Carels Nicolas, Tilli Tatiana, Tuszynski Jack A
Laboratório de Modelagem de Sistemas Biológicos, National Institute of Science and Technology for Innovation in Neglected Diseases (INCT/IDN, CNPq), Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada T6G 1Z2; Department of Physics, University of Alberta, Edmonton, Alberta, Canada T6G 2E1.
PLoS One. 2015 Jan 27;10(1):e0115054. doi: 10.1371/journal.pone.0115054. eCollection 2015.
In this report, we describe a strategy for the optimized selection of protein targets suitable for drug development against neoplastic diseases taking the particular case of breast cancer as an example. We combined human interactome and transcriptome data from malignant and control cell lines because highly connected proteins that are up-regulated in malignant cell lines are expected to be suitable protein targets for chemotherapy with a lower rate of undesirable side effects. We normalized transcriptome data and applied a statistic treatment to objectively extract the sub-networks of down- and up-regulated genes whose proteins effectively interact. We chose the most connected ones that act as protein hubs, most being in the signaling network. We show that the protein targets effectively identified by the combination of protein connectivity and differential expression are known as suitable targets for the successful chemotherapy of breast cancer. Interestingly, we found additional proteins, not generally targeted by drug treatments, which might justify the extension of existing formulation by addition of inhibitors designed against these proteins with the consequence of improving therapeutic outcomes. The molecular alterations observed in breast cancer cell lines represent either driver events and/or driver pathways that are necessary for breast cancer development or progression. However, it is clear that signaling mechanisms of the luminal A, B and triple negative subtypes are different. Furthermore, the up- and down-regulated networks predicted subtype-specific drug targets and possible compensation circuits between up- and down-regulated genes. We believe these results may have significant clinical implications in the personalized treatment of cancer patients allowing an objective approach to the recycling of the arsenal of available drugs to the specific case of each breast cancer given their distinct qualitative and quantitative molecular traits.
在本报告中，我们以乳腺癌为例，描述了一种针对肿瘤疾病开发药物的蛋白质靶点优化选择策略。我们整合了来自恶性和对照细胞系的人类相互作用组和转录组数据，因为预计在恶性细胞系中上调的高连接性蛋白质是化疗的合适蛋白质靶点，且不良副作用发生率较低。我们对转录组数据进行归一化处理，并应用统计方法客观地提取蛋白质有效相互作用的下调和上调基因的子网络。我们选择了作为蛋白质枢纽的连接性最强的那些基因，其中大多数位于信号网络中。我们表明，通过蛋白质连接性和差异表达相结合有效鉴定出的蛋白质靶点是乳腺癌成功化疗的合适靶点。有趣的是，我们发现了一些通常未被药物治疗靶向的额外蛋白质，这可能说明通过添加针对这些蛋白质设计的抑制剂来扩展现有配方是合理的，从而改善治疗效果。在乳腺癌细胞系中观察到的分子改变代表了乳腺癌发生或进展所必需的驱动事件和/或驱动途径。然而，很明显，腔面A型、B型和三阴性亚型的信号传导机制是不同的。此外，上调和下调网络预测了亚型特异性药物靶点以及上调和下调基因之间可能的补偿回路。我们相信这些结果可能对癌症患者的个性化治疗具有重要的临床意义，鉴于每种乳腺癌具有独特的定性和定量分子特征，能够以客观的方法将现有药物库重新应用于每个乳腺癌的具体情况。
