Computational analysis of gene expression space associated with metastatic cancer

<p>Abstract</p> <p>Background</p> <p>Prostate carcinoma is among the most common types of cancer affecting hundreds of thousands people every year. Once the metastatic form of prostate carcinoma is documented, the majority of patients die from their tumors as opposed to other causes. The key to successful treatment is in the earliest possible diagnosis, as well as understanding the molecular mechanisms of metastatic progression. A number of recent studies have identified multiple biomarkers for metastatic progression. However, most of the studies consider only direct comparison between metastatic and non-metastatic classes of samples.</p> <p>Results</p> <p>We propose an alternative concept of analysis that considers the entire multidimensional space of gene expression and identifies the partition of this space in which metastatic development is possible. To apply this concept in cancer gene expression studies we utilize a modification of high-dimension natural taxonomy algorithm FOREL. Our analysis of microarray data containing primary and metastatic cancer samples has revealed not only differentially expressed genes, but also relations between different groups of primary and metastatic cancer. Metastatic samples tend to occupy a distinct partition of gene expression space. Further pathway analysis suggests that this partition is delineated by a specific pattern of gene expression in cytoskeleton remodeling, cell adhesion and apoptosis/cell survival pathways. We compare our findings with both report of original analysis and recent studies in molecular mechanism of metastasis.</p> <p>Conclusion</p> <p>Our analysis indicates a single molecular mechanism of metastasis. The new approach does not contradict previously reported findings, but reveals important details unattainable with traditional methodology.</p

Glucose transporter-1 expression and the antiproliferative effects of 2-deoxy-d-glucose in osteosarcoma models

2010 Summer.Includes bibliographic references (pages 34-37).Osteosarcoma (OSA) is the most common bone tumor in the dog, more common in large to giant breed dogs. 90% of dogs diagnosed with OSA will die of metastatic disease within one year of diagnosis. There have been no great advances in therapy for canine OSA over the last 20 years. Hypoxia in tumors has been associated with an increased resistance to radiation and chemotherapy, and increased metastatic potential. Hypoxia-inducible factor 1-a (HIF-la) is a transcription factor stabilized by hypoxia. Glucose transporter 1 (GLUT-1), a downstream product of HIF-la pathway activation, is over-expressed in a variety of human tumors. We sought to determine if GLUT-1 is expressed in canine OSA and if expression is related to tumor necrosis and outcome. Immunohistochemistry was performed on 44 histologically confirmed OSA tissue samples to assess expression of GLUT-1. Of 44 cases, 27 (61%) expressed GLUT-1. There was no statistical correlation between GLUT-1 and disease-free interval, survival time, or percent necrosis. As hypothesized, GLUT-1 is present in most canine appendicular OSA. A more objective evaluation of GLUT-1 and other proteins in the HIF-la pathway may be warranted. Some cells within a tumor may be poorly perfused, and therefore less susceptible to traditional chemotherapy. Cancer cells, especially those hypoxic cells that are distant from the stromal blood vessels, require more glucose than normal cells as they utilize anaerobic glycolysis, rather than oxidative phosphorylation, to survive. 2-deoxy-Dglucose (2-DG) is a glucose analog that is preferentially captured by cancer cells, _ blocking the first step of glycolysis. We evaluated the sensitivity of various OSA (canine and murine) cell lines to 2-DG, and attempted correlation to the protein GLUT-1 with western analysis. There was no statistical correlation between 2-DG and GLUT-1 or Akt expression, although it did correlate with total ERK expression. In a murine OSA model, 2-DG was shown to inhibit metastasis, possibly through the inhibition of invasion and migration, as assessed by Boyden chamber assays in vitro using the same OSA murine cell line

Estimating Anthropometric Marker Locations from 3-D LADAR Point Clouds

An area of interest for improving the identification portion of the system is in extracting anthropometric markers from a Laser Detection and Ranging (LADAR) point cloud. Analyzing anthropometrics markers is a common means of studying how a human moves and has been shown to provide good results in determining certain demographic information about the subject. This research examines a marker extraction method utilizing principal component analysis (PCA), self-organizing maps (SOM), alpha hulls, and basic anthropometric knowledge. The performance of the extraction algorithm is tested by performing gender classification with the calculated markers