Browsing by Author "Giulietti, Matteo"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Exploring Small Extracellular Vesicles for Precision Medicine in Prostate Cancer
    (Frontiers Media, 2018-06-13) Giulietti, Matteo; Santoni, Matteo; Cimadamore, Alessia; Carrozza, Francesco; Piva, Francesco; Cheng, Liang; Lopez-Beltran, Antonio; Scarpelli, Marina; Battelli, Nicola; Montironi, Rodolfo; Pathology and Laboratory Medicine, School of Medicine
    Tumor microenvironment constitutes a complex network in which tumor cells communicate among them and with stromal and immune cells. It has been shown that cancer cells are able to exchange genetic materials through small extracellular vesicles (EVs), a heterogeneous group of vesicles with different size and shape, cargo content, and function. The importance to investigate populations of circulating EVs would be of great importance as prostate cancer (PCa) biomarkers. In several neoplasms as well as in PCa, nanometer-sized EVs of endosomal origin are implicated in supporting tumor growth and metastatic spread by both altering local stroma cells and creating a protumor environment that favors the formation of pre-metastatic niches. Several techniques are applicable for the isolation and analysis of PCa-derived small EVs and are illustrated in this article. Due to the high sensitivity and specificity of these techniques, small EVs have become ideal candidates for early diagnosis. Moreover, we discuss the role of small EVs during PCa carcinogenesis, as well as in modulating the development of drug resistance to hormonal therapy and chemotherapy, thus underlining the potential of EV-tailored strategies in PCa patients.
  • Thumbnail Image
    Item
    Predicting future cancer burden in the United States by artificial neural networks
    (Taylor & Francis, 2021) Piva, Francesco; Tartari, Francesca; Giulietti, Matteo; Aiello, Marco Maria; Cheng, Liang; Lopez-Beltran, Antonio; Mazzucchelli, Roberta; Cimadamore, Alessia; Cerqueti, Roy; Battelli, Nicola; Montironi, Rodolfo; Santoni, Matteo; Pathology and Laboratory Medicine, School of Medicine
    Aims: To capture the complex relationships between risk factors and cancer incidences in the US and predict future cancer burden. Materials & methods: Two artificial neural network (ANN) algorithms were adopted: a multilayer feed-forward network (MLFFNN) and a nonlinear autoregressive network with eXogenous inputs (NARX). Data on the incidence of the four most common tumors (breast, colorectal, lung and prostate) from 1992 to 2016 (available from National Cancer Institute online datasets) were used for training and validation, and data until 2050 were predicted. Results: The rapid decreasing trend of prostate cancer incidence started in 2010 will continue until 2018-2019; it will then slow down and reach a plateau after 2050, with several differences among ethnicities. The incidence of breast cancer will reach a plateau in 2030, whereas colorectal cancer incidence will reach a minimum value of 35 per 100,000 in 2030. As for lung cancer, the incidence will decrease from 50 per 100,000 (2017) to 31 per 100,000 in 2030 and 26 per 100,000 in 2050. Conclusion: This up-to-date prediction of cancer burden in the US could be a crucial resource for planning and evaluation of cancer-control programs.
  • Thumbnail Image
    Item
    Sequential or Concomitant Inhibition of Cyclin-Dependent Kinase 4/6 Before mTOR Pathway in Hormone-Positive HER2 Negative Breast Cancer: Biological Insights and Clinical Implications
    (Frontiers Media, 2020) Occhipinti, Giulia; Romagnoli, Emanuela; Santoni, Matteo; Cimadamore, Alessia; Sorgentoni, Giulia; Cecati, Monia; Giulietti, Matteo; Battelli, Nicola; Maccioni, Alessandro; Storti, Nadia; Cheng, Liang; Principato, Giovanni; Montironi, Rodolfo; Piva, Francesco; Pathology and Laboratory Medicine, School of Medicine
    About 75% of all breast cancers are hormone receptor-positive (HR+). However, the efficacy of endocrine therapy is limited due to the high rate of either pre-existing or acquired resistance. In this work we reconstructed the pathways around estrogen receptor (ER), mTOR, and cyclin D in order to compare the effects of CDK4/6 and PI3K/AKT/mTOR inhibitors. A positive feedback loop links mTOR and ER that support each other. We subsequently considered whether a combined or sequential inhibition of CDK4/6 and PI3K/AKT/mTOR could ensure better results. Studies indicate that inhibition of CDK4/6 activates mTOR as an escape mechanism to ensure cell proliferation. In literature, the little evidence dealing with this topic suggests that pre-treatment with mTOR pathway inhibitors could prevent or delay the onset of CDK4/6 inhibitor resistance. Additional studies are needed in order to find biomarkers that can identify patients who will develop this resistance and in whom the sensitivity to CDK4/6 inhibitors can be restored.