Back

Modeling VEGF and GLUT1 Expression as Coadapted Foraging Strategies in Cancer

Bhattacharya, R.; Gatenby, R. A.; Brown, J. S.

2026-04-17 cancer biology
10.64898/2026.04.14.718570 bioRxiv
Show abstract

Natural selection acting on cancer cells within their tumor microenvironment should favor cells with fast or efficient nutrient uptake strategies. Here, we develop and analyze a game-theoretic model focusing on the coadaptation between two foraging traits: vascular endothelial growth factor (VEGF) and glucose transporter 1 (GLUT1). Studies show that VEGF and GLUT1 are often co-expressed and are associated with more aggressive tumor phenotypes and poor clinical prognosis. VEGF is a diffusible paracrine factor that recruits blood vessels towards neighborhoods of cancer cells (angiogenesis). GLUT1 is a cell-surface transporter that enables the uptake of nutrients, especially glucose. We model these strategies operating at different scales: VEGF influences resource availability at the neighborhood level, while GLUT1 determines resource uptake at the cellular level. For VEGF, we introduce a resource-sharing continuum. With no resource sharing, cells access resources in proportion to their VEGF contribution. With uniform sharing, cells have equal access to resources, regardless of their VEGF contribution. The former leads to a tragedy of the commons and overproduction of VEGF. The latter yields a public goods game with moderate VEGF expression matching a group optimum. GLUT1 expression mediates uptake of resources recruited by VEGF and is largely independent of the degree of resource sharing. Therapeutically, both VEGF and GLUT1 inhibitors are more effective in high resource-sharing neighborhoods and less so as resource sharing declines. Overall, inhibition of GLUT1-mediated uptake emerges as more effective. The model, perhaps the first to consider VEGF and GLUT1 as coadaptations, emphasizes the need to consider cancer cell traits jointly.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
PLOS Computational Biology
1863 papers in training set
Top 1%
19.1%
2
Journal of Theoretical Biology
162 papers in training set
Top 0.2%
10.1%
3
Nature Communications
5641 papers in training set
Top 20%
8.1%
4
Bulletin of Mathematical Biology
92 papers in training set
Top 0.2%
6.5%
5
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 8%
5.6%
6
Scientific Reports
3612 papers in training set
Top 22%
4.5%
50% of probability mass above
7
Nature Ecology & Evolution
18 papers in training set
Top 0.1%
4.2%
8
eLife
5828 papers in training set
Top 33%
3.3%
9
Evolutionary Applications
108 papers in training set
Top 0.5%
3.3%
10
Science Advances
1243 papers in training set
Top 16%
2.2%
11
PLOS ONE
5266 papers in training set
Top 44%
2.2%
12
Journal of The Royal Society Interface
235 papers in training set
Top 2%
2.2%
13
Cancer Research
130 papers in training set
Top 2%
2.2%
14
iScience
1154 papers in training set
Top 19%
1.5%
15
npj Systems Biology and Applications
125 papers in training set
Top 1%
1.4%
16
Mathematical Biosciences
49 papers in training set
Top 0.8%
1.4%
17
Proceedings of the Royal Society B: Biological Sciences
393 papers in training set
Top 4%
1.4%
18
PNAS Nexus
159 papers in training set
Top 1%
1.4%
19
Royal Society Open Science
214 papers in training set
Top 4%
1.4%
20
Physical Review Research
49 papers in training set
Top 0.7%
1.0%
21
Mathematical Medicine and Biology: A Journal of the IMA
10 papers in training set
Top 0.3%
0.9%
22
Evolution, Medicine, and Public Health
14 papers in training set
Top 0.2%
0.9%
23
Physical Review E
112 papers in training set
Top 1%
0.9%
24
Cell Reports
1498 papers in training set
Top 28%
0.6%
25
Journal for ImmunoTherapy of Cancer
75 papers in training set
Top 2%
0.6%