Breast cancer is one of the major causes of death in
women worldwide.
It is a diverse illness with substantial intersubject heterogeneity,
even among individuals with the same type of tumor, and customized
therapy has become increasingly important in this sector. Because
of the clinical and physical variability of different kinds of breast
cancers, multiple staging and classification systems have been developed.
As a result, these tumors exhibit a wide range of gene expression
and prognostic indicators. To date, no comprehensive investigation
of model training procedures on information from numerous cell line
screenings has been conducted together with radiation data. We used
human breast cancer cell lines and drug sensitivity information from
Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity
in Cancer (GDSC) databases to scan for potential drugs using cell
line data. The results are further validated through three machine
learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected
top-ranked biomarkers based on their role in breast cancer and tested
them further for their resistance to radiation using the data from
the Cleveland database. We have identified six drugs named Palbociclib,
Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that
significantly perform on breast cancer cell lines. Also, five biomarkers
named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all
six shortlisted drugs and show sensitivity to the radiations. The
proposed biomarkers and drug sensitivity analysis are helpful in translational
cancer studies and provide valuable insights for clinical trial design.