Mathematical modeling of the metastatic process

Mathematical modeling in cancer has been growing in popularity and impact since its inception in 1932. The first theoretical mathematical modeling in cancer research was focused on understanding tumor growth laws and has grown to include the competition between healthy and normal tissue, carcinogenesis, therapy and metastasis. It is the latter topic, metastasis, on which we will focus this short review, specifically discussing various computational and mathematical models of different portions of the metastatic process, including: the emergence of the metastatic phenotype, the timing and size distribution of metastases, the factors that influence the dormancy of micrometastases and patterns of spread from a given primary tumor.Comment: 24 pages, 6 figures, Revie

Pathobiological Implications of MUC16 Expression in Pancreatic Cancer

MUC16 (CA125) belongs to a family of high-molecular weight O-glycosylated proteins known as mucins. While MUC16 is well known as a biomarker in ovarian cancer, its expression pattern in pancreatic cancer (PC), the fourth leading cause of cancer related deaths in the United States, remains unknown. The aim of our study was to analyze the expression of MUC16 during the initiation, progression and metastasis of PC for possible implication in PC diagnosis, prognosis and therapy. In this study, a microarray containing tissues from healthy and PC patients was used to investigate the differential protein expression of MUC16 in PC. MUC16 mRNA levels were also measured by RT-PCR in the normal human pancreatic, pancreatitis, and PC tissues. To investigate its expression pattern during PC metastasis, tissue samples from the primary pancreatic tumor and metastases (from the same patient) in the lymph nodes, liver, lung and omentum from Stage IV PC patients were analyzed. To determine its association in the initiation of PC, tissues from PC patients containing pre-neoplastic lesions of varying grades were stained for MUC16. Finally, MUC16 expression was analyzed in 18 human PC cell lines. MUC16 is not expressed in the normal pancreatic ducts and is strongly upregulated in PC and detected in pancreatitis tissue. It is first detected in the high-grade pre-neoplastic lesions preceding invasive adenocarcinoma, suggesting that its upregulation is a late event during the initiation of this disease. MUC16 expression appears to be stronger in metastatic lesions when compared to the primary tumor, suggesting a role in PC metastasis. We have also identified PC cell lines that express MUC16, which can be used in future studies to elucidate its functional role in PC. Altogether, our results reveal that MUC16 expression is significantly increased in PC and could play a potential role in the progression of this disease

Identification of polymorphisms by genomic denaturing gradient gel electrophoresis: application to the proximal region of human chromosome 21.

Genomic Denaturing Gradient Gel Electrophoresis (gDGGE) provides an alternative to the standard method of restriction fragment length polymorphism (RFLP) analysis for identifying polymorphic sequence variation in genomic DNA. For gDGGE, genomic DNA is cleaved by restriction enzymes, separated in a polyacrylamide gel containing a gradient of DNA denaturants, and then transferred by electroblotting to nylon membranes. Unlike other applications of DGGE, gDGGE is not limited by the size of the probe and does not require probe sequence information. gDGGE can be used in conjunction with any unique DNA probe. Here we use gDGGE with probes from the proximal region of the long arm of human chromosome 21 to identify polymorphic DNA sequence variation in this segment of the chromosome. Our screening panel consisted of DNA from nine individuals, which was cleaved with five restriction enzymes and submitted to electrophoresis in two denaturing gradient conditions. We detected at least one potential polymorphism for nine of eleven probes that were tested. Two polymorphisms, one at D21S4 and one at D21S90, were characterized in detail. Our study demonstrates that gDGGE is a fast and efficient method for identifying polymorphisms that are useful for genetic linkage analysis