Triple-negative breast cancers (TNBC) are resistant to standard-of-care chemotherapy and lack known targetable driver gene alterations. Identification of novel drivers could aid the discovery of new treatment strategies for this hard-to-treat patient population, yet studies using high-throughput and accurate models to define the functions of driver genes in TNBC to date have been limited. Here, we employed unbiased functional genomics screening of the 200 most frequently mutated genes in breast cancer, using spheroid cultures to model -like conditions, and identified the histone acetyltransferase CREBBP as a novel tumor suppressor in TNBC.
View Article and Find Full Text PDFBackground: Carcinoid tumors are rare tumors most commonly found in the gastrointestinal tract. They represent the most common malignancies of the appendix. As a distinct entity from both adenocarcinomas and carcinoids, Goblet cell carcinoid (GCC) was initially described in the literature in 1969.
View Article and Find Full Text PDFThe purpose of this study was to improve the prognostic value of tumour histopathology image analysis methodology by image preprocessing. Key image qualities were modified including contrast, sharpness and brightness. The texture information was subsequently extracted from images of haematoxylin/eosin-stained tumour tissue sections by GLCM, monofractal and multifractal algorithms without any analytical limitation to predefined structures.
View Article and Find Full Text PDFPurpose: Breast cancer (BC) is the most common malignancy among women, while isolated operable liver metastases (LMs) from BC are very rare and occur in only 1-5% of the patients. Besides, positive steroid receptor (SR) status for oestrogen and/or progesterone is known as a factor which improves disease free survival (DFS) and overall survival (OS). The primary aim of this study was to examine the impact of SR status on DFS and OS after liver metastasectomy in female patients with primary BC.
View Article and Find Full Text PDFBreast cancer prognosis is a subject undergoing intense study due to its high clinical relevance for effective therapeutic management and a great patient interest in disease progression. Prognostic value of fractal and gray level co-occurrence matrix texture analysis algorithms has been previously established on tumour histology images, but without any direct performance comparison. Therefore, this study was designed to compare the prognostic power of the monofractal, multifractal and co-occurrence algorithms on the same set of images.
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