Skip to main content
Top
Published in: Journal of Hematology & Oncology 1/2020

Open Access 01-12-2020 | Acute Myeloid Leukemia | Research

A single-cell survey of cellular hierarchy in acute myeloid leukemia

Authors: Junqing Wu, Yanyu Xiao, Jie Sun, Huiyu Sun, Haide Chen, Yuanyuan Zhu, Huarui Fu, Chengxuan Yu, Weigao E., Shujing Lai, Lifeng Ma, Jiaqi Li, Lijiang Fei, Mengmeng Jiang, Jingjing Wang, Fang Ye, Renying Wang, Ziming Zhou, Guodong Zhang, Tingyue Zhang, Qiong Ding, Zou Wang, Sheng Hao, Lizhen Liu, Weiyan Zheng, Jingsong He, Weijia Huang, Yungui Wang, Jin Xie, Tiefeng Li, Tao Cheng, Xiaoping Han, He Huang, Guoji Guo

Published in: Journal of Hematology & Oncology | Issue 1/2020

Login to get access

Abstract

Background

Acute myeloid leukemia (AML) is a fatal hematopoietic malignancy and has a prognosis that varies with its genetic complexity. However, there has been no appropriate integrative analysis on the hierarchy of different AML subtypes.

Methods

Using Microwell-seq, a high-throughput single-cell mRNA sequencing platform, we analyzed the cellular hierarchy of bone marrow samples from 40 patients and 3 healthy donors. We also used single-cell single-molecule real-time (SMRT) sequencing to investigate the clonal heterogeneity of AML cells.

Results

From the integrative analysis of 191727 AML cells, we established a single-cell AML landscape and identified an AML progenitor cell cluster with novel AML markers. Patients with ribosomal protein high progenitor cells had a low remission rate. We deduced two types of AML with diverse clinical outcomes. We traced mitochondrial mutations in the AML landscape by combining Microwell-seq with SMRT sequencing. We propose the existence of a phenotypic “cancer attractor” that might help to define a common phenotype for AML progenitor cells. Finally, we explored the potential drug targets by making comparisons between the AML landscape and the Human Cell Landscape.

Conclusions

We identified a key AML progenitor cell cluster. A high ribosomal protein gene level indicates the poor prognosis. We deduced two types of AML and explored the potential drug targets. Our results suggest the existence of a cancer attractor.
Appendix
Available only for authorised users
Literature
1.
go back to reference Dohner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, Buchner T, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;139:424–7.CrossRef Dohner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, Buchner T, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;139:424–7.CrossRef
2.
go back to reference Bullinger L, Dohner K, Dohner H. Genomics of acute myeloid leukemia diagnosis and pathways. J Clin Oncol. 2017;35(9):934–46.PubMedCrossRef Bullinger L, Dohner K, Dohner H. Genomics of acute myeloid leukemia diagnosis and pathways. J Clin Oncol. 2017;35(9):934–46.PubMedCrossRef
3.
go back to reference Luppi M, Fabbiano F, Visani G, Martinelli G, Venditti A. Novel agents for acute myeloid leukemia. Cancers (Basel). 2018;10:11.CrossRef Luppi M, Fabbiano F, Visani G, Martinelli G, Venditti A. Novel agents for acute myeloid leukemia. Cancers (Basel). 2018;10:11.CrossRef
4.
go back to reference Timilshina N, Breunis H, Tomlinson GA, Brandwein JM, Buckstein R, Durbano S, et al. Long-term recovery of quality of life and physical function over three years in adult survivors of acute myeloid leukemia after intensive chemotherapy. Leukemia. 2019;33(1):15–25.PubMedCrossRef Timilshina N, Breunis H, Tomlinson GA, Brandwein JM, Buckstein R, Durbano S, et al. Long-term recovery of quality of life and physical function over three years in adult survivors of acute myeloid leukemia after intensive chemotherapy. Leukemia. 2019;33(1):15–25.PubMedCrossRef
5.
6.
go back to reference Klco JM, Spencer DH, Miller CA, Griffith M, Lamprecht TL, O'Laughlin M, et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell. 2014;25(3):379–92.PubMedPubMedCentralCrossRef Klco JM, Spencer DH, Miller CA, Griffith M, Lamprecht TL, O'Laughlin M, et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell. 2014;25(3):379–92.PubMedPubMedCentralCrossRef
7.
go back to reference Tamamyan G, Kadia T, Ravandi F, Borthakur G, Cortes J, Jabbour E, et al. Frontline treatment of acute myeloid leukemia in adults. Crit Rev Oncol Hematol. 2017;110:20–34.PubMedCrossRef Tamamyan G, Kadia T, Ravandi F, Borthakur G, Cortes J, Jabbour E, et al. Frontline treatment of acute myeloid leukemia in adults. Crit Rev Oncol Hematol. 2017;110:20–34.PubMedCrossRef
8.
go back to reference Zeijlemaker W, Grob T, Meijer R, Hanekamp D, Kelder A, Carbaat-Ham JC, et al. CD34(+)CD38(-) leukemic stem cell frequency to predict outcome in acute myeloid leukemia. Leukemia. 2018. Zeijlemaker W, Grob T, Meijer R, Hanekamp D, Kelder A, Carbaat-Ham JC, et al. CD34(+)CD38(-) leukemic stem cell frequency to predict outcome in acute myeloid leukemia. Leukemia. 2018.
11.
go back to reference Povinelli BJ, Rodriguez-Meira A, Mead AJ. Single cell analysis of normal and leukemic hematopoiesis. Mol Aspects Med. 2017. Povinelli BJ, Rodriguez-Meira A, Mead AJ. Single cell analysis of normal and leukemic hematopoiesis. Mol Aspects Med. 2017.
12.
go back to reference Pellegrino M, Sciambi A, Treusch S, Durruthy-Durruthy R, Gokhale K, Jacob J, et al. High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics. Genome Res. 2018;28(9):1345–52.PubMedPubMedCentralCrossRef Pellegrino M, Sciambi A, Treusch S, Durruthy-Durruthy R, Gokhale K, Jacob J, et al. High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics. Genome Res. 2018;28(9):1345–52.PubMedPubMedCentralCrossRef
13.
go back to reference 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(281):281re2.PubMedPubMedCentralCrossRef 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(281):281re2.PubMedPubMedCentralCrossRef
14.
15.
go back to reference Shenoy N, Kessel R, Bhagat TD, Bhattacharyya S, Yu Y, McMahon C, et al. Alterations in the ribosomal machinery in cancer and hematologic disorders. J Hematol Oncol. 2012;5:32.PubMedPubMedCentralCrossRef Shenoy N, Kessel R, Bhagat TD, Bhattacharyya S, Yu Y, McMahon C, et al. Alterations in the ribosomal machinery in cancer and hematologic disorders. J Hematol Oncol. 2012;5:32.PubMedPubMedCentralCrossRef
17.
go back to reference Khajuria RK, Munschauer M, Ulirsch JC, Fiorini C, Ludwig LS, McFarland SK, et al. Ribosome levels selectively regulate translation and lineage commitment in human hematopoiesis. Cell. 2018;173(1):90–103 e19.PubMedPubMedCentralCrossRef Khajuria RK, Munschauer M, Ulirsch JC, Fiorini C, Ludwig LS, McFarland SK, et al. Ribosome levels selectively regulate translation and lineage commitment in human hematopoiesis. Cell. 2018;173(1):90–103 e19.PubMedPubMedCentralCrossRef
18.
go back to reference Liu JM, Ellis SR. Ribosomes and marrow failure: coincidental association or molecular paradigm? Blood. 2006;107(12):4583–8.PubMedCrossRef Liu JM, Ellis SR. Ribosomes and marrow failure: coincidental association or molecular paradigm? Blood. 2006;107(12):4583–8.PubMedCrossRef
19.
go back to reference Ludwig LS, Lareau CA, Ulirsch JC, Christian E, Muus C, Li LH, et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell. 2019;176(6):1325–39 e22.PubMedPubMedCentralCrossRef Ludwig LS, Lareau CA, Ulirsch JC, Christian E, Muus C, Li LH, et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell. 2019;176(6):1325–39 e22.PubMedPubMedCentralCrossRef
20.
go back to reference Xu J, Nuno K, Litzenburger UM, Qi Y, Corces MR, Majeti R, et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. Elife. 2019;8. Xu J, Nuno K, Litzenburger UM, Qi Y, Corces MR, Majeti R, et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. Elife. 2019;8.
21.
go back to reference Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, et al. Mapping the mouse cell atlas by microwell-seq. Cell. 2018;172(5):1091–107 e17.PubMedCrossRef Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, et al. Mapping the mouse cell atlas by microwell-seq. Cell. 2018;172(5):1091–107 e17.PubMedCrossRef
22.
go back to reference Velten L, Haas SF, Raffel S, Blaszkiewicz S, Islam S, Hennig BP, et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat Cell Biol. 2017;19(4):271–81.PubMedPubMedCentralCrossRef Velten L, Haas SF, Raffel S, Blaszkiewicz S, Islam S, Hennig BP, et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat Cell Biol. 2017;19(4):271–81.PubMedPubMedCentralCrossRef
23.
go back to reference Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell. 2018;174(5):1293–308 e36.PubMedPubMedCentralCrossRef Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell. 2018;174(5):1293–308 e36.PubMedPubMedCentralCrossRef
24.
go back to reference Lai S, Huang W, Xu Y, Jiang M, Chen H, Cheng C, et al. Comparative transcriptomic analysis of hematopoietic system between human and mouse by Microwell-seq. Cell Discov. 2018;4:34.PubMedPubMedCentralCrossRef Lai S, Huang W, Xu Y, Jiang M, Chen H, Cheng C, et al. Comparative transcriptomic analysis of hematopoietic system between human and mouse by Microwell-seq. Cell Discov. 2018;4:34.PubMedPubMedCentralCrossRef
25.
go back to reference Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, et al. Construction of a human cell landscape at single-cell level. Nature. 2020. Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, et al. Construction of a human cell landscape at single-cell level. Nature. 2020.
26.
go back to reference Vodyanik MA, Thomson JA, Slukvin II. Leukosialin (CD43) defines hematopoietic progenitors in human embryonic stem cell differentiation cultures. Blood. 2006;108(6):2095–105.PubMedPubMedCentralCrossRef Vodyanik MA, Thomson JA, Slukvin II. Leukosialin (CD43) defines hematopoietic progenitors in human embryonic stem cell differentiation cultures. Blood. 2006;108(6):2095–105.PubMedPubMedCentralCrossRef
27.
go back to reference Novershtern N, Subramanian A, Lawton LN, Mak RH, Haining WN, McConkey ME, et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell. 2011;144(2):296–309.PubMedPubMedCentralCrossRef Novershtern N, Subramanian A, Lawton LN, Mak RH, Haining WN, McConkey ME, et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell. 2011;144(2):296–309.PubMedPubMedCentralCrossRef
28.
go back to reference Okada S, Fukuda T, Inada K, Tokuhisa T. Prolonged expression of c-fos suppresses cell cycle entry of dormant hematopoietic stem cells. Blood. 1999;93(3):816–25.PubMedCrossRef Okada S, Fukuda T, Inada K, Tokuhisa T. Prolonged expression of c-fos suppresses cell cycle entry of dormant hematopoietic stem cells. Blood. 1999;93(3):816–25.PubMedCrossRef
29.
go back to reference Jan T, Pittois K, NicolaI P, Joseph M, Angel P. Collagenase-3 (MMP-13) and integral membrane protein 2a (Itm2a) are marker genes of chondrogenic/osteoblastic cells in bone formation: sequential temporal, and spatial expression of Itm2a, alkaline phosphatase, MMP-13, and osteocalcin in the mouse. J Bone Miner Res. 2000;15(7):1257–65.CrossRef Jan T, Pittois K, NicolaI P, Joseph M, Angel P. Collagenase-3 (MMP-13) and integral membrane protein 2a (Itm2a) are marker genes of chondrogenic/osteoblastic cells in bone formation: sequential temporal, and spatial expression of Itm2a, alkaline phosphatase, MMP-13, and osteocalcin in the mouse. J Bone Miner Res. 2000;15(7):1257–65.CrossRef
30.
go back to reference Bertoli S, Paubelle E, Berard E, Saland E, Thomas X, Tavitian S, et al. Ferritin heavy/light chain (FTH1/FTL) expression, serum ferritin levels, and their functional as well as prognostic roles in acute myeloid leukemia. Eur J Haematol. 2019;102(2):131–42.PubMedCrossRef Bertoli S, Paubelle E, Berard E, Saland E, Thomas X, Tavitian S, et al. Ferritin heavy/light chain (FTH1/FTL) expression, serum ferritin levels, and their functional as well as prognostic roles in acute myeloid leukemia. Eur J Haematol. 2019;102(2):131–42.PubMedCrossRef
31.
go back to reference Laverdiere I, Boileau M, Herold T, Rak J, Berdel WE, Wormann B, et al. Complement cascade gene expression defines novel prognostic subgroups of acute myeloid leukemia. Exp Hematol. 2016;44(11):1039–43 e10.PubMedCrossRef Laverdiere I, Boileau M, Herold T, Rak J, Berdel WE, Wormann B, et al. Complement cascade gene expression defines novel prognostic subgroups of acute myeloid leukemia. Exp Hematol. 2016;44(11):1039–43 e10.PubMedCrossRef
32.
go back to reference Bertrand J, Despeaux M, Joly S, Bourogaa E, Gallay N, Demur C, et al. Sex differences in the GSK3beta-mediated survival of adherent leukemic progenitors. Oncogene. 2012;31(6):694–705.PubMedCrossRef Bertrand J, Despeaux M, Joly S, Bourogaa E, Gallay N, Demur C, et al. Sex differences in the GSK3beta-mediated survival of adherent leukemic progenitors. Oncogene. 2012;31(6):694–705.PubMedCrossRef
33.
go back to reference Melillo L, Cascavilla N, Lombardi G, Carotenuto M. P M. Prognostic relevance of serum beta 2-microglobulin in acute myeloid leukemia. Leukemia. 1992;6(10):1076–8.PubMed Melillo L, Cascavilla N, Lombardi G, Carotenuto M. P M. Prognostic relevance of serum beta 2-microglobulin in acute myeloid leukemia. Leukemia. 1992;6(10):1076–8.PubMed
34.
go back to reference Bertazzoni U, Brusamolino E, Isernia P, Scovassi AI, Torsello S, Lazzarino M, et al. Prognostic significance of terminal transferase and adenosine deaminase in acute and chronic myeloid leukemia. Blood. 1982;60(3):685–92.PubMedCrossRef Bertazzoni U, Brusamolino E, Isernia P, Scovassi AI, Torsello S, Lazzarino M, et al. Prognostic significance of terminal transferase and adenosine deaminase in acute and chronic myeloid leukemia. Blood. 1982;60(3):685–92.PubMedCrossRef
35.
go back to reference Vaikari VP, Du Y, Wu S, Zhang T, Metzeler K, Batcha AMN, et al. Clinical and preclinical characterization of CD99 isoforms in acute myeloid leukemia. Haematologica. 2019. Vaikari VP, Du Y, Wu S, Zhang T, Metzeler K, Batcha AMN, et al. Clinical and preclinical characterization of CD99 isoforms in acute myeloid leukemia. Haematologica. 2019.
36.
go back to reference Liu L, Luo C, Luo Y, Chen L, Liu Y, Wang Y, et al. MRPL33 and its splicing regulator hnRNPK are required for mitochondria function and implicated in tumor progression. Oncogene. 2018;37(1):86–94.PubMedCrossRef Liu L, Luo C, Luo Y, Chen L, Liu Y, Wang Y, et al. MRPL33 and its splicing regulator hnRNPK are required for mitochondria function and implicated in tumor progression. Oncogene. 2018;37(1):86–94.PubMedCrossRef
37.
go back to reference Chiang CY, Pan CC, Chang HY, Lai MD, Tzai TS, Tsai YS, et al. SH3BGRL3 protein as a potential prognostic biomarker for urothelial carcinoma: a novel binding partner of epidermal growth factor receptor. Clin Cancer Res. 2015;21(24):5601–11.PubMedCrossRef Chiang CY, Pan CC, Chang HY, Lai MD, Tzai TS, Tsai YS, et al. SH3BGRL3 protein as a potential prognostic biomarker for urothelial carcinoma: a novel binding partner of epidermal growth factor receptor. Clin Cancer Res. 2015;21(24):5601–11.PubMedCrossRef
38.
go back to reference Bouchal P, Dvorakova M, Roumeliotis T, Bortlícek Z, Ihnatova I, Prochazkova I, et al. Combined proteomics and transcriptomics identifies carboxypeptidase B1 and nuclear factor B (NF- B) associated proteins as putative biomarkers of metastasis in low grade breast cancer. Mol Cell Proteomics. 2015;14(7):1814–30.PubMedPubMedCentralCrossRef Bouchal P, Dvorakova M, Roumeliotis T, Bortlícek Z, Ihnatova I, Prochazkova I, et al. Combined proteomics and transcriptomics identifies carboxypeptidase B1 and nuclear factor B (NF- B) associated proteins as putative biomarkers of metastasis in low grade breast cancer. Mol Cell Proteomics. 2015;14(7):1814–30.PubMedPubMedCentralCrossRef
39.
go back to reference Bjorkblom B, Padzik A, Mohammad H, Westerlund N, Komulainen E, Hollos P, et al. c-Jun N-terminal kinase phosphorylation of MARCKSL1 determines actin stability and migration in neurons and in cancer cells. Mol Cell Biol. 2012;32(17):3513–26.PubMedPubMedCentralCrossRef Bjorkblom B, Padzik A, Mohammad H, Westerlund N, Komulainen E, Hollos P, et al. c-Jun N-terminal kinase phosphorylation of MARCKSL1 determines actin stability and migration in neurons and in cancer cells. Mol Cell Biol. 2012;32(17):3513–26.PubMedPubMedCentralCrossRef
40.
go back to reference Scotlandi K, Remondini D, Castellani G, Manara MC, Nardi F, Cantiani L, et al. Overcoming resistance to conventional drugs in Ewing sarcoma and identification of molecular predictors of outcome. J Clin Oncol. 2009;27(13):2209–16.PubMedCrossRef Scotlandi K, Remondini D, Castellani G, Manara MC, Nardi F, Cantiani L, et al. Overcoming resistance to conventional drugs in Ewing sarcoma and identification of molecular predictors of outcome. J Clin Oncol. 2009;27(13):2209–16.PubMedCrossRef
41.
go back to reference Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet. 2016;48(8):838–47.PubMedPubMedCentralCrossRef Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet. 2016;48(8):838–47.PubMedPubMedCentralCrossRef
42.
go back to reference Shahrin NH, Diakiw S, Dent LA, Brown AL, D'Andrea RJ. Conditional knockout mice demonstrate function of Klf5 as a myeloid transcription factor. Blood. 2016;128(1):55–9.PubMedCrossRef Shahrin NH, Diakiw S, Dent LA, Brown AL, D'Andrea RJ. Conditional knockout mice demonstrate function of Klf5 as a myeloid transcription factor. Blood. 2016;128(1):55–9.PubMedCrossRef
43.
go back to reference Kawaida R, Ohtsuka T, Okutsu J, Takahashi T, Kadono Y, Oda H, et al. Jun dimerization protein 2 (JDP2), a member of the AP-1 family of transcription factor, mediates osteoclast differentiation induced by RANKL. J Exp Med. 2003;197(8):1029–35.PubMedPubMedCentralCrossRef Kawaida R, Ohtsuka T, Okutsu J, Takahashi T, Kadono Y, Oda H, et al. Jun dimerization protein 2 (JDP2), a member of the AP-1 family of transcription factor, mediates osteoclast differentiation induced by RANKL. J Exp Med. 2003;197(8):1029–35.PubMedPubMedCentralCrossRef
44.
go back to reference Chatterjee SS, Biswas M, Boila LD, Banerjee D, Sengupta A. SMARCB1 deficiency integrates epigenetic signals to oncogenic gene expression program maintenance in human acute myeloid leukemia. Mol Cancer Res. 2018;16(5):791–804.PubMedCrossRef Chatterjee SS, Biswas M, Boila LD, Banerjee D, Sengupta A. SMARCB1 deficiency integrates epigenetic signals to oncogenic gene expression program maintenance in human acute myeloid leukemia. Mol Cancer Res. 2018;16(5):791–804.PubMedCrossRef
45.
go back to reference Sun Y, Zhou B, Mao F, Xu J, Miao H, Zou Z, et al. HOXA9 reprograms the enhancer landscape to promote leukemogenesis. Cancer Cell. 2018;34(4):643–58 e5.PubMedPubMedCentralCrossRef Sun Y, Zhou B, Mao F, Xu J, Miao H, Zou Z, et al. HOXA9 reprograms the enhancer landscape to promote leukemogenesis. Cancer Cell. 2018;34(4):643–58 e5.PubMedPubMedCentralCrossRef
46.
go back to reference Roche J, Zeng C, Baron A, Gadgil S, Gemmill RM, Tigaud I, et al. Hox expression in AML identifies a distinct subset of patients with intermediate cytogenetics. Leukemia. 2004;18(6):1059–63.PubMedCrossRef Roche J, Zeng C, Baron A, Gadgil S, Gemmill RM, Tigaud I, et al. Hox expression in AML identifies a distinct subset of patients with intermediate cytogenetics. Leukemia. 2004;18(6):1059–63.PubMedCrossRef
47.
go back to reference Ozeki K, Kiyoi H, Hirose Y, Iwai M, Ninomiya M, Kodera Y, et al. Biologic and clinical significance of the FLT3 transcript level in acute myeloid leukemia. Blood. 2004;103(5):1901–8.PubMedCrossRef Ozeki K, Kiyoi H, Hirose Y, Iwai M, Ninomiya M, Kodera Y, et al. Biologic and clinical significance of the FLT3 transcript level in acute myeloid leukemia. Blood. 2004;103(5):1901–8.PubMedCrossRef
48.
go back to reference Walter RB, Othus M, Burnett AK, Lowenberg B, Kantarjian HM, Ossenkoppele GJ, et al. Significance of FAB subclassification of “acute myeloid leukemia, NOS” in the 2008 WHO classification: analysis of 5848 newly diagnosed patients. Blood. 2013;121(13):2424–31.PubMedPubMedCentralCrossRef Walter RB, Othus M, Burnett AK, Lowenberg B, Kantarjian HM, Ossenkoppele GJ, et al. Significance of FAB subclassification of “acute myeloid leukemia, NOS” in the 2008 WHO classification: analysis of 5848 newly diagnosed patients. Blood. 2013;121(13):2424–31.PubMedPubMedCentralCrossRef
49.
go back to reference Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127(20):2391–405.PubMedCrossRef Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127(20):2391–405.PubMedCrossRef
50.
go back to reference Thol F, Schlenk RF, Heuser M, Ganser A. How I treat refractory and early relapsed acute myeloid leukemia. Blood. 2015;126(3):319–27.PubMedCrossRef Thol F, Schlenk RF, Heuser M, Ganser A. How I treat refractory and early relapsed acute myeloid leukemia. Blood. 2015;126(3):319–27.PubMedCrossRef
51.
go back to reference Wallace DC, Chalkia D. Mitochondrial DNA genetics and the heteroplasmy conundrum in evolution and disease. Cold Spring Harb Perspect Biol. 2013;5(11):a021220.PubMedPubMedCentralCrossRef Wallace DC, Chalkia D. Mitochondrial DNA genetics and the heteroplasmy conundrum in evolution and disease. Cold Spring Harb Perspect Biol. 2013;5(11):a021220.PubMedPubMedCentralCrossRef
52.
go back to reference Mojtahedi M, Skupin A, Zhou J, Castano IG, Leong-Quong RY, Chang H, et al. Cell fate decision as high-dimensional critical state transition. PLoS Biol. 2016;14(12):e2000640.PubMedPubMedCentralCrossRef Mojtahedi M, Skupin A, Zhou J, Castano IG, Leong-Quong RY, Chang H, et al. Cell fate decision as high-dimensional critical state transition. PLoS Biol. 2016;14(12):e2000640.PubMedPubMedCentralCrossRef
53.
go back to reference Huang S, Eichler G, Bar-Yam Y, Ingber DE. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys Rev Lett. 2005;94(12):128701.PubMedCrossRef Huang S, Eichler G, Bar-Yam Y, Ingber DE. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys Rev Lett. 2005;94(12):128701.PubMedCrossRef
54.
go back to reference Lachmann A, Giorgi FM, Lopez G, Califanoy A. ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics. 2016;32(14):2233–5.PubMedPubMedCentralCrossRef Lachmann A, Giorgi FM, Lopez G, Califanoy A. ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics. 2016;32(14):2233–5.PubMedPubMedCentralCrossRef
55.
go back to reference Fortier JM, Payton JE, Cahan P, Ley TJ, Walter MJ, Graubert TA. POU4F1 is associated with t(8;21) acute myeloid leukemia and contributes directly to its unique transcriptional signature. Leukemia. 2010;24(5):950–7.PubMedPubMedCentralCrossRef Fortier JM, Payton JE, Cahan P, Ley TJ, Walter MJ, Graubert TA. POU4F1 is associated with t(8;21) acute myeloid leukemia and contributes directly to its unique transcriptional signature. Leukemia. 2010;24(5):950–7.PubMedPubMedCentralCrossRef
56.
go back to reference Tomasello E, Vivier E. KARAP/DAP12/TYROBP: three names and a multiplicity of biological functions. Eur J Immunol. 2005;35(6):1670–7.PubMedCrossRef Tomasello E, Vivier E. KARAP/DAP12/TYROBP: three names and a multiplicity of biological functions. Eur J Immunol. 2005;35(6):1670–7.PubMedCrossRef
57.
58.
go back to reference Delom F, Nazaraliyev A, Fessart D. The role of protein disulphide isomerase AGR2 in the tumour niche. Biol Cell. 2018;110(12):271–82.PubMedCrossRef Delom F, Nazaraliyev A, Fessart D. The role of protein disulphide isomerase AGR2 in the tumour niche. Biol Cell. 2018;110(12):271–82.PubMedCrossRef
59.
go back to reference Lo PHY, Lung HL, Cheung AKL, Apte SS, Chan KW, Kwong FM, et al. Extracellular protease ADAMTS9 suppresses esophageal and nasopharyngeal carcinoma tumor formation by inhibiting angiogenesis. Cancer Res. 2010;70(13):5567–76.PubMedPubMedCentralCrossRef Lo PHY, Lung HL, Cheung AKL, Apte SS, Chan KW, Kwong FM, et al. Extracellular protease ADAMTS9 suppresses esophageal and nasopharyngeal carcinoma tumor formation by inhibiting angiogenesis. Cancer Res. 2010;70(13):5567–76.PubMedPubMedCentralCrossRef
60.
go back to reference HAZNEDAROGLU IC, MALKAN UY. Local bone marrow renin-angiotensin system in the genesis of leukemia and other malignancies. Eur Rev Med Pharmacol Sci. 2016;20:4089–111.PubMed HAZNEDAROGLU IC, MALKAN UY. Local bone marrow renin-angiotensin system in the genesis of leukemia and other malignancies. Eur Rev Med Pharmacol Sci. 2016;20:4089–111.PubMed
61.
go back to reference Dollt C, Michel J, Kloss L, Melchers S, Schledzewski K, Becker K, et al. The novel immunoglobulin super family receptor SLAMF9 identified in TAM of murine and human melanoma influences pro-inflammatory cytokine secretion and migration. Cell Death Dis. 2018;9(10):939.PubMedPubMedCentralCrossRef Dollt C, Michel J, Kloss L, Melchers S, Schledzewski K, Becker K, et al. The novel immunoglobulin super family receptor SLAMF9 identified in TAM of murine and human melanoma influences pro-inflammatory cytokine secretion and migration. Cell Death Dis. 2018;9(10):939.PubMedPubMedCentralCrossRef
63.
go back to reference Hedlund E, Deng Q. Single-cell RNA sequencing: technical advancements and biological applications. Mol Asp Med. 2018;59:36–46.CrossRef Hedlund E, Deng Q. Single-cell RNA sequencing: technical advancements and biological applications. Mol Asp Med. 2018;59:36–46.CrossRef
65.
go back to reference Wang L, Livak KJ, Wu CJ. High-dimension single-cell analysis applied to cancer. Mol Asp Med. 2018;59:70–84.CrossRef Wang L, Livak KJ, Wu CJ. High-dimension single-cell analysis applied to cancer. Mol Asp Med. 2018;59:70–84.CrossRef
66.
go back to reference Smith CC, Paguirigan A, Jeschke GR, Lin KC, Massi E, Tarver T, et al. Heterogeneous resistance to quizartinib in acute myeloid leukemia revealed by single-cell analysis. Blood. 2017;130(1):48–58.PubMedPubMedCentralCrossRef Smith CC, Paguirigan A, Jeschke GR, Lin KC, Massi E, Tarver T, et al. Heterogeneous resistance to quizartinib in acute myeloid leukemia revealed by single-cell analysis. Blood. 2017;130(1):48–58.PubMedPubMedCentralCrossRef
67.
go back to reference Warfvinge R, Geironson L, Sommarin MNE, Lang S, Karlsson C. Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML. Blood. 2017;129:2384–94.PubMedPubMedCentralCrossRef Warfvinge R, Geironson L, Sommarin MNE, Lang S, Karlsson C. Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML. Blood. 2017;129:2384–94.PubMedPubMedCentralCrossRef
68.
go back to reference Good Z, Sarno J, Jager A, Samusik N, Aghaeepour N, Simonds EF, et al. Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat Med. 2018. Good Z, Sarno J, Jager A, Samusik N, Aghaeepour N, Simonds EF, et al. Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat Med. 2018.
69.
go back to reference de Las H-RA, Perucho L, Paciucci R, Vilardell J, LL ME. Ribosomal proteins as novel players in tumorigenesis. Cancer Metastasis Rev. 2014;33(1):115–41. de Las H-RA, Perucho L, Paciucci R, Vilardell J, LL ME. Ribosomal proteins as novel players in tumorigenesis. Cancer Metastasis Rev. 2014;33(1):115–41.
71.
go back to reference Fumagalli S, Ivanenkov VV, Teng T, Thomas G. Suprainduction of p53 by disruption of 40S and 60S ribosome biogenesis leads to the activation of a novel G2/M checkpoint. Genes Dev. 2012;26(10):1028–40.PubMedPubMedCentralCrossRef Fumagalli S, Ivanenkov VV, Teng T, Thomas G. Suprainduction of p53 by disruption of 40S and 60S ribosome biogenesis leads to the activation of a novel G2/M checkpoint. Genes Dev. 2012;26(10):1028–40.PubMedPubMedCentralCrossRef
72.
go back to reference Derenzini E, Rossi A, Trere D. Treating hematological malignancies with drugs inhibiting ribosome biogenesis: when and why. J Hematol Oncol. 2018;11(1):75.PubMedPubMedCentralCrossRef Derenzini E, Rossi A, Trere D. Treating hematological malignancies with drugs inhibiting ribosome biogenesis: when and why. J Hematol Oncol. 2018;11(1):75.PubMedPubMedCentralCrossRef
73.
go back to reference 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(6):1265–81 e24.PubMedPubMedCentralCrossRef 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(6):1265–81 e24.PubMedPubMedCentralCrossRef
74.
go back to reference Singh M, Al-Eryani G, Carswell S, Ferguson JM, Blackburn J, Barton K, et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat Commun. 2019;16(10):3120.CrossRef Singh M, Al-Eryani G, Carswell S, Ferguson JM, Blackburn J, Barton K, et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat Commun. 2019;16(10):3120.CrossRef
75.
go back to reference Weirather JL, de Cesare M, Wang Y, Piazza P, Sebastiano V, Wang XJ, et al. Comprehensive comparison of pacific biosciences and oxford nanopore technologies and their applications to transcriptome analysis. F1000Res. 2017;6:100.PubMedPubMedCentralCrossRef Weirather JL, de Cesare M, Wang Y, Piazza P, Sebastiano V, Wang XJ, et al. Comprehensive comparison of pacific biosciences and oxford nanopore technologies and their applications to transcriptome analysis. F1000Res. 2017;6:100.PubMedPubMedCentralCrossRef
76.
go back to reference Stewart JB, Chinnery PF. The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nat Rev Genet. 2015;16(9):530–42.PubMedCrossRef Stewart JB, Chinnery PF. The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nat Rev Genet. 2015;16(9):530–42.PubMedCrossRef
77.
go back to reference Li C, Wang J. Quantifying the landscape for development and cancer from a core cancer stem cell circuit. Cancer Res. 2015;75(13):2607–18.PubMedCrossRef Li C, Wang J. Quantifying the landscape for development and cancer from a core cancer stem cell circuit. Cancer Res. 2015;75(13):2607–18.PubMedCrossRef
78.
go back to reference CH W. The strategy of the genes. London: Allen and Unwin; 1957. CH W. The strategy of the genes. London: Allen and Unwin; 1957.
79.
go back to reference Lia Q, Wennborga A, Aurellb E, Dekelc E, Zoua J-Z, Xud Y, et al. Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape. PNAS. 2016;113(10):2672–7.CrossRef Lia Q, Wennborga A, Aurellb E, Dekelc E, Zoua J-Z, Xud Y, et al. Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape. PNAS. 2016;113(10):2672–7.CrossRef
80.
go back to reference Huang S, Kauffman S. How to escape the cancer attractor: rationale and limitations of multi-target drugs. Semin Cancer Biol. 2013;23(4):270–8.PubMedCrossRef Huang S, Kauffman S. How to escape the cancer attractor: rationale and limitations of multi-target drugs. Semin Cancer Biol. 2013;23(4):270–8.PubMedCrossRef
81.
go back to reference DiNardo CD, Wei AH. How I treat acute myeloid leukemia in the era of new drugs. Blood. 2020;135(2):85–96.PubMedCrossRef DiNardo CD, Wei AH. How I treat acute myeloid leukemia in the era of new drugs. Blood. 2020;135(2):85–96.PubMedCrossRef
84.
go back to reference Leung WK, Workineh A, Mukhi S, Tzannou I, Brenner D, Watanabe N, et al. Evaluation of cyclin A1-specific T cells as a potential treatment for acute myeloid leukemia. Blood Adv. 2020;4(2):387–97.PubMedPubMedCentralCrossRef Leung WK, Workineh A, Mukhi S, Tzannou I, Brenner D, Watanabe N, et al. Evaluation of cyclin A1-specific T cells as a potential treatment for acute myeloid leukemia. Blood Adv. 2020;4(2):387–97.PubMedPubMedCentralCrossRef
85.
go back to reference Gopal Krishnan PD, Golden E, Woodward EA, Pavlos NJ, Blancafort P. Rab GTPases: emerging oncogenes and tumor suppressive regulators for the editing of survival pathways in cancer. Cancers (Basel). 2020;12:2.CrossRef Gopal Krishnan PD, Golden E, Woodward EA, Pavlos NJ, Blancafort P. Rab GTPases: emerging oncogenes and tumor suppressive regulators for the editing of survival pathways in cancer. Cancers (Basel). 2020;12:2.CrossRef
86.
go back to reference Cho SH, Kuo IY, Lu PF, Tzeng HT, Lai WW, Su WC, et al. Rab37 mediates exocytosis of secreted frizzled-related protein 1 to inhibit Wnt signaling and thus suppress lung cancer stemness. Cell Death Dis. 2018;9(9):868.PubMedPubMedCentralCrossRef Cho SH, Kuo IY, Lu PF, Tzeng HT, Lai WW, Su WC, et al. Rab37 mediates exocytosis of secreted frizzled-related protein 1 to inhibit Wnt signaling and thus suppress lung cancer stemness. Cell Death Dis. 2018;9(9):868.PubMedPubMedCentralCrossRef
88.
89.
go back to reference Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–20.PubMedPubMedCentralCrossRef Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–20.PubMedPubMedCentralCrossRef
91.
go back to reference Wolf FA, Hamey FK, Plass M, Solana J, Dahlin JS, Gottgens B, et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 2019;20(1):59.PubMedPubMedCentralCrossRef Wolf FA, Hamey FK, Plass M, Solana J, Dahlin JS, Gottgens B, et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 2019;20(1):59.PubMedPubMedCentralCrossRef
92.
go back to reference Shannon P, Ramage D, Markie A, Amin N. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.PubMedPubMedCentralCrossRef Shannon P, Ramage D, Markie A, Amin N. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.PubMedPubMedCentralCrossRef
93.
go back to reference Tosches MA, Yamawaki rM, Naumann RK, Jacobi AA, Tushev G, Laurent G. Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science. 2018;360:881–8.PubMedCrossRef Tosches MA, Yamawaki rM, Naumann RK, Jacobi AA, Tushev G, Laurent G. Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science. 2018;360:881–8.PubMedCrossRef
94.
go back to reference Crow M, Paul A, Ballouz S, Huang ZJ, Gillis J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat Commun. 2018;9(1):884.PubMedPubMedCentralCrossRef Crow M, Paul A, Ballouz S, Huang ZJ, Gillis J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat Commun. 2018;9(1):884.PubMedPubMedCentralCrossRef
95.
go back to reference Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015;16:278.PubMedPubMedCentralCrossRef Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015;16:278.PubMedPubMedCentralCrossRef
96.
go back to reference Gu Z, Gu L, Eils R, Schlesner M, Brors B. Circlize implements and enhances circular visualization in R. Bioinformatics. 2014;30:2811–2.PubMedCrossRef Gu Z, Gu L, Eils R, Schlesner M, Brors B. Circlize implements and enhances circular visualization in R. Bioinformatics. 2014;30:2811–2.PubMedCrossRef
97.
go back to reference Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.PubMedCrossRef Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.PubMedCrossRef
98.
go back to reference Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1.PubMedPubMedCentralCrossRef Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1.PubMedPubMedCentralCrossRef
Metadata
Title
A single-cell survey of cellular hierarchy in acute myeloid leukemia
Authors
Junqing Wu
Yanyu Xiao
Jie Sun
Huiyu Sun
Haide Chen
Yuanyuan Zhu
Huarui Fu
Chengxuan Yu
Weigao E.
Shujing Lai
Lifeng Ma
Jiaqi Li
Lijiang Fei
Mengmeng Jiang
Jingjing Wang
Fang Ye
Renying Wang
Ziming Zhou
Guodong Zhang
Tingyue Zhang
Qiong Ding
Zou Wang
Sheng Hao
Lizhen Liu
Weiyan Zheng
Jingsong He
Weijia Huang
Yungui Wang
Jin Xie
Tiefeng Li
Tao Cheng
Xiaoping Han
He Huang
Guoji Guo
Publication date
01-12-2020
Publisher
BioMed Central
Published in
Journal of Hematology & Oncology / Issue 1/2020
Electronic ISSN: 1756-8722
DOI
https://doi.org/10.1186/s13045-020-00941-y

Other articles of this Issue 1/2020

Journal of Hematology & Oncology 1/2020 Go to the issue
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine