Iga Kołodziejczak-Guglas, Renan L S Simões, Emerson de Souza Santos, Elizabeth G Demicco, Rossana N Lazcano Segura, Weiping Ma, 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, Li Ding, Tathiane M Malta, Maciej Wiznerowicz
{"title":"基于蛋白质组学的干性评分测量致癌去分化,并能够识别可药物靶点。","authors":"Iga Kołodziejczak-Guglas, Renan L S Simões, Emerson de Souza Santos, Elizabeth G Demicco, Rossana N Lazcano Segura, Weiping Ma, 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, Li Ding, Tathiane M Malta, Maciej Wiznerowicz","doi":"10.1016/j.xgen.2025.100851","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100851"},"PeriodicalIF":11.1000,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12230239/pdf/","citationCount":"0","resultStr":"{\"title\":\"Proteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets.\",\"authors\":\"Iga Kołodziejczak-Guglas, Renan L S Simões, Emerson de Souza Santos, Elizabeth G Demicco, Rossana N Lazcano Segura, Weiping Ma, 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, Li Ding, Tathiane M Malta, Maciej Wiznerowicz\",\"doi\":\"10.1016/j.xgen.2025.100851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":72539,\"journal\":{\"name\":\"Cell genomics\",\"volume\":\" \",\"pages\":\"100851\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2025-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12230239/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xgen.2025.100851\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/4/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2025.100851","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Proteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets.
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.