Integrating mutation and gene expression cross-sectional data to infer cancer progression

BMC Systems Biology
Julia L FleckChristos G Cassandras


A major problem in identifying the best therapeutic targets for cancer is the molecular heterogeneity of the disease. Cancer is often caused by an accumulation of mutations which produce irreversible damage to the cell's control mechanisms of survival and proliferation. Different mutations may affect these cellular anachronisms through a combination of molecular interactions which may be dynamically changing during cancer progression. It has been previously shown that cancer accumulates mutations over time. In this paper we address the problem of cancer heterogeneity by modeling cancer progression using somatic mutation and gene expression cross-sectional data. We propose a novel formulation of integrating somatic mutation and gene expression data to infer the temporal sequence of events from cross-sectional data. Using a mixed integer linear program we model the interaction between groups of different mutated genes and the resulting modifications at the gene expression level. Our approach identifies a partition of mutation events which gradually produce gene expression changes to a partition of genes over time. The proposed formulation is tested using both simulated data and real breast cancer data with matched somatic mutatio...Continue Reading


Oct 19, 2017·Tumour Biology : the Journal of the International Society for Oncodevelopmental Biology and Medicine·Vishwas SharmaRavi Mehrotra
Oct 2, 2019·Molecular Omics·Julia L FleckChristos G Cassandras


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