A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma

Abstract

Glioblastoma (GBM) is the most aggressive primary brain tumor with a short median survival. Tumor recurrence is a clinical expectation of this disease and usually occurs along the resection cavity wall. However, previous clinical observations have suggested that in cases of ischemia following surgery, tumors are more likely to recur distally. Through the use of a previously established mechanistic model of GBM, the Proliferation Invasion Hypoxia Necrosis Angiogenesis (PIHNA) model, we explore the phenotypic drivers of this observed behavior. We have extended the PIHNA model to include a new nutrient-based vascular efficiency term that encodes the ability of local vasculature to provide nutrients to the simulated tumor. The extended model suggests sensitivity to a hypoxic microenvironment and the inherent migration and proliferation rates of the tumor cells are key factors that drive distal recurrence.

Recurrence Results of Other PIHNA Simulations

We present the results of PIHNA simulations that were not shown in the main text. The trends in distant recurrence patterns that we observe in the main text all hold in these simulations, supporting our observations regarding $D_h/D_c$, $\beta$, $\gamma$, $D_c$ and $\rho$ (Figs. 9, 10, 11, 12, 13, 14).

Fig. 9

Recurrence location classified for various $D_c$, $\rho$, $\beta$ and levels of ischemia for $D_h = D_c$ for $\gamma = 0.005$/day and $\gamma =0.5$/day. We see that higher values of $\beta$ and lower levels of $\gamma$ lead to a larger proportion of distant recurrences in $D_c$ and $\rho$ parameter space. Higher migration rates, $D_c$, and lower proliferation rates, $\rho$, lead to more distantly recurring simulated tumors

Fig. 10

Recurrence location classified for various $D_c$, $\rho$, $\beta$ and levels of ischemia for $D_h = 10D_c$ for $\gamma = 0.005$/day and $\gamma =0.5$/day. We see that higher values of $\beta$ and lower levels of $\gamma$ lead to a larger proportion of distant recurrences in $D_c$ and $\rho$ parameter space. Higher migration rates, $D_c$, and lower proliferation rates, $\rho$, lead to more distantly recurring simulated tumors

Fig. 11

Recurrence location classified for various $D_c$, $\rho$, $\beta$ and levels of ischemia for $D_h = 100D_c$ for $\gamma = 0.005$/day and $\gamma =0.5$/day. We see that higher values of $\beta$ and lower levels of $\gamma$ lead to a larger proportion of distant recurrences in $D_c$ and $\rho$ parameter space. Higher migration rates, $D_c$, and lower proliferation rates, $\rho$, lead to more distantly recurring simulated tumors

Fig. 12

Recurrence location classified for various $D_c$, $\rho$, $\beta$ and levels of ischemia for $D_h = 10D_c$ for $\gamma = 0.05$/day. In these simulations, perioperative ischemia was set at 10% of the pre-resection value. We see a larger proportion of local recurrence in these figures compared with those in the main text (see Fig. 4)

Fig. 13

Recurrence location classified for various $D_c$, $\rho$, $\beta$ and levels of ischemia for $D_h = 10D_c$ for $\gamma = 0.005$/day. In these simulations, perioperative ischemia was set at 10% of the pre-resection value. We see a larger proportion of local recurrence in these figures compared with those in the main text (see Fig. 5)

Fig. 14

Recurrence location classified for various $D_c$, $\rho$ and $D_h$ levels for $\beta = 0.5\rho$ and $\gamma = 0.05$/day. In these simulations, perioperative ischemia was set at 10% of the pre-resection value. We see a larger proportion of local recurrence in these figures compared with those in the main text (see Fig. 6)