Integrated Cancer Subtyping using Heterogeneous Genome-Scale Molecular Datasets
Integrated Cancer Subtyping using Heterogeneous Genome-Scale Molecular Datasets
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DOI：
10.1142/9789811215636_0049
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发表时间：
2019-11
影响因子：
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通讯作者：
S. Arslanturk;S. Drăghici;Tin Nguyen
中科院分区：
文献类型：
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作者：
S. Arslanturk;S. Drăghici;Tin Nguyen
来自现有来源的大量异类数据存储库提供了独特的机会。单独来看，每个数据集都为重要的领域和特定来源的问题提供了解决方案。总而言之，它们代表了相关数据实体的互补视图，其聚合信息价值往往远远超过其各部分的总和。因此，异类数据的集成对于i)获得更统一的画面和更全面的关系视图，ii)实现更健壮的结果，iii)提高准确性和完整性，以及iv)阐明数据特征之间的复杂交互作用至关重要。在本文中，我们提出了一种数据集成方法，利用来自不同平台的不同数据类型(mRNA、甲基化、microRNA和体细胞变体)和不同的数据尺度(微阵列、测序等)来识别癌症的亚型。癌症基因组图谱(TCGA)数据集用于构建数据集成和癌症亚型框架。提出的数据集成和疾病亚型方法准确地识别了具有显著不同生存特征的新亚组患者。在目前大量基因组学和癌症变异数据的情况下，拟议的数据集成系统将更好地区分癌症和患者亚型，以进行风险和结果预测以及有针对性的治疗计划，而不会增加成本和宝贵的时间损失。
Vast repositories of heterogeneous data from existing sources present unique opportunities. Taken individually, each of the datasets offers solutions to important domain and source-specific questions. Collectively, they represent complementary views of related data entities with an aggregate information value often well exceeding the sum of its parts. Integration of heterogeneous data is therefore paramount to i) obtain a more unified picture and comprehensive view of the relations, ii) achieve more robust results, iii) improve the accuracy and integrity, and iv) illuminate the complex interactions among data features. In this paper, we have proposed a data integration methodology to identify subtypes of cancer using multiple data types (mRNA, methylation, microRNA and somatic variants) and different data scales that come from different platforms (microarray, sequencing, etc.). The Cancer Genome Atlas (TCGA) dataset is used to build the data integration and cancer subtyping framework. The proposed data integration and disease subtyping approach accurately identifies novel subgroups of patients with significantly different survival profiles. With current availability of vast genomics, and variant data for cancer, the proposed data integration system will better differentiate cancer and patient subtypes for risk and outcome prediction and targeted treatment planning without additional cost and precious lost time.