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Kallikrein-related peptidase 6 (KLK6) expression differentiates tumor subtypes and predicts clinical outcome in breast cancer patients

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Abstract

Novel molecular markers that address the heterogeneity of breast cancer (BC) and provide meaningful prognostic information for BC patients are needed. Kallikrein-related peptidase 6 (KLK6) is aberrantly expressed and functionally implicated in BC and, like other members of the KLK family, may prove a useful molecular tool for clinical management. Our objective was to assess, for the first time, the clinical relevance of KLK6 mRNA expression in BC. Total RNA was isolated from 165 breast tumors, as well as 100 adjacent non-cancerous tumor specimens. After cDNA synthesis, and following quality control, quantitative real-time PCR for KLK6 expression analysis took place. Receiver operating characteristic curves were constructed in order to assess the ability of KLK6 mRNA expression levels to differentiate between molecular BC subtypes. Survival analyses, using DFS as endpoint, were performed at the univariate and multivariate levels. Publicly available BC databases and online survival analysis tools were used to validate our findings. A significant downregulation of KLK6 mRNA expression was observed in BC tissue sections compared to the non-cancerous component (P < 0.001). The expression of KLK6 is positively associated with tumor grade (P = 0.038) and is overexpressed in TNBC and HER2-positive tumors (P < 0.001). Aberrant KLK6 expression predicts the clinical outcome of BC patients in terms of DFS, independently of currently used prognostic markers (HR = 7.11, 95% CI = 1.19–42.45). The differential expression of KLK6 and its association with unfavorable outcome in BC patients was validated via in silico analyses. Although an independent external cohort is necessary to confirm our findings, we proved for the first time that KLK6 can provide independent prognostic information for BC patients.

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Abbreviations

AUC:

Area under the curve

BC:

Breast cancer

CI:

Confidence interval

Ct:

Threshold cycle

DFS:

Disease-free survival

DMFS:

Distant metastasis-free survival

ECM:

Extracellular matrix

EGFR:

Epidermal growth factor receptor

EMT:

Epithelial to mesenchymal transition

ER:

Estrogen receptor

HER2:

Human epidermal growth factor receptor 2

HPRT1 :

Hypoxanthine phosphoribosyltransferase 1

IHC:

Immunohistochemistry

KLK:

Kallikrein-related peptidase

PARs:

Protease-activated receptors

PR:

Progesterone receptor

ROC:

Receiver operating characteristic

RQ units:

Relative quantification units

r s :

Spearman correlation coefficient

RSSPC:

Robust single sample predictor classification

RT-qPCR:

Quantitative real-time PCR

SSPs:

Single sample predictors

TNBC:

Triple-negative breast cancer

TNM:

Tumor–node–metastasis

References

  1. Stefanini AC, da Cunha BR, Henrique T, Tajara EH. Involvement of Kallikrein-related peptidases in normal and pathologic processes. Dis Mark. 2015;2015:946572. https://doi.org/10.1155/2015/946572.

    Google Scholar 

  2. Michaelidou K, Kladi-Skandali A, Scorilas A. Kallikreins as biomarkers in human malignancies. In: Preedy VR, Patel VB, editors. Biomarkers in cancer. Dordrecht: Springer; 2015. p. 135–65.

    Chapter  Google Scholar 

  3. Schmitt M, Magdolen V, Yang F, et al. Emerging clinical importance of the cancer biomarkers kallikrein-related peptidases (KLK) in female and male reproductive organ malignancies. Radiol Oncol. 2013;47(4):319–29. https://doi.org/10.2478/raon-2013-0053.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Scorilas A, Mavridis K. Predictions for the future of kallikrein-related peptidases in molecular diagnostics. Expert Rev Mol Diagn. 2014;14(6):713–22. https://doi.org/10.1586/14737159.2014.928207.

    Article  CAS  PubMed  Google Scholar 

  5. Kryza T, Silva ML, Loessner D, Heuze-Vourc’h N, Clements JA. The kallikrein-related peptidase family: dysregulation and functions during cancer progression. Biochimie. 2016;122:283–99. https://doi.org/10.1016/j.biochi.2015.09.002.

    Article  CAS  PubMed  Google Scholar 

  6. Schmitt M, Dorn J, Kiechle M, Diamandis EP, Luo L. Clinical relevance of kallikrein-related peptidases in breast cancer. Berlin, Boston: DE GRUYTER; 2012. p. 111–44.

    Google Scholar 

  7. Bayani J, Diamandis EP. The physiology and pathobiology of human kallikrein-related peptidase 6 (KLK6). Clin Chem Lab Med. 2011;50(2):211–33. https://doi.org/10.1515/CCLM.2011.750.

    PubMed  Google Scholar 

  8. Pampalakis G, Prosnikli E, Agalioti T, Vlahou A, Zoumpourlis V, Sotiropoulou G. A tumor-protective role for human kallikrein-related peptidase 6 in breast cancer mediated by inhibition of epithelial-to-mesenchymal transition. Cancer Res. 2009;69(9):3779–87. https://doi.org/10.1158/0008-5472.CAN-08-1976.

    Article  CAS  PubMed  Google Scholar 

  9. Sidiropoulos KG, Ding Q, Pampalakis G, et al. KLK6-regulated miRNA networks activate oncogenic pathways in breast cancer subtypes. Mol Oncol. 2016;10(7):993–1007. https://doi.org/10.1016/j.molonc.2016.03.008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ehrenfeld P, Manso L, Pavicic MF, et al. Bioregulation of kallikrein-related peptidases 6, 10 and 11 by the kinin B(1) receptor in breast cancer cells. Anticancer Res. 2014;34(12):6925–38.

    CAS  PubMed  Google Scholar 

  11. Ghosh MC, Grass L, Soosaipillai A, Sotiropoulou G, Diamandis EP. Human kallikrein 6 degrades extracellular matrix proteins and may enhance the metastatic potential of tumour cells. Tumour Biol. 2004;25(4):193–9. https://doi.org/10.1159/000081102.

    Article  CAS  PubMed  Google Scholar 

  12. Klucky B, Mueller R, Vogt I, et al. Kallikrein 6 induces E-cadherin shedding and promotes cell proliferation, migration, and invasion. Cancer Res. 2007;67(17):8198–206. https://doi.org/10.1158/0008-5472.CAN-07-0607.

    Article  CAS  PubMed  Google Scholar 

  13. Scarisbrick IA, Epstein B, Cloud BA, et al. Functional role of kallikrein 6 in regulating immune cell survival. PLoS ONE. 2011;6(3):e18376. https://doi.org/10.1371/journal.pone.0018376.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Drucker KL, Paulsen AR, Giannini C, et al. Clinical significance and novel mechanism of action of kallikrein 6 in glioblastoma. Neuro Oncol. 2013;15(3):305–18. https://doi.org/10.1093/neuonc/nos313.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Michel N, Heuze-Vourc’h N, Lavergne E, et al. Growth and survival of lung cancer cells: regulation by kallikrein-related peptidase 6 via activation of proteinase-activated receptor 2 and the epidermal growth factor receptor. Biol Chem. 2014;395(9):1015–25. https://doi.org/10.1515/hsz-2014-0124.

    Article  CAS  PubMed  Google Scholar 

  16. McShane LM, Altman DG, Sauerbrei W, et al. Reporting recommendations for tumor marker prognostic studies (REMARK). Breast Cancer Res Treat. 2006;100(2):229–35. https://doi.org/10.1007/s10549-006-9242-8.

    Article  PubMed  Google Scholar 

  17. Michaelidou K, Ardavanis A, Scorilas A. Clinical relevance of the deregulated kallikrein-related peptidase 8 mRNA expression in breast cancer: a novel independent indicator of disease-free survival. Breast Cancer Res Treat. 2015;152(2):323–36. https://doi.org/10.1007/s10549-015-3470-8.

    Article  CAS  PubMed  Google Scholar 

  18. Detre S, Saclani Jotti G, Dowsett M. A “quickscore” method for immunohistochemical semiquantitation: validation for oestrogen receptor in breast carcinomas. J Clin Pathol. 1995;48(9):876–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wolff AC, Hammond ME, Hicks DG, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol. 2013;31(31):3997–4013. https://doi.org/10.1200/JCO.2013.50.9984.

    Article  PubMed  Google Scholar 

  20. Goldhirsch A, Wood WC, Coates AS, et al. Strategies for subtypes–dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol. 2011;22(8):1736–47. https://doi.org/10.1093/annonc/mdr304.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Feeley LP, Mulligan AM, Pinnaduwage D, Bull SB, Andrulis IL. Distinguishing luminal breast cancer subtypes by Ki67, progesterone receptor or TP53 status provides prognostic information. Mod Pathol. 2014;27(4):554–61. https://doi.org/10.1038/modpathol.2013.153.

    Article  CAS  PubMed  Google Scholar 

  22. Gill S, Sargent D. End points for adjuvant therapy trials: has the time come to accept disease-free survival as a surrogate end point for overall survival? Oncologist. 2006;11(6):624–9. https://doi.org/10.1634/theoncologist.11-6-624.

    Article  CAS  PubMed  Google Scholar 

  23. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402–8. https://doi.org/10.1006/meth.2001.1262.

    Article  CAS  PubMed  Google Scholar 

  24. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10(21):7252–9. https://doi.org/10.1158/1078-0432.CCR-04-0713.

    Article  CAS  PubMed  Google Scholar 

  25. Rhodes DR, Yu J, Shanker K, et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia. 2004;6(1):1–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Jezequel P, Campone M, Gouraud W, et al. bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer. Breast Cancer Res Treat. 2012;131(3):765–75. https://doi.org/10.1007/s10549-011-1457-7.

    Article  PubMed  Google Scholar 

  27. Jezequel P, Frenel JS, Campion L, et al. bc-GenExMiner 3.0: new mining module computes breast cancer gene expression correlation analyses. Database (Oxford). 2013;2013:bas060. https://doi.org/10.1093/database/bas060.

    Article  Google Scholar 

  28. Gyorffy B, Lanczky A, Eklund AC, et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat. 2010;123(3):725–31. https://doi.org/10.1007/s10549-009-0674-9.

    Article  PubMed  Google Scholar 

  29. Torre LA, Siegel RL, Ward EM, Jemal A. Global cancer incidence and mortality rates and trends–an update. Cancer Epidemiol Biomark Prev. 2016;25(1):16–27. https://doi.org/10.1158/1055-9965.EPI-15-0578.

    Article  Google Scholar 

  30. Zardavas D, Irrthum A, Swanton C, Piccart M. Clinical management of breast cancer heterogeneity. Nat Rev Clin Oncol. 2015;12(7):381–94. https://doi.org/10.1038/nrclinonc.2015.73.

    Article  CAS  PubMed  Google Scholar 

  31. Koren S, Bentires-Alj M. Breast tumor heterogeneity: source of fitness, hurdle for therapy. Mol Cell. 2015;60(4):537–46. https://doi.org/10.1016/j.molcel.2015.10.031.

    Article  CAS  PubMed  Google Scholar 

  32. Ellsworth RE, Blackburn HL, Shriver CD, Soon-Shiong P, Ellsworth DL. Molecular heterogeneity in breast cancer: state of the science and implications for patient care. Semin Cell Dev Biol. 2017;64:65–72. https://doi.org/10.1016/j.semcdb.2016.08.025.

    Article  CAS  PubMed  Google Scholar 

  33. Haynes B, Sarma A, Nangia-Makker P, Shekhar MP. Breast cancer complexity: implications of intratumoral heterogeneity in clinical management. Cancer Metastasis Rev. 2017. https://doi.org/10.1007/s10555-017-9684-y.

    PubMed  Google Scholar 

  34. Yousef GM, Yacoub GM, Polymeris ME, Popalis C, Soosaipillai A, Diamandis EP. Kallikrein gene downregulation in breast cancer. Br J Cancer. 2004;90(1):167–72. https://doi.org/10.1038/sj.bjc.6601451.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mange A, Dimitrakopoulos L, Soosaipillai A, Coopman P, Diamandis EP, Solassol J. An integrated cell line-based discovery strategy identified follistatin and kallikrein 6 as serum biomarker candidates of breast carcinoma. J Proteom. 2016;142:114–21. https://doi.org/10.1016/j.jprot.2016.04.050.

    Article  CAS  Google Scholar 

  36. Schrader CH, Kolb M, Zaoui K, et al. Kallikrein-related peptidase 6 regulates epithelial-to-mesenchymal transition and serves as prognostic biomarker for head and neck squamous cell carcinoma patients. Mol Cancer. 2015;14:107. https://doi.org/10.1186/s12943-015-0381-6.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Petraki C, Dubinski W, Scorilas A, et al. Evaluation and prognostic significance of human tissue kallikrein-related peptidase 6 (KLK6) in colorectal cancer. Pathol Res Pract. 2012;208(2):104–8. https://doi.org/10.1016/j.prp.2011.12.010.

    Article  CAS  PubMed  Google Scholar 

  38. Ahmed N, Dorn J, Napieralski R, et al. Clinical relevance of kallikrein-related peptidase 6 (KLK6) and 8 (KLK8) mRNA expression in advanced serous ovarian cancer. Biol Chem. 2016;397(12):1265–76. https://doi.org/10.1515/hsz-2016-0177.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kim TW, Lee SJ, Kim JT, et al. Kallikrein-related peptidase 6 induces chemotherapeutic resistance by attenuating auranofin-induced cell death through activation of autophagy in gastric cancer. Oncotarget. 2016;7(51):85332–48. https://doi.org/10.18632/oncotarget.13352.

    PubMed  PubMed Central  Google Scholar 

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Correspondence to Andreas Scorilas.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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10238_2018_487_MOESM1_ESM.tif

Quality control of the developed qPCR assay for quantification of KLK6 expression. Dissociation curves of (A) HPRT1 and (B) KLK6 amplicons. (C) Corresponding 3.0% w/v agarose gel electrophoresis of the RT-qPCR products of randomly selected breast tissue samples. (D) Standard curves for HPRT1 and KLK6, constructed using serial dilutions of calibrator cDNA, covering several orders of magnitude. M: molecular weight marker; PC: positive control, NC: negative control. (TIFF 9682 kb)

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Haritos, C., Michaelidou, K., Mavridis, K. et al. Kallikrein-related peptidase 6 (KLK6) expression differentiates tumor subtypes and predicts clinical outcome in breast cancer patients. Clin Exp Med 18, 203–213 (2018). https://doi.org/10.1007/s10238-018-0487-4

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  • DOI: https://doi.org/10.1007/s10238-018-0487-4

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