Breast cancer preclinical models: a vital resource for comprehending disease mechanisms and therapeutic development

Abstract

A significant obstacle in translating innovative breast cancer treatments from bench to bed side is demonstrating efficacy in preclinical settings prior to clinical trials, as the heterogeneity of breast cancer can be challenging to replicate in the laboratory. A significant number of potential medicines have not progressed to clinical trials because preclinical models inadequately replicate the complexities of the varied tumor microenvironment. Consequently, the variety of breast cancer models is extensive, and the selection of a model frequently depends on the specific inquiry presented. This review aims to present an overview of the existing breast cancer models, highlighting their advantages, limitations, and challenges in the context of innovative drug discovery, thereby offering insights that may be advantageous to future translational studies. Conventional monolayer cultures are critical for elucidating the different breast cancer types and their behavior, have limitations in adequately replicating tumor environments. The 3D models such as patient-derived xenografts, cell-derived xenografts and genetically engineered models offer better insights by maintaining tumor microenvironments and cellular heterogeneity. Results can be further enhanced when compared with breast epithelial cells, a negative control to determine early stages by investigating differences between healthy and cancerous mammary cells. While cell lines such as MCF-7, MDA-MB-231 etc are useful in vitro models, they exhibit genetic variations that may affect drug responses over time. Additionally, animal models, particularly rodents, are instrumental in breast cancer research due to their biological resemblances to humans and the relative ease of genetic modification, however, witness a low occurrence of tumors. This review thus concludes that different preclinical models have their associated benefits and pitfalls. Therefore, specific preclinical models can be created by altering the gene expression at the genetic level or could be selected as per specific experimental needs which will enable successful translation of preclinical findings into clinical trials can be possible.