Gene expression profiles of poorly differentiated, undifferentiated and metastatic cancers
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE12630 | GEO | 2009/03/29
SECONDARY ACCESSION(S): PRJNA112803
REPOSITORIES: GEO
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Project description:To investigate the identity of the tissue origin for metastases, we compared gene expression profiles of the prostate tissues and seminal vesicles from PB-Pten-NICD mice and those of 7 lung metastases from different mice. Three respective cell lines established from primary seminal vesicle tumors, primary prostate tumors, and lung metastases in PB-Pten-NICD mice were also included in the microarray analysis. We hypothesized that the gene expression profiles of primary and metastatic tumors should resemble those of their tissue origins. We identified genes differentially expressed between the seminal vesicle and prostate tumors. The expression profiles of the two cell lines established from primary tumors resembled those of their respective tissue origins, which supports our hypothesis. The expression profile of the metastatic cell line resembled those of the prostate tumor tissues, indicating the prostate as its tissue of origin. The expression profiles of 2 metastatic specimens each resembled those of the seminal vesicle tumors and the prostate tumors, respectively, while the expression profiles of the remaining 3 samples displayed a mixed pattern. This study suggests that metastatic tumors in this model may have originated from either the prostate, or the seminal vesicles, or both.
2016-09-09 | GSE72549 | GEO
Project description:Carcinomas of unknown primary origin constitute 3-5% of all newly diagnosed metastatic cancers, of which the primary source is difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor, which is why patients with metastases of unknown origin have poor prognosis and short survival. Because microRNA expression is highly tissue specific, the microRNA profile of a metastasis may be used to identify its origin. As a first step to realize this goal, we evaluated the potential of microRNA profiling for identification of the primary tumor of known metastases. 208 formalin-fixed paraffin-embedded samples representing 15 different histologies were profiled on an LNA-enhanced microarray platform, which allows for highly sensitive and specific detection of microRNA. Based on these data, we developed and cross-validated a novel classification algorithm, LASSO (Least Absolute Shrinkage and Selection Operator), which had an overall accuracy of 85%. When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy). Our findings suggest that microRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor, and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors, and eventually, enable tailored therapy. 94 samples
2013-12-01 | E-GEOD-50890 | biostudies-arrayexpress
Project description:Proteome characterization of gland confined prostate tumors and non-malignant prostate tissue. Whole cell protein extracts were purified from FFPE radical prostatectomy specimens for a total of 28 tumor samples and 8 adjacent non-malignant prostate tissues. Associated ProteomeXchange identifiers: PXD003430, PXD003452, PXD003515, PXD004132, PXD003615, PXD003636. Quantitative proteomic analysis of adjacent non-malignant prostate tissue (n=8) and gland confined prostate tumors (n=28) obtained from radical prostatectomy procedures; and bone metastatic prostate tumors (n=22) obtained from patients operated to relief spinal cord compression. At the time of surgery, most metastatic patients had relapsed after androgen-deprivation therapy, while 5 were previously untreated.
2018-08-21 | PXD009868 | Pride
Project description:Carcinomas of unknown primary origin constitute 3-5% of all newly diagnosed metastatic cancers, of which the primary source is difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor, which is why patients with metastases of unknown origin have poor prognosis and short survival. Because microRNA expression is highly tissue specific, the microRNA profile of a metastasis may be used to identify its origin. As a first step to realize this goal, we evaluated the potential of microRNA profiling for identification of the primary tumor of known metastases. 208 formalin-fixed paraffin-embedded samples representing 15 different histologies were profiled on an LNA-enhanced microarray platform, which allows for highly sensitive and specific detection of microRNA. Based on these data, we developed and cross-validated a novel classification algorithm, LASSO (Least Absolute Shrinkage and Selection Operator), which had an overall accuracy of 85%. When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy). Our findings suggest that microRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor, and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors, and eventually, enable tailored therapy.
2013-12-01 | GSE50890 | GEO
Project description:Synovial sarcoma (SynSa) is an aggressive mesenchymal tumor, comprising approximately 10% of all soft tissue sarcomas. Over half of SynSa patients develop metastasis or local recurrence, but the underlying molecular mechanisms of the aggressive clinical behavior remain poorly characterized. Sixty four frozen tumor specimens from 54 SynSa patients were subjected to array comparative genomic hybridization (aCGH) and gene expression profiling. The examined set of tumor specimens included 16 primary tumors from untreated patients who did not develop metastasis/local recurrence (SynSa1 group), 26 primary tumors from untreated patients who developed metastases or local recurrence during follow-up (SynSa2 group), and 22 metachronous metastatic/recurrent SynSa tumors (SynSa3 group). AURKA and KIF18A, which play important roles in various mitotic events, were the two most up-regulated genes in SynSa2 and SynSa3 groups compared to the SynSa1 group. Expression profiles of SynSa2 and SynSa3 tumors did not show any significant differences. Analysis of genomic index (GI) based on aCGH profiles demonstrated that the SynSa1 group consisted of tumors with significantly less complex genomes compared to SynSa2 and SynSa3 groups. There was no significant difference in genome complexity between SynSa2 and SynSa3 tumors. Primary SynSa tumors from patients who develop metastases or local recurrence share common molecular features with metastatic/recurrent tumors. Presented data suggest that the aggressive clinical SynSa behavior is determined early in tumorigenesis and might be related to impaired regulation of mitotic mechanisms. Introduction: Synovial sarcoma (SynSa) is an aggressive mesenchymal tumor, comprising approximately 10% of all soft tissue sarcomas. Over half of SynSa patients develop metastasis or local recurrence, but the underlying molecular mechanisms of the aggressive clinical behavior remain poorly characterized. Materials and methods: Sixty four frozen tumor specimens from 54 SynSa patients were subjected to array comparative genomic hybridization (aCGH) and gene expression profiling. The examined set of tumor specimens included 16 primary tumors from untreated patients who did not develop metastasis/local recurrence (SynSa1 group), 26 primary tumors from untreated patients who developed metastases or local recurrence during follow-up (SynSa2 group), and 22 metachronous metastatic/recurrent SynSa tumors (SynSa3 group). Results: AURKA and KIF18A, which play important roles in various mitotic events, were the two most up-regulated genes in SynSa2 and SynSa3 groups compared to the SynSa1 group. Expression profiles of SynSa2 and SynSa3 tumors did not show any significant differences. Analysis of genomic index (GI) based on aCGH profiles demonstrated that the SynSa1 group consisted of tumors with significantly less complex genomes compared to SynSa2 and SynSa3 groups. There was no significant difference in genome complexity between SynSa2 and SynSa3 tumors. Conclusions: Primary SynSa tumors from patients who develop metastases or local recurrence share common molecular features with metastatic/recurrent tumors. Presented data suggest that the aggressive clinical SynSa behavior is determined early in tumorigenesis and might be related to impaired regulation of mitotic mechanisms. Genomic DNA was extracted from the frozen tumor specimens using High Pure PCR Template Preparation Kit (Roche). DNA of sufficient quality was obtained from 61 tumor specimens. Agilent SurePrint G3 Human CGH 4x180K array
2014-07-31 | E-GEOD-54183 | biostudies-arrayexpress
Project description:Synovial sarcoma (SynSa) is an aggressive mesenchymal tumor, comprising approximately 10% of all soft tissue sarcomas. Over half of SynSa patients develop metastasis or local recurrence, but the underlying molecular mechanisms of the aggressive clinical behavior remain poorly characterized. Sixty four frozen tumor specimens from 54 SynSa patients were subjected to array comparative genomic hybridization (aCGH) and gene expression profiling. The examined set of tumor specimens included 16 primary tumors from untreated patients who did not develop metastasis/local recurrence (SynSa1 group), 26 primary tumors from untreated patients who developed metastases or local recurrence during follow-up (SynSa2 group), and 22 metachronous metastatic/recurrent SynSa tumors (SynSa3 group). AURKA and KIF18A, which play important roles in various mitotic events, were the two most up-regulated genes in SynSa2 and SynSa3 groups compared to the SynSa1 group. Expression profiles of SynSa2 and SynSa3 tumors did not show any significant differences. Analysis of genomic index (GI) based on aCGH profiles demonstrated that the SynSa1 group consisted of tumors with significantly less complex genomes compared to SynSa2 and SynSa3 groups. There was no significant difference in genome complexity between SynSa2 and SynSa3 tumors. Primary SynSa tumors from patients who develop metastases or local recurrence share common molecular features with metastatic/recurrent tumors. Presented data suggest that the aggressive clinical SynSa behavior is determined early in tumorigenesis and might be related to impaired regulation of mitotic mechanisms. Introduction: Synovial sarcoma (SynSa) is an aggressive mesenchymal tumor, comprising approximately 10% of all soft tissue sarcomas. Over half of SynSa patients develop metastasis or local recurrence, but the underlying molecular mechanisms of the aggressive clinical behavior remain poorly characterized. Materials and methods: Sixty four frozen tumor specimens from 54 SynSa patients were subjected to array comparative genomic hybridization (aCGH) and gene expression profiling. The examined set of tumor specimens included 16 primary tumors from untreated patients who did not develop metastasis/local recurrence (SynSa1 group), 26 primary tumors from untreated patients who developed metastases or local recurrence during follow-up (SynSa2 group), and 22 metachronous metastatic/recurrent SynSa tumors (SynSa3 group). Results: AURKA and KIF18A, which play important roles in various mitotic events, were the two most up-regulated genes in SynSa2 and SynSa3 groups compared to the SynSa1 group. Expression profiles of SynSa2 and SynSa3 tumors did not show any significant differences. Analysis of genomic index (GI) based on aCGH profiles demonstrated that the SynSa1 group consisted of tumors with significantly less complex genomes compared to SynSa2 and SynSa3 groups. There was no significant difference in genome complexity between SynSa2 and SynSa3 tumors. Conclusions: Primary SynSa tumors from patients who develop metastases or local recurrence share common molecular features with metastatic/recurrent tumors. Presented data suggest that the aggressive clinical SynSa behavior is determined early in tumorigenesis and might be related to impaired regulation of mitotic mechanisms. Gene expression profiling of 15 primary tumors from untreated patients who did not develop metastasis/local recurrence (SynSa1 group), 21 primary tumors from untreated patients who developed metastases or local recurrence during follow-up (SynSa2 group), and 21 metachronous metastatic/recurrent SynSa tumors (SynSa3 group); Agilent One-Color technology.
2014-07-31 | E-GEOD-54187 | biostudies-arrayexpress
Project description:To determine whether sarcomas – tumors of mesenchymal origin – are subject to the same technical artifacts, we profiled patient-derived tumor explants (PDXs) propagated from three aggressive subtypes: osteosarcoma, Ewing sarcoma (ES), desmoplastic small round cell tumor (DSRCT). Given the rarity of these sarcoma subtypes, we explored whether single-nuclei RNA-seq from more widely available archival frozen specimens could accurately be identified by gene expression signatures linked to tissue phenotype or pathognomonic fusion proteins.
2023-07-21 | GSE200529 | GEO
Project description:Alveolar soft part sarcoma (ASPS) is a soft tissue sarcoma with poor prognosis. Apart from the recurrent, non-reciprocal t(X,17)(p11.2;q25) translocation, there is little molecular evidence for the origin, initiation and progression of this cancer. We analysed 16 primary and metastatic ASPS samples to elucidate candidate molecular pathways involved in tumor pathogenesis. FISH analysis identified the ASPL-TFE3 fusion in all cases. High-resolution aCGH revealed a higher number of numerical aberrations in metastatic tumors relative to primaries, but failed to identify any consistent alterations in either group. Gene expression analysis highlighted 1,063 genes which were differentially expressed between primary and metastatic tumors. Gene set enrichment analysis identified 16 enriched genesets (p < 0.1) associated with differentially expressed genes. Notable among these were several stem cell gene expression signatures and pathways related to differentiation. In particular, the paired box transcription factor PAX6 was up-regulated in the primary tumors, along with several genes whose mouse orthologs have previously been implicated in Pax6-DNA binding during neural stem cell differentiation. In addition to suggesting a neural origin for ASPS, these results implicate transcriptional deregulation from fusion genes in the pathogenesis of ASPS, rather than extensive chromosomal instability. 15 different tumor and normal paired samples were analysed
2013-07-30 | E-GEOD-49324 | biostudies-arrayexpress
Project description:Pulmonary enteric adenocarcinoma (PEAD) is a rare non-small cell lung cancer subtype. It is poorly characterized and cannot be distinguished from metastatic colorectal or upper gastrointestinal adenocarcinomas (ADCs) by means of routine pathological methods. As DNA methylation patterns are known to be highly tissue specific, we aimed to develop a methylation-based algorithm to differentiate these entities. To this end, genome wide methylation profiles of 600 primary pulmonary, colorectal and upper gastrointestinal ADCs obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database were used as a reference cohort to train a machine learning algorithm. The resulting classifier correctly classified all samples from a validation cohort consisting of 680 primary pulmonary, colorectal and upper gastrointestinal ADCs from TCGA and the GEO database, demonstrating the ability of the algorithm to reliably distinguish these three entities. We then analyzed DNA methylation data of 15 PEADs as well as four pulmonary metastases and four primary colorectal ADCs with the algorithm. All 15 PEADs were reliably classified as primary pulmonary tumors and all four metastases as well as all four primary colorectal ADC samples were identified as primary colorectal ADCs. In a t-distributed stochastic neighbor embedding analysis, the PEAD samples did not form a separate methylation subclass but rather diffusely intermixed with other pulmonary ADCs. Additional characterization of the PEAD series using fluorescence in-situ hybridization, next generation sequencing and copy number analysis revealed KRAS mutations in nine of 15 samples (60%) and a high number of structural chromosomal changes. Except for an unusually high rate of chromosome 20 gain (66.7%) the molecular data was mostly reminiscent of standard pulmonary ADCs. In conclusion, we provide sound evidence of the pulmonary origin of PEAD and in addition provide a publicly available machine learning based algorithm to reliably distinguish PEAD from metastatic colorectal cancer and upper gastrointestinal adenocarcinomas.
2019-01-04 | GSE116699 | GEO
Project description:Background: Fatal cancer is often the result of spread, or metastasis, of a cancer cell from the site of its origin to a distant anatomic site. While the metastatic process and the foreign environment of the metastatic site impact a tumorâ??s biology, we continue to determine therapy for patients based upon their cancerâ??s site of origin. We have performed an unbiased analysis across metastatic solid tumors from common primary sites to determine the molecular impact of the metastatic process on site-specific biology and to identify novel therapeutic strategies. Methods: Global gene expression was used as a biological phenotype to perform a top-down analysis of 96 metastatic human tumors. Laser capture microdissection, RNA amplification, and microarray analysis were used to measure the transcription patterns of malignant epithelial cells. Genes, multi-gene expression â??signaturesâ??, and pathways associated with site of origin (SOO) and site of metastases (SOM) were identified using established computational approaches. SOO and SOM expression signatures were validated on multiple, independent datasets comprising 1217 samples (1104 samples from GSE2109 (Expression Project for Oncology) and 113 samples from GSE12630 (Monzon FA, Lyons-Weiler M, Buturovic LJ, Rigl CT et al. Multicenter validation of a 1,550-gene expression profile for identification of tumor tissue of origin. J Clin Oncol 2009 May 20;27(15):2503-8. PMID: 19332734). Reverse phase proteomics and in vitro tissue culture were used to validate associations between biological pathways, site of primary, and implicated therapeutic combinations. Findings: SOO has the dominant influence on solid tumor biology as samples segregate based upon their primary site during unsupervised hierarchical clustering. In addition, statistically significant associations are identified between single genes and pathways and each primary site investigated and SOO signatures for colon, breast, ovary, lung, and prostate cancers accurately identify primary site for independent samples of both local and metastatic tumors independent of degree of histological differentiation. The impact of SOM on tumor biology is evident as genes and pathways are significantly associated with metastatic site and SOM signatures can be generated but they are not strongly predictive when applied to localized tumors. Pathway analysis identified relatively increased expression of MYC, beta-catenin, and SRC gene sets in metastatic colorectal cancers which was confirmed with proteomic analysis of a sub-set of the original tumors. Within colorectal cancers, high SRC expression also correlates with predicted oxaliplatin sensitivity and the combination of an SRC inhibitor with oxaliplatin demonstrated synergy in three independent colorectal cancer cell lines. Interpretation: Our findings suggest that the complex alterations required for metastasis do not obscure the impact of a cancer cellâ??s origin. SOO signatures have the potential to be highly accurate diagnostic tools and the underlying site-specific biology can be used to identify novel therapeutic targets for advanced cancers. Keywords: Gene expression analysis Ninety-six laser capture microdissected adenocarcinoma patient tumor samples of various primary and metastatic sites were processed for Total RNA. Our 96-sample datatset was enriched by inclusion of previously deposited microarray data in GEO (reprocessed for this study): A total of 1217 samples (1104 samples from GSE2109, 113 samples from GSE12630) were reprocessed from the CEL files using RMA. Supplementary files: The reprocessed data matrices. A list of the 1217 Samples' GSM accession numbers and the corresponding reprocessed sample IDs.
2016-02-13 | E-GEOD-18549 | biostudies-arrayexpress