■卵巢癌患者对一线治疗的耐药性提出了重大挑战,大约70%的人经历复发并对紫杉醇等一线化疗产生强烈的耐药性。
■在预测的框架内,预防性,和个性化医疗(3PM),这项研究旨在利用人工智能来发现单个细胞的耐药性特征,并进一步构建基于这些抗性性状的分类策略和深度学习预后模型,这可以更好地促进和执行3PM。
■这项研究采用了“Beyondcell,“一种能够预测细胞药物反应的算法,计算来自卵巢癌样本的21,937个细胞的表达模式与5201个药物的特征之间的相似性,以鉴定耐药细胞。使用耐药性特征在TCGA训练集上执行10个多组学聚类,以识别具有差异药物反应的患者亚组。同时,针对该训练集构建了具有KAN架构的深度学习预后模型,该模型具有灵活的激活函数,可以更好地拟合模型.使用来自GEO的三个外部验证集评估构建的患者亚型分类器和预后模型:GSE17260、GSE26712和GSE51088。
■这项研究发现内皮细胞对紫杉醇有抗性,阿霉素,和多西他赛,提示它们作为卵巢癌患者细胞治疗靶点的潜力。基于耐药特点,10多组学聚类确定了对四种化疗药物具有不同反应的四种患者亚型,其中CS2亚型对所有四种药物的敏感性最高。其他亚型也表现出富集在不同的生物学途径和免疫浸润,允许根据他们的特点进行有针对性的治疗。此外,这项研究应用了人工智能中最新的KAN架构来取代DeepSurv预后模型中的MLP结构,最后证明了对患者预后预测的稳健表现。
■这项研究,通过对患者进行分类并根据对一线药物的耐药特征构建预后模型,有效地将多组学数据应用于3PM领域。
■在线版本包含补充材料,可在10.1007/s13167-024-00374-4获得。
UNASSIGNED: Ovarian cancer patients\' resistance to first-line treatment posed a significant challenge, with approximately 70% experiencing recurrence and developing strong resistance to first-line chemotherapies like paclitaxel.
UNASSIGNED: Within the framework of predictive, preventive, and personalized medicine (3PM), this study aimed to use artificial intelligence to find drug resistance characteristics at the single cell, and further construct the classification strategy and deep learning prognostic models based on these resistance traits, which can better facilitate and perform 3PM.
UNASSIGNED: This study employed \"Beyondcell,\" an algorithm capable of predicting cellular drug responses, to calculate the similarity between the expression patterns of 21,937 cells from ovarian cancer samples and the signatures of 5201 drugs to identify drug-resistance cells. Drug resistance features were used to perform 10 multi-omics
clustering on the TCGA training set to identify patient subgroups with differential drug responses. Concurrently, a deep learning prognostic model with KAN architecture which had a flexible activation function to better fit the model was constructed for this training set. The constructed patient subtype classifier and prognostic model were evaluated using three external validation sets from GEO: GSE17260, GSE26712, and GSE51088.
UNASSIGNED: This study identified that endothelial cells are resistant to paclitaxel, doxorubicin, and docetaxel, suggesting their potential as targets for cellular therapy in ovarian cancer patients. Based on drug resistance features, 10 multi-omics
clustering identified four patient subtypes with differential responses to four chemotherapy drugs, in which subtype CS2 showed the highest drug sensitivity to all four drugs. The other subtypes also showed enrichment in different biological pathways and immune infiltration, allowing for targeted treatment based on their characteristics. Besides, this study applied the latest KAN architecture in artificial intelligence to replace the MLP structure in the DeepSurv prognostic model, finally demonstrating robust performance on patients\' prognosis prediction.
UNASSIGNED: This study, by classifying patients and constructing prognostic models based on resistance characteristics to first-line drugs, has effectively applied multi-omics data into the realm of 3PM.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13167-024-00374-4.