Integrating cell-cycle progression, drug penetration and energy metabolism to identify improved cancer therapeutic strategies

In vitro vascularized liver and tumor tissue microenvironments on a chip for dynamic determination of nanoparticle transport and toxicity

This paper presents the development of a vascularized breast tumor and healthy or tumorigenic liver microenvironments-on-a-chip connected in series. This is the first description of a vascularized multi tissue-on-a-chip microenvironment for modeling cancerous breast and cancerous/healthy liver microenvironments, to allow for the study of dynamic and spatial transport of particles. This device enables the dynamic determination of vessel permeability, the measurement of drug and nanoparticle transport, and the assessment of the associated efficacy and toxicity to the liver. The platform is utilized to determine the effect of particle size on the spatiotemporal diffusion of particles through each microenvironment, both independently and in response to the circulation of particles in varying sequences of microenvironments. The results show that when breast cancer cells were cultured in the microenvironments they had a 2.62-fold higher vessel porosity relative to vessels within healthy liver microenvironments. Hence, the permeability of the tumor microenvironment increased by 2.35- and 2.77-fold compared with a healthy liver for small and large particles, respectively. The extracellular matrix accumulation rate of larger particles was 2.57-fold lower than smaller particles in a healthy liver. However, the accumulation rate was 5.57-fold greater in the breast tumor microenvironment. These results are in agreement with comparable in vivo studies. Ultimately, the platform could be utilized to determine the impact of the tissue or tumor microenvironment, or drug and nanoparticle properties, on transport, efficacy, selectivity, and toxicity in a dynamic, and high-throughput manner for use in treatment optimization.

The Effects of Metabolic Substrate Availability on Human Adipose-Derived Stem Cell Spheroid Survival

IMPACT STATEMENT

Human adipose-derived stem cells (hADSCs) spheroids have displayed remarkable potential for treating ischemic injury. However, low nutrient (i.e., glucose and oxygen) availability in ischemic environments results in limited tissue viability posttransplantation. To develop an understanding of the effects of nutrient availability on spheroid survival, we utilized both in vitro and computational models to examine the limiting factors in metabolic supply for avascular microtissues, revealing the critical role of glucose to improve hADSC spheroid survival in ischemic conditions. These results may impact future strategies for improving hADSC transplantation efficacy through codelivery of metabolic substrates.

Computational modelling of drug delivery to solid tumour: Understanding the interplay between chemotherapeutics and biological system for optimised delivery systems

Drug delivery to solid tumour involves multiple physiological, biochemical and biophysical processes taking place across a wide range of length and time scales. The therapeutic efficacy of anticancer drugs is influenced by the complex interplays among the intrinsic properties of tumours, biophysical aspects of drug transport and cellular uptake. Mathematical and computational modelling allows for a well-controlled study on the individual and combined effects of a wide range of parameters on drug transport and therapeutic efficacy, which would not be possible or economically viable through experimental means. A wide spectrum of mathematical models has been developed for the simulation of drug transport and delivery in solid tumours, including PK/PD-based compartmental models, microscopic and macroscopic transport models, and molecular dynamics drug loading and release models. These models have been used as a tool to identify the limiting factors and for optimal design of efficient drug delivery systems. This article gives an overview of the currently available computational models for drug transport in solid tumours, together with their applications to novel drug delivery systems, such as nanoparticle-mediated drug delivery and convection-enhanced delivery.

Tumour tissue transport after intraperitoneal anticancer drug delivery

Intraperitoneal (IP) drug delivery, either as an intraoperative chemoperfusion or as an adjuvant, repeated instillation, is an established treatment modality in patients with peritoneal carcinomatosis. The efficacy of IP drugs depends on its ability to penetrate the tumour stroma in order to reach their (sub)cellular target. It is known, that drug penetration after IP delivery is limited to a few millimetres. Here, we review the basic tissue transport mechanisms after IP delivery and discuss the biophysical barriers and obstacles that limit penetration distance. In addition, we review the physical and pharmaceutical interventions that have been studied in order to improve delivery of small molecular and macromolecular drugs after IP instillation. These interventions could inform the design of future clinical trials aiming at an improved efficacy of IP-based drug delivery in carcinomatosis patients.

Tissue transport affects how treatment scheduling increases the efficacy of chemotherapeutic drugs

A method to predict the effect of tissue transport on the scheduling of chemotherapeutic treatment could increase efficacy. Many drugs with desirable pharmacokinetic properties fail in vivo due to poor transport through tissue. To predict the effect of treatment schedule on drug efficacy we developed an in silico method that integrates diffusion through tissue and cell binding into a pharmacokinetic model. The model was evaluated with an array of theoretical drugs that had different rates of diffusivity, binding, and clearance. The efficacy of each drug, quantified as the fraction of cells killed, was calculated for twenty dosage schedules. Simulations showed that efficacy strongly depended on tissue transport, with a range of 0.00 to 99.99%, despite each drug having equal plasma areas under the curve (AUC). For most drugs, schedules that increased exposure also increased efficacy. Drugs with fast clearance benefited the most from increasing the number of doses and this was most effective for those with intermediary binding. All drugs with slow diffusivity were ineffective. For a subset of drugs, increasing the number of doses decreased efficacy. This phenomenon was unexpected because, when considering uptake into tissue, sustained plasma levels from multiple doses are generally assumed to be more effective. This counterintuitive decrease in efficacy was caused by drug retention within tumor tissue. These results established a set of rules that suggests how transport parameters affect the efficacy of drugs at different schedules. The two most predominant rules are (1) multiple doses improve efficacy for drugs with fast clearance, fast diffusivity and low to intermediate cell binding; and (2) one dose is most effective for drugs with slow clearance, slow diffusivity or strong cell binding. Understanding the role of tissue transport when determining drug treatment schedules would improve the outcome of preclinical animal experiments and early clinical trials.

Integrative proteomics and metabolomics analysis reveals the toxicity of cationic liposomes to human normal hepatocyte cell line L02

Changes in the expression of proteins and profiles of metabolites in L02 cells were investigated after exposure to CLs based on the iTRAQ and UHPLC-Q-TOF/MS, and proteomics data were coupled with metabolomics data to comprehensively assess the potential toxicity mechanisms of CLs.

Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer

Reprogramming of energy metabolism is a hallmark of cancer that enables the cancer cells to meet the increased energetic requirements due to uncontrolled proliferation. One prominent example is pancreatic ductal adenocarcinoma, an aggressive form of cancer with an overall 5-year survival rate of 5%. The reprogramming mechanism in pancreatic cancer involves deregulated uptake of glucose and glutamine and other opportunistic modes of satisfying energetic demands in a hypoxic and nutrient-poor environment. In the current study, we apply systems biology approaches to enable a better understanding of the dynamics of the distinct metabolic alterations in KRAS-mediated pancreatic cancer, with the goal of impeding early cell proliferation by identifying the optimal metabolic enzymes to target. We have constructed a kinetic model of metabolism represented as a set of ordinary differential equations that describe time evolution of the metabolite concentrations in glycolysis, glutaminolysis, tricarboxylic acid cycle and the pentose phosphate pathway. The model is comprised of 46 metabolites and 53 reactions. The mathematical model is fit to published enzyme knockdown experimental data. We then applied the model to perform in silico enzyme modulations and evaluate the effects on cell proliferation. Our work identifies potential combinations of enzyme knockdown, metabolite inhibition, and extracellular conditions that impede cell proliferation. Excitingly, the model predicts novel targets that can be tested experimentally. Therefore, the model is a tool to predict the effects of inhibiting specific metabolic reactions within pancreatic cancer cells, which is difficult to measure experimentally, as well as test further hypotheses toward targeted therapies.

Simulation-assisted design of microfluidic sample traps for optimal trapping and culture of non-adherent single cells, tissues, and spheroids

This work focuses on modelling design and operation of "microfluidic sample traps" (MSTs). MSTs regroup a widely used class of microdevices that incorporate wells, recesses or chambers adjacent to a channel to individually trap, culture and/or release submicroliter 3D tissue samples ranging from simple cell aggregates and spheroids, to ex vivo tissue samples and other submillimetre-scale tissue models. Numerous MST designs employing various trapping mechanisms have been proposed in the literature, spurring the development of 3D tissue models for drug discovery and personalized medicine. Yet, there lacks a general framework to optimize trapping stability, trapping time, shear stress, and sample metabolism. Herein, the effects of hydrodynamics and diffusion-reaction on tissue viability and device operation are investigated using analytical and finite element methods with systematic parametric sweeps over independent design variables chosen to correspond to the four design degrees of freedom. Combining different results, we show that, for a spherical tissue of diameter d < 500 μm, the simplest, closest to optimal trap shape is a cube of dimensions w equal to twice the tissue diameter: w = 2d. Furthermore, to sustain tissues without perfusion, available medium volume per trap needs to be 100× the tissue volume to ensure optimal metabolism for at least 24 hours.

Micro-dissected tumor tissues on chip: an ex vivo method for drug testing and personalized therapy

In cancer research and personalized medicine, new tissue culture models are needed to better predict the response of patients to therapies. With a concern for the small volume of tissue typically obtained through a biopsy, we describe a method to reproducibly section live tumor tissue to submillimeter sizes. These micro-dissected tissues (MDTs) share with spheroids the advantages of being easily manipulated on-chip and kept alive for periods extending over one week, while being biologically relevant for numerous assays. At dimensions below ~420 μm in diameter, as suggested by a simple metabolite transport model and confirmed experimentally, continuous perfusion is not required to keep samples alive, considerably simplifying the technical challenges. For the long-term culture of MDTs, we describe a simple microfluidic platform that can reliably trap samples in a low shear stress environment. We report the analysis of MDT viability for eight different types of tissues (four mouse xenografts derived from human cancer cell lines, three from ovarian and prostate cancer patients, and one from a patient with benign prostatic hyperplasia) analyzed by both confocal microscopy and flow cytometry over an 8-day incubation period. Finally, we provide a proof of principle for chemosensitivity testing of human tissue from a cancer patient performed using the described MDT chip method. This technology has the potential to improve treatment success rates by identifying potential responders earlier during the course of treatment and providing opportunities for direct drug testing on patient tissues in early drug development stages.

The effect of co-delivery of paclitaxel and curcumin by transferrin-targeted PEG-PE-based mixed micelles on resistant ovarian cancer in 3-D spheroids and in vivo tumors

Multicellular 3D cancer cell culture (spheroids) resemble to in vivo tumors in terms of shape, cell morphology, growth kinetics, gene expression and drug response. However, these characteristics cause very limited drug penetration into deeper parts of the spheroids. In this study, we used multi drug resistant (MDR) ovarian cancer cell spheroid and in vivo tumor models to evaluate the co-delivery of paclitaxel (PCL) and a potent NF-κB inhibitor curcumin (CUR). PCL and CUR were co-loaded into the polyethylene glycol-phosphatidyl ethanolamine (PEG-PE) based polymeric micelles modified with transferrin (TF) as the targeting ligand. Cytotoxicity, cellular association and accumulation into the deeper layers were investigated in the spheroids and compared with the monolayer cell culture. Comparing to non-targeted micelles, flow cytometry and confocal imaging proved significantly deeper and higher micelle penetration into the spheroids with TF-targeting. Both in monolayers and in spheroids, PCL cytotoxicity was significantly increased when co-delivered with CUR in non-targeted micelles or as single agent in TF-targeted micelles, whereas TF-modification of co-loaded micelles did not further enhance the cytotoxicity. In vivo tumor inhibition studies showed good correlation with the 3D cell culture experiments, which suggests the current spheroid model can be used as an intermediate model for the evaluation of co-delivery of anticancer compounds in targeted micelles.

Understanding Drug Resistance in Breast Cancer with Mathematical Oncology

Chemotherapy is mainstay of treatment for the majority of patients with breast cancer, but results in only 26% of patients with distant metastasis living 5 years past treatment in the United States, largely due to drug resistance. The complexity of drug resistance calls for an integrated approach of mathematical modeling and experimental investigation to develop quantitative tools that reveal insights into drug resistance mechanisms, predict chemotherapy efficacy, and identify novel treatment approaches. This paper reviews recent modeling work for understanding cancer drug resistance through the use of computer simulations of molecular signaling networks and cancerous tissues, with a particular focus on breast cancer. These mathematical models are developed by drawing on current advances in molecular biology, physical characterization of tumors, and emerging drug delivery methods (e.g., nanotherapeutics). We focus our discussion on representative modeling works that have provided quantitative insight into chemotherapy resistance in breast cancer and how drug resistance can be overcome or minimized to optimize chemotherapy treatment. We also discuss future directions of mathematical modeling in understanding drug resistance.

Rapid uptake of glucose and lactate, and not hypoxia, induces apoptosis in three-dimensional tumor tissue culture

The spatial arrangement of cellular metabolism in tumor tissue critically affects the treatment of cancer. However, little is known about how diffusion and cellular uptake relate to intracellular metabolism and cell death in three dimensions. To quantify these mechanisms, fluorescent microscopy and multicellular tumor cylindroids were used to measure pH and oxygen profiles, and quantify the distribution of viable, apoptotic and necrotic cells. Spheroid dissociation, enzymatic analysis, and mass spectrometry were used to measure concentration profiles of glucose, lactate and glutamine. A mathematical model was used to integrate these measurements and calculate metabolic rate parameters. It was found that large cylindroids, >500 μm in diameter, contained apoptotic and necrotic cells, whereas small cylindroids contained apoptotic but not necrotic cells. The center of cylindroids was found to be acidic but not hypoxic. From the edge to the center, concentrations of glucose, lactate and glutamine decreased rapidly. Throughout the cell masses lactate was consumed and not produced. These measurements indicate that apoptosis was the primary mechanism of cell death; acidity was not caused by lactic acid; and cell death was caused by depletion of carbon sources and not hypoxia. The mathematical model showed that the transporter enzymes for glucose and lactate were not saturated; oxygen uptake was limited by intracellular metabolism; and oxygen uptake was not limited by membrane-transport or diffusion. Unsaturated transmembrane uptake may be the cause of both proliferative and apoptotic regimes in cancer. These results suggest that transporter enzymes are excellent targets for treating well oxygenated tumors.

Towards an integrated systems-based modelling framework for drug transport and its effect on tumour cells

Background

A systematic understanding of chemotherapeutic influence on solid tumours is highly challenging and complex as it encompasses the interplay of phenomena occurring at multiple scales. It is desirable to have a multiscale systems framework capable of disentangling the individual roles of multiple contributing factors, such as transport and extracellular factors, and purely intracellular factors, as well as the interactions among these factors. Based on a recently developed systems-based modelling framework, we have developed a coupled system in order to further elucidate the role of drug transport, and its interplay with cellular signalling by incorporating intra- and extra-vascular drug transport in tumour, dynamic descriptions of intracellular signalling and tumour cell density dynamics.

Results

Different aspects of the interaction between transport and cell signalling and the effects of transport parameters have been investigated in silico. Limited drug penetration is found to be a major constraint in inducing drug effect; many aspects of the interaction of transport with cell signalling are independent of the details of cell signalling. A sensitivity analysis indicates that the effect of drug diffusivity depends on the balance between interstitial drug transport and the specific requirement for triggering apoptosis (governed by highly nonlinear signalling networks), suggesting that the effect of drug diffusivity in such cases must be considered in conjunction with descriptions of cellular dynamics.

Conclusions

The modelling framework developed in this study provides qualitative and mechanistic insights into the effect of drug on tumour cells. It provides an in silico experimental platform to investigate the interplay between extracellular factors (e.g. transport) and intracellular factors. Such a platform is essential to understanding the individual and combined effects of transport and cellular factors in solid tumour.

Lipid A controls the robustness of intratumoral accumulation of attenuated Salmonella in mice

Engineered Salmonella have the potential to treat cancers that are not responsive to standard molecular therapies. This potential has not been realized because colonization in human tumors is insufficient and variable as shown in preliminary phase I trials. Recent studies have shown that Salmonella colonization is associated with an inflammatory response mediated by tumor necrosis factor (TNF). An injectable agent, molecular lipid A, could be used to control bacterial accumulation because it induces TNF production and is rapidly cleared. We hypothesized that concurrently administrating lipid A with attenuated Salmonella would increase intratumoral accumulation, improve the robustness of tumor-targeting and be nontoxic. To test this hypothesis, Salmonella and lipid A were injected into mice with 4T1 mammary tumors. Colonization was quantified after 48 hr using anti-Salmonella immunofluorescence. A 2 μg/mouse dose of lipid A increased the area of colonized tissue fourfold, reduced variance 50% and ensured colonization in all mice. Comparatively, Salmonella failed to colonize some control mice, similar to human trials. No toxicity was observed in any treated mice. The fraction of tumor tissue with more than 25% bacterial coverage was eight times greater for treated mice compared to controls. Lipid A treatment also reduced the maximum average distance of tissue to Salmonella colonies from 1348 to 260 μm. A mathematical model of bacterial drug production predicted that 2 μg lipid A would increase tumor cell death by 82%. These results suggest that lipid A could solve the clinical challenges of Salmonella therapy and enable safe and robust treatment of cancer with bacteria.

Current advances in mathematical modeling of anti-cancer drug penetration into tumor tissues

Delivery of anti-cancer drugs to tumor tissues, including their interstitial transport and cellular uptake, is a complex process involving various biochemical, mechanical, and biophysical factors. Mathematical modeling provides a means through which to understand this complexity better, as well as to examine interactions between contributing components in a systematic way via computational simulations and quantitative analyses. In this review, we present the current state of mathematical modeling approaches that address phenomena related to drug delivery. We describe how various types of models were used to predict spatio-temporal distributions of drugs within the tumor tissue, to simulate different ways to overcome barriers to drug transport, or to optimize treatment schedules. Finally, we discuss how integration of mathematical modeling with experimental or clinical data can provide better tools to understand the drug delivery process, in particular to examine the specific tissue- or compound-related factors that limit drug penetration through tumors. Such tools will be important in designing new chemotherapy targets and optimal treatment strategies, as well as in developing non-invasive diagnosis to monitor treatment response and detect tumor recurrence.

Pharmacodynamic modeling of cell cycle and apoptotic effects of gemcitabine on pancreatic adenocarcinoma cells

PURPOSE

The standard of care for treating patients with pancreatic adenocarcinomas includes gemcitabine (2',2'-difluorodeoxycytidine). Gemcitabine primarily elicits its response by stalling the DNA replication forks of cells in the S phase of the cell cycle. To provide a quantitative framework for characterizing the cell cycle and apoptotic effects of gemcitabine, we developed a pharmacodynamic model in which the activation of cell cycle checkpoints or cell death is dependent on gemcitabine exposure.

METHODS

Three pancreatic adenocarcinoma cell lines (AsPC-1, BxPC-3, and MiaPaca-2) were exposed to varying concentrations (0-100,000 ng/mL) of gemcitabine over a period of 96 h in order to quantify proliferation kinetics and cell distributions among the cell cycle phases. The model assumes that the drug can inhibit cycle-phase transitioning in each of the 3 phases (G1, S, and G2/M) and can cause apoptosis of cells in G1 and G2/M phases. Fitting was performed using the ADAPT5 program.

RESULTS

The time course of gemcitabine effects was well described by the model, and parameters were estimated with good precision. Model predictions and experimental data show that gemcitabine induces cell cycle arrest in the S phase at low concentrations, whereas higher concentrations induce arrest in all cell cycle phases. Furthermore, apoptotic effects of gemcitabine appear to be minimal and take place at later time points.

CONCLUSION

The pharmacodynamic model developed provides a quantitative, mechanistic interpretation of gemcitabine efficacy in 3 pancreatic cancer cell lines, and provides useful insights for rational selection of chemotherapeutic agents for combination therapy.

Ovarian carcinomatosis: how the radiologist can help plan the surgical approach

Ovarian carcinoma is the most common cause of death due to gynecologic malignancy. Peritoneal involvement is present in approximately 70% of patients at the time of initial diagnosis. The disease spreads abdominally by direct extension, exfoliation of tumor cells into the peritoneal space, and dissemination of tumor cells along lymphatic pathways. Carcinomatosis characterizes an advanced stage of disease in which peritoneal disease has spread throughout the upper abdomen (stage IIIC) or in which diffuse peritoneal disease is accompanied by malignant pleural infiltration or visceral metastases (stage IV). Common sites of intraperitoneal seeding of ovarian carcinoma include the pelvis, omentum, paracolic gutters, liver capsule, and diaphragm. Soft-tissue thickening, nodularity, and enhancement are all signs of peritoneal involvement. Advanced-stage disease is treated either with initial cytoreductive surgery (debulking) followed by adjuvant chemotherapy, or with initial neoadjuvant chemotherapy followed by debulking. Radiologic imaging plays an important role in the selection of patients who may benefit from neoadjuvant chemotherapy before debulking. However, accurate interpretation of the imaging findings is challenging and requires a detailed knowledge of the complex peritoneal anatomy, directionality of flow of peritoneal fluid, and specific disease sites that are likely to present particular difficulties with regard to surgical access and technique. Although there is as yet no clear consensus on the criteria for resectability of peritoneal lesions, extensive involvement of the small bowel or mesenteric root, involved lymph nodes superior to the celiac axis, pleural infiltration, pelvic sidewall invasion, bladder trigone involvement, and hepatic parenchymal metastases or implants near the right hepatic vein are considered indicative of potential nonresectability. Implants larger than 2 cm in diameter in the diaphragm, lesser sac, porta hepatis, intersegmental fissure, gallbladder fossa, or gastrosplenic or gastrohepatic ligament also may represent nonresectable disease.

In silico modelling of treatment-induced tumour cell kill: developments and advances

Mathematical and stochastic computer (in silico) models of tumour growth and treatment response of the past and current eras are presented, outlining the aims of the models, model methodology, the key parameters used to describe the tumour system, and treatment modality applied, as well as reported outcomes from simulations. Fractionated radiotherapy, chemotherapy, and combined therapies are reviewed, providing a comprehensive overview of the modelling literature for current modellers and radiobiologists to ignite the interest of other computational scientists and health professionals of the ever evolving and clinically relevant field of tumour modelling.

The dynamics of drug resistance: a mathematical perspective

Resistance to chemotherapy is a key impediment to successful cancer treatment that has been intensively studied for the last three decades. Several central mechanisms have been identified as contributing to the resistance. In the case of multidrug resistance (MDR), the cell becomes resistant to a variety of structurally and mechanistically unrelated drugs in addition to the drug initially administered. Mathematical models of drug resistance have dealt with many of the known aspects of this field, such as pharmacologic sanctuary and location/diffusion resistance, intrinsic resistance, induced resistance and acquired resistance. In addition, there are mathematical models that take into account the kinetic/phase resistance, and models that investigate intracellular mechanisms based on specific biological functions (such as ABC transporters, apoptosis and repair mechanisms). This review covers aspects of MDR that have been mathematically studied, and explains how, from a methodological perspective, mathematics can be used to study drug resistance. We discuss quantitative approaches of mathematical analysis, and demonstrate how mathematics can be used in combination with other experimental and clinical tools. We emphasize the potential benefits of integrating analytical and mathematical methods into future clinical and experimental studies of drug resistance.

Use of mathematical models to understand anticancer drug delivery and its effect on solid tumors

The transport of anticancer drugs and their effect on tumor cells involve a number of physical and biochemical processes. Mathematical modeling provides a tool to help us understand the interaction of these complex processes, thereby contributing to the improvement and optimization of drug delivery. This article starts with a discussion of the biological and physiological properties of tumors, which are often found as barriers to anticancer drug transport and effect. A broad spectrum of mathematical models is reviewed to give an overview of the current state of modeling approaches and different categories of models are outlined. These include pharmacokinetic and transport-based models for the prediction of temporal and temporal-spatial profiles of antidrug concentrations, as well as empirical or deterministic models to describe the effect of drug. We conclude that the systematic elucidation and integration of cellular signal transduction with the biophysical aspects of drug transport will lead to a better understanding of the entire drug-delivery process.

Dynamic modeling and analysis of cancer cellular network motifs

With the advent of high-throughput biology, we now routinely scan cells and organisms at practically all levels, from genome to protein, metabolism, signaling and other cellular functions. This methodology allowed biological studies to move from a reductionist approach, such as isolation of specific pathways and mechanisms, to a more integrative approach, where biological systems are seen as a network of interconnected components that provide specific outputs and functions in response to stimuli. Recent literature on biological networks demonstrates two important concepts that we will consider in this review: (i) cellular pathways are highly interconnected and should not be studied separately, but as a network; (ii) simple, recurrent feedback motifs within the network can produce very specific functions that favor their modular use. The first theme differs from the traditional approach in biology because it provides a framework (i.e., the network view) in which large datasets are analyzed with an unbiased view. The second theme (feedback motifs) shows the importance of locally analyzing the dynamic properties of biological networks in order to better understand their functionality. We will review these themes with examples from cell signaling networks, gene regulatory networks and metabolic pathways. The deregulation of cellular networks (metabolism, signaling etc.) is involved in cancer, but the size of the networks and resulting non-linear behavior do not allow for intuitive reasoning. In that context, we argue that the qualitative classification of the 'building blocs' of biological networks (i.e. the motifs) in terms of dynamics and functionality will be critical to improve our understanding of cancer biology and rationalize the wealth of information from high-throughput experiments. From the examples highlighted in this review, it is clear that dynamic feedback motifs can be used to provide a unified view of various cellular processes involved in cancer and this will be critical for future research on personalized and predictive cancer therapies.

Enzymatic features of the glucose metabolism in tumor cells

Many tumor types exhibit an impaired Pasteur effect, i.e. despite the presence of oxygen, glucose is consumed at an extraordinarily high rate compared with the tissue from which they originate - the so-called 'Warburg effect'. Glucose has to serve as the source for a diverse array of cellular functions, including energy production, synthesis of nucleotides and lipids, membrane synthesis and generation of redox equivalents for antioxidative defense. Tumor cells acquire specific enzyme-regulatory mechanisms to direct the main flux of glucose carbons to those pathways most urgently required under challenging external conditions such as varying substrate availability, presence of anti-cancer drugs or different phases of the cell cycle. In this review we summarize the currently available information on tumor-specific expression, activity and kinetic properties of enzymes involved in the main pathways of glucose metabolism with due regard to the explanation of the regulatory basis and physiological significance of the Warburg effect. We conclude that, besides the expression level of the metabolic enzymes involved in the glucose metabolism of tumor cells, the unique tumor-specific pattern of isozymes and accompanying changes in the metabolic regulation below the translation level enable tumor cells to drain selfishly the blood glucose pool that non-transformed cells use as sparingly as possible.

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.

Mathematical modeling as a tool for planning anticancer therapy

We review a large volume of literature concerning mathematical models of cancer therapy, oriented towards optimization of treatment protocols. The review, although partly idiosyncratic, covers such major areas of therapy optimization as phase-specific chemotherapy, antiangiogenic therapy and therapy under drug resistance. We start from early cell cycle progression models, very simple but admitting explicit mathematical solutions, based on methods of control theory. We continue with more complex models involving evolution of drug resistance and pharmacokinetic and pharmacodynamic effects. Then, we consider two more recent areas: angiogenesis of tumors and molecular signaling within and among cells. We discuss biological background and mathematical techniques of this field, which has a large although only partly realized potential for contributing to cancer treatment.

Single-cell analysis demonstrates how nutrient deprivation creates apoptotic and quiescent cell populations in tumor cylindroids

Understanding how quiescent and apoptotic populations form in tumors is necessary because these cell types can considerably diminish therapeutic efficacy. Most cancer therapeutics are ineffective against quiescent cells because they target rapidly proliferating cells. Distinguishing apoptosis is important because apoptotic cells are committed to death and do not require treatment. Regrowth of quiescent cell can lead to tumor re-occurrence and metastasis, which are the leading causes of cancer mortality. We hypothesized that cylindroid cultures and acridine orange staining could be used to determine how nutrient diffusion creates apoptotic and quiescent regions in tumors. To test this hypothesis we developed a microscopy technique to measure cellular DNA and RNA content in single cells using thin cylindroids and acridine orange staining. Cell classification was compared to flow cytometry of cells grown in defined monolayer cultures. The presence of apoptosis was confirmed by morphological nuclear analysis. The effect of diffusion was determined by varying incubation time, cylindroid size, and exposing cylindroids to nutrient-deficient media. Four overlapping regions were identified as a function of cylindroid radius: an outer viable/quiescent region; a second quiescent/apoptotic region; a third late-stage apoptotic region; and an inner dead region. In monolayer cultures the absence of glutamine and growth factors induced apoptosis and hypoxia induced quiescence. Treating with nutrient-deficient media suggested that cells became quiescent near the periphery because of glucose and oxygen limitations, and became apoptotic and died further from the edge because of glutamine and growth factor limitations. These results show that cellular microenvironments can be identified in cylindroids using simple acridine orange staining and that single cell fluorescence can be measured in three-dimensional culture. The developed techniques will be useful for developing cancer therapies and determining how cell death and apoptosis are induced in three-dimensional tumor tissue.