Identifying Representative Cell Line Models for TNBC Chemotherapy Drug Resistance via Systems Biology and Bioinformatics

Abstract

Cancer cell lines, derived from tumors, have become essential tools in life science research and are commonly employed as experimental model systems in cancer research. However, researchers often overlook the similarities between clinical samples when selecting cell line models, potentially impacting the validity and applicability of their findings. In the context of triple-negative breast cancer (TNBC) chemotherapy drug resistance, our study aims to provide guidance for selecting appropriate cell line models by employing a combination of systems biology and bioinformatic approaches. These approaches, including hierarchical clustering analysis, Spearmans rank correlation, and single sample gene set enrichment analysis (ssGSEA), allowed us to identify the most representative cell models that correspond to poor chemotherapy responders among TNBC patients.