Publicação
Patient-derived explant cultures for cancer modelling
| Resumo: | "Cancer remains one of the deadliest diseases in the world. The process of drug discovery to find new drugs for oncology treatment takes more than a decade of research and it is associated with high attrition rates during clinical trials. One of the most common reasons claimed for the poor efficiency of new developed drugs is the lack of physiological relevance of the in vitro models used to select the novel compounds and to evaluate their potency. The role of tumor microenvironment on tumor progression and drug sensitivity is being increasingly studied and it is expected to provide significant clues for the development of novel therapies. Therefore, the incorporation of microenvironment features on cancer cell models during pre- clinical stages of research has the potential to improve significantly their predictive value. (...)" |
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| Autores principais: | Teixeira, Marta Carolina Jesus |
| Assunto: | Ovarian cancer Patient-derived explants Tumor microenvironment 3D cancer model |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | "Cancer remains one of the deadliest diseases in the world. The process of drug discovery to find new drugs for oncology treatment takes more than a decade of research and it is associated with high attrition rates during clinical trials. One of the most common reasons claimed for the poor efficiency of new developed drugs is the lack of physiological relevance of the in vitro models used to select the novel compounds and to evaluate their potency. The role of tumor microenvironment on tumor progression and drug sensitivity is being increasingly studied and it is expected to provide significant clues for the development of novel therapies. Therefore, the incorporation of microenvironment features on cancer cell models during pre- clinical stages of research has the potential to improve significantly their predictive value. (...)" |
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