Facing the Challenge to Mimic Breast Cancer Heterogeneity: Established and Emerging Experimental Preclinical Models Integrated with Omics Technologies

Sep 23, 2026

Breast cancer (BC) remains a major global health problem due to persistent intertumoral and intratumoral heterogeneity that shapes progression and treatment response. Current preclinical models often fail to reproduce this complexity, limiting understanding of BC pathogenesis and impeding new therapy development. This review surveys key experimental BC models used in preclinical research and analyzes their strengths, limitations, and suitability for specific questions. Omics technologies are highlighted as tools to compare models with human tumors and reveal heterogeneity and vulnerabilities.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages27

Reading Time1 h 8 min

AccessFree to download

Introduction

Breast cancer burden and molecular subtypes

Intertumoral and intratumoral heterogeneity

Preclinical experimental models and limitations

Cell lines and patient-derived organoids (PDOs)

Organ-on-chip and mouse model types

Role of omics technologies

Comparing models with human tumors

Applications for selecting appropriate models

Review Facing the Challenge to Mimic Breast Cancer Heterogeneity: Established and Emerging Experimental Preclinical Models Integrated with Omics Technologies Alessia Ciringione 1 and Federica Rizzi 1,2, * Academic Editor: Jennifer Sims-Mourtada Received: 10 April 2025 Revised: 5 May 2025 Accepted: 9 May 2025 Published: 10 May 2025 Citation: Ciringione, A.; Rizzi, F. Facing the Challenge to Mimic Breast Cancer Heterogeneity: Established and Emerging Experimental Preclinical Models Integrated with Omics Technologies. Int. J. Mol. Sci. 2025, 26, 4572. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/ijms26104572](10.3390/ijms26104572) Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Laboratory of Biochemistry, Molecular Biology and Oncometabolism, Department of Medicine and Surgery, University of Parma, Via Volturno 39, 43125 Parma, Italy; [alessia.ciringione@unipr.it](alessia.ciringione@unipr.it) 2 National Institute of Biostructure and Biosystems (INBB), 00165 Rome, Italy * Correspondence: [federica.rizzi@unipr.it](federica.rizzi@unipr.it) Abstract: Breast cancer (BC) is among the most common neoplasms globally and is the leading cause of cancer-related mortality in women. Despite significant advancementsin prevention, early diagnosis, and treatment strategies made over the past two decades, breast cancer continues to pose a significant global health challenge. One of the major obstacles in the clinical management of breast cancer patients is the high intertumoral and intratumoral heterogeneity that influences disease progression and therapeutic outcomes. The inability of preclinical experimental models to replicate this diversity has hindered the comprehensive understanding of BC pathogenesis and the development of new therapeutic strategies. An ideal experimental model must recapitulate every aspect of human BC to maintain the highest predictive validity. Therefore, a thorough understanding of each model’s inherent characteristics and limitations is essential to bridging the gap between basic research and translational medicine. In this context, omics technologies serve as powerful tools for establishing comparisons between experimental models and human tumors, which may help address BC heterogeneity and vulnerabilities. This review examines the BC models currently used in preclinical research, including cell lines, patient-derived organoids (PDOs), organ-on-chip technologies, carcinogen-induced mouse models, genetically engineered mouse models (GEMMs), and xenograft mouse models. We emphasize the advantages and disadvantages of each model and outline the most important applications of omics techniques to aid researchers in selecting the most relevant model to address their specific research questions. Keywords: breast cancer cell lines; PDOs; organ-on-chip technologies; GEMMs; xenograft mouse models; tumorheterogeneity; omics technologies 1. Introduction Breast cancer (BC) is the leading cause of cancer-related mortality among women, with an estimated incidence of 47% in 2022 [1] . Despite substantial improvements in the clinical management of BC over the past two decades, predicting the actual behavior of breast tumor pathophysiology remains challenging. The most common and widely accepted histopathological classification of BC is based on the expression of cell surface receptors: the estrogen receptor (ER), the progesterone receptor (PR), and the human epidermal growth factor receptor 2 (HER2) . The luminal A and luminal B subtypes are characterized by the presence of the ER, but they differ in the expression levels of the PR and Ki-67 [2] . Indeed, while both can benefit from endocrine the

FAQs

Why is mimicking breast cancer heterogeneity important in preclinical research?

Breast cancer exhibits both intertumoral and intratumoral heterogeneity, which strongly influences disease progression and therapeutic outcomes. If experimental models cannot replicate this diversity, it hinders understanding of disease mechanisms and limits predictive validity for developing new strategies.

Which preclinical breast cancer models are discussed in the review?

The review covers cell lines, patient-derived organoids (PDOs), organ-on-chip technologies, carcinogen-induced mouse models, genetically engineered mouse models (GEMMs), and xenograft mouse models. It evaluates advantages and disadvantages for researchers’ specific questions.

How do omics technologies help address breast cancer heterogeneity?

Omics technologies enable comparisons between experimental models and human tumors using molecular data. This helps researchers investigate heterogeneity and vulnerabilities and supports better model selection for translational research.

Sep 23, 2026

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