Pancreatic cancer acquires resistance to MAPK pathway inhibition by clonal expansion and adaptive DNA hypermethylation.
Cancer
Clonal expansion
DNA methylation
Epigenetic plasticity
PDAC
Therapy resistance
WGBS
Journal
Clinical epigenetics
ISSN: 1868-7083
Titre abrégé: Clin Epigenetics
Pays: Germany
ID NLM: 101516977
Informations de publication
Date de publication:
16 Jan 2024
16 Jan 2024
Historique:
received:
19
09
2023
accepted:
03
01
2024
medline:
17
1
2024
pubmed:
17
1
2024
entrez:
16
1
2024
Statut:
epublish
Résumé
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis. It is marked by extraordinary resistance to conventional therapies including chemotherapy and radiation, as well as to essentially all targeted therapies evaluated so far. More than 90% of PDAC cases harbor an activating KRAS mutation. As the most common KRAS variants in PDAC remain undruggable so far, it seemed promising to inhibit a downstream target in the MAPK pathway such as MEK1/2, but up to now preclinical and clinical evaluation of MEK inhibitors (MEK We found that resistant cell populations under increasing MEK Overall, the concept of acquired therapy resistance as a result of the expansion of a single cell clone with epigenetic plasticity sheds light on genetic, epigenetic and phenotypic patterns during evolvement of treatment resistance in a tumor with high adaptive capabilities and provides potential for reversion through epigenetic targeting.
Sections du résumé
BACKGROUND
BACKGROUND
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis. It is marked by extraordinary resistance to conventional therapies including chemotherapy and radiation, as well as to essentially all targeted therapies evaluated so far. More than 90% of PDAC cases harbor an activating KRAS mutation. As the most common KRAS variants in PDAC remain undruggable so far, it seemed promising to inhibit a downstream target in the MAPK pathway such as MEK1/2, but up to now preclinical and clinical evaluation of MEK inhibitors (MEK
RESULTS
RESULTS
We found that resistant cell populations under increasing MEK
CONCLUSION
CONCLUSIONS
Overall, the concept of acquired therapy resistance as a result of the expansion of a single cell clone with epigenetic plasticity sheds light on genetic, epigenetic and phenotypic patterns during evolvement of treatment resistance in a tumor with high adaptive capabilities and provides potential for reversion through epigenetic targeting.
Identifiants
pubmed: 38229153
doi: 10.1186/s13148-024-01623-z
pii: 10.1186/s13148-024-01623-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
13Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : LU-1944/3-1
Organisme : Deutsche Forschungsgemeinschaft
ID : SI1549/4-1
Organisme : Associazione Italiana per la Ricerca sul Cancro
ID : 12182
Organisme : Associazione Italiana per la Ricerca sul Cancro
ID : 12182
Organisme : Fondazione Cassa di Risparmio di Verona Vicenza Belluno e Ancona
ID : 203885/2017
Organisme : Fondazione Cassa di Risparmio di Verona Vicenza Belluno e Ancona
ID : 203885/2017
Organisme : Ministry of Science, North Rhine-Westphalia, Germany.
ID : PURE
Organisme : German Federal Ministry of Education and Research (BMBF)
ID : 01KD2206A/SATURN3
Organisme : Ministry of Culture and Science of the State of North Rhine-Westphalia
ID : research network CANcer TARgeting (CANTAR)
Informations de copyright
© 2024. The Author(s).
Références
Waddell N, Pajic M, Patch AM, Chang DK, Kassahn KS, Bailey P, et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature. 2015;518(7540):495–501.
pubmed: 25719666
pmcid: 4523082
doi: 10.1038/nature14169
Cancer Genome Atlas Research Network. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell. 2017;32(2):185–203.
doi: 10.1016/j.ccell.2017.07.007
Chan-Seng-Yue M, Kim JC, Wilson GW, Ng K, Figueroa EF, O’Kane GM, et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat Genet. 2020;52(2):231–40.
pubmed: 31932696
doi: 10.1038/s41588-019-0566-9
Ohren JF, Chen H, Pavlovsky A, Whitehead C, Zhang E, Kuffa P, et al. Structures of human MAP kinase kinase 1 (MEK1) and MEK2 describe novel noncompetitive kinase inhibition. Nat Struct Mol Biol. 2004;11(12):1192–7.
pubmed: 15543157
doi: 10.1038/nsmb859
Collisson EA, Trejo CL, Silva JM, Gu S, Korkola JE, Heiser LM, et al. A central role for RAF–>MEK–>ERK signaling in the genesis of pancreatic ductal adenocarcinoma. Cancer Discov. 2012;2(8):685–93.
pubmed: 22628411
pmcid: 3425446
doi: 10.1158/2159-8290.CD-11-0347
Walters DM, Lindberg JM, Adair SJ, Newhook TE, Cowan CR, Stokes JB, et al. Inhibition of the growth of patient-derived pancreatic cancer xenografts with the MEK inhibitor trametinib is augmented by combined treatment with the epidermal growth factor receptor/HER2 inhibitor lapatinib. Neoplasia. 2013;15(2):143–55.
pubmed: 23441129
pmcid: 3579317
doi: 10.1593/neo.121712
Vena F, Li Causi E, Rodriguez-Justo M, Goodstal S, Hagemann T, Hartley JA, et al. The MEK1/2 inhibitor pimasertib enhances gemcitabine efficacy in pancreatic cancer models by altering ribonucleotide reductase subunit-1 (RRM1). Clin Cancer Res. 2015;21(24):5563–77.
pubmed: 26228206
doi: 10.1158/1078-0432.CCR-15-0485
Van Laethem JL, Riess H, Jassem J, Haas M, Martens UM, Weekes C, et al. Phase I/II study of refametinib (BAY 86–9766) in combination with gemcitabine in advanced pancreatic cancer. Target Oncol. 2017;12(1):97–109.
pubmed: 27975152
doi: 10.1007/s11523-016-0469-y
Infante JR, Somer BG, Park JO, Li C-P, Scheulen ME, Kasubhai SM, et al. A randomised, double-blind, placebo-controlled trial of trametinib, an oral MEK inhibitor, in combination with gemcitabine for patients with untreated metastatic adenocarcinoma of the pancreas. Eur J Cancer. 2014;50(12):2072–81.
pubmed: 24915778
doi: 10.1016/j.ejca.2014.04.024
Chung V, McDonough S, Philip PA, Cardin D, Wang-Gillam A, Hui L, et al. Effect of selumetinib and MK-2206 vs axaliplatin and fluorouracil in patients with metastatic pancreatic cancer after prior therapy: SWOG S1115 study randomized clinical trial. JAMA Oncol. 2017;3(4):516–22.
pubmed: 27978579
pmcid: 5665683
doi: 10.1001/jamaoncol.2016.5383
Van Cutsem E, Hidalgo M, Canon JL, Macarulla T, Bazin I, Poddubskaya E, et al. Phase I/II trial of pimasertib plus gemcitabine in patients with metastatic pancreatic cancer. Int J Cancer. 2018;143(8):2053–64.
pubmed: 29756206
doi: 10.1002/ijc.31603
Fedele C, Ran H, Diskin B, Wei W, Jen J, Geer MJ, et al. SHP2 inhibition prevents adaptive resistance to MEK inhibitors in multiple cancer models. Cancer Discov. 2018;8(10):1237–49.
pubmed: 30045908
pmcid: 6170706
doi: 10.1158/2159-8290.CD-18-0444
Ruess DA, Heynen GJ, Ciecielski KJ, Ai J, Berninger A, Kabacaoglu D, et al. Mutant KRAS-driven cancers depend on PTPN11/SHP2 phosphatase. Nat Med. 2018;24(7):954–60.
pubmed: 29808009
doi: 10.1038/s41591-018-0024-8
Lin L, Sabnis AJ, Chan E, Olivas V, Cade L, Pazarentzos E, et al. The Hippo effector YAP promotes resistance to RAF- and MEK-targeted cancer therapies. Nat Genet. 2015;47(3):250–6.
pubmed: 25665005
pmcid: 4930244
doi: 10.1038/ng.3218
Ponz-Sarvise M, Corbo V, Tiriac H, Engle DD, Frese KK, Oni TE, et al. Identification of resistance pathways specific to malignancy using organoid models of pancreatic cancer. Clin Cancer Res. 2019;25(22):6742.
pubmed: 31492749
pmcid: 6858952
doi: 10.1158/1078-0432.CCR-19-1398
Viale A, Pettazzoni P, Lyssiotis CA, Ying H, Sánchez N, Marchesini M, et al. Oncogene ablation-resistant pancreatic cancer cells depend on mitochondrial function. Nature. 2014;514(7524):628–32.
pubmed: 25119024
pmcid: 4376130
doi: 10.1038/nature13611
Santana-Codina N, Roeth AA, Zhang Y, Yang A, Mashadova O, Asara JM, et al. Oncogenic KRAS supports pancreatic cancer through regulation of nucleotide synthesis. Nat Commun. 2018;9(1):4945.
pubmed: 30470748
pmcid: 6251888
doi: 10.1038/s41467-018-07472-8
Bryant KL, Stalnecker CA, Zeitouni D, Klomp JE, Peng S, Tikunov AP, et al. Combination of ERK and autophagy inhibition as a treatment approach for pancreatic cancer. Nat Med. 2019;25(4):628–40.
pubmed: 30833752
pmcid: 6484853
doi: 10.1038/s41591-019-0368-8
Kinsey CG, Camolotto SA, Boespflug AM, Guillen KP, Foth M, Truong A, et al. Protective autophagy elicited by RAF–>MEK–>ERK inhibition suggests a treatment strategy for RAS-driven cancers. Nat Med. 2019;25(4):620–7.
pubmed: 30833748
pmcid: 6452642
doi: 10.1038/s41591-019-0367-9
Sharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S, et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell. 2010;141(1):69–80.
pubmed: 20371346
pmcid: 2851638
doi: 10.1016/j.cell.2010.02.027
Wang Z, Hausmann S, Lyu R, Li TM, Lofgren SM, Flores NM, et al. SETD5-coordinated chromatin reprogramming regulates adaptive resistance to targeted pancreatic cancer therapy. Cancer Cell. 2020;37(6):834-49e13.
pubmed: 32442403
pmcid: 8187079
doi: 10.1016/j.ccell.2020.04.014
Hessmann E, Johnsen SA, Siveke JT, Ellenrieder V. Epigenetic treatment of pancreatic cancer: is there a therapeutic perspective on the horizon? Gut. 2017;66(1):168–79.
pubmed: 27811314
doi: 10.1136/gutjnl-2016-312539
Shen H, Laird PW. Interplay between the cancer genome and epigenome. Cell. 2013;153(1):38–55.
pubmed: 23540689
pmcid: 3648790
doi: 10.1016/j.cell.2013.03.008
Plass C, Pfister SM, Lindroth AM, Bogatyrova O, Claus R, Lichter P. Mutations in regulators of the epigenome and their connections to global chromatin patterns in cancer. Nat Rev Genet. 2013;14(11):765–80.
pubmed: 24105274
doi: 10.1038/nrg3554
Mazur PK, Herner A, Mello SS, Wirth M, Hausmann S, Sanchez-Rivera FJ, et al. Combined inhibition of BET family proteins and histone deacetylases as a potential epigenetics-based therapy for pancreatic ductal adenocarcinoma. Nat Med. 2015;21(10):1163–71.
pubmed: 26390243
pmcid: 4959788
doi: 10.1038/nm.3952
Heid I, Steiger K, Trajkovic-Arsic M, Settles M, Eßwein MR, Erkan M, et al. Co-clinical assessment of tumor cellularity in pancreatic cancer. Clin Cancer Res. 2016;23(6):1461–70.
pubmed: 27663591
doi: 10.1158/1078-0432.CCR-15-2432
Bailey P, Chang DK, Nones K, Johns AL, Patch AM, Gingras MC, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531(7592):47–52.
pubmed: 26909576
doi: 10.1038/nature16965
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. 2011;17(4):500–3.
pubmed: 21460848
pmcid: 3755490
doi: 10.1038/nm.2344
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. 2015;47(10):1168–78.
pubmed: 26343385
pmcid: 4912058
doi: 10.1038/ng.3398
Salgia R, Kulkarni P. The genetic/non-genetic duality of drug “resistance” in cancer. Trends Cancer. 2018;4(2):110–8.
pubmed: 29458961
pmcid: 5822736
doi: 10.1016/j.trecan.2018.01.001
Jager M, Wang K, Bauer S, Smedley D, Krawitz P, Robinson PN. Jannovar: a java library for exome annotation. Hum Mutat. 2014;35(5):548–55.
pubmed: 24677618
doi: 10.1002/humu.22531
Notta F, Chan-Seng-Yue M, Lemire M, Li Y, Wilson GW, Connor AA, et al. A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns. Nature. 2016;538(7625):378–82.
pubmed: 27732578
pmcid: 5446075
doi: 10.1038/nature19823
Leitao E, Beygo J, Zeschnigk M, Klein-Hitpass L, Bargull M, Rahmann S, et al. Locus-specific DNA methylation analysis by targeted deep bisulfite sequencing. Methods Mol Biol. 2018;1767:351–66.
pubmed: 29524145
doi: 10.1007/978-1-4939-7774-1_19
Roe JS, Hwang CI, Somerville TDD, Milazzo JP, Lee EJ, Da Silva B, et al. Enhancer reprogramming promotes pancreatic cancer metastasis. Cell. 2017;170(5):875-88e20.
pubmed: 28757253
pmcid: 5726277
doi: 10.1016/j.cell.2017.07.007
Yin Y, Morgunova E, Jolma A, Kaasinen E, Sahu B, Khund-Sayeed S, et al. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science. 2017;356(6337):eaai2239.
doi: 10.1126/science.aaj2239
Xuan Lin QX, Sian S, An O, Thieffry D, Jha S, Benoukraf T. MethMotif: an integrative cell specific database of transcription factor binding motifs coupled with DNA methylation profiles. Nucleic Acids Res. 2019;47(D1):D145–54.
pubmed: 30380113
doi: 10.1093/nar/gky1005
Konieczkowski DJ, Johannessen CM, Garraway LA. A convergence-based framework for cancer drug resistance. Cancer Cell. 2018;33(5):801–15.
pubmed: 29763622
pmcid: 5957297
doi: 10.1016/j.ccell.2018.03.025
Hanahan D, Coussens LM. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012;21(3):309–22.
pubmed: 22439926
doi: 10.1016/j.ccr.2012.02.022
Hata AN, Niederst MJ, Archibald HL, Gomez-Caraballo M, Siddiqui FM, Mulvey HE, et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat Med. 2016;22(3):262–9.
pubmed: 26828195
pmcid: 4900892
doi: 10.1038/nm.4040
Shibue T, Weinberg RA. EMT, CSCs, and drug resistance: the mechanistic link and clinical implications. Nat Rev Clin Oncol. 2017;14(10):611–29.
pubmed: 28397828
pmcid: 5720366
doi: 10.1038/nrclinonc.2017.44
McDonald OG, Li X, Saunders T, Tryggvadottir R, Mentch SJ, Warmoes MO, et al. Epigenomic reprogramming during pancreatic cancer progression links anabolic glucose metabolism to distant metastasis. Nat Genet. 2017;49(3):367–76.
pubmed: 28092686
pmcid: 5695682
doi: 10.1038/ng.3753
Bestor TH, Edwards JR, Boulard M. Notes on the role of dynamic DNA methylation in mammalian development. Proc Natl Acad Sci USA. 2015;112(22):6796.
pubmed: 25368180
doi: 10.1073/pnas.1415301111
Loewe S. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung. 1953;3(6):285–90.
pubmed: 13081480
Di Veroli GY, Fornari C, Wang D, Mollard S, Bramhall JL, Richards FM, et al. Combenefit: an interactive platform for the analysis and visualization of drug combinations. Bioinformatics. 2016;32(18):2866–8.
pubmed: 27153664
pmcid: 5018366
doi: 10.1093/bioinformatics/btw230
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300.
Jiang H, Lei R, Ding S-W, Zhu S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinf. 2014;15:182.
doi: 10.1186/1471-2105-15-182
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14(4):417–9.
pubmed: 28263959
pmcid: 5600148
doi: 10.1038/nmeth.4197
Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 2015;4:1521.
pubmed: 26925227
doi: 10.12688/f1000research.7563.1
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
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. 2005;102(43):15545–50.
pubmed: 16199517
pmcid: 1239896
doi: 10.1073/pnas.0506580102
Pedersen BS, Eyring K, De S, Yang IV, Schwartz DA. Fast and accurate alignment of long bisulfite-seq reads. arXiv. 2014; https://arxiv.org/abs/1401.129
Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 2012;13(10):R83.
pubmed: 23034175
pmcid: 3491411
doi: 10.1186/gb-2012-13-10-r83
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–2.
pubmed: 20110278
pmcid: 2832824
doi: 10.1093/bioinformatics/btq033
Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018;46(D1):D754–61.
pubmed: 29155950
doi: 10.1093/nar/gkx1098
Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38(4):576–89.
pubmed: 20513432
pmcid: 2898526
doi: 10.1016/j.molcel.2010.05.004
Boj SF, Hwang CI, Baker LA, Chio II, Engle DD, Corbo V, et al. Organoid models of human and mouse ductal pancreatic cancer. Cell. 2015;160(1–2):324–38.
pubmed: 25557080
doi: 10.1016/j.cell.2014.12.021
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. 2013; https://arxiv.org/abs/1303.3997
Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9(9):R137.
pubmed: 18798982
pmcid: 2592715
doi: 10.1186/gb-2008-9-9-r137
Gaspar JM. Improved peak-calling with MACS2. bioRxiv. 2018; https://doi.org/10.1101/496521
Tarasov A, Vilella AJ, Cuppen E, Nijman IJ, Prins P. Sambamba: fast processing of NGS alignment formats. Bioinformatics. 2015;31(12):2032–4.
pubmed: 25697820
pmcid: 4765878
doi: 10.1093/bioinformatics/btv098
Garrison E, Gabor M. Haplotype-based variant detection from short-read sequencing. arXiv. 2012; https://arxiv.org/abs/1207.3907
Köster J, Dijkstra LJ, Marschall T, Schönhuth A. Enhancing sensitivity and controlling false discovery rate in somatic indel discovery. bioRxiv. 2019; https://doi.org/10.1101/741256
Köster J, Rahmann S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics. 2012;28(19):2520–2.
pubmed: 22908215
doi: 10.1093/bioinformatics/bts480
Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27(21):2987–93.
pubmed: 21903627
pmcid: 3198575
doi: 10.1093/bioinformatics/btr509
R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018