Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

Cell-based computational modeling of vascular morphogenesis using Tissue Simulation Toolkit.

Computational modeling has become a widely used tool for unraveling the mechanisms of higher level cooperative cell behavior during vascular morphogenesis. However, experimenting with published simulation models or adding new assumptions to those models can be daunting for novice and even for experienced computational scientists. Here, we present a step-by-step, practical tutorial for building cell-based simulations of vascular morphogenesis using the Tissue Simulation Toolkit (TST). The TST is a freely available, open-source C++ library for developing simulations with the two-dimensional cellular Potts model, a stochastic, agent-based framework to simulate collective cell behavior. We will show the basic use of the TST to simulate and experiment with published simulations of vascular network formation. Then, we will present step-by-step instructions and explanations for building a recent simulation model of tumor angiogenesis. Demonstrated mechanisms include cell-cell adhesion, chemotaxis, cell elongation, haptotaxis, and haptokinesis.

Cooperative expression of junctional adhesion molecule-C and -B supports growth and invasion of glioma.

Brain invasion is a biological hallmark of glioma that contributes to its aggressiveness and limits the potential of surgery and irradiation. Deregulated expression of adhesion molecules on glioma cells is thought to contribute to this process. Junctional adhesion molecules (JAMs) include several IgSF members involved in leukocyte trafficking, angiogenesis, and cell polarity. They are expressed mainly by endothelial cells, white blood cells, and platelets. Here, we report JAM-C expression by human gliomas, but not by their normal cellular counterpart. This expression correlates with the expression of genes involved in cytoskeleton remodeling and cell migration. These genes, identified by a transcriptomic approach, include poliovirus receptor and cystein-rich 61, both known to promote glioma invasion, as well as actin filament associated protein, a c-Src binding partner. Gliomas also aberrantly express JAM-B, a high affinity JAM-C ligand. Their interaction activates the c-Src proto-oncogene, a central upstream molecule in the pathways regulating cell migration and invasion. In the tumor microenvironment, this co-expression may thus promote glioma invasion through paracrine stimuli from both tumor cells and endothelial cells. Accordingly, JAM-C/B blocking antibodies impair in vivo glioma growth and invasion, highlighting the potential of JAM-C and JAM-B as new targets for the treatment of human gliomas.

Quantification, self-renewal, and genetic tracing of FL1+ tumor-initiating cells in a large cohort of human gliomas.

Evidence has emerged that the initiation and growth of gliomas is sustained by a subpopulation of cancer-initiating cells (CICs). Because of the difficulty of using markers to tag CICs in gliomas, we have previously exploited more robust phenotypic characteristics, including a specific morphology and intrincic autofluorescence, to identify and isolate a subpopulation of glioma CICs, called FL1(+). The objective of this study was to further validate our method in a large cohort of human glioma and a mouse model of glioma. Seventy-four human gliomas of all grades and the GFAP-V(12)HA-ras B8 mouse model were analyzed for in vitro self-renewal capacity and their content of FL1(+). Nonneoplastic brain tissue and embryonic mouse brain were used as control. Genetic traceability along passages was assessed with microsatellite analysis. We found that FL1(+) cells from low-grade gliomas and from control nonneoplasic brain tissue show a lower level of autofluorescence and undergo a restricted number of cell divisions before dying in culture. In contrast, we found that FL1(+) cells derived from many but not all high-grade gliomas acquire high levels of autofluorescence and can be propagated in long-term cultures. Moreover, FL1(+) cells show a remarkable traceability over time in vitro and in vivo. Our results show that FL1(+) cells can be found in all specimens of a large cohort of human gliomas of different grades and in a model of genetically induced mouse glioma as well as nonneoplastic brain. However, their self-renewal capacity is variable and seems to be dependent on the tumor grade.

Tenascin-W is a specific marker of glioma-associated blood vessels and stimulates angiogenesis in vitro.

The microenvironment hosting a tumor actively participates in regulating tumor cell proliferation, migration, and invasion. Among the extracellular matrix proteins enriched in the stroma of carcinomas are the tenascin family members tenascin-C and tenascin-W. Whereas tenascin-C overexpression in gliomas is known to correlate with poor prognosis, the status of tenascin-W in brain tumors has not been investigated so far. In the present study, we analyzed protein levels of tenascin-W in 38 human gliomas and found expression of tenascin-W in 80% of the tumor samples, whereas no tenascin-W could be detected in control, nontumoral brain tissues. Double immunohistochemical staining of tenascin-W and von Willebrand factor revealed that tenascin-W is localized around blood vessels, exclusively in tumor samples. In vitro, the presence of tenascin-W increased the proportion of elongated human umbilical vein endothelial cells (HUVECs) and augmented the mean speed of cell migration. Furthermore, tenascin-W triggered sprouting of HUVEC spheroids to a similar extent as the proangiogenic factor tenascin-C. In conclusion, our study identifies tenascin-W as a candidate biomarker for brain tumor angiogenesis that could be used as a molecular target for therapy irrespective of the glioma subtype.-Martina, E., Degen, M., Rüegg, C., Merlo, A., Lino, M. M., Chiquet-Ehrismann, R., Brellier, F. Tenascin-W is a specific marker of glioma-associated blood vessels and stimulates angiogenesis in vitro.

In vivo metabolic profiling of glioma-initiating cells using proton magnetic resonance spectroscopy at 14.1 Tesla.

In the last decade, evidence has emerged indicating that the growth of a vast majority of tumors including gliomas is sustained by a subpopulation of cancer cells with stem cell properties called cancer initiating cells. These cells are able to initiate and propagate tumors and constitute only a fraction of all tumor cells. In the present study, we showed that intracerebral injection of cultured glioma-initiating cells into nude mice produced fast growing tumors showing necrosis and gadolinium enhancement in MR images, whereas gliomas produced by injecting freshly purified glioma-initiating cells grew slowly and showed no necrosis and very little gadolinium enhancement. Using proton localized spectroscopy at 14.1 Tesla, decreasing trends of N-acetylaspartate, glutamate and glucose concentrations and an increasing trend of glycine concentration were observed near the injection site after injecting cultured glioma-initiating cells. In contrast to the spectra of tumors grown from fresh cells, those from cultured cells showed intense peaks of lipids, increased absolute concentrations of glycine and choline-containing compounds, and decreased concentrations of glutamine, taurine and total creatine, when compared with a contralateral non-tumor-bearing brain tissue. A decrease in concentrations of N-acetylaspartate and γ-aminobutyrate was found in both tumor phenotypes after solid tumor formation. Further investigation is needed to determine the cause of the dissimilarities between the tumors grown from cultured glioma-initiating cells and those from freshly purified glioma-initiating cells, both derived from human glioblastomas.

Physiologically based pharmacokinetic modeling of arsenic in the mouse

A remarkable feature of the carcinogenicity of inorganic arsenic is that while human exposures to high concentrations of inorganic arsenic in drinking water are associated with increases in skin, lung, and bladder cancer, inorganic arsenic has not typically caused tumors in standard laboratory animal test protocols. Inorganic arsenic administered for periods of up to 2 yr to various strains of laboratory mice, including the Swiss CD-1, Swiss CR:NIH(S), C57Bl/6p53(+/-), and C57Bl/6p53(+/+), has not resulted in significant increases in tumor incidence. However, Ng et al. (1999) have reported a 40% tumor incidence in C57Bl/6J mice exposed to arsenic in their drinking water throughout their lifetime, with no tumors reported in controls. In order to investigate the potential role of tissue dosimetry in differential susceptibility to arsenic carcinogenicity, a physiologically based pharmacokinetic (PBPK) model for inorganic arsenic in the rat, hamster, monkey, and human (Mann et al., 1996a, 1996b) was extended to describe the kinetics in the mouse. The PBPK model was parameterized in the mouse using published data from acute exposures of B6C3F1 mice to arsenate, arsenite, monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA) and validated using data from acute exposures of C57Black mice. Predictions of the acute model were then compared with data from chronic exposures. There was no evidence of changes in the apparent volume of distribution or in the tissue-plasma concentration ratios between acute and chronic exposure that might support the possibility of inducible arsenite efflux. The PBPK model was also used to project tissue dosimetry in the C57Bl/6J study, in comparison with tissue levels in studies having shorter duration but higher arsenic treatment concentrations. The model evaluation indicates that pharmacokinetic factors do not provide an explanation for the difference in outcomes across the various mouse bioassays. Other possible explanations may relate to strain-specific differences, or to the different durations of dosing in each of the mouse studies, given the evidence that inorganic arsenic is likely to be active in the later stages of the carcinogenic process. [Authors]

Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.

DNA fingerprinting of glioma cell lines and considerations on similarity measurements.

Glioma cell lines are an important tool for research in basic and translational neuro-oncology. Documentation of their genetic identity has become a requirement for scientific journals and grant applications to exclude cross-contamination and misidentification that lead to misinterpretation of results. Here, we report the standard 16 marker short tandem repeat (STR) DNA fingerprints for a panel of 39 widely used glioma cell lines as reference. Comparison of the fingerprints among themselves and with the large DSMZ database comprising 9 marker STRs for 2278 cell lines uncovered 3 misidentified cell lines and confirmed previously known cross-contaminations. Furthermore, 2 glioma cell lines exhibited identity scores of 0.8, which is proposed as the cutoff for detecting cross-contamination. Additional characteristics, comprising lack of a B-raf mutation in one line and a similarity score of 1 with the original tumor tissue in the other, excluded a cross-contamination. Subsequent simulation procedures suggested that, when using DNA fingerprints comprising only 9 STR markers, the commonly used similarity score of 0.8 is not sufficiently stringent to unambiguously differentiate the origin. DNA fingerprints are confounded by frequent genetic alterations in cancer cell lines, particularly loss of heterozygosity, that reduce the informativeness of STR markers and, thereby, the overall power for distinction. The similarity score depends on the number of markers measured; thus, more markers or additional cell line characteristics, such as information on specific mutations, may be necessary to clarify the origin.

Marker-independent identification of glioma-initiating cells.

Tumor-initiating cells with stem cell properties are believed to sustain the growth of gliomas, but proposed markers such as CD133 cannot be used to identify these cells with sufficient specificity. We report an alternative isolation method purely based on phenotypic qualities of glioma-initiating cells (GICs), avoiding the use of molecular markers. We exploited intrinsic autofluorescence properties and a distinctive morphology to isolate a subpopulation of cells (FL1(+)) from human glioma or glioma cultures. FL1(+) cells are capable of self-renewal in vitro, tumorigenesis in vivo and preferentially express stem cell genes. The FL1(+) phenotype did not correlate with the expression of proposed GIC markers. Our data propose an alternative approach to investigate tumor-initiating potential in gliomas and to advance the development of new therapies and diagnostics.

Molecular modeling of peptide-MHC class I complexes : applications to peptide based immunotherapy

Summary The specific CD8+ T cell immune response against tumors relies on the recognition by the T cell receptor (TCR) on cytotoxic T lymphocytes (CTL) of antigenic peptides bound to the class I major histocompatibility complex (MHC) molecule. Such tumor associated antigenic peptides are the focus of tumor immunotherapy with peptide vaccines. The strategy for obtaining an improved immune response often involves the design of modified tumor associated antigenic peptides. Such modifications aim at creating higher affinity and/or degradation resistant peptides and require precise structures of the peptide-MHC class I complex. In addition, the modified peptide must be cross-recognized by CTLs specific for the parental peptide, i.e. preserve the structure of the epitope. Detailed structural information on the modified peptide in complex with MHC is necessary for such predictions. In this thesis, the main focus is the development of theoretical in silico methods for prediction of both structure and cross-reactivity of peptide-MHC class I complexes. Applications of these methods in the context of immunotherapy are also presented. First, a theoretical method for structure prediction of peptide-MHC class I complexes is developed and validated. The approach is based on a molecular dynamics protocol to sample the conformational space of the peptide in its MHC environment. The sampled conformers are evaluated using conformational free energy calculations. The method, which is evaluated for its ability to reproduce 41 X-ray crystallographic structures of different peptide-MHC class I complexes, shows an overall prediction success of 83%. Importantly, in the clinically highly relevant subset of peptide-HLAA*0201 complexes, the prediction success is 100%. Based on these structure predictions, a theoretical approach for prediction of cross-reactivity is developed and validated. This method involves the generation of quantitative structure-activity relationships using three-dimensional molecular descriptors and a genetic neural network. The generated relationships are highly predictive as proved by high cross-validated correlation coefficients (0.78-0.79). Together, the here developed theoretical methods open the door for efficient rational design of improved peptides to be used in immunotherapy. Résumé La réponse immunitaire spécifique contre des tumeurs dépend de la reconnaissance par les récepteurs des cellules T CD8+ de peptides antigéniques présentés par les complexes majeurs d'histocompatibilité (CMH) de classe I. Ces peptides sont utilisés comme cible dans l'immunothérapie par vaccins peptidiques. Afin d'augmenter la réponse immunitaire, les peptides sont modifiés de façon à améliorer l'affinité et/ou la résistance à la dégradation. Ceci nécessite de connaître la structure tridimensionnelle des complexes peptide-CMH. De plus, les peptides modifiés doivent être reconnus par des cellules T spécifiques du peptide natif. La structure de l'épitope doit donc être préservée et des structures détaillées des complexes peptide-CMH sont nécessaires. Dans cette thèse, le thème central est le développement des méthodes computationnelles de prédiction des structures des complexes peptide-CMH classe I et de la reconnaissance croisée. Des applications de ces méthodes de prédiction à l'immunothérapie sont également présentées. Premièrement, une méthode théorique de prédiction des structures des complexes peptide-CMH classe I est développée et validée. Cette méthode est basée sur un échantillonnage de l'espace conformationnel du peptide dans le contexte du récepteur CMH classe I par dynamique moléculaire. Les conformations sont évaluées par leurs énergies libres conformationnelles. La méthode est validée par sa capacité à reproduire 41 structures des complexes peptide-CMH classe I obtenues par cristallographie aux rayons X. Le succès prédictif général est de 83%. Pour le sous-groupe HLA-A*0201 de complexes de grande importance pour l'immunothérapie, ce succès est de 100%. Deuxièmement, à partir de ces structures prédites in silico, une méthode théorique de prédiction de la reconnaissance croisée est développée et validée. Celle-ci consiste à générer des relations structure-activité quantitatives en utilisant des descripteurs moléculaires tridimensionnels et un réseau de neurones couplé à un algorithme génétique. Les relations générées montrent une capacité de prédiction remarquable avec des valeurs de coefficients de corrélation de validation croisée élevées (0.78-0.79). Les méthodes théoriques développées dans le cadre de cette thèse ouvrent la voie du design de vaccins peptidiques améliorés.

Comparative localization of glioma-reactive monoclonal antibodies in vivo in an athymic mouse human glioma xenograft model.

Radioiodinated murine monoclonal antibodies (Mabs) 81C6, Me 1-14, C12, D12, and E9, made against or reactive with human gliomas but not normal brain, and Mab UJ13A, a pan-neuroectodermal Mab reactive with normal human glial and neural cells, were evaluated in paired label studies in the D-54 MG subcutaneous human glioma xenograft model system in nude mice. Following intravenous injection in the tail vein of mice bearing 200-400 mm3 tumors, specific localization of Mabs to tumor over time (6 h-9 days) was evaluated by tissue counting; each Mab demonstrated a unique localization profile. The comparison of localization indices (LI), determined as a ratio of tissue level of Mab to control immunoglobulin with simultaneous correction for blood levels of each, showed Mabs 81C6 and Me 1-14 to steadily accumulate in glioma xenografts, maintaining LI from 5-20 at 7-9 days after Mab injection. Mab UJ13A peaked at day 1, maintaining this level through day 2, and declining thereafter. Mabs D12 and C12 peaked at days 3 and 4, respectively, and E9 maintained an LI of greater than 3 from days 3-9. Percent injected dose localized/g of tumor varied from a peak high of 16% (81C6) to a low of 5% (Me 1-14 and UJ13A). Immunoperoxidase histochemistry, performed with each Mab on a battery of primary human brain neoplasms, revealed that Mabs 81C6 and E9, which demonstrated the highest levels of percent injected dose localized/g of tumor over time, reacted with antigens expressed in the extracellular matrix. This finding suggests that extracellular matrix localization of antigen represents a biologically significant factor affecting localization and/or binding in the xenograft model used. The demonstration of significant localization, varied kinetics and patterns of localization of this localizing Mab panel warrants their continued investigation as potential imaging and therapeutic agents for human trials.

Model-based Segmentation and Image Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

For radiotherapy treatment planning of retinoblastoma inchildhood, Computed Tomography (CT) represents thestandard method for tumor volume delineation, despitesome inherent limitations. CT scan is very useful inproviding information on physical density for dosecalculation and morphological volumetric information butpresents a low sensitivity in assessing the tumorviability. On the other hand, 3D ultrasound (US) allows ahigh accurate definition of the tumor volume thanks toits high spatial resolution but it is not currentlyintegrated in the treatment planning but used only fordiagnosis and follow-up. Our ultimate goal is anautomatic segmentation of gross tumor volume (GTV) in the3D US, the segmentation of the organs at risk (OAR) inthe CT and the registration of both. In this paper, wepresent some preliminary results in this direction. Wepresent 3D active contour-based segmentation of the eyeball and the lens in CT images; the presented approachincorporates the prior knowledge of the anatomy by usinga 3D geometrical eye model. The automated segmentationresults are validated by comparing with manualsegmentations. Then, for the fusion of 3D CT and USimages, we present two approaches: (i) landmark-basedtransformation, and (ii) object-based transformation thatmakes use of eye ball contour information on CT and USimages.

Regulation of peptidase activity in a three-dimensional aggregate model of brain tumor vasculature.

The abnormal vascular system of brain cancers inappropriately expresses membrane proteins, including proteolytic enzymes, ultimately resulting in blood extravasation. The production of inflammatory mediators, such as cytokines and nitric oxide, and tumor hypoxia have been implicated in these effects. We have previously shown that the activity of aminopeptidase A is increased in the abnormal vascular system of human and rat brain tumors. To study the mechanisms regulating the activities of peptidases in cerebral vasculature in brain tumors, we have developed a three-dimensional model of differentiated rat brain cells in aggregate cultures in which rat brain microvessels were incorporated. The secretion of interleukin-6 (IL-6) in the culture medium of aggregates was used as an indicator of inflammatory activation. Addition to these aggregates of C6 glioma cell medium (C6-CM) conditioned under hypoxic or normoxic conditions or serum mimicked tumor-dependent hypoxia or conditions of dysfunction of brain tumor vasculature. Hypoxic and normoxic C6-CM, but not serum, regulated peptidase activity in aggregates, and in particular it increased the activity of aminopeptidase A determined using histoenzymography. Serum, but not C6-CM, increased IL-6 production, but did not increase aminopeptidase A activity in aggregates. Thus soluble glioma-derived factors, but not serum-derived factors, induce dysfunctions of cerebral vasculature by directly regulating the activity of peptidases, not involving inflammatory activation. Tumor hypoxia is not necessary to modulate peptidase activity.