Molecular investigation of drug synergy in cancer

癌症药物协同作用的分子研究

• 批准号: 1917055
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1917055
• 关键词: Molecular; investigation; drug; synergy; cancer

A major challenge for cancer treatment rationales is predicting the actual clinical response to anti-cancer drugs for each individual patient. As well as predicting if a patient will be resistance or sensitive to a particular drug, the ability to predict which combinations of drugs may work synergistically for a particular patient is also critical i.e. the personalised medicine approach. To this end we created an expert system bioinformatic pathway that emulates the decision-making ability of a human expert, utilising drug IC-50 values for 139 drugs across 689 cell lines with gene mutation, gene regulation and copy number data, covering a multitude of cancers. This system has been validated by the "re-discovery" of many known associations between molecular signatures and drug sensitivity (paper submitted, 2016). It was also able to "re-discover" known drug combinations that work in synergy (e.g. BRAF and MEK inhibitors for melanoma); taken together this proves the success of the novel bioinformatics approach we have designed. This project would verify novel predictions of sensitivity and synergy as well as elucidating the mechanisms of synergetic relationships. The objective of this project is to investigate validity and elucidate mechanisms of predicted synergistic drug combinations. Sensitivity predictions and predictions of synergism, between drug combinations of particular interest, will be in validated in existing cancer cell lines, using a panel of cell lines for which genomic and transcriptomic profiling is available for but were not originally used in bioinformatics predictions. Validated synergism will be further investigated and the mechanism behind the synergy elucidated. Our bioinformatic pipeline above identifies gene mutations, expression values or copy numbers that are potentially crucial in predicting the synergy between drugs (e.g. common mutations which lead to sensitivity of these drugs). These predictive markers will be used to identify target molecules for investigations into synergistic mechanisms. The mechanism will then be probed by over expression, silencing, or pharmacological modulation of targets of interest. The mechanism of cell death/senescence and the cells molecular response will also be probed using various appropriate assays, immunoblotting, qPCR, etc.Strong drug combination candidates will be taken forward for investigation in primary cancer cell cultures and if time allows to in vivo models.If successful our project will validate a novel strategy for identifying clinically useful drug combinations as well as validating specific drug combinations with clinical potential.

癌症治疗原理的一个主要挑战是预测每个患者对抗癌药物的实际临床反应。除了预测患者对特定药物是否耐药或敏感外，预测哪些药物组合可能对特定患者协同工作的能力也是至关重要的，即个性化药物方法。为此，我们创建了一个模拟人类专家决策能力的专家系统生物信息路径，利用689个细胞系中139种药物的药物IC-50值，通过基因突变、基因调节和拷贝数数据，覆盖多种癌症。这一系统已经通过“重新发现”许多已知的分子签名和药物敏感性之间的关联而得到验证(2016年提交的论文)。它还能够“重新发现”起协同作用的已知药物组合(例如，治疗黑色素瘤的BRAF和MEK抑制剂)；综合起来，这证明了我们设计的新的生物信息学方法的成功。该项目将验证敏感性和协同效应的新预测，并阐明协同关系的机制。该项目的目的是调查预测的协同药物组合的有效性和阐明其机制。特别感兴趣的药物组合之间的敏感性预测和协同作用的预测将在现有的癌细胞系中得到验证，使用一组基因组和转录图谱可用于但最初不用于生物信息学预测的细胞系。经过验证的协同作用将得到进一步研究，并阐明协同作用背后的机制。我们上面的生物信息学管道确定了对预测药物之间的协同作用具有潜在关键作用的基因突变、表达值或拷贝数(例如，导致这些药物敏感性的常见突变)。这些预测标记将被用于识别目标分子，以研究协同机制。然后将通过过度表达、沉默或对感兴趣的靶点进行药物调节来探讨其机制。细胞死亡/衰老的机制和细胞的分子反应也将通过各种合适的分析方法、免疫印迹、定量聚合酶链式反应等来探索。如果时间允许，我们将在原代癌细胞培养和体内模型中引入更强的候选药物组合进行研究。如果成功，我们的项目将验证一种新的策略，用于识别临床有用的药物组合以及验证具有临床潜力的特定药物组合。