Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.
Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.
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作者：
Joisa CU;Chen KA;Berginski ME;Golitz BT;Jenner MR;Herrera Loeza G;Yeh JJ;Gomez SM
蛋白激酶活性形成细胞信息传递的骨干，既单独起作用，又作为更广泛网络（激酶组）的一部分起作用。它们在信号传导中的中心作用导致激酶组功能障碍成为疾病，特别是癌症的常见驱动因素，其中许多激酶已被鉴定为在肿瘤发展和进展中具有因果或调节作用。因此，靶向激酶的治疗方法的发展迅速增长，超过70种激酶抑制剂被批准用于临床，目前在临床试验中的数量是这个数字的两倍多。因此，了解激酶抑制剂治疗及其对下游细胞表型的影响之间的关系对于了解治疗机制和简化治疗开发中的化合物筛选具有明显的重要性。在这项工作中，我们将联合收割机两个大规模的激酶组分析数据集，并使用它们来连接激酶-激酶组相互作用与细胞系治疗反应（AUC/IC 50）。然后，我们在这个数据集上建立了计算模型，实现了高度的预测准确性（R2为0.7，RMSE为0.9），并能够识别出一组充分表征和研究不足的激酶，这些激酶显著影响细胞反应。我们通过在乳腺癌细胞系中测试预测的效应，进一步验证了这些模型，并通过在患者来源的胰腺癌细胞系中进行额外验证，扩展了模型范围。总之，这些结果表明，激酶组抑制状态的广泛定量高度预测下游细胞表型。
Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.
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