Personalized chemotherapy selection for breast cancer using gene expression profiles

Area of Science:

• Oncology
• Genomics
• Biostatistics

Background:

• Optimal chemotherapy selection remains a challenge for breast cancer patients.
• Neoadjuvant chemotherapy regimens commonly include anthracycline, paclitaxel, and docetaxel.

Purpose of the Study:

• To develop a predictive model for optimal neoadjuvant chemotherapy selection in breast cancer.
• To improve pathological complete response (pCR) rates through personalized regimen assignment.

Main Methods:

• Reanalysis of seven independent datasets (1079 breast cancer patients).
• Development of random forest models incorporating genetic and clinical variables.
• Validation using independent datasets and breast cancer cell line drug-sensitivity profiles.

Main Results:

• PRES models significantly increased expected pCR rates compared to standard treatments across seven studies.
• Model predictions showed agreement with drug-sensitivity profiles in 21 breast cancer cell lines.
• The strategy is particularly promising for HER2 and ER negative breast tumors.

Conclusions:

• Personalized REgimen Selection (PRES) offers a novel strategy to enhance breast cancer treatment efficacy.
• The PRES approach may significantly increase pCR rates, especially in challenging tumor subtypes.
• This personalized medicine strategy could optimize neoadjuvant chemotherapy selection for improved patient outcomes.