Modelling of Tirapazamine effects on solid tumour morphology

Bioreductive drugs are in clinical practice to exploit the resistance from tumour microenvironments especially in the hypoxic region of tumour. We pre-sented a tumour treatment model to capture the pharmacology of one of the most prominent bioreductive drugs, Tirapazamine (TPZ) which is in clinical trials I and II. We calculated solid tumour mass in our previous work and then integrated that model with TPZ infusion. We calculated TPZ cytotoxicity, concentration, penetra-tion with increasing distance from blood vessel and offered resistance from micro-environments for drug penetration inside the tumour while considering each cell as an individual entity. The impact of these factors on tumour morphology is also showed to see the drug behaviour inside animals/humans tumours. We maintained the heterogeneity factors in presented model as observed in real tumour mass es-pecially in terms of cells proliferation, cell movement, extracellular matrix (ECM) interaction, and the gradients of partial oxygen pressure (pO2) inside tumour cells during the whole growth and treatment activity. The results suggest that TPZ high concentration in combination with chemotherapy should be given to get maximum abnormal cell killing. This model can be a good choice for oncologists and re-searchers to explore more about TPZ action inside solid tumour

An in silico model to demonstrate the effects of Maspin on cancer cell dynamics

Most cancer treatments efficacy depends on tumor metastasis suppression, where tumor suppressor genes play an important role. Maspin (Mammary Serine Protease Inhibitor), an non-inhibitory serpin has been reported as a potential tumor suppressor to influence cell migration, adhesion, proliferation and apoptosis in in vitro and in vivo experiments in last two decades. Lack of computational investigations hinders its ability to go through clinical trials. Previously, we reported first computational model for maspin effects on tumor growth using artificial neural network and cellular automata paradigm with in vitro data support. This paper extends the previous in silico model by encompassing how maspin influences cell migration and the cell–extracellular matrix interaction in subcellular level. A feedforward neural network was used to define each cell behavior (proliferation, quiescence, apoptosis) which followed a cell-cycle algorithm to show the microenvironment impacts over tumor growth. Furthermore, the model concentrates how the in silico experiments results can further confirm the fact that maspin reduces cell migration using specific in vitro data verification method. The data collected from in vitro and in silico experiments formulates an unsupervised learning problem which can be solved by using different clustering algorithms. A density based clustering technique was developed to measure the similarity between two datasets based on the number of links between instances. Our proposed clustering algorithm first finds the nearest neighbors of each instance, and then redefines the similarity between pairs of instances in terms of how many nearest neighbors share the two instances. The number of links between two instances is defined as the number of common neighbors they have. The results showed significant resemblances with in vitro experimental data. The results also offer a new insight into the dynamics of maspin and establish as a metastasis suppressor gene for further molecular research

A proteomic and genomic investigation into the role of lamin A in colorectal cancer cell motility

Lamins are type V intermediate filament proteins found at the nuclear envelope. Expression of lamin A in colorectal cancer (CRC) tumours is correlated with poor prognosis and expression of lamin A in CRC cell lines promotes greatly increased cell motility. The aim of this study was to identify proteins that promote cell motility in response to lamin A expression and to investigate lamin A regulated changes in gene/protein expression and cytoskeletal organisation that might underpin the increased cell motility. The effects of lamin A expression were studied using quantitative proteomic and genomic methods using cells from the colorectal cancer cell line SW480 which had been transfected with GFP-lamin A (SW480/lamA) or GFP as a control (SW480/cntl). A biochemical fractionation technique was optimised for the preparation of cytoskeletal fractions which were analysed by 2D DIGE (2D difference in-gel electrophoresis) to reveal accurate and reproducible changes in the representation of proteins within the cytoskeleton in SW480/lamA cells compared to controls. The majority of proteins identified were either components of the actin/intermediate filament cytoskeleton, protein chaperones or translation initiation/elongation factors. Interestingly, tissue transglutaminase 2, a protein which modifies elements of the cytoskeleton and is associated with cancer progression, was highly over-represented in the cytoskeleton fraction of SW480/lamA cells. Ingenuity Pathway Analysis was used to analyse genome-wide Affymetrix microarray analysis of SW480/cntl and SW480/lamA cell lines. A highly significant interaction network was identified which clustered together genes linked to cancer, cellular movement and cellular growth and proliferation. Epithelial markers such as CDH1 were down-regulated and mesenchymal markers such as FN1 were up-regulated in cells expressing GFP-lamin A, which suggested that lamin A over-expression may lead to an epithelial-mesenchymal transition (EMT). As A-type lamins are known to modulate downstream effects of TGFβ signalling, and TGFβ is an inducer of EMT, changes in genes involved in TGFβ signalling were investigated. Knockdown of lamin A using siRNA led to decreased expression of TGFBI and SNAI2 followed by reduced cell motility. The data suggest that expression of lamin A in CRC cells causes changes in the organisation of the actin cytoskeleton and in TGFβ signalling, potentially involving an epithelial to mesenchymal transition, leading to increased cell motility and an increased risk of death from cancer

Induction of Epithelial-Mesenchymal Transition (EMT) in women´s cancer : protective role of differentiation factors

Metastatic spread of cancer cells to vital organs is the predominant cause of death among women suffering from breast and ovarian cancer, and invasive cancer cells are in many cases resilient to standard drugs used in the clinic. Consequently, further understanding of the metastatic process and development of new strategies to target invasive cancer cells are needed. One process that has been closely linked to cancer cell invasion and migration is epithelial-mesenchymal transition (EMT), a developmental process, which can be reactivated during cancer progression. EMT allows carcinoma cells, with an epithelial origin, to acquire mesenchymal and migratory properties that are employed to invade the surrounding tumor tissue. The overall aim of this thesis was to investigate how EMT is induced in breast and ovarian cancer cells and to study the role of EMT in drug resistance. Relapse of resilient cancer cells after surgery and first line of drug treatments is a major cause of death in ovarian and breast cancer. Currently, little is known about the functional properties of cancer cells that develop resistance to existing drug treatments and how they can be targeted. The aim of study I was to characterize the phenotypic properties of ovarian cancer cells that developed resistance to cisplatin, a chemotherapeutic drug commonly used in the clinic. We found that human SKOV-3 ovarian cancer cells that acquired resistance to cisplatin gained properties of EMT and cancer stem cells, suggesting that they were more invasive than drug-sensitive cells. Indeed, functional experiments showed that cisplatin-resistant SKOV-3 cells were more migratory in invasion assays and displayed an increased tumor initiating capacity compared to cisplatin-sensitive cells. The results from these studies link EMT to drug resistance in ovarian cancer cells, and emphasize that further understanding of EMT is needed and to be able to target EMT for therapy. In study II-IV we investigated how cellular sensitivity to EMT is regulated. In particular, we focused on identifiying epithelial differentiation factors that regulate EMT in breast cancer cells. We identified two transcription factors – C/EBPβ and Foxp4 that were lost during breast cancer progression, which conferred cells an enhanced capacity to undergo EMT as well as to gain invasive and metastatic properties in experimental in vitro and in vivo models of breast cancer. In addition, we identified the coxsackie- and adenovirus receptor (CAR), a tight junction-based cell adhesion molecule, as a novel regulator of Akt signaling and TGF-β-induced EMT in breast cancer cells. The mechanism was traced to a role of CAR in regulating localization, stability and function of the phosphatase Pten, a potent Akt inhibitor, at tight junctions. The results from these studies indicate that the EMT process is not solely regulated by factors that drive a mesenchymal differentiation program, but also, is under tight control by epithelial differentiation factors. Loss of C/EBPβ, Foxp4 and CAR may lead to increased cellular sensitivity to EMT and thereby open up the possibility that cancer cells acquire invasive and migratory properties. Based on this, we propose that novel therapies aiming to strengthen, or preserve, epithelial differentiation mechanisms in breast or ovarian cancer cells, might be useful as a type of differentiation therapy to inhibit cancer cell invasion and metastasis

Mathematical modelling of cancer invasion and metastatic spread

Metastatic spread—the dissemination of cancer cells from a primary tumour with subsequent re-colonisation at secondary sites in the body—causes around 90% of cancer-related deaths. Mathematical modelling may provide a complementary approach to help understand the complex mechanisms underlying metastasis. In particular, the spatiotemporal evolution of individual cancer cells during the so-called invasion-metastasis cascade—i.e. during cancer cell invasion, intravasation, vascular travel, extravasation and metastatic growth—is an aspect not yet explored through existing mathematical models. In this thesis, such a spatially explicit hybrid multi-organ metastasis modelling framework is developed. It describes the invasive growth dynamics of individual cancer cells both at a primary site and at potential secondary metastatic sites in the body, as well as their transport from the primary to the secondary sites. Throughout, the interactions between the cancer cells, matrix-degrading enzymes (MDEs) and the extracellular matrix (ECM) are accounted for. Furthermore, the individual-based framework models phenotypic variation by distinguishing between cancer cells of an epithelial-like, a mesenchymal-like and a mixed phenotype. It also describes permanent and transient mutations between these cell phenotypes in the form of epithelial-mesenchymal transition (EMT) and its reverse process mesenchymal-epithelial transition (MET). Both of these mechanisms are implemented at the biologically appropriate locations of the invasion-metastasis cascade. Finally, cancer cell dormancy and death at the metastatic sites are considered to model the frequently observed maladaptation of metastasised cancer cells to their new microenvironments. To investigate the EMT-process further, an additional three-dimensional discrete-continuum model of EMT- and MET-dependent cancer cell invasion is developed. It consists of a hybrid system of partial and stochastic differential equations that describe the evolution of epithelial-like and mesenchymal-like cancer cells, again under the consideration of MDE concentrations and the ECM density. Using inverse parameter estimation and sensitivity analysis, this model is calibrated to an in vitro organotypic assay experiment that examines the invasion of HSC-3 cancer cells

Identifying the molecular components that matter: a statistical modelling approach to linking functional genomics data to cell physiology

Functional genomics technologies, in which thousands of mRNAs, proteins, or metabolites can be measured in single experiments, have contributed to reshape biological investigations. One of the most important issues in the analysis of the generated large datasets is the selection of relatively small sub-sets of variables that are predictive of the physiological state of a cell or tissue. In this thesis, a truly multivariate variable selection framework using diverse functional genomics data has been developed, characterized, and tested. This framework has also been used to prove that it is possible to predict the physiological state of the tumour from the molecular state of adjacent normal cells. This allows us to identify novel genes involved in cell to cell communication. Then, using a network inference technique networks representing cell-cell communication in prostate cancer have been inferred. The analysis of these networks has revealed interesting properties that suggests a crucial role of directional signals in controlling the interplay between normal and tumour cell to cell communication. Experimental verification performed in our laboratory has provided evidence that one of the identified genes could be a novel tumour suppressor gene. In conclusion, the findings and methods reported in this thesis have contributed to further understanding of cell to cell interaction and multivariate variable selection not only by applying and extending previous work, but also by proposing novel approaches that can be applied to any functional genomics data

Understanding Proteolytic Processing of Melanoma Cell Adhesion Molecule (MCAM) in Cutaneous Melanoma

Melanoma is a type of skin cancer and is one of the most common cancers in Australia. Due to the aggressive nature of melanoma, an improved understanding of its progression is required. In this thesis, the processing of “Melanoma Cell Adhesion Molecule” (MCAM), a protein that contributes to melanoma metastasis, was explored in detail. In particular, we found that MCAM undergoes at least two cleavage events, generating protein fragments that likely contribute to melanoma spread