Breast cancer is the most common type of cancer in women and second most common cancer worldwide. Most breast cancers are ER-positive (75-80%), for which anti-hormonal therapy is used. For ER-positive metastatic breast cancer (MBC), the objective response rate to anti-hormonal treatment is only 20-40%. This shows an urgent need for biomarkers which can identify patients who will or will not benefit from the therapy. As such, for those patients, unnecessary exposure to undesirable adverse events of (anti-hormonal) therapy can be avoided. Therefore, the aim of this thesis was to find biomarkers able to predict anti-hormonal treatment responsiveness or resistance in mainly advanced ER-positive breast cancer patients.

To reach this goal different research approaches were followed. Since mutations in PIK3CA are the most prevalent mutations (up to 45%) in ER-positive breast cancers, the thesis was mainly focused on the relationship between PIK3CA genotype and PI3K pathway with treatment outcome.

It was shown that PIK3CA mutations detected in primary breast tumors have a predictive value for aromatase inhibitors (AI) response in the advanced disease setting, but not for tamoxifen response nor for prognosis. Related to the PIK3CA genotype, it was demonstrated that high expression of LRG1 can be used as biomarker for AI treatment response, which upon neo-adjuvant AI therapy showed decreased levels in patients with clinical response. At the proteomic level, high MAPK1/3 phosphorylation levels in luminal breast cancer was shown to be related with PIK3CA exon specific mutations. This MAPK1/3 phosphorylation, especially when localized in the nuclei, has prognostic value in breast cancer.

In an alternative approach, using ER-positive breast cancers with an inflammatory breast cancer phenotype, a metagene was constructed. This metagene, ABAT and STC2 were not prognostic. However, decreased expression of ABAT and STC2 were shown to be predictive for tamoxifen resistance in MBC. In the adjuvant setting, only low expression of ABAT was related to tamoxifen resistance.

Finally, using cell free DNA (cfDNA) from liquid biopsies, tumor-specific mutations were explored as biomarkers for tamoxifen resistance in MBC patients. Mutations in PIK3CA, TP53, AKAP9, CREBBP and SMAD4 were observed in serum cfDNA taken at disease progression and these mutations, except for AKAP9, were also seen in the primary tumor.

When further validated, all above biomarkers hopefully will guide us better to be able to pick the right treatment for the right breast cancer patient.

Additional Metadata
Keywords Predictive biomarkers, breast cancer, PIK3CA mutations, LRG1, ABAT, STC2, phosphorylated MAPK1/3, anti-hormonal therapy, tamoxifen, aromatase inhibitors, liquid biopsy, cfDNA, inflammatory breast cancer.
Promotor P.M.J.J. Berns (Els) , M.P.H.M. Jansen (Maurice)
Publisher Erasmus University Rotterdam
ISBN 978-94-028-0835-3
Persistent URL hdl.handle.net/1765/104014
Citation
Ramirez Ardila, D.E. (2018, January 17). PIcKing the Right Treatment for the Right Patient : anti-hormonal therapy resistance in breast cancer: PIK3CA related biomarkers and signaling pathways. Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/104014