The tissue of origin form metastatic tumors is sometimes difficult to identify from clinical and histologic information. Gene expression signatures are one potential method for identifying the tissue of origin. In the development of algorithms to identify tissue of origin, a collection of human tumor metastatic specimens with known primary sites or primary tumors with poor differentiation are very useful in identifying gene expressions signatures that can classify unknown specimens as to the tissue of origin. Here we describe a series of 276 such tumor specimens used for this purpose. The specimens are poorly differentiated, undifferentiated and metastatic specimens from tumors of the following types/tissues of origin: breast, liver, non-Hodgkin's lymphoma, non-small cell lung cancer, ovary, testicular germ cell, thyroid, kidney, pancreas, colorectal cancer, soft tissue sarcoma, bladder, gastric cancer, prostate and melanoma. This data combined with other series (GSE2109) was used to validate a proprietary tumor classification algorithm of Pathwork Diagnostics. The results of this validation set (N = 545 CEL files) showed that the algorithm correctly identified the tissue of origin for 89.4% of the specimens.
Multicenter validation of a 1,550-gene expression profile for identification of tumor tissue of origin.
Sex, Age
View SamplesFundamental research and drug development for personalized medicine necessitates cell cultures from defined genetic backgrounds. However, providing sufficient numbers of authentic cells from individuals poses a challenge. Here, we present a new strategy for rapid cell expansion that overcomes current limitations. Using a small gene library, we expanded primary cells from different tissues, donors and species. Cell type specific regimens that allow the reproducible creation of cell lines were identified. In depth characterization of a series of endothelial and hepatocytic cell lines confirmed phenotypic stability and functionality. Applying this technology enables rapid, efficient and reliable production of unlimited numbers of personalized cells. As such, these cell systems support mechanistic studies, epidemiological research and tailored drug development.
Expansion of functional personalized cells with specific transgene combinations.
Specimen part
View SamplesDue to heterogeneous multifocal nature of prostate cancer (PCa), there is currently a lack of biomarkers that stably distinguish it from benign prostatic hyperplasia (BPH), predict clinical outcome and guide the choice of optimal treatment. In this study, RNA-seq analysis was applied to formalin-fixed paraffin-embedded (FFPE) tumor and matched normal tissue samples collected from Russian patients with PCa and BPH. We identified 3384 genes differentially expressed (DE) (FDR < 0.05) between tumor tissue of PCa patients and adjacent normal tissue as well as both tissue types from BPH patients. Overexpression of four of the genes previously not associated with PCa (ANKRD34B, NEK5, KCNG3, and PTPRT) was validated by RT-qPCR. Furthermore, the enrichment analysis of overrepresented microRNA and transcription factor (TF) recognition sites within DE genes revealed common regulatory elements of which 13 microRNAs and 53 TFs were thus linked to PCa for the first time. Moreover, 8 of these TFs (FOXJ2, GATA6, NFE2L1, NFIL3, PRRX2, TEF, EBF2 and ZBTB18) were found to be differentially expressed in this study, making them not only candidate biomarkers of prostate cancer but also potential therapeutic targets. Overall design: Whole transcriptome profiling of tumor tissue and matched adjacent normal tissue from 15 patients with PCa and 2 with BPH.
Novel RNA biomarkers of prostate cancer revealed by RNA-seq analysis of formalin-fixed samples obtained from Russian patients.
Specimen part, Disease, Subject
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