Proteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets

作者：Iga Kołodziejczak-Guglas, Renan Simões, Emerson de Souza Santos, Elizabeth G. Demicco, Rossana N. Lazcano Segura, Ma Weiping, Pei Wang, Yifat Geffen, Erik Storrs, Francesca Petralia, Antonio Colaprico, Felipe da Veiga Leprevost, Pietro Pugliese, Michele Ceccarelli, Houtan Noushmehr, Alexey I. Nesvizhskii, Bożena Kamińska, Waldemar Priebe, Jan Lubiński, Bing Zhang, Alexander J. Lazar, Paweł Kurzawa, Mehdi Mesri, Ana I. Robles, Alicia Francis, Amanda G. Paulovich, Anna P. Calinawan, Antonio Iavarone, Arul M. Chinnaiyan, Bo Wen, Boris Reva, Brian J. Druker, Caleb M. Lindgren, Chandan Kumar-Sinha, Chelsea J. Newton, Chen Huang, Chet Birger, Corbin Day, D.R. Mani, Daniel Cui Zhou, Daniel W. Chan, David Fenyö, David I. Heiman, Dmitry Rykunov, Emily Huntsman, Eric E. Schadt, Eric J. Jaehnig, Eunkyung An, Fernanda Martins Rodrigues, François Aguet, Gad Getz, Galen Hostetter, Gilbert S. Omenn, Hanbyul Cho, Hui Zhang, Jared L. Johnson, Jasmin Bavarva, Jiayi Ji, Jimin Tan, Jonathan T. Lei, Joshua M. Wang, Karen A. Ketchum, Karin D. Rodland, Karl R. Clauser, Karsten Krug, Kelly V. Ruggles, Lewis C. Cantley, Liang-Bo Wang, Lijun Yao, Lizabeth Katsnelson, Marcin J. Domagalski, Marcin P. Cieslik, Mathangi Thiagarajan, Matthew A. Wyczalkowski, Matthew J. Ellis, Meenakshi Anurag, Michael A. Gillette, Michael J. Birrer, Michael Schnaubelt, Myvizhi Esai Selvan, Nadezhda V. Terekhanova, Nathan Edwards, Nicole Tignor, Özgün Babur, Qing Zhang, Ratna R. Thangudu, Richard D. Smith, Robert Oldroyd, Runyu Hong, Samuel H. Payne, Sara J.C. Gosline, Sara R. Savage, Saravana M. Dhanasekaran, Scott D. Jewell, Shankara Anand, Shankha Satpathy, Shrabanti Chowdhury, Song Cao, Stephan Schürer, Steven A. Carr, Steven M. Foltz, Tania J. Gonzalez Robles, Tao Liu, Tobias Schraink, Tomer M. Yaron, Vasileios Stathias, Wen Jiang, Wen-Wei Liang, Wenke Liu, Wilson McKerrow, Xiaoyu Song, Xinpei Yi, Xu Zhang, Yifat Geffen, Yige Wu, Ying Wang, Yingwei Hu, Yize Li, Yizhe Song, Yo Akiyama, Yongchao Dou, Yuxing Liao, Zeynep H. Gümüş, Zhen Zhang, Zhiao Shi, Li Ding, Tathiane M. Malta, Maciej Wiznerowicz · 发表于：Cell Genomics · 年份：2025 · DOI：10.1016/j.xgen.2025.100851 · 被引用次数：4 · 研究领域：Protein Degradation and Inhibitors、Cancer Genomics and Diagnostics、Histone Deacetylase Inhibitors Research

Cancer progression and therapeutic resistance are closely linked to a stemness phenotype. Here, we introduce a protein-expression-based stemness index (PROTsi) to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes. Utilizing datasets from the Clinical Proteomic Tumor Analysis Consortium across 11 tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Through integration of PROTsi with multi-omics, including protein post-translational modifications, we identify molecular features associated with stemness and proteins that act as active nodes within transcriptional networks, driving tumor aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets, both shared and tumor specific. These stemness-associated proteins demonstrate predictive value for clinical outcomes, as confirmed by immunohistochemistry in multiple samples. The findings emphasize PROTsi's efficacy as a valuable tool for selecting predictive protein targets, a crucial step in customizing anti-cancer therapy and advancing the clinical development of cures for cancer patients.