Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Tumor Biology

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Longley DB, Johnston PG. Molecular mechanisms of drug resistance. J Pathol. 2005;205(2):275–92. doi:10.1002/path.1706.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  6. Murphy CG, Modi S. HER2 breast cancer therapies: a review. Biologics. 2009;3:289–301.

    CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

  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.

    CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. 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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  41. Jia S, Meng A. Tob genes in development and homeostasis. Dev Dyn. 2007;236(4):913–21. doi:10.1002/dvdy.21092.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

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

Conflicts of interest

None

Author information

Authors and Affiliations

  1. Department of Biochemistry, School of Medicine, Konkuk University, Seoul, 143-701, Republic of Korea

    Young Seok Lee, Jin Ki Kim, Young Rae Kim, Ho Sung Myeong, Jong Duck Choi, Young Tae Ro, Yun Hee Noh & Sung Young Kim

  2. Plant Genomics Laboratory, Department of Applied Plant Science, Kangwon National University, Chuncheon, Republic of Korea

    Sun Goo Hwang & Cheol Seong Jang

  3. Department of Plastic and Reconstructive Surgery, College of Medicine, Yonsei University, Seoul, Republic of Korea

    Tae Hwan Park

  4. School of Korean Medicine, Pusan National University, Yangsan, Republic of Korea

    Kang Kwon

Authors
  1. Young Seok Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sun Goo Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Ki Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tae Hwan Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Young Rae Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ho Sung Myeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jong Duck Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kang Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Cheol Seong Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Young Tae Ro
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yun Hee Noh
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sung Young Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sung Young Kim.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 1.04 mb)

ESM 2

(XLSX 875 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, Y.S., Hwang, S.G., Kim, J.K. et al. Identification of novel therapeutic target genes in acquired lapatinib-resistant breast cancer by integrative meta-analysis. Tumor Biol. 37, 2285–2297 (2016). https://doi.org/10.1007/s13277-015-4033-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13277-015-4033-7

Keywords

Search

Navigation