15136 results for "cancer studies using rna-seq"

in 31.07 milliseconds.

• Somatic Mutation Profile by Next Generation Sequencing in HER2+ Breast Cancer

  The HER pathway is the driving force behind 30% of human breast cancers. It is important to understand how targeted therapies block different cellular pathways, and mechanisms of escape from this blockage. Therapies directed at HER2 establish a successful treatment paradigm, but de novo and acquired resistance exist. The HER signaling system is a complex network with four receptors and eleven ligands, a phosphorylation signaling cascade, and many transcription factors, all complicated by both positive and negative feedback circuits. Analysis of genomes, exomes and transcriptomes by next generation sequencing is aimed at uncovering the genetic factors responsible for patient responses to HER2-directed therapies. We are sequencing HER2-overexpressing cancers, in order to identify potential somatic changes that may better select patients who will benefit from therapy, to determine new targets that may overcome resistance, and to improve outcomes with known current HER2-targeted therapies. Whole exome capture sequencing will determine somatic mutation profiles in HER2-overexpressing tumors, to comprehensively characterize the somatic alterations, with the goal of identifying those patients most likely to respond, as well as discovering new targets that may overcome resistance to HER2-directed therapy. We augment the whole exome data with RNA-seq data to determine expression levels of somatic mutations we discover.

  Study phs000770

• CIDR: Genome Wide Association Study in Familial Parkinson Disease (PD)

  This proposal brings together the two largest NIH funded genetic studies focused on the identification of novel genes that influence the risk of PD. These two studies, PROGENI (PI: Tatiana Foroud; R01NS037167) and GenePD (PI: Richard Myers; R01NS036711) have been evaluating and recruiting families with two or more PD affected members for more than 8 years and represent the largest such cohorts world-wide. The combined sample has more than 1,000 PD families. Each study has used rigorous clinical criteria to assess their study participants. Unlike previous genome wide association studies (GWAS) in PD, all the PD cases in this proposal have a positive family history of disease. In the vast majority of these families, the index PD case has at least one sibling with the disease. Thus, the sample is unique for having substantial evidence for a genetic contribution to disease. The control group for this study consists of samples previously collected and maintained by the NINDS Repository. Genome-wide, single nucleotide polymorphism (SNP) genotyping services were provided by the Center for Inherited Disease Research (CIDR). Data analyses will focus on the identification of SNPs associated with PD susceptibility and the age of onset of disease.

  Study phs000126

• Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA)

  The OPPERA project has two sub-studies investigating effects of genetic, physiological, psychological and clinical factors on the risk of developing painful temporomandibular disorder (TMD). OPPERA I baseline case-control study of chronic TMJD (dbGaP study accession phs000762). This sub-study uses a case-control study design to investigate characteristics associated with chronic TMD. The parent OPPERA phenotype file contains an indicator variable (PHASE1CASECONTROL) identifying participants in this sub-study. Data users should use the relevant case-classification variable (CHRNONICTMDCASE) and apply statistical methods appropriate for case-control studies when analyzing TMJD in this sub-study. OPPERA I prospective cohort for first onset of TMJD (dbGaP study accession phs000850). This sub-study uses a prospective cohort study design to investigate characteristics that predict risk of developing first-onset TMD. The parent OPPERA phenotype file contains an indicator variable (PROSPECTIVECOHORT) identifying participants in this sub-study. Data users should use the relevant case-classification variable (INCIDENTCASE) and the relevant time-in-study variable (FOLLOWUPYEARS) when analyzing TMJD in this sub-study. It is recommended that data users apply statistical methods appropriate for cohort studies of a dynamic population (for example, Cox regression models, where INCIDENTCASE is the censoring variable and FOLLOWUPYEARS is time in study).

  Study phs000761

• WGS_skin_punches

  We wish to understand the degree of copy number alterations in normal human sun exposed skin. We have collected small punches of epithelia and using a bait set design have shown the these span a clone. WGS will provide copy number information on these.

  Study EGAS00001004465

• Mutational_burden_in_skin_following_UV_treatment_Nanoseq

  We wish to assess the changes in mutational burden in patients under going UV treatment for psoriasis. Using duplex sequencing we will assess mutational burden in traditionally sun exposed (arm) and non-sun exposed (buttock) skin epidermis both pre and post UV treatment.

  Study EGAS00001007681

• BGISEQ-500 Cancer Dataset - WGS tumour/normal pairs

  This study contains whole genome sequencing data for 3 matched tumour / normal pairs from Mesothelioma patients. The sequencing is 50bp paired-end generated using the BGISEQ-500 and is provided as 6 aligned and duplicate-marked BAM files.

  Study EGAS00001002298

• Mutational_burden_in_skin_following_UV_treatment_WGS

  We wish to assess the changes in mutational burden in patients under going UV treatment for psoriasis. Using duplex sequencing we will assess mutational burden in traditionally sun exposed (arm) and non-sun exposed (buttock) skin epidermis both pre and post UV treatment.

  Study EGAS00001007682

• Clonal_dynamics_and_mutation_burden_in_male_germline___DupSeq

  This work aim to study clonal dynamics of pathogenic mutations in male germline. Using Nanoseq method, sperm from normal panel will be compared with sperm from men who have either clinical phynotypes or they have child(ren) with germline predisposition syndromes.

  Study EGAS00001005135

• Impact of Mobile Element Insertions on Human Transcriptome Variation

  Mobile DNA elements are ubiquitous in nearly all species and mobile element insertions can affect nearby gene expression through many mechanisms, from disrupting coding regions to initiating DNA methylation to altering RNA splicing patterns. Thousands of mobile element insertions are still polymorphic in human populations, and these polymorphic mobile element insertions (pMEIs) can contribute to transcriptome and phenotypic variation in humans. We identified and genotyped pMEIs using the whole genome sequencing (WGS) data from the Genotype-Tissue Expression (GTEx) project. We then used the extensive multi-tissue transcriptome data from the GTEx project to systematically assess the effects of pMEIs on human transcriptome variation.

  Study phs002030

• Single-cell profiling of neoantigen-specific T cells in lung cancers treated with neoadjuvant PD-1 blockade

  We study the expression profile of MANA-specific T cells, we used the MANA functional expansion of specific T cells (MANAFEST) assay in neoadjuvant anti-PD-1-treated lung cancers and applied their T cell receptor’s (TCR) complementarity-determining region 3 (CDR3) as a “barcode” to track and analyze their transcriptional programs in the tumor microenvironment using single cell TCR sequencing coupled with single cell RNA sequencing. We revealed distinct phenotype for MANA- and virus-specific clones in TIL and adjacent normal lung, regardless of response. MANA-specific T cells show features that drive the differences between responders and non-responders.

  Study EGAS00001005343

• lnFXI_metaanalysis_summarydata

  Summary data from Meta-analysis of Genome-Wide-Association Studies for plasma levels of Coagulation Factor XI (FXI)

  Dataset EGAD00010001141

• Epigenetic Profiling of Human Colorectal Cancer

  DNA methylation, together with chromatin modifications, constitute the epigenome that functions to regulate gene expression and genome integrity. DNA methylation alterations are ubiquitous in human cancers, as many genes acquire DNA methylation in a cancer-specific manner. DNA methylation at these sites in the genome of cancer cells not only serves as a marker for tumor identification, but together with gene mutation and gene expression data, can also be used to describe subsets of tumors of the same organ source. We have previously shown distinct human colorectal cancer subtypes based on DNA methylation differences. Correlating these DNA methylation differences with clinical co-variates will serve to further understand how these distinct subtypes are generated. We have collected 100 colorectal tumor tissues (for which clinical information is known) and have obtained (unprotected) genome-wide DNA methylation and chromatin modification information for each sample for the purposes of identifying and classifying unique tumor subtypes of colorectal cancers. In addition, we have determined mutations of key genes relevant to colorectal cancer as well as gene expression profiles. We will use the clinical data for each de-identified sample to correlate with the DNA methylation, mutation and gene expression information so as to understand the driving forces behind these distinct colorectal subtypes. We have selected the most promising tumors for whole-genome bisulfite sequencing using next-generation sequencing technology to obtain complete maps of colon cancer methylomes. Researchers will be unable to identify the subjects because the samples and associated information have been de-identified and anonymized by the tissue source site. In addition, upon receipt by the USC Epigenome Center, we have assigned new random identifiers for each sample. The data generated using these new codes are not traceable to the patient identity.

  Study phs000385

• Effects of Metformin on Transcriptomic and Metabolomic Profiles in Breast Cancer Survivors Enrolled in the Randomized Placebo-Controlled MetBreCS Trial

  Metformin reduces the incidence of breast cancer in patients with obesity and type 2 diabetes. However, our knowledge of the effects of metformin on breast cancer recurrence is limited. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin. To identify metformin-regulated signaling pathways, we integrated the transcriptomic, metabolomic and steroid hormone profiles using bivariate and functional analyses. We identified MS4A1, HBA2, MT-RNR1 and MT-RNR2 expression to be differentially expressed in breast tissues from metformin-treated women. We also found metformin-associated down-regulation of EGFL6 and FDCSP in postmenopausal women. The integration of transcriptomic and metabolomic profiles revealed down-regulation of immune response genes associated with reduced levels of arginine and citrulline in the metformin-treated group. The integration of transcriptomic and steroid hormone profiles showed an enrichment of steroid hormone biosynthesis and metabolism pathways with highly negatively correlated CYP11A1 and CYP1B1 expression in breast tissue from postmenopausal metformin-treated women. Our results indicate that breast cancer survivors treated with metformin have specific changes in breast tissue gene expression that may prevent the development of new tumors. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin using bulk RNAseq (total mRNA sequencing), that is deposited in Federated EGA. The steroid hormones, and metabolomics and the insulin sensitivity serum biomarker data underlying this article may be shared upon reasonable request to the PI of the MetBreCS study (Dr. Bernardo Bonanni), following approval by the Data and Safety Monitoring Board at IEO, Milan.

  Study EGAS50000000874

• A capture methyl-seq protocol with improved efficiency and cost-effectiveness using pre-pooling and enzymatic conversion

  The opportunities for sequencing-based methylome analysis of clinical samples are increasing. To reduce its cost and the amount of genomic DNA required for library preparation, we aimed to establish a capture methyl-seq protocol, which adopts pre-pooling of multiple libraries before hybridization capture and TET2/APOBEC-mediated conversion of unmethylated cytosine to thymine. We compared a publicly available dataset generated by the standard Agilent protocol of SureSelect XT Human Methyl-Seq Kit and our dataset obtained by our modified protocol, EMCap, that adopted sample pre-pooling and enzymatic conversion. We confirmed that the quality of DNA methylation data was comparable between the two datasets. As our protocol, EMCap, is more cost-effective and reduces the amount of input genomic DNA, it would serve as a better choice for clinical methylome sequencing.

  Study JGAS000605

• ovarian cancer sample exome seq

  Tissue samples were collected from 18 ovarian cancer patients. After mechanical and enzymatic dissociation, a part of these tissues underwent growth factoromics analysis. For four cases, single-cell sequencing analysis was conducted under conditions with or without estradiol. Additionally, the remains of the tissues were frozen and subsequently subjected to exome sequencing on 16 of these samples. Sixteen cases were diagnosed as HGSOC, while two samples were clear cell or mucinous type (Extended Data Table 1). All cases were grade 3, and none had received neoadjuvant chemotherapy.

  Dataset EGAD50000002057

• Cancer genome scanning in plasma: detection of tumor-associated copy number aberrations, single nucleotide variants and tumoral heterogeneity by massively parallel sequencing

  Tumor-derived DNA can be found in the plasma of cancer patients. We explored the use of shotgun massively parallel sequencing of plasma DNA obtained from cancer patients to scan a cancer genome noninvasively. We achieved the profiling of copy number aberrations and point mutations, in a genomewide manner using this approach. By measuring the genomewide aggregated allelic loss and point mutations, we determined the fractional tumor-derived DNA concentrations in plasma and correlated these values with tumor size and surgical treatment. We have also demonstrated the application of this approach to monitoring a complex oncologic scenario, in a patient with two synchronous cancers. In particular, through the use of multi-regional sequencing of tumoral tissues and shotgun sequencing of plasma DNA, we have shown that plasma DNA sequencing is a valuable approach for studying tumoral heterogeneity. Shotgun plasma DNA sequencing is thus a powerful tool for cancer monitoring and research.

  Study EGAS00001000370

• RNA-Seq Brest Patient-derived Tumor Organoids

  This dataset includes 14 RNASeq samples from 8 patients. For some patients, organoids at different passages have been profiled. They can be identified by their alias, composed of the following structure: <subjectId>_O<organoid_number>_<passage>_RNA. For example, ICSBCS002_O1_1_RNA indicates the earlier passage of organoid 1 for subject ICSBCS002, and ICSBCS002_O1_2_RNA indicates the later passage. Additionally, WCM2137_O1_RNA and WCM2137_O2_RNA indicate two organoids (O1 and O2) derived from different tissues. More information about the passages and tissues can be found in the sample information table.

  Dataset EGAD50000000960

• Epigenome mapping in purified cells of the digestive and urogenital organs: Participation in International Human Epigenome Consortium (IHEC) (RNA-Seq)

  The aim of this study is to participate in International Human Epigenome Consortium (IHEC, http://ihec-epigenomes.org/), through disclosure of quality epigenome profiles of various normal cell lineages and diseased cells purified from surgically resected materials of multiple Japanese people.

  Study JGAS000028

• A single-cell atlas of the early COPD lung

  Single cell atlas of the human airways from 10 patients suffering of early stage Chronic Obstructive Pulmonary Disease and 12 healthy age matched volunteers. This dataset was sequenced by 10X Genomics 3’ RNA-seq profiling. It is composed of more than 400 000 cells coming from 119 samples total. Sampling was performed by brushings and biopsies from the nose to the 6 th division of the airways.

  Dataset EGAD50000001001

• Single cell RNA-seq from AD and PS patients

  We applied single cell immune profiling to gain a holistic view from immune cells infiltrating the skin to circulating immune cells, facilitating a better understanding of the two diseases in their entirety, and the identification of potential type-2 or type-3-disease-driving mechanisms.

  Study EGAS00001007055