Tumors of different molecular subtypes can show strongly deviating responses to drug treatment, making stratification of patients based on molecular markers an important part of cancer therapy. Pharmacogenomic studies have led to the discovery of selected genomic markers (e.g., BRAF^V600E), whereas transcriptomic and proteomic markers so far have been largely absent in clinical use, thus constituting a potentially valuable resource for further substratification of patients. To systematically assess the explanatory power of different -omics data types, we assembled a panel of 49 melanoma cell lines, including genomic, transcriptomic, proteomic, and pharmacological data, showing that drug sensitivity models trained on transcriptomic or proteomic data outperform genomic-based models for most drugs. These results were confirmed in eight additional tumor types using published datasets. Furthermore, we show that drug sensitivity models can be transferred between tumor types, although after correcting for training sample size, transferred models perform worse than within-tumor-type predictions. Our results suggest that transcriptomic/proteomic signals may be alternative biomarker candidates for the stratification of patients without known genomic markers.

中文翻译：

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癌细胞蛋白质组和转录组可预测对靶向药物和细胞毒性药物的敏感性。

不同分子亚型的肿瘤对药物治疗的反应可能明显不同，因此基于分子标志物的患者分层成为癌症治疗的重要组成部分。药物基因组学研究导致发现选定的基因组标记（例如，BRAF ^V600E），而到目前为止，在临床应用中基本上没有转录组和蛋白质组学标记，因此构成了潜在的有价值的资源，可进一步将患者细分。为了系统地评估不同组学数据类型的解释力，我们组装了一个由49个黑色素瘤细胞系组成的小组，包括基因组，转录组学，蛋白质组学和药理学数据，表明在转录组学或蛋白质组学数据上训练的药物敏感性模型优于基于基因组的模型对于大多数药物。使用已公开的数据集，在另外八种肿瘤类型中证实了这些结果。此外，我们显示药物敏感性模型可以在肿瘤类型之间转移，尽管在校正训练样本量后，转移模型的表现比肿瘤内类型的预测差。