1. Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
2. Department of Hematology, Second Hospital of Dalian Medical University, Dalian 116021, China
lf12034@rjh.com.cn
yintong0101@163.com
tq12221@rjh.com.cn
sjchen@stn.sh.cn
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History+
Received
Accepted
Published Online
2023-02-17
2023-07-20
2023-11-15
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Abstract
The treatment of PML/RARA+ acute promyelocytic leukemia (APL) with all-trans-retinoic acid and arsenic trioxide (ATRA/ATO) has been recognized as a model for translational medicine research. Though an altered microenvironment is a general cancer hallmark, how APL blasts shape their plasma composition is poorly understood. Here, we reported a cross-sectional correlation network to interpret multilayered datasets on clinical parameters, proteomes, and metabolomes of paired plasma samples from patients with APL before or after ATRA/ATO induction therapy. Our study revealed the two prominent features of the APL plasma, suggesting a possible involvement of APL blasts in modulating plasma composition. One was characterized by altered secretory protein and metabolite profiles correlating with heightened proliferation and energy consumption in APL blasts, and the other featured APL plasma-enriched proteins or enzymes catalyzing plasma-altered metabolites that were potential trans-regulatory targets of PML/RARA. Furthermore, results indicated heightened interferon-gamma signaling characterizing a tumor-suppressing function of the immune system at the first hematological complete remission stage, which likely resulted from therapy-induced cell death or senescence and ensuing supraphysiological levels of intracellular proteins. Overall, our work sheds new light on the pathophysiology and treatment of APL and provides an information-rich reference data cohort for the exploratory and translational study of leukemia microenvironment.
Geyer PE, Holdt LM, Teupser D, Mann M. Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol2017; 13(9): 942
[2]
Westerhoff HV, Palsson BO. The evolution of molecular biology into systems biology. Nat Biotechnol2004; 22(10): 1249–1252
[3]
Yurkovich JT, Tian Q, Price ND, Hood L. A systems approach to clinical oncology uses deep phenotyping to deliver personalized care. Nat Rev Clin Oncol2020; 17(3): 183–194
[4]
Geyer PE, Kulak NA, Pichler G, Holdt LM, Teupser D, Mann M. Plasma proteome profiling to assess human health and disease. Cell Syst2016; 2(3): 185–195
[5]
Wainberg M, Magis AT, Earls JC, Lovejoy JC, Sinnott-Armstrong N, Omenn GS, Hood L, Price ND. Multiomic blood correlates of genetic risk identify presymptomatic disease alterations. Proc Natl Acad Sci USA2020; 117(35): 21813–21820
[6]
Abu Sabaa A, Shen Q, Lennmyr EB, Enblad AP, Gammelgard G, Molin D, Hein A, Freyhult E, Kamali-Moghaddam M, Hoglund M, Enblad G, Eriksson A. Plasma protein biomarker profiling reveals major differences between acute leukaemia, lymphoma patients and controls. N Biotechnol2022; 71: 21–29
[7]
Nicholson JK, Holmes E, Kinross JM, Darzi AW, Takats Z, Lindon JC. Metabolic phenotyping in clinical and surgical environments. Nature2012; 491(7424): 384–392
[8]
Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: a review. Clin Cancer Res2009; 15(2): 431–440
[9]
Chen WL, Wang JH, Zhao AH, Xu X, Wang YH, Chen TL, Li JM, Mi JQ, Zhu YM, Liu YF, Wang YY, Jin J, Huang H, Wu DP, Li Y, Yan XJ, Yan JS, Li JY, Wang S, Huang XJ, Wang BS, Chen Z, Chen SJ, Jia W. A distinct glucose metabolism signature of acute myeloid leukemia with prognostic value. Blood2014; 124(10): 1645–1654
[10]
Sellner L, Capper D, Meyer J, Langhans CD, Hartog CM, Pfeifer H, Serve H, Ho AD, Okun JG, Kramer A, Von Deimling A. Increased levels of 2-hydroxyglutarate in AML patients with IDH1–R132H and IDH2–R140Q mutations. Eur J Haematol2010; 85(5): 457–459
[11]
Wang JH, Chen WL, Li JM, Wu SF, Chen TL, Zhu YM, Zhang WN, Li Y, Qiu YP, Zhao AH, Mi JQ, Jin J, Wang YG, Ma QL, Huang H, Wu DP, Wang QR, Li Y, Yan XJ, Yan JS, Li JY, Wang S, Huang XJ, Wang BS, Jia W, Shen Y, Chen Z, Chen SJ. Prognostic significance of 2-hydroxyglutarate levels in acute myeloid leukemia in China. Proc Natl Acad Sci U S A2013; 110(42): 17017–17022
[12]
Price ND, Magis AT, Earls JC, Glusman G, Levy R, Lausted C, McDonald DT, Kusebauch U, Moss CL, Zhou Y, Qin S, Moritz RL, Brogaard K, Omenn GS, Lovejoy JC, Hood L. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol2017; 35(8): 747–756
[13]
Bar N, Korem T, Weissbrod O, Zeevi D, Rothschild D, Leviatan S, Kosower N, Lotan-Pompan M, Weinberger A, Le Roy CI, Menni C, Visconti A, Falchi M, Spector TD; IMI DIRECT consortium; Adamski J, Franks PW, Pedersen O, Segal E. A reference map of potential determinants for the human serum metabolome. Nature2020; 588(7836): 135–140
[14]
Wang ZY, Chen Z. Acute promyelocytic leukemia: from highly fatal to highly curable. Blood2008; 111(5): 2505–2515
[15]
Lin X, Qiao N, Shen Y, Fang H, Xue Q, Cui B, Chen L, Zhu H, Zhang S, Chen Y, Jiang L, Wang S, Li J, Wang B, Chen B, Chen Z, Chen S. Integration of genomic and transcriptomic markers improves the prognosis prediction of acute promyelocytic leukemia. Clin Cancer Res2021; 27(13): 3683–3694
[16]
Tan Y, Wang X, Song H, Zhang Y, Zhang R, Li S, Jin W, Chen SJ, Fang H, Chen Z, Wang KA. PML/RARalpha direct target atlas redefines transcriptional deregulation in acute promyelocytic leukemia. Blood2021; 137(11): 1503–1516
[17]
World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA2013; 310(20): 2191–2194
[18]
Tyner JW, Tognon CE, Bottomly D, Wilmot B, Kurtz SE, Savage SL, Long N, Schultz AR, Traer E, Abel M, Agarwal A, Blucher A, Borate U, Bryant J, Burke R, Carlos A, Carpenter R, Carroll J, Chang BH, Coblentz C, d'Almeida A, Cook R, Danilov A, Dao KT, Degnin M, Devine D, Dibb J, Edwards DK 5th, Eide CA, English I, Glover J, Henson R, Ho H, Jemal A, Johnson K, Johnson R, Junio B, Kaempf A, Leonard J, Lin C, Liu SQ, Lo P, Loriaux MM, Luty S, Macey T, MacManiman J, Martinez J, Mori M, Nelson D, Nichols C, Peters J, Ramsdill J, Rofelty A, Schuff R, Searles R, Segerdell E, Smith RL, Spurgeon SE, Sweeney T, Thapa A, Visser C, Wagner J, Watanabe-Smith K, Werth K, Wolf J, White L, Yates A, Zhang H, Cogle CR, Collins RH, Connolly DC, Deininger MW, Drusbosky L, Hourigan CS, Jordan CT, Kropf P, Lin TL, Martinez ME, Medeiros BC, Pallapati RR, Pollyea DA, Swords RT, Watts JM, Weir SJ, Wiest DL, Winters RM, McWeeney SK, Druker BJ. Functional genomic landscape of acute myeloid leukaemia. Nature2018; 562(7728): 526–531
[19]
Payton JE, Grieselhuber NR, Chang LW, Murakami M, Geiss GK, Link DC, Nagarajan R, Watson MA, Ley TJ. High throughput digital quantification of mRNA abundance in primary human acute myeloid leukemia samples. J Clin Invest2009; 119(6): 1714–1726
[20]
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B1995; 57(1): 289–300
[21]
CsardiG. The igraph software package for complex network research. 2006; available from the website of SEMANTIC SCHOLAR
[22]
Šubelj L, Bajec M. Unfolding communities in large complex networks: combining defensive and offensive label propagation for core extraction. Phys Rev E Stat Nonlin Soft Matter Phys2011; 83(3): 036103
[23]
PedersenTL. ggraph: an implementation of grammar of graphics for graphs and networks. 2020; available from the website of rdrr.io
[24]
visNetwork: network visualization using ‘vis.js’. Library (Lond). 2019; available from the website of cran.r
[25]
Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, Hanspers K, Isserlin R, Kelley R, Killcoyne S, Lotia S, Maere S, Morris J, Ono K, Pavlovic V, Pico AR, Vailaya A, Wang PL, Adler A, Conklin BR, Hood L, Kuiper M, Sander C, Schmulevich I, Schwikowski B, Warner GJ, Ideker T, Bader GD. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc2007; 2(10): 2366–2382
[26]
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS2012; 16(5): 284–287
[27]
Chong J, Yamamoto M, Xia J. MetaboAnalystR 2.0: from raw spectra to biological insights. Metabolites2019; 9(3): 57
[28]
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A2005; 102(43): 15545–15550
[29]
Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst2015; 1(6): 417–425
[30]
Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics2011; 27(12): 1739–1740
[31]
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res2015; 43(D1): D447–D452
[32]
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res2017; 45(D1): D362–D368
[33]
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics2010; 26(6): 841–842
[34]
WarnesGBolkerBBonebakkerLGentlemanRHuberWLiawALumleyTMächlerMMagnussonAMöllerS. gplots: various R programming tools for plotting data. 2019
[35]
Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics2016; 32(18): 2847–2849
[36]
ChenH. VennDiagram: generate high-resolution venn and euler plots. 2018; available from the website of cran.r
[37]
Chen L, Zhu HM, Li Y, Liu QF, Hu Y, Zhou JF, Jin J, Hu JD, Liu T, Wu DP, Chen JP, Lai YR, Wang JX, Li J, Li JY, Du X, Wang X, Yang MZ, Yan JS, Ouyang GF, Liu L, Hou M, Huang XJ, Yan XJ, Xu D, Li WM, Li DJ, Lou YJ, Wu ZJ, Niu T, Wang Y, Li XY, You JH, Zhao HJ, Chen Y, Shen Y, Chen QS, Chen Y, Li J, Wang BS, Zhao WL, Mi JQ, Wang KK, Hu J, Chen Z, Chen SJ, Li JM. Arsenic trioxide replacing or reducing chemotherapy in consolidation therapy for acute promyelocytic leukemia (APL2012 trial). Proc Natl Acad Sci U S A2021; 118(6): e2020382118
[38]
Jiang N, Dai Q, Su X, Fu J, Feng X, Peng J. Role of PI3K/AKT pathway in cancer: the framework of malignant behavior. Mol Biol Rep2020; 47(6): 4587–4629
[39]
Weng XQ, Sheng Y, Ge DZ, Wu J, Shi L, Cai X. RAF-1/MEK/ERK pathway regulates ATRA-induced differentiation in acute promyelocytic leukemia cells through C/EBPbeta, C/EBPepsilon and PU.1. Leuk Res2016; 45: 68–74
[40]
Fan S, Kind T, Cajka T, Hazen SL, Tang WHW, Kaddurah-Daouk R, Irvin MR, Arnett DK, Barupal DK, Fiehn O. Systematic error removal using random forest for normalizing large-scale untargeted lipidomics data. Anal Chem2019; 91(5): 3590–3596
[41]
Jones CL, Stevens BM, Pollyea DA, Culp-Hill R, Reisz JA, Nemkov T, Gehrke S, Gamboni F, Krug A, Winters A, Pei S, Gustafson A, Ye H, Inguva A, Amaya M, Minhajuddin M, Abbott D, Becker MW, DeGregori J, Smith CA, D’Alessandro A, Jordan CT. Nicotinamide metabolism mediates resistance to venetoclax in relapsed acute myeloid leukemia stem cells. Cell Stem Cell2020; 27(5): 748–764.e4
[42]
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell2011; 144(5): 646–674
[43]
Whiteside TL. Tumor-derived exosomes and their role in cancer progression. Adv Clin Chem2016; 74: 103–141
[44]
Cantor JR, Sabatini DM. Cancer cell metabolism: one hallmark, many faces. Cancer Discov2012; 2(10): 881–898
[45]
Kumar S, Yedjou CG, Tchounwou PB. Arsenic trioxide induces oxidative stress, DNA damage, and mitochondrial pathway of apoptosis in human leukemia (HL-60) cells. J Exp Clin Cancer Res2014; 33(1): 42
[46]
Mun YC, Ahn JY, Yoo ES, Lee KE, Nam EM, Huh J, Woo HA, Rhee SG, Seong CM. Peroxiredoxin 3 has important roles on arsenic trioxide induced apoptosis in human acute promyelocytic leukemia cell line via hyperoxidation of mitochondrial specific reactive oxygen species. Mol Cells2020; 43(9): 813–820
[47]
Zheng PZ, Wang KK, Zhang QY, Huang QH, Du YZ, Zhang QH, Xiao DK, Shen SH, Imbeaud S, Eveno E, Zhao CJ, Chen YL, Fan HY, Waxman S, Auffray C, Jin G, Chen SJ, Chen Z, Zhang J. Systems analysis of transcriptome and proteome in retinoic acid/arsenic trioxide-induced cell differentiation/apoptosis of promyelocytic leukemia. Proc Natl Acad Sci U S A2005; 102(21): 7653–7658
[48]
Geoffroy MC, Esnault C, de The H. Retinoids in hematology: a timely revival? Blood 2021; 137(18): 2429–2437 doi:10.1182/blood.2020010100
[49]
Naymagon L, Moshier E, Tremblay D, Mascarenhas J. Predictors of early hemorrhage in acute promyelocytic leukemia. Leuk Lymphoma2019; 60(10): 2394–2403
[50]
Ohanian M, Rozovski U, Ravandi F, Garcia-Manero G, Jabbour E, Kantarjian HM, Estrov Z. Very high levels of lactate dehydrogenase at diagnosis predict central nervous system relapse in acute promyelocytic leukaemia. Br J Haematol2015; 169(4): 595–597
[51]
Groopman J, Ellman L. Acute promyelocytic leukemia. Am J Hematol1979; 7(4): 395–408
[52]
Morisaki T, Fujii H, Miwa S. Adenosine deaminase (ADA) in leukemia: clinical value of plasma ADA activity and characterization of leukemic cell ADA. Am J Hematol1985; 19(1): 37–45
[53]
Huang ME, Ye YC, Chen SR, Chai JR, Lu JX, Zhoa L, Gu LJ, Wang ZY. Use of all-trans retinoic acid in the treatment of acute promyelocytic leukemia. Blood1988; 72(2): 567–572