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Simulation predicts IGFBP2-HIF1α interaction drives glioblastoma growth.

Lin KW, Liao A, Qutub AA - PLoS Comput. Biol. (2015)

Bottom Line: Tremendous strides have been made in improving patients' survival from cancer with one glaring exception: brain cancer.The average overall survival remains less than 1 year.The root cause of this accelerated progression has been hypothesized to involve the insulin signaling pathway.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, Rice University, Houston, Texas, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Comparisons between computational model and literature data.(A) Relationship between IGFB2 and IGFI over time. Grey and blue points represent in vitro data obtained from Slomiany et al [74] where 0 nM and 100 nM of IGFI, were added to the media at the start of the experiment respectively. Red line represents computational simulation with no added IGFI, R2 = 0.91, and purple line indicates computational simulation with 100 nM of IGFI added at 0 hrs, R2 = 0.83. (B) HIF1α as a function of O2. Orange points are the in vitro expression data obtained in HeLa cells. Green line shows model simulations using the same initial conditions as the in vitro experiments, R2 = 0.97.
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pcbi.1004169.g003: Comparisons between computational model and literature data.(A) Relationship between IGFB2 and IGFI over time. Grey and blue points represent in vitro data obtained from Slomiany et al [74] where 0 nM and 100 nM of IGFI, were added to the media at the start of the experiment respectively. Red line represents computational simulation with no added IGFI, R2 = 0.91, and purple line indicates computational simulation with 100 nM of IGFI added at 0 hrs, R2 = 0.83. (B) HIF1α as a function of O2. Orange points are the in vitro expression data obtained in HeLa cells. Green line shows model simulations using the same initial conditions as the in vitro experiments, R2 = 0.97.

Mentions: 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.


Simulation predicts IGFBP2-HIF1α interaction drives glioblastoma growth.

Lin KW, Liao A, Qutub AA - PLoS Comput. Biol. (2015)

Comparisons between computational model and literature data.(A) Relationship between IGFB2 and IGFI over time. Grey and blue points represent in vitro data obtained from Slomiany et al [74] where 0 nM and 100 nM of IGFI, were added to the media at the start of the experiment respectively. Red line represents computational simulation with no added IGFI, R2 = 0.91, and purple line indicates computational simulation with 100 nM of IGFI added at 0 hrs, R2 = 0.83. (B) HIF1α as a function of O2. Orange points are the in vitro expression data obtained in HeLa cells. Green line shows model simulations using the same initial conditions as the in vitro experiments, R2 = 0.97.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401766&req=5

pcbi.1004169.g003: Comparisons between computational model and literature data.(A) Relationship between IGFB2 and IGFI over time. Grey and blue points represent in vitro data obtained from Slomiany et al [74] where 0 nM and 100 nM of IGFI, were added to the media at the start of the experiment respectively. Red line represents computational simulation with no added IGFI, R2 = 0.91, and purple line indicates computational simulation with 100 nM of IGFI added at 0 hrs, R2 = 0.83. (B) HIF1α as a function of O2. Orange points are the in vitro expression data obtained in HeLa cells. Green line shows model simulations using the same initial conditions as the in vitro experiments, R2 = 0.97.
Mentions: 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.

Bottom Line: Tremendous strides have been made in improving patients' survival from cancer with one glaring exception: brain cancer.The average overall survival remains less than 1 year.The root cause of this accelerated progression has been hypothesized to involve the insulin signaling pathway.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, Rice University, Houston, Texas, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus