Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE): Genome-wide Association Study Meta-analysis
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PROVIDER: PRJNA359385 | ENA |
REPOSITORIES: ENA
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Project description:OncoArray: Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE)
| PRJNA360341 | ENA
Project description:Acquired somatic variants in inherited myeloid malignancies
| PRJEB49554 | ENA
Project description:<p>Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) was one of five projects funded in 2010 as part of the NCI's Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative (<a href="http://epi.grants.cancer.gov/gameon/">http://epi.grants.cancer.gov/gameon/</a>). GAME-ON's overall goal was to foster an intra-disciplinary and collaborative approach to the translation of promising research leads deriving from the initial wave of cancer GWAS. Specific goals included replication of previous GWAS findings and identification of new susceptibility loci through meta analyses of existing GWAS data and fine mapping of identified loci to better pinpoint causal variants; and identify germline variants that are associated with risk of multiple cancers. The other four funded GAME-ON projects were: the ColoRectal TransdisciplinaryStudy (CORECT), Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE), Follow-up of Ovarian Cancer Genetic Association and Interaction Study (FOCI), and Transdisciplinary Research in Cancer of the Lung (TRICL).</p> <p>To identify additional cancer risk loci, improve the precision of fine-mapping, and facilitate cross-cancer analyses, DRIVE investigators performed a meta-analysis of eleven genome-wide association studies of breast cancer: The Australian Breast Cancer Family Study (ABCFS), the British Breast Cancer Study (BBCS), the Breast and Prostate Cancer Cohort Consortium (BPC3), the Breast Cancer Family Registries (BCFR), the Dutch Familial Bilateral Breast Cancer Study (DFBBCS), German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), the Helsinki breast cancer family Study (HEBCS), the Mammary Carcinoma Risk factor Investigation (MARIE), the Singapore and Sweden Breast Cancer Study (SASBAC), the Triple Negative Breast Cancer Study (TNBC), and the UK2 GWAS. These studies comprised a total of 16,062 cases and 46,157 controls. Imputation to the 1,000 Genomes Project Phase 1 v3 ALL reference panel was performed by study, and summary statistics from each study were combined using fixed-effect meta analysis. </p>
| phs001263 | dbGaP
Project description:EPIC array data were generated from 2 MDD case control cohorts. EWAS was performed in each cohort, followed by meta-analysis between the 2 cohort. Cohort 1: A total of 191 blood samples from 112 patients with MDD was collected up till the interim analysis (wave 1 samples) from an observational clinical study OBSERVEMDD0001 (ClinicalTrials.gov Identifier: NCT02489305) compared to 32 healthy controls; Cohort 2: The MDD cases (N = 359) were drawn from the Molecular Biomarkers of Antidepressant Response study compared to 68 healthy controls.
2022-11-03 | GSE198904 | GEO
Project description:Background: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multi-gene signatures in clinical practice is unclear and the biological importance of individual genes is difficult to assess as the published signatures virtually do not overlap. Methods: Here we describe a novel single institute cohort, including 196 non-small lung cancer (NSCLC) cases with clinical information and long-term follow-up, which was used as a training set to screen for single genes with prognostic impact. The top 450 gene probe sets identified using a univariate Cox regression model (significance level p<0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n=860). Results: The meta-analysis revealed that 17 probe sets were significantly associated with survival (p<0.0005) with a false discovery rate of 1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on a tissue microarray including 355 NSCLC samples. Low CADM1 protein expression was associated with shorter survival (p=0.028), with particular influence in the adenocarcinoma patient subgroup (p=0.002). Conclusions: We were able to validate single genes with independent prognostic impact using a novel NSCLC cohort together with a meta-analysis approach. CADM1 was identified as an immunohistochemical marker with a potential application in clinical diagnostics. Fresh frozen tissue of 196 consecutive NSCLC patients, operated between 1995 and 2005 were analyzed using Affymetrix microarrays HG-U133-Plus2. Clinical data were retrieved from the regional lung cancer registry.
2012-10-12 | E-GEOD-37745 | biostudies-arrayexpress
Project description:Background: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multi-gene signatures in clinical practice is unclear and the biological importance of individual genes is difficult to assess as the published signatures virtually do not overlap. Methods: Here we describe a novel single institute cohort, including 196 non-small lung cancer (NSCLC) cases with clinical information and long-term follow-up, which was used as a training set to screen for single genes with prognostic impact. The top 450 gene probe sets identified using a univariate Cox regression model (significance level p<0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n=860). Results: The meta-analysis revealed that 17 probe sets were significantly associated with survival (p<0.0005) with a false discovery rate of 1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on a tissue microarray including 355 NSCLC samples. Low CADM1 protein expression was associated with shorter survival (p=0.028), with particular influence in the adenocarcinoma patient subgroup (p=0.002). Conclusions: We were able to validate single genes with independent prognostic impact using a novel NSCLC cohort together with a meta-analysis approach. CADM1 was identified as an immunohistochemical marker with a potential application in clinical diagnostics.
2012-10-12 | GSE37745 | GEO