Heterogeneous Evolution of Breast Cancer Cells—An Endogenous Molecular-Cellular Network Study

Sep 21, 2026

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Breast cancer heterogeneity drives major clinical challenges including overtreatment and drug resistance, rooted in unclear normal epithelial origins, evolutionary stability, and subtype transitions. The study develops quantitative bio-process modeling using an endogenous molecular–cellular network hypothesis in which both pathological and physiological phenotypes emerge from conserved modules and interacting cellular factors. The model generates cell states matching four molecular subtypes and validates them using TCGA data, identifies a singular progression gateway via Luminal A, and distinguishes positive feedback loops that stabilize states from negative loops that enable transitions. Results support tracing subtype dynamics and may guide targeted therapy development.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

1. Introduction

2. Materials and Methods

3. Results

3.1 Endogenous core network model

3.2 Emergent cell states and subtype alignment

3.3 Topological progression gateway and feedback loops

4. Discussion

biology Article Heterogeneous Evolution of Breast Cancer Cells—An Endogenous Molecular-Cellular Network Study Tianqi Li 1, Yong-Cong Chen 1,* and Ping Ao 2 Citation: Li, T.; Chen, Y.-C.; Ao, P. Heterogeneous Evolution of Breast Cancer Cells—An Endogenous Molecular-Cellular Network Study. Biology 2024, 13, 564. [https://](https://)[ ](https://)[doi.org/10.3390/biology13080564](doi.org/10.3390/biology13080564) Academic Editor: Katia Cortese Received: 2 July 2024 Revised: 23 July 2024 Accepted: 25 July 2024 Published: 26 July 2024 Copyright: © 2024 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Center for Quantitative Life Sciences & Physics Department, Shanghai University, Shanghai 200444, China; [tancygd@shu.edu.cn](tancygd@shu.edu.cn) 2 School of Biomedical Engineering, Sichuan University, Chengdu 610065, China; [aoping@sjtu.edu.cn](aoping@sjtu.edu.cn) * Correspondence: [chenyongcong@shu.edu.cn](chenyongcong@shu.edu.cn) Simple Summary: The classification of breast cancer has been a complex subject, with diverse origins of normal breast epithelial cells and evolutionary pathways contributing to its extensive heterogeneity, which further complicates the treatment process. We developed a dynamic network model to simulate the emergence of different cellular states and explore the underlying biological mechanisms. This model revealed cellular states that align with four recognized molecular subtypes of breast cancer. It also identified feedback loops that either reinforce or facilitate phenotypic transitions, and allowed us to trace these transitions through the model’s topological structure. This approach provides new insights into the mechanisms driving breast cancer heterogeneity and may benefit the development of more targeted therapeutic strategies. Abstract: Breast cancer heterogeneity presents a significant challenge in clinical therapy, such as over-treatment and drug resistance. These challenges are largely due to its obscure normal epithelial origins, evolutionary stability, and transitions on the cancer subtypes. This study aims to elucidate the cellular emergence and maintenance of heterogeneous breast cancer via quantitative bio-process modeling, with potential benefit to therapeutic strategies for the disease. An endogenous molecular– cellular hypothesis posits that both pathological and physiological states are phenotypes evolved from and shaped by interactions among a number of conserved modules and cellular factors within a biological network. We hereby developed a model of core endogenous network for breast cancer in accordance with the theory, quantifying its intrinsic dynamic properties with dynamic modeling. The model spontaneously generates cell states that align with molecular classifications at both the molecular and modular level, replicating four widely recognized molecular subtypes of the cancer and validating against data extracted from the TCGA database. Further analysis shows that topologically, a singular progression gateway from normal breast cells to cancerous states is identified as the Luminal A-type breast cancer. Activated positive feedback loops are found to stabilize cellular states, while negative feedback loops facilitate state transitions. Overall, more routes are revealed on the cellular transition between stable states, and a traceable count explains the origin of breast cancer heterogeneity. Ultimately, the research intended to strength the search for therapeutic targets. Keywords: breast cancer; cell subtype; molecular regulatory network; systems biology 1. Introduction The prevalence of breast cancer (BC) has been rising annually at a rate of approximately 0.5%

FAQs

What problem does the study address in breast cancer care?

It addresses breast cancer heterogeneity as a key cause of overtreatment and drug resistance, which complicates clinical diagnosis and therapy selection.

How does the model explain the emergence of different breast cancer states?

It uses an endogenous molecular–cellular network hypothesis and quantitative dynamic modeling to spontaneously generate cell states that align with recognized molecular subtypes.

What roles do feedback loops play in the model?

Activated positive feedback loops stabilize cellular states, while negative feedback loops facilitate transitions between states, enabling traceable dynamics across stable regions.

Sep 21, 2026

Uploaded by Ezra