CancerMap project prostate cancer microarray dataset
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA373993 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Microarray expression profiling has currently failed to provide a consistent classification for human prostate cancer. Such classifications are important because they provide a framework for the identification of new biomarkers of clinical behavior and for the development of targeted therapies. We hypothesize that previous studies have been unsuccessful because of their failure to take into account the well documented occurrence of prostate cancer multifocality and genetic heterogeneity. We have invented a novel method for collecting whole RNALater preserved ‘research slices’ from prostatectomy specimens that, for the first time, allows the mapping of multifocality and of genetic heterogeneity in prostate cancer to be integrated with the selection of samples for expression microarray analysis. For each specimen we will construct a map of the regions of cancer and of their ERG gene rearrangement status from whole mount formalin fixed sections immediately juxtaposed to the ‘research slice’. Only foci of cancers containing a homogeneous pattern of ERG gene alteration will be selected for study. A pilot study has already demonstrated the feasibility of this approach, and provides initial evidence that cancers may be stratified into at least two prognostically distinct categories. Novel biomarkers defining distinct prostate cancer categories will be verified and validated in future studies linked to clinical trials.
2017-03-13 | GSE94767 | GEO
Project description:Prostate Cancer Genome Sequencing Project
| PRJNA81143 | ENA
Project description:We present a meta-dataset comprising of a total of 237 samples including both primary tumors and tumor-free prostate tissues from six independent GEO datasets. To minimise inter-platform variation, only datasets generated from the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array) were processed to develop the meta-dataset. Using multiple open source R packages implemented in our previously developed bioinformatics pipeline, each dataset has been preprocessed with RMA normalisation, merged, and batch effect-corrected via Combat method. With increased sample size, the present meta-dataset serves an excellent 'discovery cohort' for discovering differentially expressed in diseased phenotype.
2019-04-11 | E-MTAB-6694 | biostudies-arrayexpress
Project description:Hinchcliff 165 microarray dataset
| PRJNA256198 | ENA
Project description:The Proteogenomic Landscape of Curable Prostate Cancer [microarray]
| PRJNA419650 | ENA
Project description:Count Me In: The Metastatic Prostate Cancer Project
| PRJNA592223 | ENA
Project description:Count Me In: The Metastatic Prostate Cancer Project
| PRJNA592225 | ENA
Project description:Custom Microarray Developmental Disabilities Clinical Dataset
| PRJNA377195 | ENA
Project description:DNA sequencing studies have identified specific recurrent somatic mutations that drive the aggressiveness of localized prostate cancers. Surprisingly, though, it is poorly understood how the prostate cancer proteome is shaped by genomic, epigenomic and transcriptomic dysregulation. To fill this gap, we profiled the whole genomes, methylomes, epigenomes, transcriptomes and proteomes of 55 localized, intermediate-risk, prostate cancers. This multi-modal dataset revealed that the genomic subtypes of prostate cancer converge on four proteomic subtypes, which are associated with distinct clinical trajectories. ETS fusion genes, the most common mutation in prostate tumours, perturb the proteome and transcriptome in divergent ways – with different genes and pathways affected at each level. Indeed, mRNA abundance changes explain only ~10% of variability in protein levels. Perhaps as a direct result, prognostic biomarkers that combine genomic or epigenomic features with proteomic ones significantly outperform those comprised of either molecular feature alone. These data suggest that the proteome of prostate cancer is shaped by a complex interplay of genomic, epigenomic, transcriptomic and post-transcriptional dysregulation.
2019-06-30 | GSE107299 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE18916: Expression data from 42 prostate cancer samples - 16 recurrent and 26 recurrence-free GSE18917: Expression data from 22 prostate cancer samples - 6 recurrent and 16 recurrence-free from the validation dataset Refer to individual Series
2011-06-15 | E-GEOD-18918 | biostudies-arrayexpress