Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

ELUCIDATING FUNCTIONAL ROLES FOR MYOGENIN IN ADULT SKELETAL MUSCLE METABOLISM, EXERCISE CAPACITY, AND REGENERATION

The four basic helix-loop-helix myogenic transcription factors, myogenin, Myf5, MRF4, and MyoD are critical for embryonic skeletal muscle development. Myogenin is necessary for the terminal differentiation of myoblasts into myofibers during embryogenesis, but little is known about the roles played by myogenin in adult skeletal muscle function and metabolism. Furthermore, while metabolism is a well-studied physiological process, how it is regulated at the transcriptional level remains poorly understood. In this study, my aim was to determine the function of myogenin in adult skeletal muscle metabolism, exercise capacity, and regeneration. To investigate this, I utilized a mouse strain harboring the Myogflox allele and a Cre recombinase transgene, enabling the efficient deletion of myogenin in the adult mouse. Myogflox/flox mice were stressed physically through involuntary treadmill running and by breeding them with a strain harboring the Duchenne’s muscular dystrophy (DMDmdx) allele. Surprisingly, Myog-deleted animals exhibited an enhanced capacity for exercise, running farther and faster than their wild-type counterparts. Increased lactate production and utilization of glucose as a fuel source indicated that Myog-deleted animals exhibited an increased glycolytic flux. Hypoglycemic Myog-deleted mice no longer possessed the ability to outrun their wild-type counterparts, implying the ability of these animals to further deplete their glucose reserves confers their enhanced exercise capacity. Moreover, Myog-deleted mice exhibited an enhanced response to long-term exercise training. The mice developed a greater proportion of type 1 oxidative muscle fibers, and displayed increased levels of succinate dehydrogenase activity, indicative of increased oxidative metabolism. Mdx:Myog-deleted mice exhibited a similar phenotype, outperforming their mdx counterparts, although lagging behind wild-type animals. The morphology of muscle tissue from mdx:Myog-deleted mice appears to mimic that of mdx animals, indicating that myogenin is dispensable for adult skeletal muscle regeneration. Through global gene expression profiling and quantitative (q)RT-PCR, I identified a unique set of putative myogenin-dependent genes involved in regulating metabolic processes. These data suggest myogenin’s functions during adulthood are distinctly different than those during embryogenesis, and myogenin acts as a high-level transcription factor regulating metabolic activity in adult skeletal muscle.

Transcriptional regulation and functional analysis of the Wilms' tumor gene WT1

The Wilms' tumor gene, WT1, encodes a zinc finger transcription factor which functions as a tumor suppressor. Defects in the WT1 gene can result in the development of nephroblastoma. WT1 is expressed during development, primarily in the metanephric kidney, the mesothelial lining of the abdomen and thorax, and the developing gonads. WT1 expression is tightly regulated and is essential for renal development. The WT1 gene encodes a protein with a proline-rich N-terminus which functions as a transcriptional repressor and C-terminus contains 4 zinc fingers that mediate DNA binding. WT1 represses transcription from a number of growth factors and growth factor receptors. WT1 mRNA undergoes alternative splicing at two sites, resulting in 4 mRNA species and polypeptide products. Exon 5, encoding 17 amino acids is alternatively spliced, and is located between the transcriptional repression domain and the DNA binding domain. The second alternative splice is the terminal 9 nucleotides of zinc finger 3, encoding the tripeptide Lys-Thr-Ser (KTS). The presence or absence of KTS within the zinc fingers of WT1 alters DNA binding.^ I have investigated transcriptional regulation of WT1, characterizing two means of repressing WT1 transcription. I have cloned a transcriptional silencer of the WT1 promoter which is located in the third intron of the WT1 gene. The silencer is 460 bp in length and contains an Alu repeat. The silencer functions in cells of non-renal origin.^ I have found that WT1 protein can autoregulate the WT1 promoter. Using the autoregulation of the WT1 promoter as a functional assay, I have defined differential consensus DNA binding motifs of WT1 isoforms lacking and containing the KTS tripeptide insertion. With these refined consensus DNA binding motifs, I have identified two additional targets of WT1 transcriptional repression, the proto-oncogenes bcl-2 and c-myc.^ I have investigated the ability of the alternatively spliced exon 5 to influence cell growth. In cell proliferation assays, isoforms of WT1 lacking exon 5 repress cell growth. WT1 isoforms containing exon 5 fail to repress cell growth to the same extent, but alter the morphology of the cells. These experiments demonstrate that the alternative splice isoforms of WT1 have differential effects on the function of WT1. These findings suggest a role for the alternative splicing of WT1 in metanephric development. ^