Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response.

BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contains crucial information about tumour heterogeneity and the transport limitations that reduce drug efficacy. Mathematical modelling of drug delivery and cellular responsiveness based on underutilised DCE-MRI data has the unique potential to predict therapeutic responsiveness for individual patients. METHODS: To interpret DCE-MRI data, we created a modelling framework that operates over multiple time and length scales and incorporates intracellular metabolism, nutrient and drug diffusion, trans-vascular permeability, and angiogenesis. The computational methodology was used to analyse DCE-MR images collected from eight breast cancer patients at Baystate Medical Center in Springfield, MA. RESULTS: Computer simulations showed that trans-vascular transport was correlated with tumour aggressiveness because increased vessel growth and permeability provided more nutrients for cell proliferation. Model simulations also indicate that vessel density minimally affects tissue growth and drug response, and nutrient availability promotes growth. Finally, the simulations indicate that increased transport heterogeneity is coupled with increased tumour growth and poor drug response. CONCLUSION: Mathematical modelling based on DCE-MRI has the potential to aid treatment decisions and improve overall cancer care. This model is the critical first step in the creation of a comprehensive and predictive computational method.

Tumour-targeted delivery of TRAIL using Salmonella typhimurium enhances breast cancer survival in mice.

BACKGROUND: An effective cancer therapeutic must selectively target tumours with minimal systemic toxicity. Expression of a cytotoxic protein using Salmonella typhimurium would enable spatial and temporal control of delivery because these bacteria preferentially target tumours over normal tissue. METHODS: We engineered non-pathogenic S. typhimurium to secrete murine TNF-related apoptosis-inducing ligand (TRAIL) under the control of the prokaryotic radiation-inducible RecA promoter. The response of the RecA promoter to radiation was measured using fluorometry and immunoblotting. TRAIL toxicity was determined using flow cytometry and by measuring caspase-3 activation. A syngeneic murine tumour model was used to determine bacterial accumulation and the response to expressed TRAIL. RESULTS: After irradiation, engineered S. typhimurium secreted TRAIL, which caused caspase-3-mediated apoptosis and death in 4T1 mammary carcinoma cells in culture. Systemic injection of Salmonella and induction of TRAIL expression using 2 Gy gamma-irradiation caused a significant delay in mammary tumour growth and reduced the risk of death by 76% when compared with irradiated controls. Repeated dosing with TRAIL-bearing Salmonella in conjunction with radiation improved the 30-day survival from 0 to 100%. CONCLUSION: These results show the pre-clinical utility of S. typhimurium as a TRAIL expression vector that effectively reduces tumour growth and extends host survival.

Optimization of cytochrome P4502D6 (CYP2D6) phenotype assignment using a genotyping algorithm based on allele frequency data.

Cytochrome P4502D6 (CYP2D6) is a highly polymorphic gene locus with > 50 variant alleles which lead to a wide range in enzymatic activity. So called poor metabolizers are carriers of any two non-functional alleles of the CYP2D6 gene. CYP2D6 genotyping is cumbersome and the question of how much genotyping is necessary for an accurate phenotype prediction is still debated. The goal of this study was to determine the optimum amount of genotyping required to accurately predict the phenotype at a reasonable cost in a white North American population. To address this issue, we designed a polymerase chain reaction (PCR)/restriction fragment length polymorphism-based genotyping strategy to detect 'key' mutations linked to extensive metabolizer or poor metabolizer associated alleles in combination with extra-long PCR (XL-PCR). All mutations with the exception of gene deletions and duplications are detectable by simple restriction digestion analysis and agarose gel electrophoresis. In addition, we utilized a genotyping algorithm based on our own and published allele frequency data and phenotype analysis to calculate the probability of a correct genotype (and thus, phenotype) assignment. As little as one XL-PCR reaction followed by a maximum of six reamplification reactions allows an accurate prediction of an individual's genotype to 99.15%. As few as four reamplification reactions identify 97.9% of poor metabolizer individuals. We evaluated our model in 208 white North Americans by testing for the presence of 'key' mutations linked to CYP2D6*2, *3, *4, *6, *7, *8, *9, *10, *11, *12, *15, *17 and *18 alleles and the *5, *13 and *16 gene deletions. For all individuals, the correct phenotype has been predicted. Discordant phenotype assignment occurred in only two individuals which subsequently was attributed to CYP2D6 inhibition by concomitant drug therapy.

In tumors Salmonella migrate away from vasculature toward the transition zone and induce apoptosis.

Motile bacteria can overcome diffusion resistances that substantially reduce the efficacy of standard cancer therapies. Many reports have also recently described the ability of Salmonella to deliver therapeutic molecules to tumors. Despite this potential, little is known about the spatiotemporal dynamics of bacterial accumulation in solid tumors. Ultimately this timing will affect how these microbes are used therapeutically. To determine how bacteria localize, we intravenously injected Salmonella typhimurium into BALB/c mice with 4T1 mammary carcinoma and measured the average bacterial content as a function of time. Immunohistochemistry was used to measure the extent of apoptosis, the average distance of bacteria from tumor vasculature and the location of bacteria in four different regions: the core, transition, body and edge. Bacteria accumulation was also measured in pulmonary and hepatic metastases. The doubling time of bacterial colonies in tumors was measured to be 16.8 h, and colonization was determined to delay tumor growth by 48 h. From 12 and 48 h after injection, the average distance between bacterial colonies and functional vasculature significantly increased from 130 to 310 µm. After 48 h, bacteria migrated away from the tumor edge toward the central core and induced apoptosis. After 96 h, bacteria began to marginate to the tumor transition zone. All observed metastases contained Salmonella and the extent of bacterial colocalization with metastatic tissue was 44% compared with 0.5% with normal liver parenchyma. These results demonstrate that Salmonella can penetrate tumor tissue and can selectively target metastases, two critical characteristics of a targeted cancer therapeutic.

Using isotopomer path tracing to quantify metabolic fluxes in pathway models containing reversible reactions.

As a more complete picture of the genetic and enzymatic composition of cells becomes available, there is a growing need to describe how cellular regulatory elements interact with the cellular environment to affect cell physiology. One means for describing intracellular regulatory mechanisms is concurrent measurement of multiple metabolic pathways and their interactions by metabolic flux analysis. Flux of carbon through a metabolic pathway responds to all cellular regulatory systems, including changes in enzyme and substrate concentrations, enzyme activation or inhibition, and ultimately genetic control. The extent to which metabolic flux analysis can describe cellular physiology depends on the number of pathways in the model and the quality of the data. Intracellular information is obtainable from isotopic tracer experiments, the most extensive being the determination of the isotopomer distribution, or specific labeling pattern, of intracellular metabolites. We present a rapid and novel solution method that determines the flux of carbon through complex pathway models using isotopomer data. This time-consuming problem was solved with the introduction of isotopomer path tracing, which drastically reduces the number of isotopomer variables to the number of isotopomers observed experimentally. We propose a partitioned solution method that takes advantage of the nearly linear relationship between fluxes and isotopomers. Whereas the stoichiometric matrix and the isotopomer matrix are invertible, simulated annealing and the Newton-Raphson method are used for the nonlinear components. Reversible reactions are described by a new parameter, the association factor, which scales hyperbolically with the rate of metabolite exchange. Automating the solution method permits a variety of models to be compared, thus enhancing the accuracy of results. A simplified example that contains all of the complexities of a comprehensive pathway model is presented.