Abstract LB004: Deciphering oncogenic dedifferentiation: A proteomic approach to quantifying stemness scores and unveiling druggable targets
Authors: Kołodziejczak-Guglas Iga , Simões Renan , Demicco Elizabeth G. , Segura Rossana L. , Lazar Alexander J. , Ma Weiping , Storrs Erik , Petralia Francesca , Colaprico Antonio , da Leprevost Felipe , Pugliese Pietro , Ceccarelli Michele , Nesvizhski Alexey I. , Kamińska Bożena , Zhang Bing , Rodriguez Henry , Mesri Mehdi , Robles Ana I. ,   Clinical Proteomic Tumor Analysis Consortium, Ding Li , Malta Tathiane M. , Wiznerowicz Maciej 
Published: April 2024

Cancer progression and therapeutic resistance are closely linked to the acquisition of a stemness phenotype. In this context, we introduce a novel protein expression-based stemness index (PROTsi) designed to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes in tumor samples. The methodology involved the application of a machine learning model to predict the stemness molecular phenotype, leveraging proteomic data. The prediction model was built from human pluripotent stem cells from the Human Induced Pluripotent Stem Cells Consortium (HipSci). Using datasets sourced from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) across eleven distinct tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Integrating the PROTsi with gene expression, DNA methylation, microRNA expression, copy number alteration, and protein post-translational modification data such as protein acetylation, glycosylation, and phosphorylation, we unveiled proteogenomic associations related to stemness. This comprehensive analysis enabled the identification of proteins and modified proteins functioning as active nodes within transcriptional networks, thereby steering the cancer aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets both tumor-specific and shared among tumor types. The stemness-associated proteins we identified demonstrate prognostic value for clinical outcomes, as confirmed through immunohistochemistry in independent samples. This underscores PROTsi's efficacy as a valuable tool for selecting both prognostic protein and drug targets. This dual functionality enhances its utility in customizing anti-cancer therapy and propels the clinical development of effective cures for diverse cancer patient populations. In conclusion, our study not only introduces PROTsi as a robust method for evaluating stemness in cancer but also sheds light on potential opportunities for targeted therapies, emphasizing the significance of personalized treatment strategies in the pursuit of more effective cancer interventions.

Citation Format: Iga Kołodziejczak-Guglas, Renan Simões, Elizabeth G. Demicco, Rossana L. Segura, Alexander J. Lazar, Weiping Ma, Erik Storrs, Francesca Petralia, Antonio Colaprico, Felipe da Leprevost, Pietro Pugliese, Michele Ceccarelli, Alexey I. Nesvizhski, Bożena Kamińska, Bing Zhang, Henry Rodriguez, Mehdi Mesri, Ana I. Robles, Clinical Proteomic Tumor Analysis Consortium, Li Ding, Tathiane M. Malta, Maciej Wiznerowicz. Deciphering oncogenic dedifferentiation: A proteomic approach to quantifying stemness scores and unveiling druggable targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB004.