Tremendous strides have been made in improving patients’ survival from cancer with one glaring exception: brain cancer. Glioblastoma is the most common, aggressive and highly malignant type of primary brain tumor. The average overall survival remains less than 1 year. Notably, cancer patients with obesity and diabetes have worse outcomes and accelerated progression of glioblastoma. The root cause of this accelerated progression has been hypothesized to involve the insulin signaling pathway. However, while the process of invasive glioblastoma progression has been extensively studied macroscopically, it has not yet been well characterized with regards to intracellular insulin signaling. In this study we connect for the first time microscale insulin signaling activity with macroscale glioblastoma growth through the use of computational modeling. Results of the model suggest a novel observation: feedback from IGFBP2 to HIF1α is integral to the sustained growth of glioblastoma. Our study suggests that downstream signaling from IGFI to HIF1α, which has been the target of many insulin signaling drugs in clinical trials, plays a smaller role in overall tumor growth. These predictions strongly suggest redirecting the focus of glioma drug candidates on controlling the feedback between IGFBP2 and HIF1α.
A variety of multiscale modeling methods has been used to describe the growth of solid tumors, including both discrete and continuous approaches. A non-inclusive set of references include [25–29], along with several recent reviews [30–36]. Previous mathematical models of glioma progression have primarily focused on the growth or migration of cancerous cells from a tumor core [37–41]. Despite the increasing number and sophistication of the models, these studies have not considered insulin signaling. Conversely, computational models of insulin signaling exist [42,43], but have only been applied to other applications, including articular cartilage [44], ovarian cancer [45], and human skeletal muscle [46], and exclude molecules of interest for brain cancer cells [44,47]. Thus, we created for the first time, a computational chemical-kinetic model linking the insulin signaling pathway to glioblastoma growth.
We developed a chemical-kinetic model that characterizes the network architecture and dynamics of the insulin signaling pathway—and then links these molecular interactions to cell and tissue level responses. Based on previous literature on the insulin signaling pathway, we constructed a model comprised of 4 differential equations and 1 mass conservation equation which describe interactions between components in the insulin signaling system (see Fig 1B). Our aim was to create the minimal model necessary to capture all the following interactions of key molecules: The growth rate of the glioblastoma tumor, Eq 5, was determined by regression analysis using the data from both our previous experiments on spheroid growth in vitro using the U87 glioblastoma cell line and LN229 glioblastoma growth in mice [70]. The U87 and LN229 glioblastoma cell lines were used to compare glioblastoma cell lines which were more dependent on insulin signaling (LN229) and less dependent on insulin signaling (U87) [3]. Growth of the glioblastoma is normally measured experimentally by changes in the volume or the diameter of the cancerous spheroid/tumor mass. In the model, glioblastoma growth is a time-varying function, defined as net growth of the glioblastoma spheroid/tumor volume and is assumed to depend on its basal growth and the additional growth that is promoted by HIF1α. A genetic algorithm was used to determine default values for the unknown kinetic rates (the genetic algorithm was employed in Matlab, and refined using fminsearch). The estimated initial conditions and fitted rate constants are shown in Tables 1 and 2. The model was fitted for three outputs: glioblastoma growth rate; HIF1α vs. O2 levels; and IGFI as a function of IGFBP2. The glioblastoma growth rates were found for two distinct experiments (U87 and LN229) by fitting the same model and obtaining different initial conditions and growth rates for the two cell lines. Results from fitting the in vitro U87 spheroid growth and literature data of LN229 growth in mice are shown in Fig 2A and 2B, respectively. HIF1α is a function of oxygen levels, and it was fitted using data from Jiang et al. [71] which monitored how the HIF1α levels changed in HeLa cells as a function of O2. The rate constants were simultaneously fitted using data of IGFI and IGFBP2 levels as a function of each other and time (see Fig 3A, Slomiany et al. [41]). In those experiments, the IGFBP2 concentration was monitored as a function of time under two external concentrations of IGFI (0 nM and 100 nM). The experiments used the human retinal pigment epithelial (RPE) cell line D407; and it is an assumption of the model that the same relationships hold in glioma cells (these measurements are the only ones we are aware of that measure IGFBP2 as a function of IGFI levels). We also estimated that the IGFBP2 response was the same as that of IGFBP3, which is the IGFBP species available from the in vitro experimental data. Initial conditions were also determined from experiments. The concentration of IGFI under normal conditions was calculated based on the data by Lonn et al [72]. Similarly the mean concentration of IGFBP2 in patients with glioblastoma was calculated from a previous study [73]. Both of the calculations for IGFI and IGFBP2 are shown in the S1 File. Initial concentrations of all molecular factors involved in the system were varied independently between 0. 1×-10× of the fitted concentrations, and the effect on each compound and overall glioma growth was simulated. Oxygen levels were tested between 2–21%. The sensitivity of glioblastoma growth to changes in kinetic rate constants was determined for kinetic rates of 0. 1×-10× the fitted values individually. The results from the complete sensitivity analysis can be found in S2 File. Sensitivity analysis was summarized by calculating the sensitivity index (see below) at 40 days for the LN229 cell line in Table 1. The time duration of 40 days was chosen as it matched the duration of studies performed in the in vivo LN229 work from literature. The following equation was used to calculate the sensitivity index to quantify the levels of sensitivity. The sensitivity index was plotted in Fig 4. The definitions of each variable in the sensitivity index can be found in Table 3. In addition to varying the rate constants individually, we simultaneously explored the entire parameter space of the rate constants (varying between 0. 1×–10× of the fitted values) using the Latin Hypercube Sampling method [75]. From this sampling, 500 sets of rate constants were simulated in the model for glioma growth over 40 days where the glioblastoma diameter was recorded. Principal component analysis illustrating the resulting glioblastoma diameters as a function of multi-varied kinetics rates is shown in S1–S4 Figs. Additionally, to confirm the kinetic parameters that most significantly influence glioma progression, glioblastoma diameters were correlated to the rate constants by calculating partial correlations (Fig 5). To simulate the effect of using different drug targeting factors in glioblastoma, we set each rate constant to 0 separately, modeling the effects of removing each interaction, with the exception of the basal production and degradation of HIF1α. The exception is because HIF1α is ubiquitous in cells; targeting HIF1α would not only affect glioblastoma cells but also other cells. Setting the rate constant to 0 simulated the removal of each reaction from the system. The diameter of the glioblastoma for both cell lines U87 and LN229 was then compared to the original pathway before the removal of the reaction. The glioblastoma diameter was simulated over 40 days. Results are shown in Fig 6. Unknown rate constants were found by fitting existing literature data. Fig 4A shows the model simulations compared to the literature in vitro data, to which the model was fit, that monitored the IGFBP2 concentration as a function of time under two external concentrations of IGFI (0 nM and 100 nM) in the system [74]. For the case with 0 nM external IGFI, the model simulations that best fitted the in vitro data was found to be internal IGFI concentration levels of 92. 5 nM of IGFI. Fig 3B shows the model simulations compared to literature in vitro data that monitored HIF1α as a function of oxygen [71].
Results of the sensitivity analysis on the initial model conditions showed that HIF1α and IGFBP2 levels in the insulin signaling system were most sensitive to reduced oxygen (2%) and also elevated IGFItotal levels (Fig 7). At higher concentrations of IGFItotal, elevated steady state concentrations of IGFI and IGFBP2 were observed. In hypoxic conditions (2% oxygen), HIF1α and IGFBP2 concentrations were increased initially and reached a steady-state of 7× and 1. 25× baseline values, respectively. Varying initial conditions in the model showed that the insulin system is highly sensitive to reduced oxygen concentrations and elevated IGFI concentrations compared to the default initial conditions (control). For the remaining initial conditions, the insulin signaling system in glioblastoma was robust over changes in initial HIF1α concentrations and the (IGFI-IGFBP2) complex concentration. In order to analyze the contribution of each rate constant to glioblastoma growth, the sensitivity index was calculated for each rate constant, for LN229 tumor growth (shown in Table 4 in descending order). Results are plotted in Fig 4, which shows that LN229 glioblastoma growth was most sensitive to the production of HIF1α (k8) production of IGFBP2 (k1), growth rate due to HIF1α (k11) and promotion of HIF1α by IGFBP2 (k10). Results of the Latin Hypercube Sampling confirmed these findings. After computing the Partial Correlation Coefficients between rate constants and glioblastoma growth, we found that the production of HIF1α (k8) was the highest correlated rate constant to glioblastoma growth, as shown in Fig 6. When we removed each reaction independently from the model, the results were striking. When the feedback from IGFBP2 to HIF1α was removed in LN229 cells, the glioblastoma volume over the simulation of 40 days was halved as compared to when the downstream signal from IGFI to HIF1α was removed shown in Fig 6. Removal of the HIF1α to IGFBP2 connection had minimal effect on the glioblastoma growth. When a similar simulation was conducted for the U87 cell line, there was not a significant change in the glioblastoma volume when either the IGFBP2 to HIF1α or the IGFI to HIF1α connection was removed, see S5 Fig.
We have developed a chemical-kinetic model that predicts glioblastoma growth as a function of insulin signaling. Our model agrees with experimental in vitro data on interactions between IGFI, IGFBP2 and HIF1α. Sensitivity analysis on initial conditions found the insulin signaling pathway to be most sensitive to IGFI concentration and oxygen levels. Current literature data on the relationship between HIF1α and oxygen shows that glioblastoma growth is insensitive to high oxygen levels, but highly sensitive at low oxygen concentrations. This is significant as glioblastoma spheroids are generally under hypoxic conditions. There is maximal HIF1α expression at low oxygen levels [76]. In addition, there are more pronounced changes in HIF1α expression at these low oxygen levels. Small changes in oxygen levels result in large changes in HIF1α levels. As the oxygen levels increase towards 21%, HIF1α levels are exponentially decreased. This relationship explains how glioblastoma tumors have a fairly constant response at higher oxygen levels. However, at low oxygen levels, glioblastoma will have drastically higher HIF1α levels which result in a much different phenotype and growth rate. Drugs have been developed to target the IGFIR pathway by suppressing the IGFI to HIF1α pathway using three main types of compounds: IGFIR targeting antibodies, tyrosine kinase inhibitors for kinase domains of IGFIR, and IGFI ligand neutralizing antibodies [24,77–79]. However, these compounds have failed to control glioblastoma growth clinically, and have not made it past phase III clinical trials [24]. Our sensitivity analysis on the rate constants showed that the contribution of basal HIF1α production to LN229 glioblastoma growth is greater than contribution of the IGFI-dependent HIF1α production. This suggests that HIF1α would be a more effective target to reduce glioblastoma growth than targeting the IGFIR molecular interactions by current drugs. In fact, the top four rate constants that glioblastoma growth was most sensitive to when individually perturbed were the production of HIF1α (k8), production of IGFBP2 (k1), growth rate due to HIF1α (k11) and promotion of HIF1α by IGFBP2 (k10). However, since the HIF1α effects are ubiquitous in all cells, alterations in HIF1α and production of IGFBP2 would be difficult to target in cancerous cells only. On the other hand, IGFBP2 overexpression is specific to glioblastoma multiforme compared to gliomas. Thus we focused on the effect of promotion of HIF1α by IGFBP2 (k10), which had the third highest correlation found by Partial Correlation to glioblastoma growth in Fig 5. Our results from the growth reduction analysis showed that glioblastoma growth was more sensitive to the removal of feedback from IGFBP2 to HIF1α as compared to the IGFI to HIF1α interaction. There have not been any published drugs that have specifically blocked feedback between IGFBP2 and HIF1α in glioblastoma. Our model predicts that this pathway could result in significantly reduced growth of glioblastoma and should be targeted by the next generation of glioblastoma drugs. This study offers an explanation for the difficulties encountered by current drugs targeting IGFIR to reduce glioblastoma cell growth: a secondary mechanism that upregulates HIF1α. We found that glioblastoma growth was highly sensitive to this new hypothesized interaction, IGFBP2 to HIF1α signaling. While other researchers have highlighted the importance of IGFBP2 in glioblastoma growth [80], we have been able to suggest a specific mechanism that can be potentially targeted. In our predictions, removing the feedback from IGFBP2 to HIF1α resulted in almost half of the growth in the glioblastoma diameter over 40 days as compared to removing the downstream signal from IGFI to HIF1α. By using two different glioblastoma cell lines in our analysis, we have found that glioblastoma growth through the insulin signaling pathway is tumor specific. When we conducted the glioblastoma growth reduction analyses of the LN229 and U87 cell lines, there was almost no change in growth observed in the U87 cell lines, while the LN229 showed a reduction in the glioblastoma tumors’ growth. Glioblastoma cells lines that rely on the insulin signaling pathway for their aggressive growth phenotype will be more affected by drugs that target the insulin signaling pathway. Conversely, if the glioblastoma cells do not rely on the signaling from insulin for their growth, then targeting the insulin signaling pathway would not be effective in controlling the growth. This explains why when U87 and LN229 were targeted using TAE226 (IGFIR tyrosine kinase inhibitor), a larger amount of apoptosis was observed for the LN229 cell line compared to the U87 cells [3]. Thus, targeting the insulin signaling pathway through the IGFBP2-HIF1α interaction could be effective for those glioblastoma cells dependent on insulin signaling. Compensatory pathways may also influence cancer growth, and the computational results presented here warrant targeted experimental testing focusing on the IGFBP2-HIF1α interaction in the context of other signaling networks. In conclusion, we have been able to achieve a deeper understanding of the interactions between key factors in the insulin signaling pathway through our computational model. The model allowed us to simulate the effects of removing different reactions in the insulin signaling pathway network, to test in silico potential therapeutic targets. These model predictions provide the impetus for future experimental studies exploring the role of IGFBP2-HIF1α interactions. In sum, we have found a possible target in the insulin signaling system that merits exploration as a candidate drug target for glioblastoma patients and other patients with cancers sensitive to the insulin signaling pathway.
Current treatment for glioblastoma patients is limited to nonspecific methods: surgery followed by a combination of radio- and chemotherapy. With these methods, glioma patient survival is less than one year post-diagnosis. Targeting specific protein signaling pathways offers potentially more potent therapies. One promising potential target is the insulin signaling pathway, which is known to contribute to glioblastoma progression. However, drugs targeting this pathway have shown mixed results in clinical trials, and the detailed mechanisms of how the insulin signaling pathway promotes glioblastoma growth remain to be elucidated. Here, we developed a computational model of insulin signaling in glioblastoma in order to study this pathway’s role in tumor progression. Using the model, we systematically test contributions of different insulin signaling protein interactions on glioblastoma growth. Our model highlights a key driver for the growth of glioblastoma: IGFBP2-HIF1α feedback. This interaction provides a target that could open the door for new therapies in glioma and other solid tumors.
Simulation Predicts IGFBP2-HIF1α Interaction Drives Glioblastoma Growth