| Summary: | Disease states are often associated with radical rearrangements of cellular metabolism; suggesting the transcriptome underlying these changes follows a distinctive pattern. Identification of these patterns is complicated by the hugely heterogeneous nature of these diseases, such as cancer, and the patterns remain hidden within noise of large datasets. A new biclustering algorithm called Massively Correlating Biclustering (MCbiclust) was developed to identify these patterns. Taking a large gene set such as those known to be associated with the mitochondria, samples are selected in which these genes are highly correlated. Rigorous benchmarking of this method with other biclustering methods on synthetic gene expression data and an E. coli data set show it to be superior in finding these patterns. This method was used to identify the role mitochondrial biogenesis plays in cancer; applied on the Cancer Cell Line Encyclopedia (CCLE) it identified differences in mitochondrial function based on the different tissue of origin of the cell line. In patient breast tumour samples a change in mitochondrial function was identified and linked to differences in known breast cancer subtypes. Breast cancer cell lines were identified that matched this pattern. Experimentally testing these cell lines confirming the significant difference in gene expression expected and also showed significant changes in mitochondrial function demonstrated by measurements in oxygen consumption, proteomics and metabolomics. MCbiclust has been developed into an R package. Using this method, new cancer subtypes can be identified, based on fundamental changes to known pathways. The benefit is twofold: first to increase understanding of these complex systems and second to guide treatment using drug compounds known to target these pathways. The methods described here while applied to cancer and mitochondria, are versatile and can be applied to any large dataset of gene expression measurements.
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