This study focuses on ETV7, a transcriptional repressor, known to be up-regulated in breast cancer (BC). Firstly, we demonstrated a new role of ETV7 in promoting breast cancer stem-like cell (BCSC) plasticity and resistance to chemotherapy in BC cells. We observed that ETV7 repressed a large panel of interferon response genes and increased BCSC cell plasticity, leading to resistance to 5-fluorouracil. Then, we investigated the role of ETV7 in inflammatory and immune responses in BC cells. We identified TNFRSF1A, encoding for TNFR1, as one of the genes repressed by ETV7. We demonstrated that ETV7 directly bound to the intron I of this gene, and we showed that the ETV7-mediated down-regulation of TNFRSF1A reduced the activation of NF-κB signaling. These results suggest that ETV7 can reduce the inflammatory responses in BC cells by repressing the TNFR1/NF-κB axis. Moreover, we analyzed the role of ETV7 in the regulation of antigen presentation and confirmed that ETV7 downregulated genes involved in the antigen-presenting pathway, potentially leading to cancer immune evasion We also analyzed if the silencing of ETV7 affected the viability of cancer cells and observed that knock-down of ETV7 can induce p53-mediated apoptosis in cancer cells. Lastly, we analyzed the pro-tumorigenic potential of ETV7 using in vivo model and we observed that mammary gland tumor cells overexpressing ETV7 formed bigger tumors with higher proliferation potential. Taken collectively, the data acquired during this project confirm the role of ETV7 as an important regulator of BC aggressiveness both in vitro and in vivo and propose ETV7 as a novel player in BC immunity, opening a new research direction and giving useful insights for more effective therapeutic strategies.

The purpose of this study is to determine the impact that breast cancer has on identity and how emotional support affects identity formation in women after cancer diagnosis and treatment. While there is much research on the medical treatment of breast cancer and the impact it has on women, there is a gap in research on the identity construction of women who have gone through breast cancer and the impact of emotional support for them during and after treatment. This is an important aspect of research as demonstrated by the rising number of women diagnosed with breast cancer each year. Because of advances in medicine and emphasis in early intervention, more women are impacted by the emotional aftermath of cancer. In order to accomplish this aim, this study utilizes narrative-based interviews to learn about breast cancer survivors’ stories of emotional support and its impact on identity. This uses a sociological approach of symbolic interactionism. This research helps evaluate the impact of emotional support for breast cancer patients in connection with medical support.

This thesis compares different methods that could be used to construct a computer aided diagnosis (CAD) system that analyzes mammograms. Such systems have many steps, but this thesis focuses on feature extraction, feature selection, and classification. The main comparison is between the simplified Rubber Band Straightening Transform (SRBST) and the Onion Transform, which are used to extract texture features. Another comparison is between 2D and 3D co-occurrence matrices. Next, features are selected using a greedy algorithm. This section mainly compares the effectiveness of Receiver Operating Characteristic (ROC) and Class Overlap Rating (COR). Also evaluated are the effectiveness of Linear Discriminate Analysis (LDA) and the sort order of features. Then the selected features are used to classify the lesions. In this part, Nearest Mean, Nearest Neighbor, and Maximum Likelihood are compared. The results are then used to determine the best combination of methods to use in a CAD system.

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