Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction.
Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction.
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用于癌症预后预测的基因表达和体细胞改变数据的泛癌评估。
DOI：
10.1186/s12885-021-08796-3
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发表时间：
2021-09-25
期刊：
影响因子：
3.8
通讯作者：
Frost HR
中科院分区：
文献类型：
--
作者：
Zheng X;Amos CI;Frost HR
在过去的几十年里，诊断和治疗癌症的方法有了显著的改进。然而，患者和肿瘤特征的多样性限制了预后预测方法的进展。高通量组学技术的发展现在为肿瘤的特征提供了多种方法。尽管大量已发表的研究集中于整合多组学数据和使用通路水平模型来预测癌症预后，但对于使用基因水平和通路水平预测因子的多种癌症类型的多组学数据的预后图景，仍存在知识差距。在这项研究中，我们系统地评估了三种常用的组学数据类型(基因表达、拷贝数变异和体细胞点突变)，涵盖了DNA水平和RNA水平的特征。我们使用套索或套索惩罚的COX模型以及基因或通路水平的预测因子，在TCGA中评估了这三种组学方法对33种癌症类型的预测效果。我们使用三种类型的组学数据在基因和途径水平上构建了33种癌症的预后图景。基于这种情况，我们发现预测性能取决于癌症类型，我们还强调了支持最准确预后模型的癌症类型和组学模式。一般来说，基于基因表达数据估计的模型在基因或通路水平上提供了最好的预测性能，并且将拷贝数变化或体细胞点突变数据添加到基因表达数据中并不能改善预测性能，一些特殊的队列包括低级别胶质瘤和甲状腺癌。总的来说，与基因水平模型相比，通路水平模型在多种癌症类型和组学数据类型中具有更好的解释性能、更高的稳定性和更小的模型规模。基于这一前景和综合比较，基于基因表达数据估计的模型在基因或途径水平上提供了最好的预测性能。与基因水平模型相比，路径水平模型具有更好的解释性能、更高的稳定性和更小的模型规模。网上版载有补充材料，可在10.1186/s12885-021-08796-3查阅。
Over the past decades, approaches for diagnosing and treating cancer have seen significant improvement. However, the variability of patient and tumor characteristics has limited progress on methods for prognosis prediction. The development of high-throughput omics technologies now provides multiple approaches for characterizing tumors. Although a large number of published studies have focused on integration of multi-omics data and use of pathway-level models for cancer prognosis prediction, there still exists a gap of knowledge regarding the prognostic landscape across multi-omics data for multiple cancer types using both gene-level and pathway-level predictors. In this study, we systematically evaluated three often available types of omics data (gene expression, copy number variation and somatic point mutation) covering both DNA-level and RNA-level features. We evaluated the landscape of predictive performance of these three omics modalities for 33 cancer types in the TCGA using a Lasso or Group Lasso-penalized Cox model and either gene or pathway level predictors. We constructed the prognostic landscape using three types of omics data for 33 cancer types on both the gene and pathway levels. Based on this landscape, we found that predictive performance is cancer type dependent and we also highlighted the cancer types and omics modalities that support the most accurate prognostic models. In general, models estimated on gene expression data provide the best predictive performance on either gene or pathway level and adding copy number variation or somatic point mutation data to gene expression data does not improve predictive performance, with some exceptional cohorts including low grade glioma and thyroid cancer. In general, pathway-level models have better interpretative performance, higher stability and smaller model size across multiple cancer types and omics data types relative to gene-level models. Based on this landscape and comprehensively comparison, models estimated on gene expression data provide the best predictive performance on either gene or pathway level. Pathway-level models have better interpretative performance, higher stability and smaller model size relative to gene-level models. The online version contains supplementary material available at 10.1186/s12885-021-08796-3.
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影响因子：
64.8
作者：
通讯作者：
--
影响因子：
5.2
作者：
Krajewska-Kulak E;Sengupta P
通讯作者：
Sengupta P
影响因子：
2.2
作者：
Breheny P;Huang J
通讯作者：
Huang J
影响因子：
3.7
作者：
Bennett, Brian D.;Xiong, Qing;Furey, Terrence S.
通讯作者：
Furey, Terrence S.
DOI：
10.1016/s0360-3016(96)00619-0
发表时间：
1997-03-01
影响因子：
7
作者：
Gaspar, L;Scott, C;Byhardt, R
通讯作者：
Byhardt, R