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OMICS. 2016 Jul;20(7):442-7. doi: 10.1089/omi.2016.0059.

Study of Clinical Survival and Gene Expression in a Sample of Pancreatic Ductal Adenocarcinoma by Parsimony Phylogenetic Analysis.

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1 Department of Biochemistry, Cellular and Molecular Biology, School of Medicine, Georgetown University , Washington, DC.
2 Laboratory of Immunology, Section of Immunopathology, National Eye Institute , Bethesda, Maryland.
3 Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University , Washington, DC.


Pancreatic ductal adenocarcinoma (PDAC) is one of the rapidly growing forms of pancreatic cancer with a poor prognosis and less than 5% 5-year survival rate. In this study, we characterized the genetic signatures and signaling pathways related to survival from PDAC, using a parsimony phylogenetic algorithm. We applied the parsimony phylogenetic algorithm to analyze the publicly available whole-genome in silico array analysis of a gene expression data set in 25 early-stage human PDAC specimens. We explain here that the parsimony phylogenetics is an evolutionary analytical method that offers important promise to uncover clonal (driver) and nonclonal (passenger) aberrations in complex diseases. In our analysis, parsimony and statistical analyses did not identify significant correlations between survival times and gene expression values. Thus, the survival rankings did not appear to be significantly different between patients for any specific gene (pā€‰>ā€‰0.05). Also, we did not find correlation between gene expression data and tumor stage in the present data set. While the present analysis was unable to identify in this relatively small sample of patients a molecular signature associated with pancreatic cancer prognosis, we suggest that future research and analyses with the parsimony phylogenetic algorithm in larger patient samples are worthwhile, given the devastating nature of pancreatic cancer and its early diagnosis, and the need for novel data analytic approaches. The future research practices might want to place greater emphasis on phylogenetics as one of the analytical paradigms, as our findings presented here are on the cusp of this shift, especially in the current era of Big Data and innovation policies advocating for greater data sharing and reanalysis.

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