AMIGO2 characterizes cancer-associated fibroblasts in metastatic colon cancer and induces the release of paracrine active tumorigenic secretomes - Yong, Yong-song; Demmler, Richard; Zohud, Bisan; Fang, Qi; Zhang, Tong; Zhou, Yonghua; Petter, Katja; Flierl, Christian; Gass, Tobias; Geppert, Carol-Immanuel; Merkel, Susanne; Schellerer, Vera; et al. (Journal of Pathology, 2024)
Secretomes of cancer-associated fibroblasts (CAFs) in colorectal cancer (CRC) contribute to malignancy. Detailed knowledge is available on the components and functions of CAF secretomes. Little is known about the regulation of CAF secretomes. Here, we searched for receptor-like membrane-bound molecules in CAFs, which may regulate the production and release of tumor-activating secretomes. The adhesion molecule with Ig-like domain 2 (AMIGO2) was significantly upregulated in cultivated CAFs compared to normal tissue-associated fibroblasts (NAFs), and this was confirmed in patient-derived tissues. AMIGO2 expression was low or absent in healthy colon, significantly increased in fibroblasts of primary CRC, and highest in the stromal tissues of CRC-derived liver metastases. AMIGO2 expression in CAFs correlated with a higher T-category, increased lymph node metastasis, progressed tumor stages and was associated with reduced survival in different cohorts of CRC patients. Interestingly, AMIGO2 expression was induced by transforming growth factor-β and higher in female patients, who exhibit a more aggressive disease course. In functional studies, conditioned media of NAFs with experimentally induced AMIGO2 overexpression enhanced proliferation and migration of different CRC tumor cells, while siRNA-mediated inhibition of AMIGO2 in CAFs attenuated these effects. Accordingly, therapeutic inhibition of the receptor-like AMIGO2 protein in CRC CAFs could prevent tumorigenic secretomes in CRC. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
CRIS-Team Scopus Importer:2024-11-22
Keywords: cancer-associated fibroblasts; colorectal cancer; secretion; secretomes; tumor microenvironment

Automated Lesion Detection in Endoscopic Imagery for Small Animal Models - Eixelberger, Thomas; Fang, Qi; Zohud, Bisan; Hackner, Ralf; Jackstadt, Rene; Stürzl, Michael; Naschberger, Elisabeth; Wittenberg, Thomas (Bildverarbeitung für die Medizin 2024. BVM 2024, 2024)
Murine animal models are routinely used in research of gastrointestinal diseases, for example to analyze colorectal cancer or chronic inflammatory bowel diseases. By using suitable (miniaturized) endoscopy systems, it is possible to examine the large intestine of mice with respect to inflammatory, vascular or neoplastic changes without the need to sacrifice the animals. This enables the acquisition of high-resolution colonoscopy image sequences that can be used for the visual examination of tumors, the assessment of inflammation or the vasculature. Since the human resources for analyzing a multitude of videos, are limited, an automated evaluation of such image data is desirable. Video recordings (n = 49) of mice with and without colorectal cancer (CRC) were employed for this purpose and scored by clinical experts. The videos contained mice with tumors in 33 cases and 16 are pathologically normal. For the automatic detection of neoplastic changes (e.g. polyps), a deep neural network based on the YOLOv7- tiny architecture was applied. This network was previously trained on >36,000 human colon images with neoplasias visible in all frames. On test data with human images, the precision of the network is Prec = 0.92, and Rec = 0.90. The network was applied to the mouse data without any changes. To avoid falsepositive detections a color-based method was added to differentiate between stool residues and polyps. With the framework for the detection of neoplastic changes and classification of stool residues, we achieve results of Prec = 0.90, Rec = 0.98, F1 score = 0.94.Without the detection of stool residues, the values were dropping to Prec = 0.65 and Rec = 0.98, as 19 occurrences of stool are incorrectly classified as tumors. Our network trained on human data for neoplasia detection is able to accurately detect tumors in the murine colon. An additional module for the separation of stool residues is essential to avoid integration of wrongly positive cases.
CRIS-Team Scopus Importer:2024-03-29