Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA283186 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Uveal melanoma (UM) is a rare form of melanoma with a genetics and immunology that is different from skin melanoma. Previous studies have identified genetic driver events of early stage disease when the tumor is confined to the eye. In this study, we have characterized genomic events in UM metastases using whole-genome and RNA sequencing from thirty-two and twenty-eight patients, respectively, and profiled individual tumor infiltrating lymphocytes in a number of the metastases. We find that 91% of the patients have metastases carrying inactivating events in the tumor suppressor BAP1 and this coincided with somatic alterations in GNAQ, GNA11, CYSLTR2, PLCB4, SF3B1 and/or CDKN2A. Mutational signature analysis revealed a rare subset of tumors with prominent signs of UV damage, associated with outlier mutational burden. We study copy number variations (CNV) and find overrepresented events, some of which were not altered in matched primary eye tumors. A focused siRNA screen identified functionally significant genes of some of the segments recurrently gained. We reintroduced a functional copy of BAP1 into a patient-derived BAP1 deficient tumor cell line and found broad transcriptomic changes of genes associated with subtype distinction and prognosis in primary UM. Lastly, our analysis of the immune microenvironments of metastases revealed a presence of tumor-reactive T cells. However, a large fraction expressed the immune checkpoint receptors such as TIM-3, TIGIT and LAG3. These results provide an updated view of genomic events represented in metastatic UM and immune interactions in advanced lesions.
2020-03-18 | PXD017743 | Pride
Project description:A comprehensive genomic survey for somatic events in systemic metastatic prostate tumors.
2015-05-07 | GSE68597 | GEO
Project description:Background: Metastases result in 90% of all cancer deaths. Prostate cancer primary tumors evolve to become metastatic through selective alterations, such as amplification and deletion of genomic DNA. Methods: Genomic DNA copy number alterations were used to develop a gene signature that measured the metastatic potential of a prostate cancer primary tumor. We studied the genomic landscape of these alterations in 294 primary tumors and 49 metastases from 5 independent cohorts. Receiver-operating characteristic cross-validation and Kaplan-Meier survival analysis were performed to assess the accuracy of our predictive model. The signature was measured in a panel of 337 cancer cell lines from 29 different tissue origins. Results: We identified 399 copy number alterations around genes that were over-represented in metastases and predictive of whether a primary tumor will metastasize. Cross-validation analysis resulted in a predictive accuracy of 80.5% and log rank analysis of the metastatic potential score was significantly related to the endpoint of metastasis-free survival (p=0.014). The metastatic signature was observed in cell lines originating from lung, breast, colon, thyroid, rectum, pancreas and melanoma. The signature was comprised in part of genes of known or putative metastatic role — 8 solute carrier genes, 6 Cadherin family genes and 5 potassium channel genes — that function in metabolism, cell-to-cell adhesion and escape from anoikis/apoptosis. Conclusions: Somatic Copy number alterations are an independent predictor of metastatic potential. The data indicate a prognostic utility for using primary tumor genomics to assist in clinical decision making and developing therapeutics for prostate and likely other cancers. genomic DNA from 29 prostate cancer tumors with matched normals run on Affymetrix 6.0 SNP arrays.
2013-12-01 | E-GEOD-27105 | biostudies-arrayexpress
Project description:Intratumor mutational heterogeneity has been documented in primary non-small cell lung cancer. Here, we elucidate mechanisms of tumor evolution and heterogeneity in metastatic thoracic tumors (lung adenocarcinoma and thymic carcinoma) using whole-exome and transcriptome sequencing, SNP array for copy number alterations (CNA) and mass spectrometry-based quantitative proteomics of metastases obtained by rapid autopsy. APOBEC-mutagenesis, promoted by increased expression of APOBEC3 region transcripts and associated with a high-risk germline APOBEC3 variant, strongly correlated with mutational tumor heterogeneity. TP53 mutation status was associated with APOBEC hypermutator status. Interferon pathways were enriched in tumors with high APOBEC mutagenesis and IFN- induced expression of APOBEC3B in lung adenocarcinoma cells in culture suggesting a role for the immune microenvironment in the generation of mutational heterogeneity. CNA occurring late in tumor evolution correlated with downstream transcriptomic and proteomic heterogeneity, although global proteomic heterogeneity was significantly greater than transcriptomic and CNA heterogeneity. These results illustrate key mechanisms underlying multi-dimensional heterogeneity in metastatic thoracic tumors.
2019-05-07 | PXD012845 | Pride
Project description:Background: Metastases result in 90% of all cancer deaths. Prostate cancer primary tumors evolve to become metastatic through selective alterations, such as amplification and deletion of genomic DNA. Methods: Genomic DNA copy number alterations were used to develop a gene signature that measured the metastatic potential of a prostate cancer primary tumor. We studied the genomic landscape of these alterations in 294 primary tumors and 49 metastases from 5 independent cohorts. Receiver-operating characteristic cross-validation and Kaplan-Meier survival analysis were performed to assess the accuracy of our predictive model. The signature was measured in a panel of 337 cancer cell lines from 29 different tissue origins. Results: We identified 399 copy number alterations around genes that were over-represented in metastases and predictive of whether a primary tumor will metastasize. Cross-validation analysis resulted in a predictive accuracy of 80.5% and log rank analysis of the metastatic potential score was significantly related to the endpoint of metastasis-free survival (p=0.014). The metastatic signature was observed in cell lines originating from lung, breast, colon, thyroid, rectum, pancreas and melanoma. The signature was comprised in part of genes of known or putative metastatic role — 8 solute carrier genes, 6 Cadherin family genes and 5 potassium channel genes — that function in metabolism, cell-to-cell adhesion and escape from anoikis/apoptosis. Conclusions: Somatic Copy number alterations are an independent predictor of metastatic potential. The data indicate a prognostic utility for using primary tumor genomics to assist in clinical decision making and developing therapeutics for prostate and likely other cancers.
2013-12-01 | GSE27105 | GEO
Project description:Intratumor heterogeneity fosters the evolution of the genome leading to metastatic progress and therapy resistance . Here, we investigate the relative contribution of tumor aneuploidy and genomic heterogeneity involving CNAs and mutational events as prognostic and predictive determinants for disease recurrence in early-stage colon cancer patients. We combined SNP arrays, targeted next-generation sequencing, fluorescence in situ hybridization and inmunohistochemistry on a retrospective cohort of 84 untreated stage II colon cancer patients. We assessed the subclonality of copy-number alterations (CNAs) and mutations, CD8+ lymphocyte infiltration levels and their association with time to recurrence (TTR).
2021-12-01 | GSE171777 | GEO
Project description:Multisampled Lethal Metastatic Prostate Cancer Copy Number Analysis
| PRJNA111809 | ENA
Project description:The integration of diverse ‘omic’ datasets will increase our understanding of the key signaling pathways that drive disease. Here, we used clinical tissue cohorts corresponding to lethal metastatic castration resistant prostate cancer (CRPC) obtained at rapid autopsy to integrate mutational, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed transcriptional master regulators, functionally mutated genes, and differentially ‘activated’ kinases in CRPC tissues to synthesize a robust signaling network consisting of pathways with known and novel gene interactions. For 6 individual CRPC patients for which we had transcriptomic and phosphoproteomic data we observed distinct pathway activation states for each patient profile. In one patient, the activated pathways were strikingly similar to a prostate cancer cell line, 22Rv1, providing us with a good pre-clinical model to test targeted, combination therapies. In all, synthesis of multiple ‘omic’ datasets revealed a plethora of pathway information suitable for targeted therapies in lethal prostate cancer.
2016-08-19 | PXD002286 | Pride
Project description:Characterization of the prostate cancer transcriptome and genome has identified chromosomal rearrangements and copy number gains/losses, including ETS gene fusions, PTEN loss and androgen receptor (AR) amplification, that drive prostate cancer development and progression to lethal, metastatic castrate resistant prostate cancer (CRPC)1. As less is known about the role of mutations2-4, here we sequenced the exomes of 50 lethal, heavily-pretreated metastatic CRPCs obtained at rapid autopsy (including three different foci from the same patient) and 11 treatment naïve, high-grade localized prostate cancers. We identified low overall mutation rates even in heavily treated CRPC (2.00/Mb) and confirmed the monoclonal origin of lethal CRPC. Integrating exome copy number analysis identified disruptions of CHD1, which define a subtype of ETS? prostate cancer. Similarly, we demonstrate that ETS2, which is deleted in ~1/3 of CRPCs (commonly through TMPRSS2:ERG fusions), is a prostate cancer tumor suppressor that can also be deregulated through mutation. Further, we identified recurrent mutations in multiple chromatin/histone modifying genes, including MLL2 (mutated in 8.6% of prostate cancers), and demonstrate interaction of the MLL complex with AR, which is required for AR mediated signaling. We also identified novel recurrent mutations in the AR collaborating factor FOXA1, which is mutated in 5 of 147 (3.4%) prostate cancers (both untreated localized prostate cancer and CRPC) , and showed that mutated FOXA1 represses androgen signaling and increases tumor growth in vitro and in vivo. Proteins that physically interact with AR, such as the ERG gene fusion product, FOXA1, MLL2, UTX, and ASXL1 were found to be mutated in CRPC, suggesting novel drivers of prostate cancer progression and potential resistance mechanisms to anti-androgen therapies. In summary, we describe the mutational landscape of a heavily treated metastatic cancer, identify novel mechanisms of AR signaling deregulated in prostate cancer, and prioritize candidates for future study. Gene expression profiling and array CGH (aCGH) was performed on matched benign prostate tissues (n=28), localized prostate cancer (n=59), and metastatic castrate resistant prostate cancer (CRPC, n=35). For gene expression profiling, frozen prostate tissue samples (channel 2), were hybridized against a commercial pool of benign prostate tissue (Clontech, channel 1). For aCGH, frozen prostate tissue samples (channel 2) were hybridized against a commerical sample of Human Male Genomic DNA (Promega, channel 1).
2012-05-22 | E-GEOD-35988 | biostudies-arrayexpress
Project description:The systematic characterization of somatic mutations in cancer genomes is essential for understanding the disease and for developing targeted therapeutics. Here we report the identification of 2,576 somatic mutations across approximately 1,800 megabases of DNA representing 1,507 coding genes from 441 tumours comprising breast, lung, ovarian and prostate cancer types and subtypes. Additionally, 373 tumors were assayed for copy number alterations via Agilent 244A CGH arrays and 153 breast, lung, and colon samples were assayed for mRNA abundance with Affymetrix HuEx1 Exon Arrays. This SuperSeries is composed of the SubSeries listed below.
2010-08-24 | GSE23768 | GEO