Biomedical Informatics Training Program, Stanford University, Stanford, California, United States of America.
Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, California, United States of America.
PLoS Comput Biol. 2021 Feb 5;17(2):e1008631. doi: 10.1371/journal.pcbi.1008631. eCollection 2021 Feb.
For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.
对于许多常见的复杂疾病，治疗方案往往效果不佳。例如，尽管有多种可用的免疫调节剂和免疫抑制剂，但炎症性肠病 (IBD) 仍然难以治疗。患者之间疾病的异质性使得选择最佳治疗方案具有挑战性，并且一些患者对任何现有治疗选择都没有反应。IBD 的药物再利用策略在临床上取得的成功有限，通常也没有提供针对个体患者的治疗建议。在这项工作中，我们提出了 NetPTP，这是一种基于网络的个性化治疗预测框架，它可以从基因表达数据中模拟测量的药物效果，并将其应用于患者样本，以生成个性化的治疗排序列表。为了实现这一点，我们结合了公开的网络、药物靶点和药物效果数据，以患者数据生成治疗排序。然后可以使用这些排序列表来优先考虑现有治疗方法，并为个别患者发现新的治疗方法。我们展示了 NetPTP 如何捕获和建模药物效果，并且我们将我们的框架应用于单个 IBD 样本，以提供对 IBD 治疗的新见解。
