整合网络分析鉴定卵巢癌的潜在靶点和药物。

Integrative network analysis identifies potential targets and drugs for ovarian cancer.

机构信息

Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, 63130, USA.

Dalian University of Technology, Dalian, 116024, China.

出版信息

BMC Med Genomics. 2020 Sep 21;13(Suppl 9):132. doi: 10.1186/s12920-020-00773-2.

DOI:10.1186/s12920-020-00773-2

PMID:32958005

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7504661/

Abstract

BACKGROUND

Though accounts for 2.5% of all cancers in female, the death rate of ovarian cancer is high, which is the fifth leading cause of cancer death (5% of all cancer death) in female. The 5-year survival rate of ovarian cancer is less than 50%. The oncogenic molecular signaling of ovarian cancer are complicated and remain unclear, and there is a lack of effective targeted therapies for ovarian cancer treatment.

METHODS

In this study, we propose to investigate activated signaling pathways of individual ovarian cancer patients and sub-groups; and identify potential targets and drugs that are able to disrupt the activated signaling pathways. Specifically, we first identify the up-regulated genes of individual cancer patients using Markov chain Monte Carlo (MCMC), and then identify the potential activated transcription factors. After dividing ovarian cancer patients into several sub-groups sharing common transcription factors using K-modes method, we uncover the up-stream signaling pathways of activated transcription factors in each sub-group. Finally, we mapped all FDA approved drugs targeting on the upstream signaling.

RESULTS

The 427 ovarian cancer samples were divided into 3 sub-groups (with 100, 172, 155 samples respectively) based on the activated TFs (with 14, 25, 26 activated TFs respectively). Multiple up-stream signaling pathways, e.g., MYC, WNT, PDGFRA (RTK), PI3K, AKT TP53, and MTOR, are uncovered to activate the discovered TFs. In addition, 66 FDA approved drugs were identified targeting on the uncovered core signaling pathways. Forty-four drugs had been reported in ovarian cancer related reports. The signaling diversity and heterogeneity can be potential therapeutic targets for drug combination discovery.

CONCLUSIONS

The proposed integrative network analysis could uncover potential core signaling pathways, targets and drugs for ovarian cancer treatment.

摘要

背景

尽管卵巢癌在女性中仅占所有癌症的 2.5%，但其死亡率却很高，是女性癌症死亡的第五大主要原因（占所有癌症死亡的 5%）。卵巢癌的 5 年生存率低于 50%。卵巢癌的致癌分子信号复杂且仍不清楚，并且缺乏有效的卵巢癌治疗靶向药物。

方法

在这项研究中，我们建议研究个体卵巢癌患者和亚组的激活信号通路；并确定能够破坏激活信号通路的潜在靶标和药物。具体来说，我们首先使用马尔可夫链蒙特卡罗（MCMC）方法识别单个癌症患者的上调基因，然后识别潜在的激活转录因子。使用 K-模式方法将卵巢癌患者分为几个具有共同转录因子的亚组后，我们揭示了每个亚组中激活转录因子的上游信号通路。最后，我们将所有靶向作用于上游信号的 FDA 批准药物映射到这些通路。

结果

根据激活的 TF（分别有 14、25、26 个激活 TF），将 427 个卵巢癌样本分为 3 个亚组（分别有 100、172、155 个样本）。揭示了多个上游信号通路，例如 MYC、WNT、PDGFRA（RTK）、PI3K、AKT TP53 和 MTOR，这些通路激活了发现的 TF。此外，确定了 66 种针对发现的核心信号通路的 FDA 批准药物。其中 44 种药物已在卵巢癌相关报告中报道。信号的多样性和异质性可能是药物组合发现的潜在治疗靶点。