Perfil MultiOmico de Vesículas Extracelulares do Cancro da Próstata: Desvendar assinaturas moleculares para a descoberta de biomarcadores

PROMISE's ultimate objective is to understand the extracellular vesicle (EV) communication network in prostate cancer (PCa) through a multiomic analysis approach (proteomic, transcriptomic and methylomic) using primary tumors, with matched normal adjacent tissue and liquid biopsies. PROMISE will take advantage of the fact that our institution is a leading cancer care provider with its own Biobank, that has been systematically collecting different clinical samples. Thus, we firstly aim to isolate and characterize PCa-derived EVs, using the full spectrum of samples, to understand and identify the cargo/information that is being passed from the tumors to plasma and urine through EV transportation. Moreover, we intend to verify if the content of tumor-derived EVs is faithfully mirrored in the corresponding circulating EVs from the same patients, and understand if there is a most relevant body fluid for biomarker discovery. Making use of leading-edge high-throughput multiomic techniques and analysis to assess protein, RNA and DNA cargo from the EVs, we aim to create a database with information from the EVs’ proteome, transcriptome and methylome. This comprehensive dataset will encompass the EV information with clinical and histopathological information from the patients and will be a spark for catalyzing future EV biomarker studies in our group, allowing us to pursue new lines of investigation. Moreover, as this is still mostly unknown information, this project creates a chance to contribute to the dissemination of scientific knowledge by publishing this database online and making it publicly available for other investigators and advertise it through ISEV and other relevant national and international technical communication channels. As our compromise and final purpose is with the patients and with making discoveries that can be subsequently implemented in the clinical practice, PROMISE second major goal is to unveil and make use of EV-specific signatures that allow for metastatic PCa early identification using a liquid biopsy approach. We aspire to reach this with a machine learning-based model using one or more relevant omics and the most pertinent liquid biopsy type chosen from the first goal. We will use three groups of PCa patient samples: (1) diagnosed without clinical evidence of metastasis or during follow-up; (2) diagnosed without clinical evidence of metastasis but that developed metastasis during follow-up; and (3) diagnosed with evidence of metastasis at the initial plasma collection moment. Subsequently, we will train a machine learning model to successfully identify PCa metastasis hallmarks in the liquid biopsy EV cargo that may allow for early metastasis detection and to consequently improve PCa patient management and treatment. As it stands, this project will not only contribute to expanding our basic biological knowledge of PCa-derived EV content, but is also a possibility to generate a minimally invasive state-of-the-art technological approach for metastatic PCa patients clinical management, particularly in scenarios where the current gold standard approaches find limitations.