Cross-sectional network analysis of plasma proteins/metabolites correlated with pathogenesis and therapeutic response in acute promyelocytic leukemia

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.

Keywords

acute promyelocytic leukemia / plasma proteomics / plasma metabolomics / cross-sectional correlation network / pathogenesis / treatment

References

[1] Geyer PE, Holdt LM, Teupser D, Mann M. Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol 2017; 13(9): 942

[2] Westerhoff HV, Palsson BO. The evolution of molecular biology into systems biology. Nat Biotechnol 2004; 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 Oncol 2020; 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 Syst 2016; 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 USA 2020; 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 Biotechnol 2022; 71: 21–29

[7] Nicholson JK, Holmes E, Kinross JM, Darzi AW, Takats Z, Lindon JC. Metabolic phenotyping in clinical and surgical environments. Nature 2012; 491(7424): 384–392

[8] Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: a review. Clin Cancer Res 2009; 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. Blood 2014; 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 Haematol 2010; 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 A 2013; 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 Biotechnol 2017; 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. Nature 2020; 588(7836): 135–140

[14] Wang ZY, Chen Z. Acute promyelocytic leukemia: from highly fatal to highly curable. Blood 2008; 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 Res 2021; 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. Blood 2021; 137(11): 1503–1516

[17] World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 2013; 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. Nature 2018; 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 Invest 2009; 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 B 1995; 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 Phys 2011; 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 Protoc 2007; 2(10): 2366–2382

[26] Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284–287

[27] Chong J, Yamamoto M, Xia J. MetaboAnalystR 2.0: from raw spectra to biological insights. Metabolites 2019; 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 A 2005; 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 Syst 2015; 1(6): 417–425

[30] Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 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 Res 2015; 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 Res 2017; 45(D1): D362–D368

[33] Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010; 26(6): 841–842

[34] WarnesGBolker BBonebakkerLGentlemanRHuberW LiawALumley TMächlerMMagnussonAMöller S. 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. Bioinformatics 2016; 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 A 2021; 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 Rep 2020; 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 Res 2016; 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 Chem 2019; 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 Cell 2020; 27(5): 748–764.e4

[42] Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011; 144(5): 646–674

[43] Whiteside TL. Tumor-derived exosomes and their role in cancer progression. Adv Clin Chem 2016; 74: 103–141

[44] Cantor JR, Sabatini DM. Cancer cell metabolism: one hallmark, many faces. Cancer Discov 2012; 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 Res 2014; 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 Cells 2020; 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 A 2005; 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 Lymphoma 2019; 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 Haematol 2015; 169(4): 595–597

[51] Groopman J, Ellman L. Acute promyelocytic leukemia. Am J Hematol 1979; 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 Hematol 1985; 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. Blood 1988; 72(2): 567–572

Acknowledgements

This study was supported by the State Key Laboratory of Medical Genomics, the Double First-Class Project (No. WF510162602) from the Ministry of Education, the Shanghai Collaborative Innovation Program on Regenerative Medicine and Stem Cell Research (No. 2019CXJQ01), the Overseas Expertise Introduction Project for Discipline Innovation (111 Project; No. B17029), the National Natural Science Foundation of China (Nos. 82230006 and 32170663), the Shanghai Clinical Research Center for Hematological disease (No. 19MC1910700), the Shanghai Shenkang Hospital Development Center (No. SHDC2020CR5002), the Shanghai Major Project for Clinical Medicine (No. 2017ZZ01002), the Innovative Research Team of High-level Local Universities in Shanghai and the Yangfan Program of the Science and Technology Commission of Shanghai Municipality (No. 22YF1425500). We thank all members of the Shanghai Institute of Hematology and the National Research Center for Translational Medicine at Shanghai.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11684-023-1022-x and is accessible for authorized users.

Compliance with ethics guidelines

Conflict of interests Niu Qiao, Yizhu Lyu, Feng Liu, Yuliang Zhang, Xiaolin Ma, Xiaojing Lin, Junyu Wang, Yinyin Xie, Ruihong Zhang, Jing Qiao, Hongming Zhu, Li Chen, Hai Fang, Tong Yin, Zhu Chen, and Qiang Tian declare no competing interests. Saijuan Chen is the Editor-in-Chief of Frontiers of Medicine, who was excluded from the peer-review process and all editorial decisions related to the acceptance and publication of this article. Peer-review was handled independently by the other editors to minimize bias.

The study was approved by Ethics Committee, Rui Jin Hospital, and the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from all patients for being included in the study.

RIGHTS & PERMISSIONS

2023 Higher Education Press