Limits...
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

In silico reduction of glioblastoma growth for LN229 glioblastoma cell line.Glioblastoma growth was simulated for (A) control conditions, and when two separate interactions were removed from the model: (B) IGFI to HIF1α and (C) IGFBP2 to HIF1α. (D) Removal of the IGFBP2 to HIF1α interaction had the greatest reduction in the glioblastoma growth as compared to control conditions.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4401766&req=5

pcbi.1004169.g006: In silico reduction of glioblastoma growth for LN229 glioblastoma cell line.Glioblastoma growth was simulated for (A) control conditions, and when two separate interactions were removed from the model: (B) IGFI to HIF1α and (C) IGFBP2 to HIF1α. (D) Removal of the IGFBP2 to HIF1α interaction had the greatest reduction in the glioblastoma growth as compared to control conditions.

Mentions: 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.


Simulation predicts IGFBP2-HIF1α interaction drives glioblastoma growth.

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

In silico reduction of glioblastoma growth for LN229 glioblastoma cell line.Glioblastoma growth was simulated for (A) control conditions, and when two separate interactions were removed from the model: (B) IGFI to HIF1α and (C) IGFBP2 to HIF1α. (D) Removal of the IGFBP2 to HIF1α interaction had the greatest reduction in the glioblastoma growth as compared to control conditions.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004169.g006: In silico reduction of glioblastoma growth for LN229 glioblastoma cell line.Glioblastoma growth was simulated for (A) control conditions, and when two separate interactions were removed from the model: (B) IGFI to HIF1α and (C) IGFBP2 to HIF1α. (D) Removal of the IGFBP2 to HIF1α interaction had the greatest reduction in the glioblastoma growth as compared to control conditions.
Mentions: 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.

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