Multicenter Validation of a 1,550-Gene Expression Profile for Identification of Tumor Tissue of Origin
ABSTRACT Malignancies found in unexpected locations or with poorly differentiated morphologies can pose a significant challenge for tissue of origin determination. Current histologic and imaging techniques fail to yield definitive identification of the tissue of origin in a significant number of cases. The aim of this study was to validate a predefined 1,550-gene expression profile for this purpose.
Four institutions processed 547 frozen specimens representing 15 tissues of origin using oligonucleotide microarrays. Half of the specimens were metastatic tumors, with the remainder being poorly differentiated and undifferentiated primary cancers chosen to resemble those that present as a clinical challenge.
In this blinded multicenter validation study the 1,550-gene expression profile was highly informative in tissue determination. The study found overall sensitivity (positive percent agreement with reference diagnosis) of 87.8% (95% CI, 84.7% to 90.4%) and overall specificity (negative percent agreement with reference diagnosis) of 99.4% (95% CI, 98.3% to 99.9%). Performance within the subgroup of metastatic tumors (n = 258) was found to be slightly lower than that of the poorly differentiated and undifferentiated primary tumor subgroup, 84.5% and 90.7%, respectively (P = .04). Differences between individual laboratories were not statistically significant.
This study represents the first adequately sized, multicenter validation of a gene-expression profile for tissue of origin determination restricted to poorly differentiated and undifferentiated primary cancers and metastatic tumors. These results indicate that this profile should be a valuable addition or alternative to currently available diagnostic methods for the evaluation of uncertain primary cancers.
Full-textDOI: · Available from: Maureen Lyons, May 30, 2015
SourceAvailable from: Anthony Wing Hung Chan[Show abstract] [Hide abstract]
ABSTRACT: Ying Yang 1 (YY1) is a transcription factor that regulates diverse biological processes and increasing recognized to have important roles in carcinogenesis. The function and clinical significance of YY1 in gastric adenocarcinoma (GAC) have not been elucidated. In this study, the functional role of YY1 in gastric cancer was investigated by MTT proliferation assays, monolayer colony formation, cell cycle analysis, signaling pathway analysis, Western blot analysis and in vivo study through YY1 knockdown or overexpression. Immunohistochemical study with YY1 antibody was performed on tissue microarray consisting of 247 clinical GAC samples. The clinical correlation and prognosis significance were evaluated. YY1 expression was up-regulated in gastric cancer cell lines and primary gastric cancers. Knocking down YY1 by siYY1 inhibited cell growth, inducing G1 phase accumulation and apoptosis. Ectopic YY1 expression enhanced cell proliferation in vitro and in vivo. Knocking down YY1 in gastric cancer cells suppressed proliferation by inhibiting Wnt/beta-catenin pathway, whereas its overexpression exerted oncogenic property by activating Wnt/beta-catenin pathway. In primary GAC samples, YY1 nuclear expression correlated with shorter survival and predicted poor prognosis in early stage GACs. Our data demonstrated that YY1 contributes to gastric carcinogenesis in gastric cancer. In early stage GACs YY1 might serve as a poor prognostic marker and possibly as a potential therapeutic target.Journal of Translational Medicine 03/2014; 12(1):80. DOI:10.1186/1479-5876-12-80 · 3.99 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta.PLoS ONE 03/2014; 9(3):e91240. DOI:10.1371/journal.pone.0091240 · 3.53 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: The Tumor protein D52 (TPD52) gene was identified nearly 20 years ago through its overexpression in human cancer, and a substantial body of data now strongly supports TPD52 representing a gene amplification target at chromosome 8q21.13. This review updates progress toward understanding the significance of TPD52 overexpression and targeting, both in tumors known to be characterized by TPD52 overexpression/amplification, and those where TPD52 overexpression/amplification has been recently or variably reported. We highlight recent findings supporting microRNA regulation of TPD52 expression in experimental systems and describe progress toward deciphering TPD52's cellular functions, particularly in cancer cells. Finally, we provide an overview of TPD52's potential as a cancer biomarker and immunotherapeutic target. These combined studies highlight the potential value of genes such as TPD52, which are overexpressed in many cancer types, but have been relatively understudied.Tumor Biology 05/2014; 35(8). DOI:10.1007/s13277-014-2006-x · 2.84 Impact Factor