Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.

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 metabo

SUBMITTER: Diener C 

PROVIDER: S-EPMC5186797 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature