[en] [en] BACKGROUND: N6-methyladenosine (m6A) is the most abundant mRNA modification. Whether m6A regulators can determine tumor aggressiveness and risk of immune evasion in pancreatic ductal adenocarcinoma (PDAC) remains unknown.
METHODS: An integrated model named "m6Ascore" is constructed based on RNA-seq data of m6A regulators in PDAC. Association of m6Ascore and overall survival is validated across several different datasets. Overlaps of m6Ascore and established molecular classifications of PDAC is examined. Immune infiltration, enriched pathways, somatic copy number alterations (SCNAs), mutation profiles and response to immune checkpoint inhibitors are compared between m6Ascore-high and m6Ascore-low tumors.
FINDINGS: m6Ascore is associated with dismal overall survival and increased tumor recurrence in PDAC as well as several other solid tumors including colorectal cancer and breast cancer. Basal-like (Squamous) PDAC has higher m6Ascore than that in the classical PDAC. Mechanism study showed m6Ascore-high tumors are characterized with reduced immune infiltration and T cells exhaustion. Meanwhile, m6Ascore is associated with genes regulating cachexia and chemoresistance in PDAC. Furthermore, distinct SCNAs patterns and mutation profiles of KRAS and TP53 are present in m6Ascore-high tumors, indicating immune evasion. m6Ascore-low tumors have higher response rates to immune checkpoint inhibitors (ICIs).
INTERPRETATION: These findings indicate m6Ascore can predict aggressiveness and immune evasion in pancreatic cancer. This model has implications for pancreatic cancer prognosis and treatment response to ICIs.
FUNDING: This work was supported in part by National Institutes of Health (NIH) grants to M. Li (R01 CA186338, R01 CA203108, R01 CA247234 and the William and Ella Owens Medical Research Foundation) and NIH/National Cancer Institute Q39 award P30CA225520 to Stephenson Cancer Center.
Disciplines :
Biochemistry, biophysics & molecular biology
Author, co-author :
Zhou, Zhijun; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Zhang, Junxia; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Xu, Chao ; Department of Biostatistics and Epidemiology, Hudson College of Public Health, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Yang, Jingxuan; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Zhang, Yuqing; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Liu, Mingyang; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Shi, Xiuhui ; Université de Liège - ULiège > Département de pharmacie ; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Li, Xiaoping; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Zhan, Hanxiang; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Chen, Wei; Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, Guangdong 518107, China
McNally, Lacey R; Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Fung, Kar-Ming; Department of Pathology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Luo, Wenyi; Department of Pathology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
Houchen, Courtney W; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States
He, Yulong; Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, Guangdong 518107, China
Zhang, Changhua; Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, Guangdong 518107, China. Electronic address: zhchangh@mail.sysu.edu.cn
Li, Min; Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States. Electronic address: Min-Li@ouhsc.edu
NCI - National Cancer Institute William and Ella Owens Medical Research Foundation NIH - National Institutes of Health
Funding text :
We would like to thank Prof. Qianghu Wang for providing valuable suggestions for this study. This work was supported in part by National Institutes of Health (NIH) grants to M. Li (R01 CA186338, R01 CA203108, R01 CA247234 and the William and Ella Owens Medical Research Foundation) and NIH/National Cancer Institute Q39 award P30CA225520 to Stephenson Cancer Center.
Liu, L, Bai, X, Wang, J, Tang, XR, Wu, DH, Du, SS, et al. Combination of TMB and CNA stratifies prognostic and predictive responses to immunotherapy across metastatic cancer. Clin Cancer Res 25:24 (2019), 7413–7423.
Delaunay, S, Frye, M., RNA modifications regulating cell fate in cancer. Nat Cell Biol 21:5 (2019), 552–559.
Frye, M, Harada, BT, Behm, M, He, C., RNA modifications modulate gene expression during development. Science 361:6409 (2018), 1346–1349.
Han, D, Liu, J, Chen, C, Dong, L, Liu, Y, Chang, R, et al. Anti-tumour immunity controlled through mRNA m(6)A methylation and YTHDF1 in dendritic cells. Nature 566:7743 (2019), 270–274.
Li, Y, Xiao, J, Bai, J, Tian, Y, Qu, Y, Chen, X, et al. Molecular characterization and clinical relevance of m(6)A regulators across 33 cancer types. Mol Cancer, 18(1), 2019, 137.
Kandimalla, R, Gao, F, Li, Y, Huang, H, Ke, J, Deng, X, et al. RNAMethyPro: a biologically conserved signature of N6-methyladenosine regulators for predicting survival at pan-cancer level. NPJ Precis Oncol, 3, 2019, 13.
Zhang, B, Wu, Q, Li, B, Wang, D, Wang, L, Zhou, YL., m(6)A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer. Mol Cancer, 19(1), 2020, 53.
Siegel, RL, Miller, KD, Jemal, A., Cancer statistics, 2020. CA Cancer J Clin 70:1 (2020), 7–30.
Moffitt, RA, Marayati, R, Flate, EL, Volmar, KE, Loeza, SG, Hoadley, KA, et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat Genet 47:10 (2015), 1168–1178.
Collisson, EA, Sadanandam, A, Olson, P, Gibb, WJ, Truitt, M, Gu, S, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med 17:4 (2011), 500–503.
Zou, Y, Hu, X, Zheng, S, Yang, A, Li, X, Tang, H, et al. Discordance of immunotherapy response predictive biomarkers between primary lesions and paired metastases in tumours: a multidimensional analysis. EBioMedicine, 63, 2020, 103137.
Li, B, Dewey, CN., RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12, 2011, 323.
Friedman, J, Hastie, T, Tibshirani, R., Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1 (2010), 1–22.
Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102:43 (2005), 15545–15550.
Robinson, MD, McCarthy, DJ, Smyth, GK., edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:1 (2010), 139–140.
Warde-Farley, D, Donaldson, SL, Comes, O, Zuberi, K, Badrawi, R, Chao, P, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38 (2010), W214–W220 Web Server issue.
Shannon, P, Markiel, A, Ozier, O, Baliga, NS, Wang, JT, Ramage, D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:11 (2003), 2498–2504.
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, 12(4), 2011, R41.
Mayakonda, A, Lin, DC, Assenov, Y, Plass, C, Koeffler, HP., Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 28:11 (2018), 1747–1756.
Cancer Genome Atlas Research Network. Electronic address WBE, cancer genome Atlas research n. comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169:7 (2017), 1327–1341 e23.
Cursons, J, Souza-Fonseca-Guimaraes, F, Foroutan, M, Anderson, A, Hollande, F, Hediyeh-Zadeh, S, et al. A gene signature predicting natural killer cell infiltration and improved survival in melanoma patients. Cancer Immunol Res 7:7 (2019), 1162–1174.
Collin, M, McGovern, N, Haniffa, M., Human dendritic cell subsets. Immunology 140:1 (2013), 22–30.
Newman, AM, Steen, CB, Liu, CL, Gentles, AJ, Chaudhuri, AA, Scherer, F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol 37:7 (2019), 773–782.
Barbieri, I, Tzelepis, K, Pandolfini, L, Shi, J, Millan-Zambrano, G, Robson, SC, et al. Promoter-bound METTL3 maintains myeloid leukaemia by m(6)A-dependent translation control. Nature 552:7683 (2017), 126–131.
Liu, M, Yang, J, Zhang, Y, Zhou, Z, Cui, X, Zhang, L, et al. ZIP4 Promotes Pancreatic Cancer Progression by Repressing ZO-1 and Claudin-1 through a ZEB1-Dependent Transcriptional Mechanism. Clin Cancer Res 24:13 (2018), 3186–3196.
Liu, M, Zhang, Y, Yang, J, Cui, X, Zhou, Z, Zhan, H, et al. ZIP4 increases expression of transcription factor ZEB1 to promote integrin alpha3beta1 signaling and inhibit expression of the gemcitabine transporter ENT1 in pancreatic cancer cells. Gastroenterology 158:3 (2020), 679–692 e1.
Yang, J, Zhang, Z, Zhang, Y, Ni, X, Zhang, G, Cui, X, et al. ZIP4 promotes muscle wasting and cachexia in mice with orthotopic pancreatic tumors by stimulating RAB27B-regulated release of extracellular vesicles from cancer cells. Gastroenterology 156:3 (2019), 722–734 e6.
Zhang, Y, Yang, J, Cui, X, Chen, Y, Zhu, VF, Hagan, JP, et al. A novel epigenetic CREB-miR-373 axis mediates ZIP4-induced pancreatic cancer growth. EMBO Mol Med 5:9 (2013), 1322–1334.
Romero, JM, Grunwald, B, Jang, GH, Bavi, PP, Jhaveri, A, Masoomian, M, et al. A four-chemokine signature is associated with a T-cell-inflamed phenotype in primary and metastatic pancreatic cancer. Clin Cancer Res 26:8 (2020), 1997–2010.
Hugo, W, Zaretsky, JM, Sun, L, Song, C, Moreno, BH, Hu-Lieskovan, S, et al. Genomic and transcriptomic features of response to Anti-PD-1 therapy in metastatic melanoma. Cell 165:1 (2016), 35–44.
Van Allen, EM, Miao, D, Schilling, B, Shukla, SA, Blank, C, Zimmer, L, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350:6257 (2015), 207–211.
Auslander, N, Zhang, G, Lee, JS, Frederick, DT, Miao, B, Moll, T, et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med 24:10 (2018), 1545–1549.
Carter, JA, Gilbo, P, Atwal, GS., IMPRES does not reproducibly predict response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med 25:12 (2019), 1833–1835.
Li, Z, Peng, Y, Li, J, Chen, Z, Chen, F, Tu, J, et al. N(6)-methyladenosine regulates glycolysis of cancer cells through PDK4. Nat Commun, 11(1), 2020, 2578.
Zhang, C, Huang, S, Zhuang, H, Ruan, S, Zhou, Z, Huang, K, et al. YTHDF2 promotes the liver cancer stem cell phenotype and cancer metastasis by regulating OCT4 expression via m6A RNA methylation. Oncogene 39:23 (2020), 4507–4518.
Yang, F, Jin, H, Que, B, Chao, Y, Zhang, H, Ying, X, et al. Dynamic m(6)A mRNA methylation reveals the role of METTL3-m(6)A-CDCP1 signaling axis in chemical carcinogenesis. Oncogene 38:24 (2019), 4755–4772.
Wang, H, Hu, X, Huang, M, Liu, J, Gu, Y, Ma, L, et al. Mettl3-mediated mRNA m(6)A methylation promotes dendritic cell activation. Nat Commun, 10(1), 2019, 1898.
Li, HB, Tong, J, Zhu, S, Batista, PJ, Duffy, EE, Zhao, J, et al. m(6)A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature 548:7667 (2017), 338–342.
Su, R, Dong, L, Li, C, Nachtergaele, S, Wunderlich, M, Qing, Y, et al. R-2HG exhibits anti-tumor activity by targeting FTO/m(6)A/MYC/CEBPA signaling. Cell 172:1-2 (2018), 90–105 e23.
Hegde, S, Krisnawan, VE, Herzog, BH, Zuo, C, Breden, MA, Knolhoff, BL, et al. Dendritic cell paucity leads to dysfunctional immune surveillance in pancreatic cancer. Cancer Cell 37:3 (2020), 289–307 e9.
House, IG, Savas, P, Lai, J, Chen, AXY, Oliver, AJ, Teo, ZL, et al. Macrophage-derived CXCL9 and CXCL10 are required for antitumor immune responses following immune checkpoint blockade. Clin Cancer Res 26:2 (2020), 487–504.
Herbst, RS, Soria, JC, Kowanetz, M, Fine, GD, Hamid, O, Gordon, MS, et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515:7528 (2014), 563–567.
Mayoux, M, Roller, A, Pulko, V, Sammicheli, S, Chen, S, Sum, E, et al. Dendritic cells dictate responses to PD-L1 blockade cancer immunotherapy. Sci Transl Med, 12(534), 2020.
Perkhofer, L, Gout, J, Roger, E, Kude de Almeida, F, Baptista Simoes, C, Wiesmuller, L, et al. DNA damage repair as a target in pancreatic cancer: state-of-the-art and future perspectives. Gut, 2020.
Davoli, T, Uno, H, Wooten, EC, Elledge, SJ., Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science, 355(6322), 2017.
Knudsen, ES, Kumarasamy, V, Chung, S, Ruiz, A, Vail, P, Tzetzo, S, et al. Targeting dual signalling pathways in concert with immune checkpoints for the treatment of pancreatic cancer. Gut, 2020.
Xu, LX, He, MH, Dai, ZH, Yu, J, Wang, JG, Li, XC, et al. Genomic and transcriptional heterogeneity of multifocal hepatocellular carcinoma. Ann Oncol 30:6 (2019), 990–997.
Rosenthal, R, Cadieux, EL, Salgado, R, Bakir, MA, Moore, DA, Hiley, CT, et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567:7749 (2019), 479–485.
Balachandran, VP, Luksza, M, Zhao, JN, Makarov, V, Moral, JA, Remark, R, et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551:7681 (2017), 512–516.
Zhou, Z, Xia, G, Xiang, Z, Liu, M, Wei, Z, Yan, J, et al. A C-X-C chemokine receptor type 2-dominated cross-talk between tumor cells and macrophages drives gastric cancer metastasis. Clin Cancer Res 25:11 (2019), 3317–3328.
Lin, C, He, H, Liu, H, Li, R, Chen, Y, Qi, Y, et al. Tumour-associated macrophages-derived CXCL8 determines immune evasion through autonomous PD-L1 expression in gastric cancer. Gut 68:10 (2019), 1764–1773.
Liu, M, Zhang, Y, Yang, J, Zhan, H, Zhou, Z, Jiang, Y, et al. Zinc dependent regulation of ZEB1 and YAP1 co-activation promotes emt plasticity and metastasis in pancreatic cancer. Gastroenterology, 2021.
