Abstract
Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients.
Highlights
Cancer is a heterogeneous disease that manifests in a wide variety of geno- and phenotypes
In order to include the majority of cancer panels in TCGA into the analysis, we tried to identify a subset of genes that showed similar global expression across NCI60 and TCGA
For the NCI60 data set this mean log expression was calculated across all cell lines for which proliferation rates were available (57 of 60), whereas the mean log expression for the TCGA data set was obtained by averaging over all samples
Summary
Cancer is a heterogeneous disease that manifests in a wide variety of geno- and phenotypes. Many of the reconstruction methods use discretized enzyme expression values in order to exclude biochemical reactions with a lacking enzyme (Wang et al, 2012; Pornputtapong et al, 2015; Schultz and Qutub, 2016) This strategy has shown to be a promising approach in constraining the feasible metabolic space in cells or tissues and predicting the metabolic capacities of several cancers (Agren et al, 2014). Our study provides a computational strategy that is able to predict the proliferation rate of cancer biopsies from cell line gene expression data alone and this allows detailed surveys of the potential metabolic activity underlying each case. Our methodology can contribute to the identification of the common and specific metabolic alterations associated with cancers across different tissues, which is of importance during the development of personalized treatments for cancer
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