Mechanistic Metabolic Model of Cancer Using DNA Dosages

Abstract

Background: Among other peculiarities, cancerous tissue is marked by its abnormal energy metabolism. The genetic basis for these metabolic changes is not well understood. We used metabolic modelling to characterize genetic differences between tissues sampled from cancer patients and healthy subjects. Methods: We developed DNA Dosages, a mathematical model and statistical exome data analysis algorithm based on linear programming and a constraint-based model. The algorithm can highlight genetic characteristics of cancer beyond single nucleotide polymorphisms, capturing copy number variations. We applied the algorithm to exome data from studies of three types of malignancy (familial breast cancer, esophageal squamous cell carcinoma, and urothelial bladder cancer). Findings: The DNA Dosages technique revealed that tumour tissue, but also non-tumour tissue in cancer patients shows significant metabolic differences from tissue samples of healthy subjects. Interpretation: Exome analysis with the DNA Dosages approach promises to yield cancer biomarkers as well as to support cancer diagnostics from blood and other non-tumour biopsy samples. Funding Statement: This project was funded by the authors as part of their personal interest in the advancement of science. Declaration of Interests: The authors declare that we have no competing interests. Ethics Approval Statement: Data used in this study are taken from open domain. Ethical committee approvals were taken by the original authors for their respective works.