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An investigation of cancer cell line-based drug response prediction methods on patient data

机译：患者数据癌细胞系的药物反应预测方法研究

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The most significant goal of precision medicine is to identify the right treatment for individual patients based on their molecular profiles. Several big projects have been provided with a large amount of -omics and drug response data for human cell lines such as GDSC and CCLE and for patients such as GEO. Based on these useful datasets, many computational methods are increasingly being applied to predict not only untested drug responses on cell lines but also those on the patients. Such approaches built prediction models for drug response on cell line data then applied the learned models to predict drug response on the patient. In this way, it also helps to tackle the disparity between models trained on cell lines and their clinical applications. However, the datasets are highly heterogeneous in terms of the used array techniques, drug response measurements, and so on, thus leading to inconsistent results across computational methods on different datasets. Therefore, in this study, we assessed seven machine learning models built on the cell line datasets and then applied them to the patient datasets. Experimental results show that models built on pan-cancer cell lines cannot work well on every cancer-specific patient dataset Also, patient datasets with larger sizes were suggested to measure the prediction performance of each method correctly.
机译：精密药物最重要的目标是鉴定基于其分子型材的个体患者的正确治疗方法。已经为人类细胞系（如GDSC和CCL）提供了大量的大项目，以及GDSC和CCLE等患者，以及GEO等患者。基于这些有用的数据集，许多计算方法越来越多地应用于预测细胞系上未经测试的药物反应，而且还应用于患者的药物反应。这种方法在细胞系数据上构建了用于药物反应的预测模型，然后应用学习模型以预测对患者的药物反应。通过这种方式，它还有助于解决在细胞系和临床应用上培训的模型之间的视差。然而，在使用的阵列技术，药物响应测量等方面，数据集是高度异构的，因此导致不同数据集上的计算方法的结果不一致。因此，在本研究中，我们评估了在单元线数据集上构建的七种机器学习模型，然后将其应用于患者数据集。实验结果表明，在泛癌细胞系上构建的模型在每个癌症特定的患者数据集上都不能很好地工作，也建议具有较大尺寸的患者数据集来正确测量每种方法的预测性能。

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