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

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Title: Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models
Authors: Catarina Franco Jones, Diogo Dias, Ana C. Moreira, Gil Gonçalves, Stefano Cinti, Mustafa B.A. Djamgoz, Frederico Castelo Ferreira, Paola Sanjuán-Alberte, Rosalia Moreddu
Source: iScience, Vol 28, Iss 10, Pp 113579- (2025)
Publisher Information: Elsevier, 2025.
Publication Year: 2025
Collection: LCC:Science
Subject Terms: Technical aspects of cell biology, Cancer, Biological sciences research methodologies, Science
Description: Summary: Breast cancer cell lines are indispensable tools for unraveling disease mechanisms, enabling drug discovery, and developing personalized treatments, yet their heterogeneity and inconsistent classification pose significant challenges in model selection and data reproducibility. This review aims at providing a comprehensive and user-friendly framework for broadly mapping the features of breast cancer types and commercially available human breast cancer cell lines, defining absolute criteria, i.e., objective features such as origin (e.g., MDA-MB, MCF), histological subtype (ductal, lobular), hormone receptor status (ER/PR/HER2), and genetic mutations (BRCA1, TP53), and relative criteria, which contextualize functional behaviors such as metastatic potential, drug sensitivity, and genomic instability. It then examines how the proposed framework could be applied to cell line screening in advanced and emerging disease models. By supporting better informed choices, this work aims to improve experimental design and strengthen the connection between in vitro breast cancer studies and their clinical translation.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2589-0042
Relation: http://www.sciencedirect.com/science/article/pii/S2589004225018401; https://doaj.org/toc/2589-0042
DOI: 10.1016/j.isci.2025.113579
Access URL: https://doaj.org/article/0d2a84bc0d9e4d8e9283f9c46d48c545
Accession Number: edsdoj.0d2a84bc0d9e4d8e9283f9c46d48c545
Database: Directory of Open Access Journals
Description
Abstract:Summary: Breast cancer cell lines are indispensable tools for unraveling disease mechanisms, enabling drug discovery, and developing personalized treatments, yet their heterogeneity and inconsistent classification pose significant challenges in model selection and data reproducibility. This review aims at providing a comprehensive and user-friendly framework for broadly mapping the features of breast cancer types and commercially available human breast cancer cell lines, defining absolute criteria, i.e., objective features such as origin (e.g., MDA-MB, MCF), histological subtype (ductal, lobular), hormone receptor status (ER/PR/HER2), and genetic mutations (BRCA1, TP53), and relative criteria, which contextualize functional behaviors such as metastatic potential, drug sensitivity, and genomic instability. It then examines how the proposed framework could be applied to cell line screening in advanced and emerging disease models. By supporting better informed choices, this work aims to improve experimental design and strengthen the connection between in vitro breast cancer studies and their clinical translation.
ISSN:25890042
DOI:10.1016/j.isci.2025.113579