28 research outputs found

    The growth pattern of transplanted normal and nodular hepatocytes

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    Overt neoplasia is often the end result of a long biological process beginning with the appearance of focal lesions of altered tissue morphology. While the putative clonal nature of focal lesions has often been emphasized, increasing attention is being devoted to the possible role of an altered growth pattern in the evolution of carcinogenesis. Here we compare the growth patterns of normal and nodular hepatocytes in a transplantation system that allows their selective clonal proliferation in vivo. Rats were pre-treated with retrorsine, which blocks the growth of resident hepatocytes, and were then transplanted with hepatocytes isolated from either normal liver or hepatocyte nodules. Both cell types were able to proliferate extensively in the recipient liver, as expected. However, their growth pattern was remarkably different. Clusters of normal hepatocytes integrated in the host liver, displaying a normal histology; however, transplanted nodular hepatocytes formed new hepatocyte nodules, with altered morphology and sharp demarcation from surrounding host liver. Both the expression and distribution of proteins involved in cell polarity, cell communication, and cell adhesion, including connexin 32, E-cadherin, and matrix metalloproteinase-2, were altered in clusters of nodular hepatocytes. Furthermore, we were able to show that down-regulation of connexin 32 and E-cadherin in nodular hepatocyte clusters was independent of growth rate. These results support the concept that a dominant pathway towards neoplastic disease in several organs involves defect(s) in tissue pattern formation

    Cancer Biomarker Discovery: The Entropic Hallmark

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    Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases
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