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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Systems Immunology
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1497251
This article is part of the Research Topic Systems Immunology and Computational Omics for Transformative Medicine View all 3 articles

Computational analysis of the functional impact of MHC-II-expressing triple-negative breast cancer

Provisionally accepted
Yang Cui Yang Cui 1Weihang ZHANG Weihang ZHANG 1Xin Zeng Xin Zeng 1Yitao Yang Yitao Yang 1Sung-Joon Park Sung-Joon Park 2Kenta Nakai Kenta Nakai 1,2*
  • 1 Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
  • 2 Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato, Tokyo, Japan

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

    The tumor microenvironment (TME) plays a crucial role in tumor progression and immunoregulation. Major histocompatibility complex class II (MHC-II) is essential for immune surveillance within the TME. While MHC-II genes are typically expressed by professional antigenpresenting cells, they are also expressed in tumor cells, potentially facilitating anti-tumor immune responses. To understand the role of MHC-II-expressing tumor cells, we analyzed triple-negative breast cancer (TNBC), an aggressive subtype with poor prognosis and limited treatment options, using public bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics datasets. Our analysis revealed a distinct tumor subpopulation that upregulates MHC-II genes and actively interacts with immune cells. We implicated that this subpopulation is preferentially present in proximity to regions in immune infiltration of TNBC patient cohorts with a better prognosis, suggesting the functional importance of MHC-II-expressing tumor cells in modulating the immune landscape and influencing patient survival outcomes. Remarkably, we identified a prognostic signature comprising 40 significant genes in the MHC-II-expressing tumors in which machine leaning models with the signature successfully predicted patient survival outcomes and the degree of immune infiltration. This study advances our understanding of the immunological basis of cancer progression and suggests promising new directions for therapeutic strategies.

    Keywords: breast cancer, machine learning, MHC-II pathway, Multi-omics data Integration, Tumor Microenvironment

    Received: 16 Sep 2024; Accepted: 08 Nov 2024.

    Copyright: © 2024 Cui, ZHANG, Zeng, Yang, Park and Nakai. 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: Kenta Nakai, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan

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