A., Theodoropoulos, J., Forstén, S., Warfvinge, R., Dufva, O., Bouhlal, J., Dhapola, P., Duàn, H., Laajala, E., et al. (2023) · Leukemia · doi:10.1038/s41375-023-02074-w · Cited by 48 · see citing works
“…co-stimulatory genes in NK cells with and without co-culture of CML cells. g Expression of IFN-γ response score in primary CML LSCs. h Scaled average expressions (avg exp) and proportion of active NK cells expressing (pct.exp) some genes induced in the co-culture of NK cells, in patient samples.”
BackgroundMethodInfluentialSelf-citationNew therapeutic opportunities from dissecting the pre-B leukemia bone marrow microenvironment
Cheung, L. C., Tickner, J. C., Hughes, A. M., Skut, P., Howlett, M., Foley, B. A., Oommen, J., Wells, J. E., He, B., Singh, S., et al. (2018) · Leukemia · doi:10.1038/s41375-018-0144-7 · Cited by 48 · see citing works
“Single-cell transcriptomics has revealed deregulation of stromal cells in patients with chronic myeloid leukemia [60].”
BackgroundImmature CML cells implement a BMP autocrine loop to escape TKI treatment
Grockowiak, É., Laperrousaz, B., Jeanpierre, S., Voeltzel, T., Guyot, B., Gobert, S., Nicolini, F. E., & Maguer‐Satta, V. (2017) · Blood · doi:10.1182/blood-2017-08-801019 · Cited by 46 · see citing works
Monitoring of Minimal Residual Disease (MRD) in Chronic Myeloid Leukemia: Recent Advances
Cumbo, C., Anelli, L., Specchia, G., & Albano, F. (2020) · Cancer Management and Research · doi:10.2147/cmar.s232752 · Cited by 46 · see citing works
Dissecting human disease with single-cell omics: application in model systems and in the clinic
Strzelecka, P. M., Ranzoni, A. M., & Cvejic, A. (2018) · Disease Models & Mechanisms · doi:10.1242/dmm.036525 · Cited by 45 · see citing works
“Furthermore, Giustacchini et al. looked at the molecular signature of stem cells in chronic myeloid leukaemia (CLM), applying scRNA-seq to analyse the CSCs (Giustacchini et al., 2017).”
Mapping and targeting of the leukemic microenvironment
Witkowski, M. T., Kousteni, S., & Aifantis, I. (2019) · The Journal of Experimental Medicine · doi:10.1084/jem.20190589 · Cited by 45 · see citing works
“…al., 2004 The CXCR4/CXCL12 axis and adhesive interactions through CD44 promote leukemic cell survival Schneider et al., 2002; Spoo et al., 2007; Passaro et al., 2015; Pitt et al., 2015; Sison and Brown, 2011; Fedorchenko et al., 2013; Godavarthy et al., 2019; Jin et al., 2006; Krause et al., 2006”
BackgroundRodríguez-Meira, A., O’Sullivan, J., Rahman, H., & Mead, A. J. (2020) · STAR Protocols · doi:10.1016/j.xpro.2020.100125 · Cited by 44 · see citing works
Self-citationMARVEL: an integrated alternative splicing analysis platform for single-cell RNA sequencing data
Wen, W. X., Mead, A. J., & Thongjuea, S. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1260 · Cited by 42 · see citing works
“Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying transcriptional heterogeneity in normal tissues (1–5) and pathological conditions (6–11).”
BackgroundSelf-citationhsa-mir183/EGR1–mediated regulation of E2F1 is required for CML stem/progenitor cell survival
Pellicano, F., Park, L., Hopcroft, L. E. M., Shah, M., Jackson, L., Scott, M. T., Clarke, C. J., Sinclair, A., Abraham, S. A., Hair, A., et al. (2018) · Blood · doi:10.1182/blood-2017-05-783845 · Cited by 44 · see citing works
Jeanpierre, S., Arizkane, K., Thongjuea, S., Grockowiak, É., Geistlich, K., Barral, L., Voeltzel, T., Guillemin, A., Gonin-Giraud, S., Gandrillon, O., et al. (2020) · Haematologica · doi:10.3324/haematol.2019.232793 · Cited by 43 · see citing works
“Single-cell RNA sequencing and Gene-set enrichment analyses were performed as previously described.(7) The BMP signature was designed as presented in the Online Supplementary Table 1.”
BackgroundMethodInfluentialSelf-citationQin, P., Pang, Y., Hou, W., Fu, R., Zhang, Y., Wang, X. J., Meng, G., Liu, Q., Zhu, X., Hong, N., et al. (2021) · Cell Discovery · doi:10.1038/s41421-020-00223-4 · Cited by 43 · see citing works
Exploiting Single-Cell Tools in Gene and Cell Therapy
Bode, D., Cull, A. H., Rubio-Lara, J. A., & Kent, D. G. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.702636 · Cited by 43 · see citing works
Novel approaches to therapy in CML
Bhatia, R. (2017) · Hematology · doi:10.1182/asheducation-2017.1.115 · Cited by 41 · see citing works
“LSC populations vary greatly in their capacity to generate leukemia, associated with variability in gene expression profiles and differential sensitivity to TKIs and other treatments.(9,10) An important question is whether LSC retention on TKI reflects retention of preexisting populations vs.”
BackgroundLearning deep features and topological structure of cells for clustering of scRNA-sequencing data
Wang, H., & Ma, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac068 · Cited by 40 · see citing works
Kok, C. H., Yeung, D. T. O., Liu, L., Watkins, D. B., Leclercq, T. M., Dang, P., Saunders, V. A., Reynolds, J., White, D. L., & Hughes, T. P. (2019) · Blood Advances · doi:10.1182/bloodadvances.2019000195 · Cited by 40 · see citing works
“Among the 17 genes, we found 8 (IGFBP2, SRSF11, BAX,CDKN1B,BNIP3L, FZD7,PRSS57, andRPS28) that overlapped with other TKI resistance and CML progression studies, including the acute myeloid leukemia LSC17 study.(20,21,23,52-56) Particularly, IGFBP2 and PRSS57, which were expressed significantly higher in the EMR failur…”
ResultBencomo-Alvarez, A. E., Rubio, A. J., Olivas, I. M., Gonzalez, M. A., Ellwood, R. A., Fiol, C. R., Eide, C. A., Lara, J. J., Barreto‐Vargas, C., Jave‐Suárez, L. F., et al. (2021) · Oncogene · doi:10.1038/s41388-021-01732-6 · Cited by 41 · see citing works
Single-Cell Genomic Analysis in Plants
Yuan, Y., Lee, H., Hu, H., Scheben, A., & Edwards, D. (2018) · Genes · doi:10.3390/genes9010050 · Cited by 38 · see citing works
“Single-cell genomic analysis has successfully described cancer cell states, for example, of stem cells in leukaemia patients [28] and biological developmental processes such as ageing [29].”
BackgroundMolecular Testing in CML between Old and New Methods: Are We at a Turning Point?
Soverini, S., Bernardi, S., & Galimberti, S. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9123865 · Cited by 39 · see citing works
“This might offer scientists the possibility to design ab initio a personalized therapeutic project [76].”
BackgroundHeterogeneous disease-propagating stem cells in juvenile myelomonocytic leukemia
Louka, E., Povinelli, B., Rodríguez-Meira, A., Buck, G., Wen, W. X., Wang, G., Sousos, N., Ashley, N., Hamblin, A., Booth, C. A. G., et al. (2020) · The Journal of Experimental Medicine · doi:10.1084/jem.20180853 · Cited by 6 · see citing works
“We have shown that in CML and MPN, such an approach can help to resolve normal and malignant stem cells (Giustacchini et al., 2017; Psaila et al., 2020; Rodriguez-Meira et al., 2019).”
BackgroundSelf-citationClinically relevant fusion oncogenes: detection and practical implications
Sorokin, M., Rabushko, E., Rozenberg, J. M., Mohammad, T., Seryakov, A., Sekacheva, M. I., & Buzdin, A. A. (2022) · Therapeutic Advances in Medical Oncology · doi:10.1177/17588359221144108 · Cited by 37 · see citing works
Potential application of cell reprogramming techniques for cancer research
Saito, S., Lin, Y., Nakamura, Y., Eckner, R., Wuputra, K., Kuo, K., Lin, C., & Yokoyama, K. K. (2018) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-018-2924-7 · Cited by 37 · see citing works
Komorowski, L., Fidyt, K., Patkowska, E., & Firczuk, M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21165776 · Cited by 37 · see citing works
“This property was not only limited to Ph+ LSCs, as Ph-negative (Ph-) HSCs in CML patients poorly responding to therapy also showed increased levels of TGF-β and TNF-α signaling, indicating disruption of the whole microenvironment [27].”
BackgroundTargeting Leukemic Stem Cells in Chronic Myeloid Leukemia: Is It Worth the Effort?
Soverini, S., Santis, S. D., Monaldi, C., Bruno, S., & Mancini, M. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22137093 · Cited by 37 · see citing works
Genetic Biomarkers in Chronic Myeloid Leukemia: What Have We Learned So Far?
Abdulmawjood, B., Costa, B. G. L. D. M., Roma‐Rodrigues, C., Baptista, P. V., & Fernandes, A. R. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222212516 · Cited by 36 · see citing works
“FOS (FOS) Fos proto-oncogene, AP-1 transcription factor 2353 Regulation of cell proliferation, differentiation, and transformation TKIs 1 resistance [44]”
BackgroundInfluential
- Authors
- Giustacchini, A., Thongjuea, S., Barkas, N., Woll, P. S., Povinelli, B. J., Booth, C. A. G., Sopp, P., Norfo, R., Rodriguez-Meira, A., Ashley, N., Jamieson, L., Vyas, P., Anderson, K., Segerstolpe, Å., Qian, H., Olsson-Strömberg, U., Mustjoki, S., Sandberg, R., Jacobsen, S. E. W., & Mead, A. J.
- Affiliations
- University of Oxford (GB)
- Oslo University Hospital (NO)
- Karolinska Institutet (SE)
- University of Helsinki (FI)
- Karolinska University Hospital (SE)
- Churchill Hospital (GB)
- Corresponding author
- Jacobsen, Sten Eirik W
- Mead, Adam J
- ORCID
- Venue
- Nature Medicine
- Published
- 2017-05-15
- Volume
- 23
- Issue
- 6
- Pages
- 692-702
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2614253029
- Cited by
- 403
- References
- 63
- Citations by year
- 2026: 16 · 2025: 27 · 2024: 44 · 2023: 44 · 2022: 44 · 2021: 57 · 2020: 65 · 2019: 43 · 2018: 38 · 2017: 24
- FWCI
- 35.89
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- Funders
- Cancer Research UK (S_3279)
- Medical Research Council (MC_PC_13073, MC_PC_12020, MR/M00919X/1, MR/L008963/1, MR/L006340/1, MC_UU_00016/5)
- Topics
- Health Sciences › Medicine › Hematology › Chronic Myeloid Leukemia Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Keywords
- chronic myeloid leukemia, cancer stem cells, intratumoral heterogeneity, targeted therapy resistance, somatic mutation detection, single-cell transcriptomics, hematopoietic stem cells, disease progression, blast crisis, subclonal evolution, molecular signatures
- MeSH terms
- Adult, Aged, Blast Crisis, Female, Flow Cytometry, Humans, Male, Middle Aged, Neoplastic Stem Cells, Leukemia, Myelogenous, Chronic, BCR-ABL Positive, Gene Library, Gene Expression Regulation, Neoplastic, Genes, abl, In Situ Hybridization, Fluorescence, Sequence Analysis, DNA, Sequence Analysis, RNA, Reverse Transcriptase Polymerase Chain Reaction, Gene Expression Profiling, Chromatin Immunoprecipitation, Core Binding Factor Alpha 2 Subunit, Young Adult, Single-Cell Analysis, Transcriptome
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