Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges

Diletta Piana, Federica Iavarone, Elisa De Paolis, Gennaro Daniele, Federico Parisella, Angelo Minucci, Viviana Greco, Andrea Urbani

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
RivistaInternational Journal of Molecular Sciences
Volume25
DOI
Stato di pubblicazionePubblicato - 2024

Keywords

  • cancer
  • personalized medicine
  • proteogenomics
  • proteomics

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