Learning contextual gene set interaction networks of cancer with condition specificity
1 Integrated Cancer Genomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
2 School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, USA
3 Pharmaceutical Genomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
4 Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
5 Cancer and Cell Biology Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
6 Computational Biology Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
BMC Genomics 2013, 14:110 doi:10.1186/1471-2164-14-110Published: 19 February 2013
Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients.
In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations.
The analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes.