RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity.
RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity.
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Radsigbench：基准测试癌细胞放射敏性功能基因组学特征的框架。
DOI：
10.1093/bib/bbab561
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发表时间：
2022-03-10
影响因子：
9.5
通讯作者：
McMahon SJ
中科院分区：
文献类型：
--
作者：
O'Connor JD;Overton IM;McMahon SJ
已经提出了多种肿瘤细胞放射敏感性(RS)的转录预测指标，但它们还没有相互对照或对照模型。为了解决这一问题，我们提出了一个全面的RS签名基准测试框架RadSigBch。该方法将候选模型与根据随机重采样的控制信号和辐射响应积分的细胞过程开发的模型进行比较。对总体和单个组织的签名准确性进行稳健的评估。NCI60和癌症细胞系百科全书数据集被集成到我们的工作流程中。对RS的两个指标的预测进行了评估：2Gy后的存活率和平均灭活剂量。我们将RadSigBtch框架应用于七个重要的已发表的辐射敏感性签名，并测试其与控制签名的等价性。已发表模型在测试数据上的平均样本外R2非常差，在癌细胞系百科全书中为0.01(范围：NCI0.05至0.09)，在−60数据中为0.00(范围：−0.19至0.19)。所研究的已发表的和细胞过程信号的准确性与重新采样的对照相同，这表明这些信号包含有限的辐射特定信息。为了有效地预测固有RS以指导临床治疗方案，需要改进的建模策略。我们提出了改进方法的建议，例如纳入扰动数据、多组学、高级机器学习和机械建模。我们的验证框架为内在RS预测的持续发展提供了稳健的性能评估。
Multiple transcriptomic predictors of tumour cell radiosensitivity (RS) have been proposed, but they have not been benchmarked against one another or to control models. To address this, we present RadSigBench, a comprehensive benchmarking framework for RS signatures. The approach compares candidate models to those developed from randomly resampled control signatures and from cellular processes integral to the radiation response. Robust evaluation of signature accuracy, both overall and for individual tissues, is performed. The NCI60 and Cancer Cell Line Encyclopaedia datasets are integrated into our workflow. Prediction of two measures of RS is assessed: survival fraction after 2 Gy and mean inactivation dose. We apply the RadSigBench framework to seven prominent published signatures of radiation sensitivity and test for equivalence to control signatures. The mean out-of-sample R2 for the published models on test data was very poor at 0.01 (range: −0.05 to 0.09) for Cancer Cell Line Encyclopedia and 0.00 (range: −0.19 to 0.19) in the NCI60 data. The accuracy of both published and cellular process signatures investigated was equivalent to the resampled controls, suggesting that these signatures contain limited radiation-specific information. Enhanced modelling strategies are needed for effective prediction of intrinsic RS to inform clinical treatment regimes. We make recommendations for methodological improvements, for example the inclusion of perturbation data, multiomics, advanced machine learning and mechanistic modelling. Our validation framework provides for robust performance assessment of ongoing developments in intrinsic RS prediction.
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影响因子：
48
作者：
Huber W;Carey VJ;Gentleman R;Anders S;Carlson M;Carvalho BS;Bravo HC;Davis S;Gatto L;Girke T;Gottardo R;Hahne F;Hansen KD;Irizarry RA;Lawrence M;Love MI;MacDonald J;Obenchain V;Oleś AK;Pagès H;Reyes A;Shannon P;Smyth GK;Tenenbaum D;Waldron L;Morgan M
通讯作者：
Morgan M
影响因子：
3.7
作者：
Hall JS;Iype R;Senra J;Taylor J;Armenoult L;Oguejiofor K;Li Y;Stratford I;Stern PL;O'Connor MJ;Miller CJ;West CM
通讯作者：
West CM
影响因子：
5.7
作者：
Jang, Bum-Sup;Kim, In Ah
通讯作者：
Kim, In Ah
影响因子：
11.5
作者：
Eschrich, Steven A.;Fulp, William J.;Torres-Roca, Javier F.
通讯作者：
Torres-Roca, Javier F.
DOI：
10.1177/2515245918770963
发表时间：
2018-06-01
影响因子：
13.6
作者：
Lakens, Daniel;Scheel, Anne M.;Isager, Peder M.
通讯作者：
Isager, Peder M.