Nalbantoglu Sinem, Abu-Asab Mones, Tan Ming, Zhang Xuemin, Cai Ling, Amri Hakima
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.
OMICS. 2016 Jul;20(7):442-7. doi: 10.1089/omi.2016.0059.
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 (p > 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.
胰腺导管腺癌（PDAC）是胰腺癌中增长迅速的一种形式，预后较差，5年生存率低于5%。在本研究中，我们使用简约系统发育算法，对与PDAC生存相关的基因特征和信号通路进行了表征。我们应用简约系统发育算法，对25例早期人类PDAC标本的基因表达数据集进行公开可用的全基因组计算机阵列分析。我们在此解释，简约系统发育学是一种进化分析方法，有望揭示复杂疾病中的克隆（驱动）和非克隆（乘客）畸变。在我们的分析中，简约分析和统计分析未发现生存时间与基因表达值之间存在显著相关性。因此，对于任何特定基因，患者之间的生存排名似乎没有显著差异（p>0.05）。此外，在本数据集中，我们未发现基因表达数据与肿瘤分期之间存在相关性。虽然目前的分析未能在这个相对较小的患者样本中识别出与胰腺癌预后相关的分子特征，但考虑到胰腺癌的毁灭性本质及其早期诊断，以及对新型数据分析方法的需求，我们建议未来在更大的患者样本中使用简约系统发育算法进行研究和分析是值得的。未来的研究实践可能希望更加强调系统发育学作为分析范式之一，因为我们在此呈现的研究结果正处于这一转变的前沿，尤其是在当前倡导更大数据共享和重新分析的大数据和创新政策时代。
