Abstract
BackgroundIdentifying 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.ResultsIn 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.ConclusionsThe 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.
Highlights
Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research
Overview of learning contextual gene set interaction networks and identifying condition specificity Learning contextual gene set interaction networks and identifying condition specificity involves several steps of data transformation, as illustrated in Figure 1, and is described further in the Methods section. It consists of four major steps: (STEP I) identifying contextual gene sets as basic functional modules, (STEP II) summarizing contextual gene sets to transform the data of genes to the data of contextual gene sets, (STEP III) learning contextual gene set interaction networks where each interaction represents dependency in expression between two contextual gene sets, and (STEP IV) identifying condition specificity of each interaction
Identifying contextual gene sets first requires the identification of samples where biological contexts are involved, and we use the context-mining algorithm [10,12] to find such contextual conditions, where a contextual condition is a subset of samples where groups of closely related coherent expression patterns are found
Summary
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. Many computational and mathematical techniques have been developed to infer molecular patterns of biological and translational interest from gene expression data profiled from human tumors. As most of these methodologies are highly dependent on simple correlation of changes in mRNA abundance as the primary measure of relatedness, they are intrinsically limited in their sensitivity and specificity by the highly heterogeneous, idiosyncratic nature of tumor gene expression patterns. As the biochemical mechanisms of tumor growth and survival have been subjected to ever more detailed analysis, it has become clear that for most tumor types there is substantial variation in how tumors use available normal and altered cellular functions to achieve relentless growth and disproportionate survival
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