Publications by authors named "Fabiola Pedrini"

Article Synopsis
  • Understanding cancer involves exploring mechanisms, categorizing subtypes, predicting outcomes, and evaluating treatment effectiveness, with gene-expression signatures being vital tools over the last decade.
  • Recent technological advancements like single-cell RNA sequencing have exposed the complexity of tumor cells, prompting the need for new computational tools to analyze this heterogeneity accurately.
  • The R Bioconductor package "signifinder" helps streamline the use of cancer transcriptional signatures across various data types, enhancing the analysis of tumor features through case studies that illustrate its effectiveness in oncology research.
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Introduction: The Hippo pathway and its transcriptional effectors yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) are targets for cancer therapy. It is important to determine if the activation of one factor compensates for the inhibition of the other. Moreover, it is unknown if YAP/TAZ-directed perturbation affects cell-cell communication of non-malignant liver cells.

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Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier.

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