Search Results - EGA European Genome-Phenome Archive

Childhood Cancer Data Initiative (CCDI): Enhancement of Data Sharing in Pediatric, Adolescent and Young Adult Cancers

NCI's Childhood Cancer Data Initiative (CCDI) is building a community centered around childhood cancer care and research data. Through enhanced data sharing, we can improve our understanding of cancer biology to improve preventive measures, treatment, quality of life, and survivorship, as well as ensure that researchers learn from every child with cancer. While childhood cancers represent the leading cause of death in children over the age of 1, they are collectively rare, comprising approximately 1%‒3% of cancers diagnosed annually in the United States. Information on diagnosis, treatment, and outcomes is often stored at the hospital or institution where a child is treated, making it difficult to answer scientific questions about childhood cancer. Sharing clinical care and research data generated by children's hospitals, clinics, or networks broadly with the community can help us learn faster and, on a scale much larger than any single institution caring for children can learn on its own. To exploit the rapid advances in high-throughput DNA sequencing technologies and realize the goals of precision cancer medicine, the UMICH Cancer Center established the Michigan Oncology Sequencing Center (MI-ONCOSEQ). This UMICH Cancer Center provisions genomic data derived from the Michigan Oncology Sequencing Center (MI-ONCOSEQ) clinical sequencing assay for patients that fall within the inclusion criteria of the CCDI data sharing initiative. An "integrative sequencing approach" carried out in a CLIA-certified laboratory is utilized to provide a comprehensive landscape of the genetic alterations in individual tumor specimens for the purpose of identifying informative and/or actionable mutations. Using a number of computational pipelines that we have developed in-house or have adapted from the public domain, candidate molecular aberrations are nominated and then analyzed by scientists for clinical significance and/or relevance. This approach enables the detection of point mutations, insertions/deletions, gene fusions and rearrangements, amplifications/deletions, and outlier expressed genes. Furthermore, we can identify certain germline alterations that may also be relevant.

Study phs002431

Somatic Mutations and Their Etiological Determinants for Breast Cancer in African American Women (B-CAUSE Study)

The breast cancer health disparity between women of African ancestry (AA) and European ancestry (EA) remains a major public health challenge in the United States. AA women are afflicted by higher rate of triple-negative breast cancer (TNBC) and bear the highest mortality rate of all populations from the disease. Even within TNBC, some data suggest that AA women have poorer outcomes than EA women. Mounting evidence points to population differences in cancer biology, a fundamental question that remains unsettled. The disparity also manifests in representativeness in tumor sequencing projects, due to the low inclusion of AAs in genomic studies. A recent analysis of The Cancer Genome Atlas (TCGA) data revealed that the number of AA cases sequenced was sufficient only for studying frequently mutated genes (≥10%), and the power was worse for TNBC as a separate disease entity. As a result, the breast tumor mutational landscape elucidated to date is derived mostly from EA cases, and may not necessarily represent AA cancer genomes. Moreover, as discovery potential for new driver genes has come close to a plateau, cancer in AAs derived from distinct genetic and environmental contexts may provide a powerful venue for discovering mutations that are rare in EA cases. This is showcased in recent studies in colon and prostate cancer. Thus, we carried out a collaborative project, B-CAUSE, to characterize the mutational landscape of AA breast cancer genomes by pooling resources from five established studies, including the Women's Circle of Health Study (WCHS), the Black Women's Health Study (BWHS), the Southern Community Cohort Study (SCCS), the Nashville Breast Health Study (NBHS), and the Southern Tri-State Breast Health Study (STSBHS). This effort has created the largest AA-specific breast tumor mutation dataset to date in an efficient manner. The data deposited here are from the whole-exome sequencing (WES) of matched tumor-normal pairs of 462 TNBC in AA women from the B-CAUSE Study.

Study phs003962

Mixture of 2

Mixture of 2 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

Dataset EGAD00001008726

HiC-sequencing in human monocyte differentiation

This dataset consists of in situ HiC-seq data from human monocytes, monocyte-derived dendritic cells as well as monocyte-derived cells that were subjected to siRNA treatment targeting CTCF or RAD21. In total, the data set includes 42 samples.

Dataset EGAD00001007955

scRNA-seq of nonhematopoietic cells in human lymph nodes and lymphoma

Single-cell count data generated by the Cellranger (10X Genomics).

Dataset EGAD00001008311

AML-PMP miRNA-Seq

miRNA libraries of the AML-PMP project were sequenced on the Illumina HiSeq 2000 instrument, approximately 16 samples per HiSeq lane, to a median depth of 5.7 million reads per library.

Dataset EGAD00001009772

Low-pass whole-genome DNA sequencing of cohesin-mutated and wildtype adult AMLs

Low-pass whole-genome DNA sequencing of cohesin-mutated (STAG2 or RAD21 mutations) and wildtype (CTRL-AML) adult AMLs generated generated from ultrasound-fragmented genomic DNA. Samples were only sequenced shallow (20-40 Mio reads) Used for digital karyotyping and ChIP-seq background/copy-nuber normalization/correction.

Dataset EGAD00001011207

11805 results for "cancer rna-seq"

in 22.83 milliseconds.