Name
Abstract | ||
|---|---|---|
Metabolic reprogramming is a hallmark of cancer. The main aim of this work is to integrate a genome-scale metabolic description of tumor cells into a tumor growth model that accounts for the spatiotemporally heterogeneous tumor microenvironment, in order to study the effects of microscopic characteristics on tumor evolution. A lactate maximization metabolic strategy that allows near optimal growth solution, while maximizing lactate secretion, is assumed. The proposed sub-cellular metabolic model is then incorporated into a hybrid discrete-continuous model of tumor growth. We produced several phenotypes by applying different constraints and optimization criteria in the metabolic model and explored the tumor evolution of the various phenotypes in different vasculature conditions and ECM densities. At first, we showed that the metabolic capabilities of phenotypes depending on resource availability can vary in a counter-intuitive manner. We then showed that i) tumor population, morphology and spread are affected differently in different conditions, allowing thus phenotypes to be superior than others in different conditions and that ii) polyclonal tumors consisting of different phenotypes can exploit their different metabolic capabilities to enhance further tumor evolution. The proposed framework comprises a proof-of-concept demonstration showing the importance of considering the metabolic capabilities of phenotypes on predicting tumor evolution. The proposed framework allows the incorporation of context-specific and patient-specific data for the study of personalized tumor evolution and therapy efficacy, linking genome to metabolic capabilities and tumor dynamics. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JBHI.2018.2890708 | IEEE journal of biomedical and health informatics |
Keywords | Field | DocType |
Tumors,Biochemistry,Cancer,Evolution (biology),Genomics,Bioinformatics,Mathematical model | Tumor microenvironment,Population,Growth model,Reprogramming,Pattern recognition,Phenotype,Computer science,Genomics,Artificial intelligence,Computational biology,Extracellular matrix,Glycolysis | Journal |
Volume | Issue | ISSN |
23 | 5 | 2168-2208 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eleftheria Tzamali | 1 | 12 | 2.65 |
| georgios tzedakis | 2 | 8 | 1.64 |
| Vangelis Sakkalis | 3 | 110 | 23.68 |