Widespread redundancy in -omics profiles of cancer mutation states.
Widespread redundancy in -omics profiles of cancer mutation states.
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DOI：
10.1186/s13059-022-02705-y
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发表时间：
2022-06-27
期刊：
影响因子：
12.3
通讯作者：
中科院分区：
文献类型：
--
作者：
在癌症细胞功能的研究中，研究人员越来越能够从多种组学分析中进行选择作为功能读数。为给定的研究选择正确的读数可能很困难，并且哪一层细胞功能最适合捕获相关信号仍不清楚。我们认为从功能组学数据预测癌症突变状态（存在或不存在）是一个代表性问题，它提供了一个机会来量化和比较不同组学读数捕获癌症失调信号的能力。从包含遗传改变数据的 TCGA 泛癌症图谱中，我们重点关注 RNA 测序、DNA 甲基化阵列、反相蛋白阵列 (RPPA)、microRNA 和体细胞突变特征作为组学读数。在癌症中反复突变的一系列基因中，RNA 测序往往是突变状态最有效的预测器。我们发现许多基因的一种或多种其他数据类型几乎是同样有效的预测因子。对于同一突变，突变之间的性能比数据类型之间的性能差异更大，并且顶级数据类型之间几乎没有差异。我们还发现，将数据类型组合到单个多组学模型中，与最佳单个数据类型相比，预测能力几乎没有提高或没有提高。根据我们的结果，对于专注于癌症突变功能结果的研究设计，通常有多种组学类型可以作为有效的读数，尽管基因表达似乎是合理的默认选项。在线版本包含可在 10.1186/s13059-022-02705-y 获取的补充材料。
In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear. We consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type. Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option. The online version contains supplementary material available at 10.1186/s13059-022-02705-y.
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影响因子：
64.8
作者：
Ghandi, Mahmoud;Huang, Franklin W.;Sellers, William R.
通讯作者：
Sellers, William R.
将KMT2C/MLL3募集到DNA损伤部位介导DNA损伤反应并调节癌症中的PARP抑制剂敏感性。
影响因子：
11.2
作者：
Chang A;Liu L;Ashby JM;Wu D;Chen Y;O'Neill SS;Huang S;Wang J;Wang G;Cheng D;Tan X;Petty WJ;Pasche BC;Xiang R;Zhang W;Sun P
通讯作者：
Sun P
影响因子：
12.3
作者：
Heim D;Montavon G;Hufnagl P;Müller KR;Klauschen F
通讯作者：
Klauschen F
DOI：
10.1016/j.tig.2014.07.005
发表时间：
2014-10
期刊：
Trends in genetics : TIG
影响因子：
--
作者：
Huang Y;Rao A
通讯作者：
Rao A
影响因子：
2.7
作者：
Coudray, Alexandre;Battenhouse, Anna M.;Iyer, Vishwanath R.
通讯作者：
Iyer, Vishwanath R.