11987 results for "cancer rna-seq"

in 18.11 milliseconds.

• Peter MacCallum Cancer Centre Research Data Access Committee

  Dac EGAC00001002975

• EGAD00010000622

  SNP array data for gastric cancer cell lines

  Dataset EGAD00010000622

• nanostring analysis

  mRNA expression profile of kidney cancer

  Dataset EGAD00010001535

• OvCan1

  Digital images of ovarian cancer sections

  Dataset EGAD00010000881

• Whole exome sequencing from small cell lung cancer patients

  Study EGAS00001005087

• Accelerated single cell seeding in relapsed multiple myeloma

  Study EGAS00001004404

• AUBTRG - Whole Exome Sequencing of Diffuse Glioma Samples

  Study EGAS00001003035

• Molecular analysis of inflammatory myofibroblastic tumor (WGS and WES)

  Study EGAS00001005081

• Molecular characterization of a renal cell carcinoma PDX cohort

  Study EGAS00001006249

• Validation of a targeted sequencing panel for multiple myeloma

  Study EGAS00001006164

• Study of the microenvironment of angioimmunoblastic T-cell lymphoma

  Study EGAS00001006401

• RNAseq analysis on primary sites Colorectal Cancer xenografts (PRX) samples

  Study EGAS00001007051

• Characterization of alternative promoter usage in advanced prostate cancer

  Study EGAS00001006275

• GBM stem cell lines and PRMT5 inhibitor

  Study EGAS00001004397

• Elucidating the molecular landscape of KRAS/BRAF mutant colorectal cancers

  Study EGAS00001007459

• Yale Policy for head and neck cancer and HPV clinical trials

  Dac EGAC00001003519

• Aberrant EZHIP Expression Drives Tumorigenesis in Osteosarcoma

  Study EGAS00001007531

• Integrated genetic analysis of primary CNS lymphoma

  Study EGAS00001007222

• SickKids_Cancer Molecular Diagnostics

  Cancer Molecular Diagnostics at Hospital for Sick Children

  Dac EGAC50000000375

• Fasting-mimicking diet reshapes antitumor immunity in cancer patients

  Study EGAS00001004944

• Whole genome sequencing to identify structural variants in early-stage breast cancer

  Dac EGAC50000000461

• Carolina Breast Cancer Study (CBCS)

  The Carolina Breast Cancer Study (CBCS), phase III, is a population-based study aiming to understand the social and molecular correlates of breast cancer outcomes, particularly among Black and young women. CBCS recruited first primary breast cancer cases from 44 North Carolina counties between 2008-2013. All cases were between age 20 and 74 and self-identified as female. Black women and young women were systematically oversampled by randomized recruitment such that the study includes approximately 50% Black women and approximately 50% women diagnosed before age 50. A total of 2,998 participants were enrolled. In the ten years following diagnosis, women completed surveys approximately every 1-2 years and diagnoses of subsequent distant local recurrence and second primary tumors were ascertained via linked medical records. We identified records of women who developed secondary breast tumors (including second primary tumors, contralateral breast cancers, and locoregional recurrences), and these participants were oversampled for targeted DNA sequencing using the UNCseq panel (~1200 genes).CBCS germline pathogenic mutations cannot be investigated because participants were consented between 2008 and 2013 with specific language prohibiting this. However, germline data can be used to call somatic mutations or to standardize somatic genomic data.For sharing of study data not included here, please visit unclineberger.org for instructions on submitting a data use agreement.

  Study phs003725

• CancerLocator: Non-Invasive Cancer Diagnosis and Tissue-of-Origin Prediction Using Methylation Profiles of Cell-Free DNA

  Background: The detection and characterization of cell-free DNA in plasma is one of the most promising new areas in cancer diagnosis. Liquid biopsy, unlike traditional tissue biopsy, has the potential to diagnose a variety of different malignancies. Results: Here we propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data. We comprehensively evaluate CancerLocator with simulations and real data, and compare its performance with that of two established multi-class classification methods. We show that the predicted tumor burdens are highly consistent with the true values. In addition, when the proportion of tumor-derived DNAs in the cell-free DNAs is low, the two popular machine learning methods completely fail for cancer diagnosis, while CancerLocator successfully overcomes the challenge. CancerLocator also achieves promising results on patient plasma samples, despite the fact that the DNA methylation data from these samples has very low sequencing coverage. Conclusions: CfDNA methylation may be developed as an important approach for non-invasive early cancer diagnosis.

  Study EGAS00001002211

• CancerDetector: Ultrasensitive and Non-Invasive Cancer Detection at the Resolution of Individual Reads using Cell-free DNA Methylation Sequencing Data

  The detection of tumor-derived cell-free DNA in plasma is one of the most promising directions in cancer diagnosis. The major challenge in such approach is how to identify the tiny amount of tumor DNAs out of total cell-free DNAs in blood. Here we propose an ultrasensitive cancer detection method, termed “CancerDetector”, using the DNA methylation profiles of cell-free DNAs. The key of our method is to probabilistically model the joint methylation patterns of multiple adjacent CpG sites on an individual sequencing read, in order to exploit the pervasive nature of DNA methylation for signal amplification. Therefore, CancerDetector can sensitively identify a trace amount of tumor cfDNAs in plasma, at the level of individual reads. We evaluated CancerDetector on the simulated data, and showed a high concordance of the predicted and true tumor burden. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor DNAs. In addition, the predicted tumor burden showed great consistency with tumor size and survival outcome. Note that all of those testing were performed on sequencing data at low to medium coverage (1X to 10X). Therefore, CancerDetector holds the great potential to detect cancer early and cost-effectively.

  Study EGAS00001002728

• Exome sequencing of a novel cervical cancer cell line

  Human cancer cell lines are largely used in the searching for new antineoplastic agents. However, due to the artifacts of a long-term in culture, cell lines do not always represent the realistic tumor cell behavior. This has motivated the development of models that better mimetics the tumor tissue, among them, the establishment of primary cell cultures. In this work, we establish and characterized a low-passage cervix cancer cell line from a Brazilian patient with squamous cell carcinoma. The phenotype confirms the epithelial and tumor origin, through cytokeratins, EpCAM, and p16 staining. Whole exome sequencing showed relevant somatic mutations in several genes including BRCA2, TGFBR1, and IRX2 genes. CNV analysis by nanostring and WGS revealed amplification in genes related mainly with kinases proteins, involved in proliferation, migration and cell differentiation, such as EGFR, PIK3CA, and MAPK7. Overexpression of EGFR was confirmed by phospho RTK-array and western blot analysis. Furthermore, the cell was sensitivity to cisplatin, with IC50 13 times lower than SiHa cell line. In conclusion, this cervical cancer cell line presents molecular alterations that are an important tool for leading pre-clinical studies of new drugs that target one or more of the altered pathways.

  Study EGAS00001003343