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Review Article | Open Access

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Xing Wanga,b,c,1Jinshou Yanga,b,c,1Bo Rena,b,c,1Gang Yanga,b,cXiaohong Liua,b,cRuiling Xiaoa,b,cJie Rena,b,cFeihan Zhoua,b,cLei Youa,b,c( )Yupei Zhaoa,b,c( )
Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100023, China
Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing 100023, China
National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing 100023, China

Peer review under responsibility of Chongqing Medical University.

1 These authors contributed equally to this work.

Show Author Information

Abstract

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

References

1

Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11): 2913–2921.

2

Neuzillet C, Tijeras-Raballand A, Bourget P, et al. State of the art and future directions of pancreatic ductal adenocarcinoma therapy. Pharmacol Ther. 2015;155:80–104.

3
Conroy T, Hammel P, Hebbar M, et al. Unicancer GI PRODIGE 24/CCTG PA.6 trial: a multicenter international randomized phase Ⅲ trial of adjuvant mFOLFIRINOX versus gemcitabine (gem) in patients with resected pancreatic ductal adenocarcinomas. 2018;36(18_suppl):LBA4001–LBA4001.
4

Rhim AD, Mirek ET, Aiello NM, et al. EMT and dissemination precede pancreatic tumor formation. Cell. 2012;148(1–2):349–361.

5

Wilson JS, Pirola RC, Apte MV. Stars and stripes in pancreatic cancer: role of stellate cells and stroma in cancer progression. Front Physiol. 2014;5:52.

6

Bailey P, Chang DK, Nones K, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531(7592):47–52.

7

Zhou DC, Jayasinghe RG, Chen S, et al. Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer. Nat Genet. 2022;54(9):1390–1405.

8

Liu J, Dang H, Wang XW. The significance of intertumor and intratumor heterogeneity in liver cancer. Exp Mol Med. 2018;50(1):e416.

9

International Cancer Genome Consortium, Hudson TJ, Anderson W, et al. International network of cancer genome projects. Nature. 2010;464(7291):993–998.

10

Waddell N, Pajic M, Patch AM, et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature. 2015;518(7540):495–501.

11

Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011;12(4):R41.

12

Xu D, Wang Y, Liu X, et al. Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma. Pharmacol Res. 2021;164:105370.

13

Humphris JL, Patch AM, Nones K, et al. Hypermutation in pancreatic cancer. Gastroenterology. 2017;152(1):68–74.e2.

14

Cancer Genome Atlas Research Network. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell. 2017;32(2):185–203.e13.

15

Tiriac H, Belleau P, Engle DD, et al. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer. Cancer Discov. 2018;8(9):1112–1129.

16

Sivakumar S, de Santiago I, Chlon L, Markowetz F. Master regulators of oncogenic KRAS response in pancreatic cancer: an integrative network biology analysis. PLoS Med. 2017;14(1):e1002223.

17

Le Large TYS, Bijlsma MF, Kazemier G, van Laarhoven HWM, Giovannetti E, Jimenez CR. Key biological processes driving metastatic spread of pancreatic cancer as identified by multi-omics studies. Semin Cancer Biol. 2017;44:153–169.

18

Moffitt RA, Marayati R, Flate EL, et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat Genet. 2015;47(10):1168–1178.

19

Collisson EA, Sadanandam A, Olson P, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 2011;17(4):500–503.

20

Li Z, Rangaraju PR. Effect of sand content on properties of self-consolidating, high-performance cementitious mortar. Transport Res Rec. 2015;2508(1):84–92.

21

Chien W, Sudo M, Ding LW, et al. Functional genome-wide screening identifies targets and pathways sensitizing pancreatic cancer cells to dasatinib. J Cancer. 2018;9(24):4762–4773.

22

Bloomston M, Frankel WL, Petrocca F, et al. microRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. JAMA. 2007;297(17):1901–1908.

23

Greither T, Grochola LF, Udelnow A, Lautenschläger C, Würl P, Taubert H. Elevated expression of microRNAs 155, 203, 210 and 222 in pancreatic tumors is associated with poorer survival. Int J Cancer. 2010;126(1):73–80.

24

Zhang Q, Wang JY, Zhou SY, Yang SJ, Zhong SL. Circular RNA expression in pancreatic ductal adenocarcinoma. Oncol Lett. 2019;18(3):2923–2930.

25

Hernandez YG, Lucas AL. microRNA in pancreatic ductal adenocarcinoma and its precursor lesions. World J Gastrointest Oncol. 2016;8(1):18–29.

26

Frampton AE, Krell J, Jamieson NB, et al. microRNAs with prognostic significance in pancreatic ductal adenocarcinoma: a meta-analysis. Eur J Cancer. 2015;51(11):1389–1404.

27

Hanahan D. Hallmarks of cancer: new dimensions. Cancer Discov. 2022;12(1):31–46.

28

Li S, Peng Y, Panchenko AR. DNA methylation: precise modulation of chromatin structure and dynamics. Curr Opin Struct Biol. 2022;75:102430.

29

Lomberk G, Blum Y, Nicolle R, et al. Distinct epigenetic landscapes underlie the pathobiology of pancreatic cancer subtypes. Nat Commun. 2018;9(1):1978.

30

Ehrlich M, Jiang G, Fiala E, et al. Hypomethylation and hypermethylation of DNA in Wilms tumors. Oncogene. 2002;21(43):6694–6702.

31

Lindsey JC, Lusher ME, Strathdee G, et al. Epigenetic inactivation of MCJ (DNAJD1) in malignant paediatric brain tumours. Int J Cancer. 2006;118(2):346–352.

32

Honda S, Minato M, Suzuki H, et al. Clinical prognostic value of DNA methylation in hepatoblastoma: four novel tumor suppressor candidates. Cancer Sci. 2016;107(6):812–819.

33

Qi G, Kudo Y, Tang B, et al. PARP6 acts as a tumor suppressor via downregulating Survivin expression in colorectal cancer. Oncotarget. 2016;7(14):18812–18824.

34

Fan Y, Zhan Q, Xu H, et al. Epigenetic identification of ZNF545 as a functional tumor suppressor in multiple myeloma via activation of p53 signaling pathway. Biochem Biophys Res Commun. 2016;474(4):660–666.

35

Schneider G, Krämer OH, Fritsche P, Schüler S, Schmid RM, Saur D. Targeting histone deacetylases in pancreatic ductal adenocarcinoma. J Cell Mol Med. 2010;14(6a):1255–1263.

36

Ren B, Yang J, Wang C, et al. High-resolution Hi-C maps highlight multiscale 3D epigenome reprogramming during pancreatic cancer metastasis. J Hematol Oncol. 2021;14(1):120.

37

Radon TP, Massat NJ, Jones R, et al. Identification of a three-biomarker panel in urine for early detection of pancreatic adenocarcinoma. Clin Cancer Res. 2015;21(15):3512–3521.

38

Honda K, Kobayashi M, Okusaka T, et al. Plasma biomarker for detection of early stage pancreatic cancer and risk factors for pancreatic malignancy using antibodies for apolipoprotein-AII isoforms. Sci Rep. 2015;5:15921.

39

Balasenthil S, Huang Y, Liu S, et al. A plasma biomarker panel to identify surgically resectable early-stage pancreatic cancer. J Natl Cancer Inst. 2017;109(8):djw341.

40

Kosanam H, Makawita S, Judd B, Newman A, Diamandis EP. Mining the malignant ascites proteome for pancreatic cancer biomarkers. Proteomics. 2011;11(23):4551–4558.

41

Chen R, Pan S, Ottenhof NA, et al. Stromal galectin-1 expression is associated with long-term survival in resectable pancreatic ductal adenocarcinoma. Cancer Biol Ther. 2012;13(10):899–907.

42

Chen R, Dawson DW, Pan S, et al. Proteins associated with pancreatic cancer survival in patients with resectable pancreatic ductal adenocarcinoma. Lab Invest. 2015;95(1):43–55.

43

Britton D, Zen Y, Quaglia A, et al. Quantification of pancreatic cancer proteome and phosphorylome: indicates molecular events likely contributing to cancer and activity of drug targets. PLoS One. 2014;9(3):e90948.

44

Besmer DM, Curry JM, Roy LD, et al. Pancreatic ductal adenocarcinoma mice lacking mucin 1 have a profound defect in tumor growth and metastasis. Cancer Res. 2011;71(13):4432–4442.

45

Kim MS, Zhong Y, Yachida S, et al. Heterogeneity of pancreatic cancer metastases in a single patient revealed by quantitative proteomics. Mol Cell Proteomics. 2014;13(11):2803–2811.

46

Nigjeh EN, Chen R, Allen-Tamura Y, Brand RE, Brentnall TA, Pan S. Spectral library-based glycopeptide analysis-detection of circulating galectin-3 binding protein in pancreatic cancer. Proteonomics Clin App. 2017;11:9–10. https://doi.org/10.1002/prca.201700064.

47

Pan S, Chen R, Tamura Y, et al. Quantitative glycoproteomics analysis reveals changes in N-glycosylation level associated with pancreatic ductal adenocarcinoma. J Proteome Res. 2014;13(3):1293–1306.

48

Muñoz-Pinedo C, El Mjiyad N, Ricci JE. Cancer metabolism: current perspectives and future directions. Cell Death Dis. 2012;3(1):e248.

49

Xiong Y, Shi C, Zhong F, Liu X, Yang P. LC-MS/MS and SWATH based serum metabolomics enables biomarker discovery in pancreatic cancer. Clin Chim Acta. 2020;506:214–221.

50

Luo X, Liu J, Wang H, Lu H. Metabolomics identified new biomarkers for the precise diagnosis of pancreatic cancer and associated tissue metastasis. Pharmacol Res. 2020;156:104805.

51

Phua LC, Goh S, Tai DWM, et al. Metabolomic prediction of treatment outcome in pancreatic ductal adenocarcinoma patients receiving gemcitabine. Cancer Chemother Pharmacol. 2018;81(2):277–289.

52

Battini S, Faitot F, Imperiale A, et al. Metabolomics approaches in pancreatic adenocarcinoma: tumor metabolism profiling predicts clinical outcome of patients. BMC Med. 2017;15(1):56.

53

Daemen A, Peterson D, Sahu N, et al. Metabolite profiling stratifies pancreatic ductal adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors. Proc Natl Acad Sci U S A. 2015;112(32):E4410–E4417.

54

Ren Z, Jiang J, Xie H, et al. Gut microbial profile analysis by MiSeq sequencing of pancreatic carcinoma patients in China. Oncotarget. 2017;8(56):95176–95191.

55

Nagata N, Nishijima S, Kojima Y, et al. Metagenomic identification of microbial signatures predicting pancreatic cancer from a multinational study. Gastroenterology. 2022;163(1):222–238.

56

Zhou W, Zhang D, Li Z, et al. The fecal microbiota of patients with pancreatic ductal adenocarcinoma and autoimmune pancreatitis characterized by metagenomic sequencing. J Transl Med. 2021;19(1):215.

57

Tintelnot J, Xu Y, Lesker TR, et al. Microbiota-derived 3-IAA influences chemotherapy efficacy in pancreatic cancer. Nature. 2023;615(7950):168–174.

58

Hamdan FH, Johnsen SA. DeltaNp63-dependent super enhancers define molecular identity in pancreatic cancer by an interconnected transcription factor network. Proc Natl Acad Sci U S A. 2018;115(52):E12343–E12352.

59

Sinkala M, Mulder N, Martin D. Machine learning and network analyses reveal disease subtypes of pancreatic cancer and their molecular characteristics. Sci Rep. 2020;10(1):1212.

60

Rodriguez E, Boelaars K, Brown K, et al. Analysis of the glyco-code in pancreatic ductal adenocarcinoma identifies glycan-mediated immune regulatory circuits. Commun Biol. 2022;5(1):41.

61

Karasinska JM, Topham JT, Kalloger SE, et al. Altered gene expression along the glycolysis-cholesterol synthesis axis is associated with outcome in pancreatic cancer. Clin Cancer Res. 2020;26(1):135–146.

62

Nicolle R, Blum Y, Marisa L, et al. Pancreatic adenocarcinoma therapeutic targets revealed by tumor-stroma cross-talk analyses in patient-derived xenografts. Cell Rep. 2017;21(9):2458–2470.

63

Mishra NK, Guda C. Genome-wide DNA methylation analysis reveals molecular subtypes of pancreatic cancer. Oncotarget. 2017;8(17):28990–29012.

64

Espinet E, Gu Z, Imbusch CD, et al. Aggressive PDACs show hypomethylation of repetitive elements and the execution of an intrinsic IFN program linked to a ductal cell of origin. Cancer Discov. 2021;11(3):638–659.

65

Zhou D, Guo S, Wang Y, et al. Functional characteristics of DNA N6-methyladenine modification based on long-read sequencing in pancreatic cancer. Brief Funct Genomics. 2023:elad021.

66

Humphrey ES, Su SP, Nagrial AM, et al. Resolution of novel pancreatic ductal adenocarcinoma subtypes by global phosphotyrosine profiling. Mol Cell Proteomics. 2016;15(8):2671–2685.

67

Tong Y, Sun M, Chen L, et al. Proteogenomic insights into the biology and treatment of pancreatic ductal adenocarcinoma. J Hematol Oncol. 2022;15(1):168.

68

Tao L, Zhong L, Li Y, Li D, Xiu D, Zhou J. Integrated proteomics and phosphoproteomics reveal perturbed regulative pathways in pancreatic ductal adenocarcinoma. Mol Omics. 2021;17(2):230–240.

69

Connor AA, Denroche RE, Jang GH, et al. Association of distinct mutational signatures with correlates of increased immune activity in pancreatic ductal adenocarcinoma. JAMA Oncol. 2017;3(6):774–783.

70

Kong L, Liu P, Zheng M, Xue B, Liang K, Tan X. Multi-omics analysis based on integrated genomics, epigenomics and transcriptomics in pancreatic cancer. Epigenomics. 2020;12(6):507–524.

71

Ju J, Wismans LV, Mustafa DAM, et al. Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients. iScience. 2021;24(12):103415.

72

Chan-Seng-Yue M, Kim JC, Wilson GW, et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat Genet. 2020;52(2):231–240.

73

Mahajan UM, Alnatsha A, Li Q, et al. Plasma metabolome profiling identifies metabolic subtypes of pancreatic ductal adenocarcinoma. Cells. 2021;10(7):1821.

74

Ho WJ, Jaffee EM, Zheng L. The tumour microenvironment in pancreatic cancer — clinical challenges and opportunities. Nat Rev Clin Oncol. 2020;17(9):527–540.

75

Knudsen ES, Vail P, Balaji U, et al. Stratification of pancreatic ductal adenocarcinoma: combinatorial genetic, stromal, and immunologic markers. Clin Cancer Res. 2017;23(15):4429–4440.

76

Wang X, Li L, Yang Y, Fan L, Ma Y, Mao F. Reveal the heterogeneity in the tumor microenvironment of pancreatic cancer and analyze the differences in prognosis and immunotherapy responses of distinct immune subtypes. Front Oncol. 2022;12:832715.

77

Grünwald BT, Devisme A, Andrieux G, et al. Spatially confined sub-tumor microenvironments in pancreatic cancer. Cell. 2021;184(22):5577–5592.e18.

78

Neuzillet C, Tijeras-Raballand A, Ragulan C, et al. Inter- and intra-tumoural heterogeneity in cancer-associated fibroblasts of human pancreatic ductal adenocarcinoma. J Pathol. 2019;248(1):51–65.

79

Tu M, Klein L, Espinet E, et al. TNF-α-producing macrophages determine subtype identity and prognosis via AP1 enhancer reprogramming in pancreatic cancer. Nat Cancer. 2021;2(11):1185–1203.

80

Wang L, Liu Y, Dai Y, et al. Single-cell RNA-seq analysis reveals BHLHE40-driven pro-tumour neutrophils with hyperactivated glycolysis in pancreatic tumour microenvironment. Gut. 2023;72(5):958–971.

81

Zheng H, Li Y, Zhao Y, Jiang A. Single-cell and bulk RNA sequencing identifies T cell marker genes score to predict the prognosis of pancreatic ductal adenocarcinoma. Sci Rep. 2023;13:3684.

82

Schnurr M, Duewell P, Bauer C, et al. Strategies to relieve immunosuppression in pancreatic cancer. Immunotherapy. 2015;7(4):363–376.

83

Lin W, Noel P, Borazanci EH, et al. Single-cell transcriptome analysis of tumor and stromal compartments of pancreatic ductal adenocarcinoma primary tumors and metastatic lesions. Genome Med. 2020;12(1):80.

84

Puleo F, Nicolle R, Blum Y, et al. Stratification of pancreatic ductal adenocarcinomas based on tumor and microenvironment features. Gastroenterology. 2018;155(6):1999–2013.e3.

85

Lowery MA, Jordan EJ, Basturk O, et al. Real-time genomic profiling of pancreatic ductal adenocarcinoma: potential actionability and correlation with clinical phenotype. Clin Cancer Res. 2017;23(20):6094–6100.

86

Frampton AE, Castellano L, Colombo T, et al. microRNAs cooperatively inhibit a network of tumor suppressor genes to promote pancreatic tumor growth and progression. Gastroenterology. 2014;146(1):268–277.e18.

87

Eder JP, Vande Woude GF, Boerner SA, LoRusso PM. Novel therapeutic inhibitors of the c-Met signaling pathway in cancer. Clin Cancer Res. 2009;15(7):2207–2214.

88

Pedersen SF, Stock C. Ion channels and transporters in cancer: pathophysiology, regulation, and clinical potential. Cancer Res. 2013;73(6):1658–1661.

89

Bach DH, Hong JY, Park HJ, Lee SK. The role of exosomes and miRNAs in drug-resistance of cancer cells. Int J Cancer. 2017;141(2):220–230.

90

Bach DH, Park HJ, Lee SK. The dual role of bone morphogenetic proteins in cancer. Mol Ther Oncolytics. 2018;8:1–13.

91

Keklikoglou I, Hosaka K, Bender C, et al. microRNA-206 functions as a pleiotropic modulator of cell proliferation, invasion and lymphangiogenesis in pancreatic adenocarcinoma by targeting ANXA2 and KRAS genes. Oncogene. 2015;34(37):4867–4878.

92

Lee DK, Long NP, Jung J, et al. Integrative lipidomic and transcriptomic analysis of X-linked adrenoleukodystrophy reveals distinct lipidome signatures between adrenomyeloneuropathy and childhood cerebral adrenoleukodystrophy. Biochem Biophys Res Commun. 2019;508(2):563–569.

93

Long NP, Park S, Anh NH, et al. Efficacy of integrating a novel 16-gene biomarker panel and intelligence classifiers for differential diagnosis of rheumatoid arthritis and osteoarthritis. J Clin Med. 2019;8(1):50.

94

Donahue TR, Tran LM, Hill R, et al. Integrative survival-based molecular profiling of human pancreatic cancer. Clin Cancer Res. 2012;18(5):1352–1363.

95

Long NP, Jung KH, Anh NH, et al. An integrative data mining and omics-based translational model for the identification and validation of oncogenic biomarkers of pancreatic cancer. Cancers. 2019;11(2):E155.

96

Wang Z, Wei P. IMIX: a multivariate mixture model approach to association analysis through multi-omics data integration. Bioinformatics. 2021;36(22–23):5439–5447.

97

Mancera-Arteu M, Giménez E, Balmaña M, et al. Multivariate data analysis for the detection of human alpha-acid glycoprotein aberrant glycosylation in pancreatic ductal adenocarcinoma. J Proteonomics. 2019;195:76–87.

98

Wang H, Guo H, Sun J, Wang Y. Multi-omics analyses based on genes associated with oxidative stress and phospholipid metabolism revealed the intrinsic molecular characteristics of pancreatic cancer. Sci Rep. 2023;13(1):13564.

99

Wang Z, Yuan Q, Chen X, et al. A prospective prognostic signature for pancreatic adenocarcinoma based on ubiquitination-related mRNA-lncRNA with experimental validation in vitro and vivo. Funct Integr Genomics. 2023;23(3):263.

100

Ke M. Identification and validation of apparent imbalanced epi-lncRNAs prognostic model based on multi-omics data in pancreatic cancer. Front Mol Biosci. 2022;9:860323.

101

Gress TM, Lausser L, Schirra LR, et al. Combined microRNA and mRNA microfluidic TaqMan array cards for the diagnosis of malignancy of multiple types of pancreatico-biliary tumors in fine-needle aspiration material. Oncotarget. 2017;8(64):108223–108237.

102

Starzyńska T, Karczmarski J, Paziewska A, et al. Differences between well-differentiated neuroendocrine tumors and ductal adenocarcinomas of the pancreas assessed by multi-omics profiling. Int J Mol Sci. 2020;21(12):4470.

103

Yang G, Guan W, Cao Z, et al. Integrative genomic analysis of gemcitabine resistance in pancreatic cancer by patient-derived xenograft models. Clin Cancer Res. 2021;27(12):3383–3396.

104

Yang B, Zhou M, Wu Y, et al. The impact of immune microenvironment on the prognosis of pancreatic ductal adenocarcinoma based on multi-omics analysis. Front Immunol. 2021;12:769047.

105

Zhang L, Yu S, Wang C, Jia C, Lu Z, Chen J. Establishment of a non-coding RNAomics screening platform for the regulation of KRAS in pancreatic cancer by RNA sequencing. Int J Oncol. 2018;53(6):2659–2670.

106

Armstrong A, Haque MR, Mirbagheri S, et al. Multiplex patient-based drug response assay in pancreatic ductal adenocarcinoma. Biomedicines. 2021;9(7):705.

107

Liu E, Zhang ZZ, Cheng X, Liu X, Cheng L. SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma. BMC Med Genom. 2020;13(suppl 5):50.

108

Lee HS, Kim E, Lee J, et al. Profiling of conditionally reprogrammed cell lines for in vitro chemotherapy response prediction of pancreatic cancer. EBioMedicine. 2021;65:103218.

109

Yan J, Risacher SL, Shen L, Saykin AJ. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Briefings Bioinf. 2018;19(6):1370–1381.

110

Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83.

111

Zhang J, Baran J, Cros A, et al. International cancer genome Consortium data portal: a one-stop shop for cancer genomics data. Database. 2011;2011:bar026.

112

Network CGAR, Weinstein JN, Collisson EA, et al. The cancer genome Atlas pan-cancer analysis project. Nat Genet. 2013;45(10):1113–1120.

113

Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet. 2023;24(8):494–515.

114

Duan M, Hao J, Cui S, et al. Diverse modes of clonal evolution in HBV-related hepatocellular carcinoma revealed by single-cell genome sequencing. Cell Res. 2018;28(3):359–373.

115

Ling S, Hu Z, Yang Z, et al. Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution. Proc Natl Acad Sci U S A. 2015;112(47):E6496–E6505.

116

Wiener D, Schwartz S. The epitranscriptome beyond m6A. Nat Rev Genet. 2021;22(2):119–131.

117

Athreya A, Iyer R, Neavin D, et al. Augmentation of physician assessments with multi-omics enhances predictability of drug response: a case study of major depressive disorder. IEEE Comput Intell Mag. 2018;13(3):20–31.

118

Cavallo F, De Giovanni C, Nanni P, Forni G, Lollini PL. 2011: the immune hallmarks of cancer. Cancer Immunol Immunother. 2011;60(3):319–326.

119

Liu Q, Hu P. Association analysis of deep genomic features extracted by denoising autoencoders in breast cancer. Cancers. 2019;11(4):E494.

120

Bauer DC, Gaff C, Dinger ME, et al. Genomics and personalised whole-of-life healthcare. Trends Mol Med. 2014;20(9):479–486.

121

Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11(3):333–337.

122

Gray SW, Hicks-Courant K, Cronin A, Rollins BJ, Weeks JC. Physicians' attitudes about multiplex tumor genomic testing. J Clin Oncol. 2014;32(13):1317–1323.

123

Hira MT, Razzaque MA, Angione C, Scrivens J, Sawan S, Sarker M. Author Correction: integrated multi-omics analysis of ovarian cancer using variational autoencoders. Sci Rep. 2021;11(1):16671.

124

Shen Q, Zhang S. Approximate distance correlation for selecting highly interrelated genes across datasets. PLoS Comput Biol. 2021;17(11):e1009548.

125

Liao J, Qian J, Fang Y, et al. De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution. Nat Commun. 2022;13(1):6498.

126

Wood LD, Hruban RH. Pathology and molecular genetics of pancreatic neoplasms. Cancer J. 2012;18(6):492–501.

127

Artifon ELA, Guedes HG, Cheng S. Maximizing the diagnostic yield of endoscopic ultrasound-guided fine-needle aspiration biopsy. Gastroenterology. 2017;153(4):881–885.

128

Valero V, Saunders TJ, He J, et al. Reliable detection of somatic mutations in fine needle aspirates of pancreatic cancer with next-generation sequencing. Ann Surg. 2016;263(1):153–161.

129

Witkiewicz AK, McMillan EA, Balaji U, et al. Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets. Nat Commun. 2015;6:6744.

130

Jones S, Zhang X, Parsons DW, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008;321(5897):1801–1806.

131

Du Y, Zhao B, Liu Z, et al. Molecular subtyping of pancreatic cancer: translating genomics and transcriptomics into the clinic. J Cancer. 2017;8(4):513–522.

132

Herberts C, Wyatt AW. Technical and biological constraints on ctDNA-based genotyping. Trends Cancer. 2021;7(11):995–1009.

133

Lianidou E. Detection and relevance of epigenetic markers on ctDNA: recent advances and future outlook. Mol Oncol. 2021;15(6):1683–1700.

134

Hinzman CP, Singh B, Bansal S, et al. A multi-omics approach identifies pancreatic cancer cell extracellular vesicles as mediators of the unfolded protein response in normal pancreatic epithelial cells. J Extracell Vesicles. 2022;11(6):e12232.

135

Collisson EA, Bailey P, Chang DK, Biankin AV. Molecular subtypes of pancreatic cancer. Nat Rev Gastroenterol Hepatol. 2019;16(4):207–220.

136

Tang B, Chen Y, Wang Y, Nie J. A wavelet-based learning model enhances molecular prognosis in pancreatic adenocarcinoma. BioMed Res Int. 2021;2021:7865856.

Genes & Diseases
Article number: 101143
Cite this article:
Wang X, Yang J, Ren B, et al. Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma. Genes & Diseases, 2024, 11(6): 101143. https://doi.org/10.1016/j.gendis.2023.101143

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Received: 19 May 2023
Revised: 04 September 2023
Accepted: 10 September 2023
Published: 14 October 2023
© 2023 The Authors.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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