Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models

Area of Science:

• Oncology
• Cell Biology
• Translational Medicine

Background:

• Breast cancer cell lines are crucial for research but suffer from heterogeneity and inconsistent classification.
• Challenges in cell line selection impact data reproducibility and drug discovery efforts.

Purpose of the Study:

• To present a comprehensive framework for mapping breast cancer cell line features.
• To define objective (absolute) and functional (relative) criteria for cell line characterization.
• To guide cell line selection for improved experimental design and clinical translation.

Main Methods:

• Defining absolute criteria: origin, histological subtype, hormone receptor status (ER/PR/HER2), and genetic mutations (BRCA1, TP53).
• Defining relative criteria: metastatic potential, drug sensitivity, and genomic instability.
• Applying the framework to cell line screening in advanced and emerging breast cancer models.

Main Results:

• A structured approach to classifying breast cancer cell lines based on defined criteria.
• Identification of key features for distinguishing cell line characteristics.
• Demonstration of framework applicability in advanced and emerging disease models.

Conclusions:

• The proposed framework enhances informed cell line selection for breast cancer research.
• Improved cell line characterization strengthens the link between in vitro studies and clinical outcomes.
• This systematic approach aims to boost reproducibility and accelerate personalized medicine development.