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Ahmad, Dena A. J.; Negm, Ola H.; Alabdullah, M. Layth; Mirza, Sameer; Hamed, Mohamed R.; Band, Vimla; Green, Andrew R.; Ellis, Ian O.; Rakha, Emad A. (2016)
Publisher: Springer US
Journal: Breast Cancer Research and Treatment
Languages: English
Types: Article
Subjects: Reverse phase protein microarray (RPPA), MAPK signalling pathways, Breast cancer, Preclinical Study, Cancer Research, Oncology
Background Mitogen-activated protein kinases (MAPKs) are signalling transduction molecules that have different functions and diverse behaviour in cancer. In breast cancer, MAPK is related to oestrogen receptor (ER) and HER2. Methods Protein expression of a large panel of MAPKs (JNK1/2, ERK, p38, C-JUN and ATF2 including phosphorylated forms) were assessed immunohistochemically in a large (n = 1400) and well-characterised breast cancer series prepared as tissue microarray. Moreover, reverse phase protein array was applied to quantify protein expression of MAPKs in six breast cancer cell lines with different phenotypes including HER2-transfected cells. Results MAPKs expression was associated with clinicopathological variables characteristic of good prognosis. These associations were most significant in the whole series and in the ER+ subgroup compared to other BC classes. Most of MAPKs showed a positive association with ER, BCL2 and better outcome and were negatively associated with the proliferation marker Ki67 and p53. Association of MAPK with HER2 was mainly seen in the ER- subgroup. Reverse phase protein array confirmed immunohistochemistry results and revealed differential expression of MAPK proteins in ER+ and ER− cell lines. Conclusions MAPKs are associated with good prognosis and their expression is mainly related to ER. Studying a large panel rather than individual biomarkers may provide improved understanding of the pathway. Electronic supplementary material The online version of this article (doi:10.1007/s10549-016-3967-9) contains supplementary material, which is available to authorized users.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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