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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

Results of model sensitivity to single rate constants as measured by the sensitivity index.Sensitivity index of each model parameter as defined in the main text. The sensitivity index is shown in descending order going from left to right. Rate constant k8 (production of HIF1α) had the highest sensitivity when varying the rate constants individually.
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pcbi.1004169.g004: Results of model sensitivity to single rate constants as measured by the sensitivity index.Sensitivity index of each model parameter as defined in the main text. The sensitivity index is shown in descending order going from left to right. Rate constant k8 (production of HIF1α) had the highest sensitivity when varying the rate constants individually.

Mentions: 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.


Simulation predicts IGFBP2-HIF1α interaction drives glioblastoma growth.

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

Results of model sensitivity to single rate constants as measured by the sensitivity index.Sensitivity index of each model parameter as defined in the main text. The sensitivity index is shown in descending order going from left to right. Rate constant k8 (production of HIF1α) had the highest sensitivity when varying the rate constants individually.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004169.g004: Results of model sensitivity to single rate constants as measured by the sensitivity index.Sensitivity index of each model parameter as defined in the main text. The sensitivity index is shown in descending order going from left to right. Rate constant k8 (production of HIF1α) had the highest sensitivity when varying the rate constants individually.
Mentions: 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.

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