Machine learning-assisted elucidation of CD81–CD44 interactions in promoting cancer stemness and extracellular vesicle integrity
Year: 2022
Type: article
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 coexpressed 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.
Source: eLife
DOI: 10.7554/elife.82669
Authors: Erika K. Ramos, Chia-Feng Tsai, Nurmaa K. Dashzeveg, Yuzhi Jia, Yue Cao, Megan Manu, Rokana Taftaf, Andrew D. Hoffmann, Lamiaa El-Shennawy, Marina A. Gritsenko, Valery Adorno-Cruz, Emma J. Schuster, David Scholten, Dhwani Patel, Xia Liu, Priyam Patel, Brian Wray, Youbin Zhang, Shanshan Zhang, Ronald J. Moore, Jeremy V. Mathews, Matthew J. Schipma, Tao Liu, Valerie L. Tokars, Massimo Cristofanilli, Tujin Shi, Yang Shen, Huiping Liu
Language: English
Topic: Cancer Cells and Metastasis
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,500
Funders: National Science Foundation, U.S. Department of Defense, American Cancer Society, Northwestern University, Case Western Reserve University, Susan G. Komen, National Institutes of Health, National Cancer Institute, National Institute of General Medical Sciences, National Center for Advancing Translational Sciences
Awards: CHARACTERIZATION OF CLUSTERED CTCS TO ELIMINATE BREAST CANCER METASTASIS, G1799050767, G1881085264, G1892409510, Targeted proteomics technology for accurate quantitative single-cell proteomics, G2575617957, G3411591045, Northwestern University Clinical and Translational Science Institute (NUCATS), G5118416777, G556791467, G8563464415, CAREER: Physics-Constrained Modeling of Molecular Texts, Graphs, and Images for Deciphering Protein-Protein Interactions, Carcinogenesis Training Program
OpenAlex ID: W4300716327