Experimental and computational analyses reveal dynamics of tumor vessel cooption and optimal treatment strategies

Abstract

Cooption of the host vasculature is a strategy that some cancers use to sustain tumor progression without-or before-angiogenesis or in response to antiangiogenic therapy. Facilitated by certain growth factors, cooption can mediate tumor infiltration and confer resistance to antiangiogenic drugs. Unfortunately, this mode of tumor progression is difficult to target because the underlying mechanisms are not fully understood. Here, we analyzed the dynamics of vessel cooption during tumor progression and in response to antiangiogenic treatment in gliomas and brain metastases. We followed tumor evolution during escape from antiangiogenic treatment as cancer cells coopted, and apparently mechanically compressed, host vessels. To gain deeper understanding, we developed a mathematical model, which incorporated compression of coopted vessels, resulting in hypoxia and formation of new vessels by angiogenesis. Even if antiangiogenic therapy can block such secondary angiogenesis, the tumor can sustain itself by coopting existing vessels. Hence, tumor progression can only be stopped by combination therapies that judiciously block both angiogenesis and cooption. Furthermore, the model suggests that sequential blockade is likely to be more beneficial than simultaneous blockade.

Keywords: Ang2; VEGF; antiangiogenic treatment; glioblastoma; hypoxia.

Fig. 1.

Schematic of components comprising the mathematical model and their interactions. The model considers events at both cellular and tissue levels. At the cellular level, cancer cells move toward the more oxygenated regions, coopting existing normal blood vessels. Cancer cell proliferation next to blood vessels can cause vessel compression, resulting in hypoxia, which drives a secondary angiogenic response as VEGF and SDF1α levels increase. This initiates angiogenesis through the proliferation and migration of vessels. Angiogenesis is enhanced by high levels of Ang2, which destabilizes vessels, and inhibited by Ang1 and PDGF-B, which recruit pericytes and stabilize vessels. From the vascular endothelial cell population, a tissue level functional vessel density is calculated that, in turn, determines the macroscopic oxygen transport, cancer cell proliferation, and overall tumor growth.

Fig. 2.

In vivo measurements of vessel cooption. (A) Using intravital microscopy, newly implanted CNS-1 tumor cells (red) were detected growing along the meningeal vessels of the brain vasculature (green). (Scale bar: 50 µm.) (B) In cases where the cells were implanted deeper in the brain parenchyma, after 2 to 3 wk (day 16 and 21, respectively), the tumor was fully established in the brain, as evidenced by the 3D projections. It should be noted that, at this stage of growth, a central tumor mass had formed below the displayed 3D volume. (C) Antiangiogenic treatment increases vessel cooption. Quantification performed in vivo or using immunostaining of harvested tissue showed increased vessel cooption by tumor cells, with less central mass growth and more preferential tumor growth along brain vessels. (D) The extent of tumor spread from the primary mass (white arrow) by cooption is shown in immunofluorescence sections of GL261 tumors following cediranib treatment (red, GL261 tumor cells; green, CD31⁺ vessels; blue, nuclei). (Scale bar: 200 µm.) (E) Typical trajectory of coopting tumor cells. Following a single cell division (solid and dash arrows), the daughter cells subsequently migrated in opposite directions along the vessel. (Scale bar: 50 μm.). The Right-hand panel shows an overlay of the displacements over the 2-h period for the region shown by the solid arrow, with the initial position appearing in red and the final positions in blue. (Scale bar: 20 μm.) (Magnification: B and C, 200×.)

Fig. 3.

Proliferation of cancer cells that coopt blood vessels can compress tumor blood vessels. (A and B) Cooption of tumor blood vessels (red) by metastatic MDA-231BR cells (green) imaged by intravital microscopy. The two fields of view show typical cooption events. (C) The close association of cancer cells with blood vessels is further confirmed by immunofluorescence imaging. (D–F) H&E images of brain tumor sections show the compression of blood vessels due to the accumulation of perivascular cells (arrows), caused by cancer cell migration and proliferation along the vessel (blood vessel dimensions: compressed 4 to 6 µm and uncompressed 22 to 45 µm). The bright red objects are red blood cells trapped within the collapsed blood vessels and the dashed oval shows the vessel periphery. (Scale bars: A and C, 100 μm; scale identical in B.) (Magnification: D–F, 400×.)

Fig. 4.

Comparison between experimental data and the mathematical model of dynamics of vessel cooption. (A) Model simulations were compared with experimental data (1) for the temporal evolution of vessel cooption around a single vessel. Red represents glioblastoma cells (DsRed-expressing CNS-1 cells), and green represents perfused blood vessels (FITC-dextran injected intravenously). In our model, we adjusted the migration/diffusion coefficient of cancer cells to match the experimental observations of cooption (Color scale: low, 0; high, 18, dimensionless units). Reprinted from ref. . Copyright (2014), with permission from Elsevier. (Magnification: 200×.) (B) The decrease in vessel diameter as a function of the number of cancer cells attached to the vessel wall agrees well with published experimental data for D54 glioma cells found in ref. . Reprinted from ref. . Copyright (2018), with permission from Elsevier. (C) Model simulations for the evolution of cancer cell cooption around a blood vessel and the decrease in coopted vessel diameter were in agreement with experimental data in Fig. 2D (Color scale: low, 0; high = 18, dimensionless units). (D) Blood vessel cooption imaged using GFP tagged MGG8 cells and perfused blood vessels labeled with TAMRA-dextran. Images were created by high-resolution 3D reconstructions of intravital microscopy images of tumors from vehicle- and LGK974-treated MGG8-bearing mice (9). (Scale bar: 25 µm.) Reprinted by permission from ref. , Springer Nature: Nature Communications, copyright (2014). (E) Comparison of experimental data (squares, mean ± SEM) and model simulations (dash lines) for the number of MGG8 cancer cells in contact with the vessel wall. The y axis represents the ratio of the number of cancer cells during treatment divided by the number of cancer cells at day 0 for the experimental values and with the corresponding concentration of cancer cells in the model.

Fig. 5.

Model validation for VEGF, Ang2, and vascular density based on data taken from Holash et al. (6) using a rat glioma model. (A, Left) In situ hybridization analysis of Ang2 and VEGF mRNA in 2-wk-old rat gliomas and a 4-wk-old rat glioma. (Right) Model simulations of Ang2 and VEGF during tumor growth showing the concentrations of the two proteins at the tumor periphery at 4 wk of tumor growth. (B, Top) Sections from rat C6 gliomas showing vessel growth. At 2 wk, tumors continue to have extensive internal vasculature although the vessel density is less than that in the surrounding brain tissue. At 4 wk, the vessels inside the tumor regress, but robust angiogenesis is apparent at the tumor periphery. (Scale bar: 1 mm.) (Bottom) Model simulations agree with the experimental observations. The oval in C shows the region of robust angiogenesis at the tumor margin. From ref. . Reprinted with permission from AAAS.

Fig. 6.

Phase diagrams of tumor volume, cancer cell concentration, and vascular density for (A) combined anti-VEGF and anticooption treatment. (B) Table summarizing the main effects of individual VEGF and cooption blockade from the results in A. (C) Schematic summarizing the optimal treatment condition as a function of cancer cell proliferation and migration rates based on the model results of SI Appendix, Fig. S5.