In Vitro Exploration of EGFR in Breast Cancer: Unlocking the Cellular Signaling Network
Keywords:
Epidermal Growth Factor Receptor (EGFR), Breast Cancer Subtypes, EGFR-Estrogen Receptor Cross-Talk, In Vitro Models, Triple-Negative Breast Cancer (TNBC), Cell-Based Assays, Network Pharmacology, PhytochemicalsAbstract
Breast cancer remains a leading cause of cancer-related mortality worldwide, with an estimated 2.3 million new cases and 670,000 deaths in 2022, and projections indicating a continued rise in incidence by 1–5% annually in many regions. The epidermal growth factor receptor (EGFR) plays a pivotal role in tumor progression, particularly through its overexpression in aggressive subtypes such as triple-negative breast cancer (TNBC) and HER2-positive breast cancer, where it drives proliferation, invasion, and therapeutic resistance. This review explores how in vitro models have illuminated the intricate cross-talk between EGFR and estrogen receptor (ER) pathways, revealing mechanisms of bidirectional signaling that exacerbate endocrine resistance and metastatic potential. EGFR overexpression in TNBC correlates with heightened aggressiveness and poor prognosis, while in HER2-positive tumors, it contributes to resistance against antibody-drug conjugates like trastuzumab deruxtecan, underscoring the need for dual-targeting strategies. Cell-based assays, including MTT for viability, wound-healing for migration, and Western blot for phosphorylation events, have been instrumental in validating EGFR inhibitors' efficacy and dissecting pathway interactions in 2D and 3D models like MDA-MB-231 and MCF-7 cells. Furthermore, network pharmacology approaches have predicted phytochemicals (e.g., from Nigella sativa or Eclipta prostrata) that disrupt EGFR signaling hubs, offering multi-target potential with reduced resistance risks. By synthesizing these in vitro insights, this article highlights opportunities for precision therapeutics, emphasizing the transition from cellular networks to clinical applications in overcoming breast cancer heterogeneity