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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1485123

Machine Learning-Informed Liquid-Liquid Phase Separation for Personalized Breast Cancer Treatment Assessment

Provisionally accepted
Tao Wang Tao Wang Shu Wang Shu Wang *Zhuolin Li Zhuolin Li *Jie Xie Jie Xie *Huan Chen Huan Chen *Jing Hou Jing Hou *
  • Guizhou Provincial People's Hospital, Guiyang, China

The final, formatted version of the article will be published soon.

    Background: Breast cancer, characterized by its heterogeneity, is a leading cause of mortality among women. The study aims to develop a Machine Learning-Derived Liquid-Liquid Phase Separation (MDLS) model to enhance the prognostic accuracy and personalized treatment strategies for breast cancer patients.The study employed ten machine learning algorithms to construct 108 algorithm combinations for the MDLS model. The robustness of the model was evaluated using multi-omics and single-cell data across 14 breast cancer cohorts, involving 9,723 patients. Genetic mutation, copy number alterations, and single-cell RNA sequencing were analyzed to understand the molecular mechanisms and predictive capabilities of the MDLS model. Immunotherapy targets were predicted by evaluating immune cell infiltration and immune checkpoint expression. Chemotherapy targets were identified through correlation analysis and drug responsiveness prediction.The MDLS model demonstrated superior prognostic power, with a mean C-index of 0.649, outperforming 69 published signatures across ten cohorts. High-MDLS patients exhibited higher tumor mutation burden and distinct genomic alterations, including significant gene amplifications and deletions. Single-cell analysis revealed higher MDLS activity in tumor-aneuploid cells and identified key regulatory factors involved in MDLS progression. Cell-cell communication analysis indicated stronger interactions in high-MDLS groups, and immunotherapy response evaluation showed better outcomes for low-MDLS patients.The MDLS model offers a robust and precise tool for predicting breast cancer prognosis and tailoring personalized treatment strategies. Its integration of multi-omics and machine learning highlights its potential clinical applications, particularly in improving the effectiveness of immunotherapy and identifying therapeutic targets for high-MDLS patients.

    Keywords: breast cancer, Liquid-liquid phase separation, machine learning, Immunotherapy, Methotrexate

    Received: 23 Aug 2024; Accepted: 31 Oct 2024.

    Copyright: © 2024 Wang, Wang, Li, Xie, Chen and Hou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Shu Wang, Guizhou Provincial People's Hospital, Guiyang, China
    Zhuolin Li, Guizhou Provincial People's Hospital, Guiyang, China
    Jie Xie, Guizhou Provincial People's Hospital, Guiyang, China
    Huan Chen, Guizhou Provincial People's Hospital, Guiyang, China
    Jing Hou, Guizhou Provincial People's Hospital, Guiyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.