Tumor Biology

, Volume 37, Issue 2, pp 2285–2297 | Cite as

Identification of novel therapeutic target genes in acquired lapatinib-resistant breast cancer by integrative meta-analysis

  • Young Seok Lee
  • Sun Goo Hwang
  • Jin Ki Kim
  • Tae Hwan Park
  • Young Rae Kim
  • Ho Sung Myeong
  • Jong Duck Choi
  • Kang Kwon
  • Cheol Seong Jang
  • Young Tae Ro
  • Yun Hee Noh
  • Sung Young Kim


Acquired resistance to lapatinib is a highly problematic clinical barrier that has to be overcome for a successful cancer treatment. Despite efforts to determine the mechanisms underlying acquired lapatinib resistance (ALR), no definitive genetic factors have been reported to be solely responsible for the acquired resistance in breast cancer. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets related to breast cancer with ALR, using the R-based RankProd package. From the meta-analysis, we were able to identify a total of 990 differentially expressed genes (DEGs, 406 upregulated, 584 downregulated) that are potentially associated with ALR. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs showed that “response to organic substance” and “p53 signaling pathway” may be largely involved in ALR process. Of these, many of the top 50 upregulated and downregulated DEGs were found in oncogenesis of various tumors and cancers. For the top 50 DEGs, we constructed the gene coexpression and protein–protein interaction networks from a huge database of well-known molecular interactions. By integrative analysis of two systemic networks, we condensed the total number of DEGs to six common genes (LGALS1, PRSS23, PTRF, FHL2, TOB1, and SOCS2). Furthermore, these genes were confirmed in functional module eigens obtained from the weighted gene correlation network analysis of total DEGs in the microarray datasets (“GSE16179” and “GSE52707”). Our integrative meta-analysis could provide a comprehensive perspective into complex mechanisms underlying ALR in breast cancer and a theoretical support for further chemotherapeutic studies.


Meta-analysis Microarray Differentially expressed genes Breast cancer Acquired lapatinib resistance 



This study was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2013R1A1A1075999).

Conflicts of interest


Supplementary material

13277_2015_4033_MOESM1_ESM.docx (1.1 mb)
ESM 1 (DOCX 1.04 mb)
13277_2015_4033_MOESM2_ESM.xlsx (875 kb)
ESM 2 (XLSX 875 kb)


  1. 1.
    Saraswathy M, Gong S. Different strategies to overcome multidrug resistance in cancer. Biotechnol Adv. 2013;31(8):1397–407. doi: 10.1016/j.biotechadv.2013.06.004.CrossRefPubMedGoogle Scholar
  2. 2.
    Szakacs G, Paterson JK, Ludwig JA, Booth-Genthe C, Gottesman MM. Targeting multidrug resistance in cancer. Nat Rev Drug Discov. 2006;5(3):219–34. doi: 10.1038/nrd1984.CrossRefPubMedGoogle Scholar
  3. 3.
    Chen KG, Sikic BI. Molecular pathways: regulation and therapeutic implications of multidrug resistance. Clin Cancer Res. 2012;18(7):1863–9. doi: 10.1158/1078-0432.CCR-11-1590.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Longley DB, Johnston PG. Molecular mechanisms of drug resistance. J Pathol. 2005;205(2):275–92. doi: 10.1002/path.1706.CrossRefPubMedGoogle Scholar
  5. 5.
    Foo J, Michor F. Evolution of acquired resistance to anti-cancer therapy. J Theor Biol. 2014;355:10–20. doi: 10.1016/j.jtbi.2014.02.025.CrossRefPubMedGoogle Scholar
  6. 6.
    Murphy CG, Modi S. HER2 breast cancer therapies: a review. Biologics. 2009;3:289–301.PubMedPubMedCentralGoogle Scholar
  7. 7.
    Tevaarwerk AJ, Kolesar JM. Lapatinib: a small-molecule inhibitor of epidermal growth factor receptor and human epidermal growth factor receptor-2 tyrosine kinases used in the treatment of breast cancer. Clin Ther. 2009;31(Pt 2):2332–48. doi: 10.1016/j.clinthera.2009.11.029.CrossRefPubMedGoogle Scholar
  8. 8.
    Bilancia D, Rosati G, Dinota A, Germano D, Romano R, Manzione L. Lapatinib in breast cancer. Ann Oncol. 2007;18 Suppl 6:vi26–30. doi: 10.1093/annonc/mdm220.PubMedGoogle Scholar
  9. 9.
    Medina PJ, Goodin S. Lapatinib: a dual inhibitor of human epidermal growth factor receptor tyrosine kinases. Clin Ther. 2008;30(8):1426–47. doi: 10.1016/j.clinthera.2008.08.008.CrossRefPubMedGoogle Scholar
  10. 10.
    Roskoski Jr R. The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacol Res. 2014;79:34–74. doi: 10.1016/j.phrs.2013.11.002.CrossRefPubMedGoogle Scholar
  11. 11.
    Lovly CM, Shaw AT. Molecular pathways: resistance to kinase inhibitors and implications for therapeutic strategies. Clin Cancer Res. 2014;20(9):2249–56. doi: 10.1158/1078-0432.CCR-13-1610.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Chen FL, Xia W, Spector NL. Acquired resistance to small molecule ErbB2 tyrosine kinase inhibitors. Clin Cancer Res. 2008;14(21):6730–4. doi: 10.1158/1078-0432.CCR-08-0581.CrossRefPubMedGoogle Scholar
  13. 13.
    Rosenzweig SA. Acquired resistance to drugs targeting receptor tyrosine kinases. Biochem Pharmacol. 2012;83(8):1041–8. doi: 10.1016/j.bcp.2011.12.025.CrossRefPubMedGoogle Scholar
  14. 14.
    Wetterskog D, Shiu KK, Chong I, Meijer T, Mackay A, Lambros M, et al. Identification of novel determinants of resistance to lapatinib in ERBB2-amplified cancers. Oncogene. 2014;33(8):966–76. doi: 10.1038/onc.2013.41.CrossRefPubMedGoogle Scholar
  15. 15.
    Kumler I, Tuxen MK, Nielsen DL. A systematic review of dual targeting in HER2-positive breast cancer. Cancer Treat Rev. 2014;40(2):259–70. doi: 10.1016/j.ctrv.2013.09.002.CrossRefPubMedGoogle Scholar
  16. 16.
    Mohd Sharial MS, Crown J, Hennessy BT. Overcoming resistance and restoring sensitivity to HER2-targeted therapies in breast cancer. Ann Oncol. 2012;23(12):3007–16. doi: 10.1093/annonc/mds200.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Ramasamy A, Mondry A, Holmes CC, Altman DG. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med. 2008;5(9), e184. doi: 10.1371/journal.pmed.0050184.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Liu J, Li J, Li H, Li A, Liu B, Han L. A comprehensive analysis of candidate genes and pathways in pancreatic cancer. Tumour Biol: J Int Soc Oncodevelopmental Biol Med. 2015;36(3):1849–57. doi: 10.1007/s13277-014-2787-y.CrossRefGoogle Scholar
  19. 19.
    Tulalamba W, Larbcharoensub N, Sirachainan E, Tantiwetrueangdet A, Janvilisri T. Transcriptome meta-analysis reveals dysregulated pathways in nasopharyngeal carcinoma. Tumour Biol: J Int Soc Oncodevelopmental Biol Med. 2015. doi: 10.1007/s13277-015-3268-7.Google Scholar
  20. 20.
    Komurov K, Tseng JT, Muller M, Seviour EG, Moss TJ, Yang L, et al. The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant ErbB2-positive breast cancer cells. Mol Syst Biol. 2012;8:596. doi: 10.1038/msb.2012.25.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Liu L, Greger J, Shi H, Liu Y, Greshock J, Annan R, et al. Novel mechanism of lapatinib resistance in HER2-positive breast tumor cells: activation of AXL. Cancer Res. 2009;69(17):6871–8. doi: 10.1158/0008-5472.CAN-08-4490.CrossRefPubMedGoogle Scholar
  22. 22.
    Bailey ST, Miron PL, Choi YJ, Kochupurakkal B, Maulik G, Rodig SJ, et al. NF-kappaB activation-induced anti-apoptosis renders HER2-positive cells drug resistant and accelerates tumor growth. Mol Cancer Res. 2014;12(3):408–20. doi: 10.1158/1541-7786.MCR-13-0206-T.CrossRefPubMedGoogle Scholar
  23. 23.
    Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7), e1000097. doi: 10.1371/journal.pmed.1000097.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Xia J, Fjell CD, Mayer ML, Pena OM, Wishart DS, Hancock RE. INMEX—a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res. 2013;41(Web server issue):W63–70. doi: 10.1093/nar/gkt338.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Fang F, Pan J, Xu L, Wang J. Identification of potential transcriptomic markers in developing ankylosing spondylitis: a meta-analysis of gene expression profiles. Biomed Res Int. 2015;2015:826316. doi: 10.1155/2015/826316.PubMedPubMedCentralGoogle Scholar
  26. 26.
    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. 2010;38(Web Server issue):W214–20. doi: 10.1093/nar/gkq537.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Molina-Navarro MM, Trivino JC, Martinez-Dolz L, Lago F, Gonzalez-Juanatey JR, Portoles M, et al. Functional networks of nucleocytoplasmic transport-related genes differentiate ischemic and dilated cardiomyopathies. A new therapeutic opportunity. PLoS One. 2014;9(8), e104709. doi: 10.1371/journal.pone.0104709.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Firoz A, Malik A, Singh SK, Jha V, Ali A. Comparative analysis of glycogene expression in different mouse tissues using RNA-Seq Data. Int J Genomics. 2014;2014:837365. doi: 10.1155/2014/837365.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Gupta A, Schulze TG, Nagarajan V, Akula N, Corona W, Jiang XY, et al. Interaction networks of lithium and valproate molecular targets reveal a striking enrichment of apoptosis functional clusters and neurotrophin signaling. Pharmacogenomics J. 2012;12(4):328–41. doi: 10.1038/tpj.2011.9.CrossRefPubMedGoogle Scholar
  30. 30.
    Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods. 2012;9(5):471–2. doi: 10.1038/nmeth.1938.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4, Article17. doi: 10.2202/1544-6115.1128.PubMedGoogle Scholar
  32. 32.
    Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006;7 Suppl 1:S7. doi: 10.1186/1471-2105-7-S1-S7.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Jiang J, Jia P, Zhao Z, Shen B. Key regulators in prostate cancer identified by co-expression module analysis. BMC Genomics. 2014;15:1015. doi: 10.1186/1471-2164-15-1015.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Zhou X, Li D, Wang X, Zhang B, Zhu H, Zhao J, et al. Galectin-1 is overexpressed in CD133+ human lung adenocarcinoma cells and promotes their growth and invasiveness. Oncotarget. 2014.Google Scholar
  35. 35.
    Miao JH, Wang SQ, Zhang MH, Yu FB, Zhang L, Yu ZX, et al. Knockdown of galectin-1 suppresses the growth and invasion of osteosarcoma cells through inhibition of the MAPK/ERK pathway. Oncol Rep. 2014;32(4):1497–504. doi: 10.3892/or.2014.3358.PubMedGoogle Scholar
  36. 36.
    Chan HS, Chang SJ, Wang TY, Ko HJ, Lin YC, Lin KT, et al. Serine protease PRSS23 is upregulated by estrogen receptor alpha and associated with proliferation of breast cancer cells. PLoS One. 2012;7(1), e30397. doi: 10.1371/journal.pone.0030397.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Inder KL, Ruelcke JE, Petelin L, Moon H, Choi E, Rae J, et al. Cavin-1/PTRF alters prostate cancer cell-derived extracellular vesicle content and internalization to attenuate extracellular vesicle-mediated osteoclastogenesis and osteoblast proliferation. J Extracell Vesicles. 2014;3. doi: 10.3402/jev.v3.23784.
  38. 38.
    Yi JS, Mun DG, Lee H, Park JS, Lee JW, Lee JS, et al. PTRF/cavin-1 is essential for multidrug resistance in cancer cells. J Proteome Res. 2013;12(2):605–14. doi: 10.1021/pr300651m.CrossRefPubMedGoogle Scholar
  39. 39.
    Xu J, Zhou J, Li MS, Ng CF, Ng YK, Lai PB, et al. Transcriptional regulation of the tumor suppressor FHL2 by p53 in human kidney and liver cells. PLoS One. 2014;9(8), e99359. doi: 10.1371/journal.pone.0099359.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    McGrath MJ, Binge LC, Sriratana A, Wang H, Robinson PA, Pook D, et al. Regulation of the transcriptional coactivator FHL2 licenses activation of the androgen receptor in castrate-resistant prostate cancer. Cancer Res. 2013;73(16):5066–79. doi: 10.1158/0008-5472.CAN-12-4520.CrossRefPubMedGoogle Scholar
  41. 41.
    Jia S, Meng A. Tob genes in development and homeostasis. Dev Dyn. 2007;236(4):913–21. doi: 10.1002/dvdy.21092.CrossRefPubMedGoogle Scholar
  42. 42.
    O’Malley S, Su H, Zhang T, Ng C, Ge H, Tang CK. TOB suppresses breast cancer tumorigenesis. Int J Cancer. 2009;125(8):1805–13. doi: 10.1002/ijc.24490.CrossRefPubMedGoogle Scholar
  43. 43.
    Helms MW, Kemming D, Contag CH, Pospisil H, Bartkowiak K, Wang A, et al. TOB1 is regulated by EGF-dependent HER2 and EGFR signaling, is highly phosphorylated, and indicates poor prognosis in node-negative breast cancer. Cancer Res. 2009;69(12):5049–56. doi: 10.1158/0008-5472.CAN-08-4154.CrossRefPubMedGoogle Scholar
  44. 44.
    Iglesias-Gato D, Chuan YC, Wikstrom P, Augsten S, Jiang N, Niu Y, et al. SOCS2 mediates the cross talk between androgen and growth hormone signaling in prostate cancer. Carcinogenesis. 2014;35(1):24–33. doi: 10.1093/carcin/bgt304.CrossRefPubMedGoogle Scholar

Copyright information

© International Society of Oncology and BioMarkers (ISOBM) 2015

Authors and Affiliations

  • Young Seok Lee
    • 1
  • Sun Goo Hwang
    • 2
  • Jin Ki Kim
    • 1
  • Tae Hwan Park
    • 3
  • Young Rae Kim
    • 1
  • Ho Sung Myeong
    • 1
  • Jong Duck Choi
    • 1
  • Kang Kwon
    • 4
  • Cheol Seong Jang
    • 2
  • Young Tae Ro
    • 1
  • Yun Hee Noh
    • 1
  • Sung Young Kim
    • 1
  1. 1.Department of Biochemistry, School of MedicineKonkuk UniversitySeoulRepublic of Korea
  2. 2.Plant Genomics Laboratory, Department of Applied Plant ScienceKangwon National UniversityChuncheonRepublic of Korea
  3. 3.Department of Plastic and Reconstructive Surgery, College of MedicineYonsei UniversitySeoulRepublic of Korea
  4. 4.School of Korean MedicinePusan National UniversityYangsanRepublic of Korea

Personalised recommendations