Search

Radiotherapy is recognized globally as a mainstay of treatment in most solid tumors and is essential in both curative and palliative settings. Ionizing radiation is frequently combined with surgery, either preoperatively or postoperatively, and with systemic chemotherapy. Recent advances in imaging have enabled precise targeting of solid lesions yet substantial intratumoral heterogeneity means that treatment planning and monitoring remains a clinical challenge as therapy response can take weeks to manifest on conventional imaging and early indications of progression can be misleading. Photoacoustic imaging (PAI) is an emerging modality for molecular imaging of cancer, enabling non-invasive assessment of endogenous tissue chromophores with optical contrast at unprecedented spatio-temporal resolution. Preclinical studies in mouse models have shown that PAI could be used to assess response to radiotherapy and chemoradiotherapy based on changes in the tumor vascular architecture and blood oxygen saturation, which are closely linked to tumor hypoxia. Given the strong relationship between hypoxia and radio-resistance, PAI assessment of the tumor microenvironment has the potential to be applied longitudinally during radiotherapy to detect resistance at much earlier time-points than currently achieved by size measurements and tailor treatments based on tumor oxygen availability and vascular heterogeneity. Here, we review the current state-of-the-art in PAI in the context of radiotherapy research. Based on these studies, we identify promising applications of PAI in radiation oncology and discuss the future potential and outstanding challenges in the development of translational PAI biomarkers of early response to radiotherapy.

The main reason why therapeutic schemes fail in Glioblastoma lies on its own peculiarities as a cancer and on our failure to fully decipher them. Fast tumor evolution, invasiveness and incomplete surgical resection contribute to disease recurrence, therapy resistance and high mortality. More faithfull models must be developed to address Glioblastoma biology and better clinical guidance. Research studies are discussed in this review that: i) improve understanding and assessment of the growth mechanisms of Glioblastoma and ii) develop preclinical models (in vitro-in vivo-in silico) that mimic patients tumor (phenocopying) in order to provide better prediction of response to therapies.

Glioblastoma is the most malignant brain tumor among adults. Despite multimodality treatment, it remains incurable, mainly because of its extensive heterogeneity and infiltration in the brain parenchyma. Recent evidence indicates dysregulation of the expression of the Promyelocytic Leukemia Protein (PML) in primary Glioblastoma samples. PML is implicated in various ways in cancer biology. In the brain, PML participates in the physiological migration of the neural progenitor cells, which have been hypothesized to serve as the cell of origin of Glioblastoma. The role of PML in Glioblastoma progression has recently gained attention due to its controversial effects in overall Glioblastoma evolution. In this work, we studied the role of PML in Glioblastoma pathophysiology using the U87MG cell line. We genetically modified the cells to conditionally overexpress the PML isoform IV and we focused on its dual role in tumor growth and invasive capacity. Furthermore, we targeted a PML action mediator, the Enhancer of Zeste Homolog 2 (EZH2), via the inhibitory drug DZNeP. We present a combined in vitro–in silico approach, that utilizes both 2D and 3D cultures and cancer-predictive computational algorithms, in order to differentiate and interpret the observed biological results. Our overall findings indicate that PML regulates growth and invasion through distinct cellular mechanisms. In particular, PML overexpression suppresses cell proliferation, while it maintains the invasive capacity of the U87MG Glioblastoma cells and, upon inhibition of the PML-EZH2 pathway, the invasion is drastically eliminated. Our in silico simulations suggest that the underlying mechanism of PML-driven Glioblastoma physiology regulates invasion by differential modulation of the cell-to-cell adhesive and diffusive capacity of the cells. Elucidating further the role of PML in Glioblastoma biology could set PML as a potential molecular biomarker of the tumor progression and its mediated pathway as a therapeutic target, aiming at inhibiting cell growth and potentially clonal evolution regarding their proliferative and/or invasive phenotype within the heterogeneous tumor mass.

Breast cancer and Glioblastoma brain cancer are aggressive malignancies with poor prognosis. In this study primary Glioblastoma and secondary breast cancer spheroids are formed and treated with the well-known Temozolomide and Doxorubicin chemotherapeutics, respectively. A custom multi-angle Light Sheet Fluorescence Microscope is employed for high resolution imaging of both cancer cell spheroids. Such a technique is successful in realizing pre-clinical drug screening, while enables the discrimination among physiologic tumor parameters. LSFM technique, parameters and method followed are also presented.

The application of accurate cancer predictive algorithms validated with experimental data is a field concerning both basic researchers and clinicians, especially regarding a highly aggressive form of cancer, such as Glioblastoma. In an aim to enhance prediction accuracy in realistic patient-specific environments, accounting for both inter- and intratumoral heterogeneity, we use patient-derived Glioblastoma cells from different patients. We focus on cell proliferation using in vitro experiments to estimate cell doubling times and sizes for established primary Glioblastoma cell lines. A preclinically driven mathematical model parametrization is accomplished by taking into account the experimental measurements. As a control cell line we use the well-studied U87MG cells. Both in vitro and in silico results presented support that the variance between tumor staging can be attributed to the differential proliferative capacity of the different Glioblastoma cells. More specifically, the intratumoral heterogeneity together with the overall proliferation reflected in both the proliferation rate and the mechanical cell contact inhibition can predict the in vitro evolution of different Glioblastoma cell lines growing under the same conditions. Undoubtedly, additional imaging techniques capable of providing spatial information of tumor cell physiology and microenvironment will enhance our understanding regarding Glioblastoma nature and verify and further improve our predictability.

Major Glioblastoma’s hallmarks include proliferation, invasion and heterogeneity. Biological 3D tumor spheroid models can serve as intermediate systems between traditional 2D cell culture and complex in vivo models. Tumor spheroids have been shown to more accurately reproduce the spatial organization and microenvironmental factors of in vivo micro-tumors, such as relevant gradients of nutrients and other molecular agents, while they maintain cell-to-cell and cell-to-matrix interactions. In vitro 3D assays are useful to monitor these properties. Here, we test the suitability of the well-known T98 G Glioblastoma cell line in such a 3D assay. The doubling time and death rate parameters of T98 G are estimated, as well as their spheroidal growth-expansion curves with and without the presence of basement membrane substrate. The T98 G invasive profile is characterized by collective morphology and proliferation-associated invasion. We show that the T98 G secondary GB cell line exhibits both invasive and proliferative capabilities in 3D and thus, can serve as control cell line for the 3D in vitro study of primary GB cell cultures.

Glioblastoma cells adopt migration strategies to invade into the brain parenchyma ranging from individual to collective mechanisms, whose role and dynamics are not yet fully understood. In this work, we explore Glioblastoma heterogeneity and recapitulate its invasive patterns both in vitro, by utilizing primary cells along with the U87MG cell line, and in silico, by adopting discrete, individual cell-based mathematics. Glioblastoma cells are cultured three-dimensionally in an ECM-like substrate. The primary Glioblastoma spheroids adopt a novel cohesive pattern, mimicking perivascular invasion in the brain, while the U87MG adopt a typical, starburst invasive pattern under the same experimental setup. Mathematically, we focus on the role of the intrinsic heterogeneity with respect to cell-to-cell adhesion. Our proposed mathematical approach mimics the invasive morphologies observed in vitro and predicts the dynamics of tumour expansion. The role of the proliferation and migration is also explored showing that their effect on tumour morphology is different per cell type. The proposed model suggests that allowing cell-to-cell adhesive heterogeneity within the tumour population is sufficient for variable invasive morphologies to emerge which remain originally undetectable by conventional imaging, indicating that exploration in pathological samples is needed to improve our understanding and reveal potential patient-specific therapeutic targets.

Glioblastoma multiforme is the most aggressive type of glioma and the most common malignant primary intra-axial brain tumor. In an effort to predict the evolution of the disease and optimize therapeutical decisions, several models have been proposed for simulating the growth pattern of glioma. One of the latest models incorporates cell proliferation and invasion, angiogenic net rates, oxygen consumption, and vasculature. These factors, particularly oxygenation levels, are considered fundamental factors of tumor heterogeneity and compartmentalization. This paper focuses on the initialization of the cancer cell populations and vasculature based on imaging examinations of the patient and presents a feasibility study on vasculature prediction over time. To this end, pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging using Toft's model are used in order to feed the model. Ktrans is used as a metric of the density of endothelial cells (vasculature); at the same time, it also helps to discriminate distinct image areas of interest, under a set of assumptions. Feasibility results of applying the model to a real clinical case are presented, including a study on the effect of certain parameters on the pattern of the simulated tumor.

(1)H NMR spectroscopy was employed to investigate the repercussion of Origanum dictamnus tea ingestion in several volunteers' urine metabolic profiles, among them two with chronic inflammatory bowel diseases (IBD), mild IBD and Crohn's disease. Herein, we demonstrate that the concentrations of a lot of urinary metabolites such as hippurate, trimethylamine oxide (TMAO), citrate, and creatinine are altered, which prompts the intestinal microflora function/content perturbation as well as kidney function regulation by dictamnus tea. Interestingly, our preliminary results showed that a high dose of dictamnus tea intake appeared to be toxic for a person with Crohn's disease, since it caused high endogenous ethanol excretion in urine. All subjects' metabolic effects caused by the dictamnus tea appeared to be reversible, when all volunteers stopped its consumption. Finally, we highlight that individuals' metabolic phenotype is reflected in their urine biofluid before and after the dictamnus tea effect while all individuals have some common and different metabolic responses to this tea, implying that each phenotype has a quite different response to this tea consumption.

Brain tumor lesions (BTL), i.e. high grade gliomas, are known to have a prominence of vasculature, which is promoted through hypoxia mechanisms and differential expression of vascular endothelial growth factor (VEGF). Imaging techniques such as dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with intra-venous (i.v.) administration of a Gd-based contrast agent (GBCA) are successfully used for the diagnosis and characterization of the BTLs. Tracer kinetics plays an important role in DCE-MRI by assessing the vessel leakage through estimation of the transfer and disposition of GBCAs in a lesion. Physiologically-based pharmacokinetic modeling (PBPK) represents a well-documented approach to estimate in silico the disposition of pharmacologic agents in the body. In this work, we sought to i)present a whole-body PBPK approach in order to estimate the PK profile of Gd-DTPA (Gadopentetic acid, Magnevist®) and ii)evaluate the impact of vascular fraction of tracer's extravascular-extracellular disposition in a simulated BTL. PK profile was assessed through the application of Simcyp® simulator platform and the generation of whole-body PBPK model. BTL was introduced as an additional compartment (∼5% of total brain weight) with tissue characteristics matching those of a brain tumor. In silico clinical trials (ISCTs) were designed by integrating literature data for a virtual population of patients with cancer for Simcyp® and Gd-DTPA properties. The ISCTs were generated for a representative individual and concentrations were estimated for 15 minutes following i.v. bolus injection of Gd-DTPA (0.1 mmol/kg). Given the simulated BTL's size, all parameters were kept constant except for capillary fraction in order to evaluate the impact of vasculature. RESULTS: from the whole-body PBPK simulations estimate the maximum plasma concentration of Gd-DTPA to be 3.0 mM whereas the intracranial blood concentration to be 1.7 mM. Regarding the simulated BTL, concentrations varied between 1.5-1.7 mM for the extravascular-extracellular space following modulation of tissue percentage in capillaries. Finally, zero concentrations are predicted for brain parenchyma and intracellular tissues due to the presence of blood-brain barrier and the absence of cellular intake, transfer or passive mediated, for Gd-DTPA, respectively. The presented in silico approach was capable of assessing the PK profiles of Gd-DTPA and especially its disposition in the extravascular-extracellular space of a putative BTL. The need for exploitation of the imaging techniques with studies correlating image analysis with clinical outcome and in silico tools is of great interest. The incorporation of PBPK models along with in silico tools for tumor growth and/or clinical data could reveal novel approaches for improving DCE-MRI techniques applied in diagnosis and treatment protocols (i.e. novel anti-VEGF treatments).