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Microfluidic Platforms for High-Throughput Pancreatic Ductal Adenocarcinoma Organoid Culture and Drug Screening

Pancreatic Ductal Adenocarcinoma (PDAC), the most common pancreatic cancer type, is believed to become the second leading cause of cancer-related deaths by 2030 with mortality rates of up to 93%. It is often detected at a late stage due to lacking symptoms, and therefore surgical removal of the tumo...

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Detalles Bibliográficos
Autores principales: Geyer, Marlene, Queiroz, Karla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733732/
https://www.ncbi.nlm.nih.gov/pubmed/35004672
http://dx.doi.org/10.3389/fcell.2021.761807
Descripción
Sumario:Pancreatic Ductal Adenocarcinoma (PDAC), the most common pancreatic cancer type, is believed to become the second leading cause of cancer-related deaths by 2030 with mortality rates of up to 93%. It is often detected at a late stage due to lacking symptoms, and therefore surgical removal of the tumor is the only treatment option for patients. Only 20% of the tumors are resectable, mainly due to early metastasis. Therefore, for 80% of cases chemotherapeutic treatment is the leading therapy for patients. PDAC is characterized by high-density stroma which induces hypoxic conditions and high interstitial pressure. These factors impact carcinogenesis and progression of PDAC and support the formation of an immunosuppressive microenvironment that renders this tumor type refractory to immunotherapies. Most in vitro PDAC models have limited translational relevance, as these fail to recapitulate relevant aspects of PDAC complexity. Altogether, there is an urgent need for novel and innovative PDAC modeling platforms. Here, we discuss the relevance of microfluidic and organoid technologies as platforms for modeling bio- and physicochemical features of PDAC and as translational models that enable high-throughput phenotypic drug screenings, while also allowing for the development of novel personalized models used to identify treatment responsive patient subsets.