Abstract
BackgroundBreast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets.ResultsOur results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods.ConclusionThe integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers there exists solid evidence for involvement in the development of breast carcinomas. Our approach also unraveled the complex regulatory interactions comprising the identified key drivers. These genomic drivers could be further investigated in the wet lab as potential candidates for new drug targets. This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes.
Highlights
Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate
This study presents an integrative network-based approach based on whole-genome gene expression profiling, DNA methylome, miRNA expression, and genomic mutations of breast cancer samples from the The Cancer Genome Atlas (TCGA) portal [30]
Differential analysis We developed and applied an integrative network-based approach to conduct combinatorial regulatory network analysis in the context of breast invasive carcinoma with the aim of identifying the major genetic drivers that lead to tumorigenesis (Figure 1)
Summary
Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets. Treatment and prognosis of cancer development relies largely on a correct classification of the histological grade and identification of the major determinants driving the tumorigenesis process. Cava et al presented an effective discrimination of breast cancer types based on a support vector machine classifier combining copy number variations, SNP data, and the expression values of miRNAs, and mRNAs [5]. The combination of microarray studies with mathematical models such as network theory allows to define relationships between genes and to discover interacting networks and pathways, improving the understanding of complex diseases [24]
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