CIBERONC, Carlos III Health Institute (Ministry of Economy and Competitiveness), 28029, Madrid, Spain.
CIBERONC, Carlos III Health Institute (Ministry of Economy and Competitiveness), 28029, Madrid, Spain; Department of Pathology, Medical School, University of Valencia - INCLIVA Biomedical Health Research Institute, 46010, Valencia, Spain.
Comput Biol Med. 2023 Sep;164:107364. doi: 10.1016/j.compbiomed.2023.107364. Epub 2023 Aug 14.
Digital pathology and artificial intelligence are promising emerging tools in precision oncology as they provide more robust and reproducible analysis of histologic, morphologic and topologic characteristics of tumor cells and the surrounding microenvironment. This study aims to develop digital image analysis workflows for therapeutic assessment in preclinical in vivo models. For this purpose, we generated pipelines that enable automatic detection and quantification of vitronectin and αvβ3 in heterotopic high-risk neuroblastoma xenografts, demonstrating that digital analysis workflows can be used to provide robust detection of vitronectin secretion and αvβ3 expression by malignant neuroblasts and to evaluate the possibility of combining traditional chemotherapy (etoposide) with extracellular matrix-targeted therapies (cilengitide). Digital image analysis added evidence for the relevance of territorial vitronectin as a therapeutic target in neuroblastoma, since its expression is modified after treatment, with a mean percentage of 60.44% in combined therapy tumors vs 45.08% in control ones. In addition, the present study revealed the efficacy of cilengitide for reducing αvβ3 expression, with a mean αvβ3 positivity of 34.17% in cilengitide treated material vs 66.14% in control and with less tumor growth when combined with etoposide, with a final mean volume of 0.04 cm in combined therapy vs 1.45 cm in control. The results of this work highlight the importance of extracellular matrix-focused therapies in preclinical studies to improve therapeutic assessment for high-risk neuroblastoma patients.
数字病理学和人工智能是精准肿瘤学中很有前途的新兴工具，因为它们提供了更强大和可重复的组织学、形态学和拓扑学特征分析，包括肿瘤细胞和周围微环境。本研究旨在开发用于临床前体内模型治疗评估的数字图像分析工作流程。为此，我们生成了能够自动检测和量化异质高危神经母细胞瘤异种移植物中 vitronectin 和 αvβ3 的管道，证明数字分析工作流程可用于提供恶性神经母细胞瘤 vitronectin 分泌和 αvβ3 表达的可靠检测，并评估将传统化疗（依托泊苷）与细胞外基质靶向治疗（西仑吉肽）相结合的可能性。数字图像分析为神经母细胞瘤中细胞外基质靶向治疗作为治疗靶点的相关性提供了更多证据，因为其表达在治疗后发生了改变，联合治疗组肿瘤的 vitronectin 表达平均百分比为 60.44%，而对照组为 45.08%。此外，本研究还揭示了西仑吉肽降低αvβ3 表达的功效，西仑吉肽治疗组的αvβ3 阳性率平均为 34.17%，而对照组为 66.14%，当与依托泊苷联合使用时肿瘤生长减少，联合治疗组的最终平均体积为 0.04 cm，而对照组为 1.45 cm。这项工作的结果强调了细胞外基质靶向治疗在临床前研究中的重要性，以改善高危神经母细胞瘤患者的治疗评估。
