I-Corps: Combination targeted drug design for personalized cancer therapy

据估计，去年美国有160万人患上了某种形式的癌症，预计这一数字在未来几年还会增加。对于某些癌症（如乳腺癌或前列腺癌）的标准治疗方法提供了很高的存活率，而对于其他类型的癌症，如胰腺癌或脑癌，存活率要低得多。当前方法的一个重要问题是，癌症患者的治疗是基于他们的队列，而不是基于他们的个性化基因组成。这往往导致治疗无效，对所有风险水平的患者预后不良。所提出的技术是一种优化的算法和软件，用于发现潜在的多靶点蛋白质组合，从而产生高效的联合药物治疗。所提出的方法在整合药物筛选和基因组特征数据以预测有效药物组合方面是新颖的。提出的过程可以产生真正个性化的治疗方案与一小组输入数据。具体来说，该团队正在研究能够产生高效联合药物治疗的多靶点蛋白质组合，目前的重点是蛋白质激酶。蛋白激酶已被证明对多种类型的癌症有效，并已被用于治疗CML（一种白血病）。随着更多的数据通过可执行的并行测试（如外显子组测序、RNA测序和siRNA敲低实验）获得，该框架允许团队改进和集中预测。大多数现有的个性化方法主要集中于根据其他类似模型的情况，应用药物来靶向特定的突变。该团队的方法旨在通过专门寻找多靶点组合和多药物治疗来改进这一点，以克服将靶向治疗扩展到新癌症类型所面临的许多障碍。

An estimated 1.6 million people in the U.S. developed some form of cancer last year and this number is predicted to increase in coming years. Standard treatment approaches for some forms of cancer (such as breast or prostrate) provide high chances of survival, whereas for other types of cancer such as pancreas or brain cancer, survivals rates are significantly lower. One significant issue with the current approaches is that a cancer patient is treated based on their cohort and not based on their personalized genetic makeup. This often leads to ineffective treatments and poor outcomes for patients at all levels of risk. The proposed technology is an optimized algorithm and software to discover potential multi-target protein combinations that can produce highly effective combination drug treatments. The proposed approach is novel in integrating drug screen and genomic characterization data for predicting effective drug combinations.The proposed process can produce truly personalized therapeutic options with a small set of input data. Specifically, the team is looking at multi-target protein combinations that can produce highly effective combination drug treatments, with a current focus on protein kinases. Protein kinases been shown to be effective in multiple types of cancer, and have led to a treatment for CML, a type of leukemia. The framework allows the team to refine and focus predictions as more data becomes available through parallel tests that can be performed, such as exome sequencing, RNA sequencing and siRNA knockdown experiments. Most existing personalized approaches are primarily focused on applying drugs to target specific mutations based on what happens in other similar models. This team's approach intends to improve on this by specifically looking for multi-target combinations and multi-drug therapeutics to overcome many of the obstacles faced in the expansion of targeted therapies to new cancer types.