Modeling of Tumor Progression in NSCLC and Intrinsic Resistance to TKI in Loss of PTEN Expression

<div><p>EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.</p> </div

Important species kinetics comparison in Ras-ERK pathway between normal and NSCLC models at 50 ng/ml EGF.

<p>A) Kinetics of Ras-GTP formation. B) Kinetics of Raf1 activation. C) Phosphorylation kinetics of MEK leading to ppMEK double phosphorylation. D) Phosphorylation kinetics of ERK leading to ppERK double phosphorylation: <b>NSCLC model factor (red); normal model factors (blue).</b></p

Computational simulation of EGFR and ΔEGFR autophosphorylation and internalization at 50 ng/ml EGF.

<p>A) Kinetics of EGFR autophosphorylation and internalization in 100secs. B) Kinetics of Δ EGFR autophosphorylation and internalization in 100 secs.</p

Computational simulation of Akt phosphorylation (pAkt) in NSCLC that involve loss of PTEN (at 50 ng/ml EGF). For more information refer to the context.

<p>Computational simulation of Akt phosphorylation (pAkt) in NSCLC that involve loss of PTEN (at 50 ng/ml EGF). For more information refer to the context.</p

Normal EGFR signaling computational simulation at 50 ng/ml of EGF.

<p>A) Kinetics of EGFR autophosphorylation (pEGF-EGFR2). B) Kinetics of Ras-GTP formation. C) Kinetics of Raf1 activation (Raf1active). D) Phosphorylation kinetics of MEK leading to ppMEK double phosphorylation. E) Phosphorylation kinetics of ERK leading to ppERK double phosphorylation. f) Kinetics of PI3K phosphorylation (pPI3K). G) Activation kinetics of Akt as result of phosphorylation (pAkt). H) Kinetics of phosphorylated STAT3 dimerization in cytoplasm. I) kinetics of dimer STAT3 import into the nucleus.</p

Important species kinetics comparison in PI3K/Akt and STAT3 activation pathways between normal and NSCLC models at 50 ng/ml EGF.

<p>A) Kinetics of PI3K phosphorylation. B) Activation kinetics of Akt as result of phosphorylation. C) Kinetics of phosphorylated STAT3 dimerization in cytoplasm. D) Kinetics of dimer STAT3 import into the nucleus: <b>NSCLC model factor (red); normal model factors (blue).</b></p

Schematic overview of the EGFR signaling. For more information refer to the context.

<p>Schematic overview of the EGFR signaling. For more information refer to the context.</p

Inhibition of EGFR signaling in the presence of erlotinib (IC 50 ) and comparison with kinetics of three important factors in NSCLC model (at 50 ng/ml EGF).

<p>A) Phosphorylation kinetics of ERK leading to ppERK double phosphorylation. B) Activation kinetics of Akt as result of phosphorylation. C) Kinetics of dimer STAT3 localization into the nucleus: <b>NSCLC model factors (red); NSCLC model factors in the presence of erlotinib (yellow).</b></p

Computational simulation of Akt phosphrylation (pAkt) in 10 µmol/liter erlotinib (IC 50) in NSCLC that involve loss of PTEN (at 50 ng/ml EGF).

<p>Computational simulation of Akt phosphrylation (pAkt) in 10 µmol/liter erlotinib (IC 50) in NSCLC that involve loss of PTEN (at 50 ng/ml EGF).</p

Cluster density view of the weighted cellular signaling networks by applying similarity measure.

<p>(A) human cancer cell signaling, and (B) mouse hippocampus CA1 neural networks were weighted by similarity measure. Nodes were labeled according to their position in the cell, including cell membrane, adducin, cell adhesion, centrosome, cytoskeleton, endothelial, endoplasmic reticulum, cytosolic, extracellular space, golgi apparatus, lysosome, mitochondria, nucleus, ribosome and vesicles. VOSveiwer program was used for visualizing connectivity-based clustering patterns <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039643#pone.0039643-Nees1" target="_blank">[35]</a>. This tool provides visualization of similarities, where objects with high similarity are located close to each other and those with low similarity are located far from each other.</p