Munquad Sana, Das Asim Bikas
Department of Biotechnology, National Institute of Technology Warangal, Warangal, 506004, Telangana, India.
Heliyon. 2024 Mar 1;10(5):e27190. doi: 10.1016/j.heliyon.2024.e27190. eCollection 2024 Mar 15.
The poor prognosis of glioma patients brought attention to the need for effective therapeutic approaches for precision therapy. Here, we deployed algorithms relying on network medicine and artificial intelligence to design the framework for subtype-specific target identification and drug response prediction in glioma. We identified the driver mutations that were differentially expressed in each subtype of lower-grade glioma and glioblastoma multiforme and were linked to cancer-specific processes. Driver mutations that were differentially expressed were also subjected to subtype-specific disease module identification. The drugs from the drug bank database were retrieved to target these disease modules. However, the efficacy of anticancer drugs depends on the molecular profile of the cancer and varies among cancer patients due to intratumor heterogeneity. Hence, we developed a deep-learning-based drug response prediction framework using the experimental drug screening data. Models for 30 drugs that can target the disease module were developed, where drug response measured by IC50 was considered a response and gene expression and mutation data were considered predictor variables. The model construction consists of three steps: feature selection, data integration, and classification. We observed the consistent performance of the models in training, test, and validation datasets. Drug responses were predicted for particular cell lines derived from distinct subtypes of gliomas. We found that subtypes of gliomas respond differently to the drug, highlighting the importance of subtype-specific drug response prediction. Therefore, the development of personalized therapy by integrating network medicine and a deep learning-based approach can lead to cancer-specific treatment and improved patient care.
胶质瘤患者的预后较差，这使得人们开始关注精准治疗的有效方法。在此，我们运用了基于网络医学和人工智能的算法，来设计识别胶质瘤亚型特异性靶点及预测药物反应的框架。我们确定了在低级别胶质瘤和多形性胶质母细胞瘤的每个亚型中差异表达且与癌症特异性过程相关的驱动突变。差异表达的驱动突变也用于识别亚型特异性疾病模块。从药物库数据库中检索药物以靶向这些疾病模块。然而，抗癌药物的疗效取决于癌症的分子特征，并且由于肿瘤内异质性在癌症患者中存在差异。因此，我们利用实验性药物筛选数据开发了一个基于深度学习的药物反应预测框架。针对30种可靶向疾病模块的药物建立了模型，其中将通过IC50测量的药物反应视为一种反应，将基因表达和突变数据视为预测变量。模型构建包括三个步骤：特征选择、数据整合和分类。我们在训练、测试和验证数据集中观察到模型具有一致的性能。对源自不同胶质瘤亚型的特定细胞系的药物反应进行了预测。我们发现胶质瘤的亚型对药物的反应不同，这突出了亚型特异性药物反应预测的重要性。因此，通过整合网络医学和基于深度学习的方法来开发个性化治疗，可以实现癌症特异性治疗并改善患者护理。
