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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

Partial Correlation of rate constants to glioblastoma growth.Latin Hypercube sampling of all rate constants within 0.1× to 10× of fitted values followed by Partial Correlation of rate constants to glioblastoma growth. Blue bars represent U87 cell line while orange bars represent LN229 cell line. Rate constant k8 (production of HIF1α) had the highest sensitivity when varying the rate constants in combination using the Latin Hypercube sampling method. Inset shows the schematic of the simplified insulin signaling pathway used in the computational model.
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pcbi.1004169.g005: Partial Correlation of rate constants to glioblastoma growth.Latin Hypercube sampling of all rate constants within 0.1× to 10× of fitted values followed by Partial Correlation of rate constants to glioblastoma growth. Blue bars represent U87 cell line while orange bars represent LN229 cell line. Rate constant k8 (production of HIF1α) had the highest sensitivity when varying the rate constants in combination using the Latin Hypercube sampling method. Inset shows the schematic of the simplified insulin signaling pathway used in the computational model.

Mentions: 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).


Simulation predicts IGFBP2-HIF1α interaction drives glioblastoma growth.

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

Partial Correlation of rate constants to glioblastoma growth.Latin Hypercube sampling of all rate constants within 0.1× to 10× of fitted values followed by Partial Correlation of rate constants to glioblastoma growth. Blue bars represent U87 cell line while orange bars represent LN229 cell line. Rate constant k8 (production of HIF1α) had the highest sensitivity when varying the rate constants in combination using the Latin Hypercube sampling method. Inset shows the schematic of the simplified insulin signaling pathway used in the computational model.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004169.g005: Partial Correlation of rate constants to glioblastoma growth.Latin Hypercube sampling of all rate constants within 0.1× to 10× of fitted values followed by Partial Correlation of rate constants to glioblastoma growth. Blue bars represent U87 cell line while orange bars represent LN229 cell line. Rate constant k8 (production of HIF1α) had the highest sensitivity when varying the rate constants in combination using the Latin Hypercube sampling method. Inset shows the schematic of the simplified insulin signaling pathway used in the computational model.
Mentions: 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).

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