Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America.
Carlsbad, California, United States.
PeerJ. 2023 Sep 29;11:e16088. doi: 10.7717/peerj.16088. eCollection 2023.
Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions.
We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication.
As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.
最近，人们努力将现有药物重新用于不同的适应症，并结合了一些整合蛋白质-蛋白质相互作用网络和信号通路的计算方法，以帮助确定现有目标和/或药物的优先级。然而，许多现有方法都侧重于整合仅适用于一小部分疾病或病症的额外数据。
我们设计并实现了一种新的基于 R 的开源目标优先级排序和重新利用方法，该方法整合了来自五个公共途径数据库的规范细胞内信号信息以及来自 OpenTargets.org 等公共来源的目标信息。Pathway2Targets 算法接受一组显著途径作为输入，然后检索并整合给定条件下这些途径中所有目标的公共数据。它还包含一个可由用户自定义权重的方案，以支持各种用例，包括目标优先级排序、药物再利用以及确定针对不同适应症具有生物学相关性的新目标。
作为概念验证，我们将该算法应用于具有 144 个病例和对照样本的公共结直肠癌 RNA 测序数据集。我们的分析根据差异基因表达和信号通路富集确定了 430 个目标和~700 种独特药物。我们发现，我们排名最高的预测目标在结直肠癌的 FDA 批准治疗药物的目标中显著富集（-值<0.025），其中包括 EGFR、VEGFA 和 PTGS2。有趣的是，在同一列表中没有发现其他癌症目标的统计学显著富集，这表明结果具有高度特异性。我们还调整了权重方案，以优先考虑 CRC 中更具创新性的目标。第二次分析揭示了表皮生长因子受体 (EGFR)、磷酸肌醇 3-激酶 (PI3K) 和两种丝裂原活化蛋白激酶 (MAPK14 和 MAPK3)。这些观察结果表明，我们具有可自定义权重方案的开源方法可以准确地确定与所关注的疾病或病症特异性和相关的目标，以及处于开发早期阶段的目标。我们预计，这种方法将补充其他用于各种适应症的药物再利用方法，从而有助于提高此类患者的生活质量和整体健康水平。
