Machine learning-assisted elucidation of CD81-CD44 interactions in promoting cancer stemness and extracellular vesicle integrity
- Northwestern University, United States
- Pacific Northwest National Laboratory, United States
- Texas A&M University, United States
- University of Kentucky, United States
Abstract
Tumor-initiating cells with reprogramming plasticity or stem-progenitor cell properties (stemness) are thought to be essential for cancer development and metastatic regeneration in many cancers; however, elucidation of the underlying molecular network and pathways remains demanding. Combining machine learning and experimental investigation, here we report CD81, a tetraspanin transmembrane protein known to be enriched in extracellular vesicles (EVs), as a newly identified driver of breast cancer stemness and metastasis. Using protein structure modeling and interface prediction-guided mutagenesis, we demonstrate that membrane CD81 interacts with CD44 through their extracellular regions in promoting tumor cell cluster formation and lung metastasis of triple negative breast cancer (TNBC) in human and mouse models. In-depth global and phosphoproteomic analyses of tumor cells deficient with CD81 or CD44 unveils endocytosis-related pathway alterations, leading to further identification of a quality-keeping role of CD44 and CD81 in EV secretion as well as in EV-associated stemness-promoting function. CD81 is co-expressed along with CD44 in human circulating tumor cells (CTCs) and enriched in clustered CTCs that promote cancer stemness and metastasis, supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights machine learning as a powerful tool in facilitating the molecular understanding of new molecular targets in regulating stemness and metastasis of TNBC.
Data availability
RNA sequencing data have been deposited to GEO database with accession number GSE174087.Mass spec raw data sets have been deposited in the Japan ProteOmeSTandard Repository (https://repository.jpostdb.org/) (98). The accession numbers are PXD029529 for ProteomeXchange (99) and JPST001321 for jPOST. The access link is https://repository.jpostdb.org/preview/1370203119618182ba1c0f2
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RNAseq of MDA-MB-231 siCon and siCD81 cellsNCBI Gene Expression Omnibus, GSE174087.
Article and author information
Author details
Marina A Gritsenko
Funding
National Cancer Institute (R01CA245699)
- Erika K Ramos
- Huiping Liu
National Institute of General Medical Sciences (R35GM124952)
- Yang Shen
U.S. Department of Defense (W81XWH-16-1-0021)
- Huiping Liu
Susan G. Komen (CCR18548501)
- Xia Liu
American Cancer Society (ACS127951-RSG-15-025-01-CSM)
- Huiping Liu
National Cancer Institute (T32 CA009560)
- Erika K Ramos
National Cancer Institute (T32GM008061)
- Emma J Schuster
National Science Foundation (CCF-1943008)
- Yang Shen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All mice used in this study were kept in specific pathogen-free facilities in the Animal Resources Center at Northwestern University. All animal procedures complied with the NIH Guidelines for the Care and Use of Laboratory Animals and were approved by the respective Institutional Animal Care and Use Committees.
Reviewing Editor
- Caigang Liu, Shengjing Hospital of China Medical University, China
Publication history
- Received: August 14, 2022
- Accepted: August 26, 2022
- Accepted Manuscript published: October 4, 2022 (version 1)
- Version of Record published: October 19, 2022 (version 2)
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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Machine learning-assisted elucidation of CD81-CD44 interactions in promoting cancer stemness and extracellular vesicle integrity
eLife 11:e82669.
https://doi.org/10.7554/eLife.82669
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