Non-Invasive Monitoring of Increased Fibrotic Tissue and Hyaluronan Deposition in the Tumor Microenvironment in the Advanced Stages of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinomas are characterized by a complex and robust tumor microenvironment (TME) consisting of fibrotic tissue, excessive levels of hyaluronan (HA), and immune cells. We utilized quantitative multi-parametric magnetic resonance imaging (mp-MRI) methods at 14 Tesla in a genetically engineered KPC (KrasLSL-G12D/+, Trp53LSL-R172H/+, Cre) mouse model to assess the complex TME in advanced stages of tumor development. The whole tumor, excluding cystic areas, was selected as the region of interest for data analysis and subsequent statistical analysis. Pearson correlation was used for statistical inference. There was a significant correlation between tumor volume and T2 (r = −0.66), magnetization transfer ratio (MTR) (r = 0.60), apparent diffusion coefficient (ADC) (r = 0.48), and Glycosaminoglycan-chemical exchange saturation transfer (GagCEST) (r = 0.51). A subset of mice was randomly selected for histological analysis. There were positive correlations between tumor volume and fibrosis (0.92), and HA (r = 0.76); GagCEST and HA (r = 0.81); and MTR and CD31 (r = 0.48). We found a negative correlation between ADC low-b (perfusion) and Ki67 (r = −0.82). Strong correlations between mp-MRI and histology results suggest that mp-MRI can be used as a non-invasive tool to monitor the tumor microenvironment

The dynamics of stress p53-Mdm2 network regulated by p300 and HDAC1.

We construct a stress p53-Mdm2-p300-HDAC1 regulatory network that is activated and stabilised by two regulatory proteins, p300 and HDAC1. Different activation levels of [Formula: see text] observed due to these regulators during stress condition have been investigated using a deterministic as well as a stochastic approach to understand how the cell responds during stress conditions. We found that these regulators help in adjusting p53 to different conditions as identified by various oscillatory states, namely fixed point oscillations, damped oscillations and sustain oscillations. On assessing the impact of p300 on p53-Mdm2 network we identified three states: first stabilised or normal condition where the impact of p300 is negligible, second an interim region where p53 is activated due to interaction between p53 and p300, and finally the third regime where excess of p300 leads to cell stress condition. Similarly evaluation of HDAC1 on our model led to identification of the above three distinct states. Also we observe that noise in stochastic cellular system helps to reach each oscillatory state quicker than those in deterministic case. The constructed model validated different experimental findings qualitatively

Stability curve induced by .

<p>The variation of concentration level versus for different exposure times  = 10–100, keeping fixed. The inset is the enlarged portion of the actively activated regime.</p

Stability curve induced by .

<p>Plots of concentration level as a function of for different values of exposure times i.e. 10-100 (at constant value of ). The inset is the enlarged portion of the activated regime. In the curve stabilized and activated regimes are demarcated.</p

dynamics for various levels.

<p>The plots of concentration levels as a function of time in hours for various values: (a) , (b) , (c) , (d) , (e) and (f) respectively at constant value of .</p

Activation of via variation of level.

<p>Plots of concentration levels as a function of time in hours for various values 0.0002, 0.002, 0.008, 0.01, 0.02 and 0.04 respectively (at constant value of ), showing activation and stabilization of .</p

Stability curve induced by .

<p>Plots of concentration level as a function of for different values of exposure times i.e. 10–100 (at constant value of ). In the curve stabilized and activated regimes are demarcated.</p

Stability curve induced by .

<p>The variation of concentration level versus for different exposure times  = 10–100, keeping fixed. The inset is the enlarged portion of the activated and stabilized regimes.</p

Noise contribution on dynamics in stochastic system.

<p>The variation of as a function of time in hours in stochastic system for different values of system size,  = 1, 10, 15, 20, 25, 50 (at constant values of and ).</p

Two-dimensional recurrence plots of and .

<p>Recurrence plots between (), () and () for different values of rate constants , i.e. 0.04, 0.05, 0.06, 0.08, 0.09 and 0.1 respectively.</p