Investigating biomarkers of response to CDK4/6 inhibition in combination with anti-estrogen therapy in breast cancer cell lines

Breast cancer is the most common cancer diagnosed in women and remains the second leading cause of cancer related deaths. For decades, anti-estrogen therapy (AET) has been used to treat estrogen receptor positive (ER+) breast cancer. The development of novel targeted therapies, like cyclin dependent kinase 4/6 inhibitors (CDK4/6i), provide additional treatment options for women with late-stage disease. CDK4/6i in combination with AET has delayed cancer progression and is approved for use in metastatic ER+, human epidermal growth factor receptor 2 negative breast cancer. However, it is estimated that almost half of these patients don’t derive a meaningful benefit from this treatment combination, resulting in unnecessary drug toxicities. Furthermore, unnecessary cancer treatment is a burden to the healthcare system, particularly with expensive drugs like CDK4/6i which cost ~$8000 per month of treatment. The identification of predictive biomarkers represents a top cancer research priority. Biomarker testing improves treatment selection so that patients and our health care system derive maximal benefit from these effective but expensive drugs. To date, ER positivity remains the only molecular biomarker for CDK4/6i treatment selection. For this research, I performed a comprehensive literature review of CDK4/6i biomarkers and analyzed publicly available molecular/clinical databases to identify potential biomarkers. I then conducted in vitro tests of palbociclib (CDK4/6i) and tamoxifen (AET) on a comprehensive panel of breast cancer cell lines. From this, I optimized the in vitro experimental assays to ensure they measured the cytostatic activity of palbociclib to accurately rank drug efficacy amongst the cell lines. Next, using reverse phase protein array (RPPA), I performed a discovery-based proteomic analysis to identify biomarkers of resistance to palbociclib +/- tamoxifen. My analysis of public databases failed to identify any statistically significant biomarkers, further highlighting the need for biomarker research. Western blot analysis identified p16 overexpression and Rb loss as markers of resistance to palbociclib. RPPA analyses identified an additional thirteen differentially expressed proteins associated with palbociclib resistance. I also identified twelve proteomic biomarkers of resistance to the combination therapy. This research identifies predictive biomarker candidates for validation and highlights the importance of experimental assay optimization for translatable in vitro testing.Medicine, Faculty ofObstetrics and Gynaecology, Department ofGraduat