Cancer Identification
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Breast cancer poses a significant global health challenge. In 2020 alone, a staggering 2.3 million women received a breast cancer diagnosis, and tragically, 685,000 lost their lives to this disease. As one of the most prevalent cancers globally, breast cancer manifests in various types, each characterized by distinct factors such as the attributes of cancer cells and the presence or absence of specific receptors.
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Among the common types of breast cancer are Ductal Carcinoma In Situ (DCIS), Invasive Ductal Carcinoma (IDC), Lobular Carcinoma In Situ (LCIS), and several other subtypes. Accurate diagnosis and precise classification of breast cancer are paramount as they determine the most appropriate treatment strategy for individual patients.
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Epithelial-to-mesenchymal transition (EMT) and its reversal process, mesenchymal-to-epithelial transition (MET), play critical roles in tumor progression, and their dynamics are closely linked to alterations in the extracellular matrix (ECM). Understanding the influence of the ECM on these malignant switches holds great promise for advancing cancer therapy. However, replicating and comprehending these complex events in vitro remains a formidable challenge.
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By delving deeper into the interplay between EMT, MET, and the ECM, we strive to unlock new insights and therapeutic opportunities for combating breast cancer. Our research endeavors to decipher the intricate mechanisms underlying these processes, shedding light on their contribution to tumor development and progression. Through this understanding, we aim to pave the way for novel treatment approaches that effectively target EMT, ultimately improving patient outcomes.
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While recreating these multifaceted events in vitro poses challenges, our dedicated team of researchers and scientists are committed to pushing the boundaries of scientific knowledge. By utilizing innovative approaches and cutting-edge technologies, we aim to bridge the gap between in vitro models and the complex tumor microenvironment, bringing us closer to developing more effective interventions and personalized therapies for breast cancer patients.
P. Barros da Silva†, X. Zhao†, et al , “Unravelling the interplay of matrix cues in epithelial-to-mesenchymal transition: insights from a 3D model combining native and artificial ECM”, To be submitted, 2023.