11932 results for "cancer rna-seq"

in 169.65 milliseconds.

• Whole exome sequencing data for paired non-serous endometrial and ovarian carcinomas from 27 patients with concurrent tumours.

  Dataset for Synchronous Endometrial and Ovarian Cancer

  Dataset EGAD00001015680

• Targeted gene sequencing of gallbladder cancer

  The dataset includes the targeted gene sequencing data from51 pairs of gallbladder caner tissues and patient-matched normal tissues.

  Dataset EGAD00001000902

• GIST SSGXVIII trial targeted gene sequencing

  Targeted cancer gene sequencing of samples enrolled in the SSGXVIII trial from Finland.

  Dataset EGAD00001001427

• WES

  WES was performed on 2 TURP, 6 biopsies, 1 CDX, 1 cell line, 6 CTCs, 1 DNA germline and 1WBC from prostate cancer

  Dataset EGAD00001006009

• scATAC-seq of CD4+ T cells from blood and tumor of NSCLC patients

  This study explore the CD4+ T cells diversity in paired blood and tumor of 2 different NSCLC patients. After FACS-sorted of Tregs (DAPI- CD45+ CD4+ CD25hi CD127lo) and Tconvs (DAPI- CD45+ CD4+CD25lo CD127lo/hi), cells were mixed in equal proportion (fifty/fifty ratio) and procesed with 10X Chromium (10X Genomics) scATAC kit and sequenced using Illumina platforms.

  Study EGAS50000000294

• Integration of T Cell Repertoire, CyTOF, genotyping and symptomatology data reveals subphenotypic variability in COVID-19 Patients

  A total of 61 patients (33 mild and 28 severe) with a median age of 51 years who recovered from COVID-19 were included in this study (Table 1). All collected samples were confirmed in diagnosis of COVID-19 by RT-PCR and positive IgG serolog. For the 61 patients, TCR-seq, CyTOF, and genotyping data were generated and analyzed in separate studies, as part of larger cohorts. In total, there are 143 variables, with 129 being quantitative (TCRseq and CyTOF metrics) and 13 being qualitative (symptomatology and genotyping). For more information about cohort characteristics, data adquisition an pre-processing, please visit PMID: 39227859, 36969980 and 37287057.

  Dataset EGAD50000000840

• Targeted long-read snIso-Seq of the human brain in neurodegenerative diseases

  Nuclei were extracted from postmortem human brain, and cDNA libraries were generated using the 10X Genomics 3' v3.1 kit. Prior to fragmentation, 75% of the library was reserved for long-read sequencing. This unfragmented library was used for target enrichment with a custom 50 gene probe panel from Twist Biosciences. The resulting amplified cDNA libraries were sequenced on a Sequel II. The files provided have undergone CCS generation, and we recommend following the guidelines in https://isoseq.how/ for further analysis starting with removal of primers using lima. The primer sequences that should be supplied are the Read 1 and TSO primers from the 10X Genomics kit.

  Dataset EGAD50000000179

• WGBS Discovery Samples

  The dataset contains valuable genomic data from a discovery cohort consisting of 29 individuals. This cohort is comprised of 8 healthy individuals (control), 8 patients with ST-segment elevation myocardial infarction (STEMI), 7 patients with non-ST-segment elevation myocardial infarction (NSTEMI), and 6 patients with unstable angina (UA). The genomic data was obtained by isolating cell-free circulating DNA (ccfDNA) and subjecting it to bisulfite conversion using a low-input BS-seq (PBAT) protocol. Sequencing was done on the Novaseq 6000 platform.

  Dataset EGAD00001010935

• LLD PhIPSeq

  LLD PhIP-Seq reads. Oligopeptides were designed at Eran's Segal group at Weizmann Institute of Science. Dataset include 1,783 plasma samples. In 340 participants, a second time point was taken after a 4-year follow-up.

  Dataset EGAD00001010104

• DPY30 ChIP-seq

  H9 human embryonic stem cells (hESCs) were cultured in feeder-free chemically-defined conditions in medium containing 10ng/ml Activin A and 12ng/ml FGF2 (Vallier L. 2011, Methods in Molecular Biology, 690: 57-66). Chromatin immunoprecipitation was performed as described in Brown S. et al. 2011. Stem Cells 29: 1176-85 by using 5ug of anti-DPY30 antibody (Sigma, cat. number HPA043761). This protocol was performed in control hESCs (expressing a scrambled shRNA) and in hESCs stably expressing an shRNA against DPY30 (Sigma, clone n. TRCN0000131112).This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001001268

• Cell-Free, Methylated DNA in Blood Samples Reveals Tissue-Specific, Cellular Damage from Radiation Treatment

  Radiation therapy is an effective cancer treatment, although damage to surrounding healthy tissues can also occur. Cell-type specific DNA methylation patterns can be used to decode the cellular origins of cell-free DNA fragments, allowing for minimally-invasive monitoring of tissue damage. To evaluate whether changes in cell-free DNA methylation can indicate damages to tissues in patients treated with radiation, we collected serum samples from 15 breast cancer patients at three timepoints during their standard-of-care radiation therapy after surgery. A baseline sample was taken for each patient before onset of radiation therapy and a second End-Of-Treatment (EOT) sample was taken 30 minutes after the last treatment fraction. Finally, a recovery sample was taken one month after completion of radiation therapy. From serum samples, cell-free DNA was isolated and bisulfite capture-sequencing DNA methylation data were generated. Bisulfite capture-sequencing DNA methylation data were also generated from buffy coat and tissue-specific endothelial gDNA samples as well as from serum/plasma samples of healthy controls. The analysis of serum samples from breast cancer patients undergoing radiation treatment revealed distinct tissue-specific epithelial and endothelial responses to radiation across multiple organs. In conclusion, as a proof of concept we show that cell-type specific methylation signatures can be applied to detect cellular injury from radiation treatment using minimally invasive cell-free DNA in blood samples.

  Study phs003290

• University of Texas PDX Development and Trial Center Grant

  The goal for the University Texas PDX Development and Trial Center (UTPDTC) is to optimize personalized biomarker-based cancer therapy and identify effective targeted drugs based on the molecular characteristics of each tumor. Our short-term goals are to establish a biobank of clinically, and molecularly-annotated Patient-Derived Xenografts (PDXs) and to use PDXs as a platform for preclinical drug development and biomarker discovery. The primary goal for UTPDTC investigators will be to develop PDX trial strategies for preclinical testing of single agents and drug combinations. These models will allow the determination of the optimal treatments (single drugs or combinations) that should be tested in clinical trials in increasingly individualized, molecularly defined subsets of tumors. The goal of the Patient-Derived Xenograft Core is to provide high-quality clinically relevant and molecularly annotated PDX models for the research projects proposed in the University of Texas PDX Development and Trial Center (UTPDTC) grant application and to the research activity of the NCI PDX Development and Trial Centers Research Network (PDXNet) by leveraging PDX resources at our institutions and developing new PDX models from human cancer specimens using rigorous quality standards so that the models can be used to guide clinical trial development. The PDX models developed and/or characterized by the PDX Core will be available to other cancer researchers through the PDXNet and NCI Patient-Derived Models Repository (PDMR).

  Study phs001980

• Whole genome sequencing with linked reads of pediatric glioblastoma samples

  Pediatric glioblastoma (pGBM) is a lethal cancer with no effective therapies. To understand mechanisms of tumor evolution in this cancer, we performed whole genome sequencing with linked reads on longitudinally resected pGBM samples. Our analyses showed that all diagnostic and recurrent samples were collections of genetically diverse subclones. Clonal composition rapidly evolved at recurrence, with less than 8% of non-synonymous single nucleotide variants being shared in diagnostic-recurrent pairs. In order to track the origins of the mutational events we observed in pGBM, we generated whole genome datasets for two patients and their parents. These trios showed that genetic variants could be (i) somatic, (ii) inherited from a healthy parent, or (iii) arose de novo in the germlines of pGBM patients. Analysis of variant allele frequencies supported a model of tumor growth involving slow-cycling cancer stem cells that give rise to fast-proliferating progenitor-like cells and to non-dividing cells. Interestingly, radiation and anti-mitotic chemotherapeutics did not increase overall tumor burden upon recurrence. These findings support an important role for slow-cycling stem cell populations in contributing to recurrences, since slow-cycling cell populations are expected to be less prone to genotoxic stress induced by these treatments and to accumulate few mutations. Our results highlight the need for new targeted treatments that account for the complex functional hierarchies and genomic heterogeneity of pGBM.

  Study EGAS00001003432

• Partner-independent fusion gene detection by multiplexed CRISPR/Cas9 enrichment and long-read Nanopore sequencing

  Fusion genes are hallmarks of various cancer types and important determinants for diagnosis, prognosis and treatment possibilities. The promiscuity of fusion genes with respect to partner choice and exact breakpoint-positions restricts their detection in the diagnostic setting, even for known and recurrent fusion gene configurations. To accurately identify these gene fusions in an unbiased manner, we developed FUDGE: a FUsion gene Detection assay from Gene Enrichment. FUDGE couples target-selected and strand-specific CRISPR/Cas9 activity for enrichment and detection of fusion gene drivers (e.g. BRAF, EWSR1, KMT2A/MLL) - without prior knowledge of fusion partner or breakpoint-location - to long-read Nanopore sequencing. FUDGE encompasses a dedicated bioinformatics approach (NanoFG) to detect fusion genes from Nanopore sequencing data. Our strategy is flexible with respect to target choice and enables multiplexed enrichment for simultaneous analysis of several genes in multiple samples in a single sequencing run. We observe on average a 508-fold on-target enrichment and identify fusion breakpoints at nucleotide resolution - all within two days. We demonstrate that FUDGE effectively identifies fusion genes in cancer cell lines, tumor samples and on whole genome amplified DNA irrespective of partner gene or breakpoint-position in 100% of cases. Furthermore, we show that FUDGE is superior to routine diagnostic methods for fusion gene detection. In summary, we have developed a rapid and versatile fusion gene detection assay, providing an unparalleled opportunity for pan-cancer detection of fusion genes in routine diagnostics.

  Study EGAS00001003964

• Data Access Committee of divisions B060 and B062, German Cancer Research Center (DKFZ)

  Dac EGAC00001000219

• EGAD00010000644

  Affymetrix SNP6.0 cancer cell line exome sequencing data

  Dataset EGAD00010000644

• Genentech_cell_line_SNP

  SNP array data for 668 cancer cell lines

  Dataset EGAD00010000951

• FunGeST - Functional Genomics from cancer research to personalized medicine

  Dac EGAC00001002924

• Rucaparib in patients presenting a metastatic breast cancer with Homologous Recombination Deficiency, without germline BRCA1/2 mutation, pronostic value of high LOH genomic score and predictive value of HRDetect (WGS)

  Study EGAS00001004756

• Point-of-care monitoring of head and neck cancer treatment response and recurrence development using nanopore-based ctDNA consensus sequencing

  Study EGAS00001007090

• UCL Translation Uro-Oncology Data Access Committee will govern access to data generated from the Translation Uro-Oncology groups of the UCL Cancer Institute and UCL Division of Surgery & Interventional Science

  Dac EGAC00001000471

• V4_Colorectal_panel_test

  Targeted gene screen of cell line tumours for testing the new V4 Colorectal gene panel.

  Study EGAS00001001807

• sWGS of non-cancer controls

  Paired end shallow whole genome sequencing (sWGS) data of cell-free DNA from plasma from self-reporting healthy individuals (MonRec Cohort)

  Dataset EGAD00001005805

• RBWES

  Retinoblastoma is a rare childhood cancer of the retina. We studied retinoblastoma by Whole-Exome-Sequencing.

  Dataset EGAD00001007591

• Osteosarcoma RNAseq

  We propose to definitively characterise the somatic genetics of Osteosarcoma cancer through generation of comprehensive catalogues of somatic mutations by high coverage genome and transcriptome sequencing.

  Dataset EGAD00001000826

• Multiple_myeloma_precursor_genomics

  Whole genome sequencing of up to 45 multiple myeloma precursor samples and matched normals pairs.

  Study EGAS00001006312

• Mutational_signatures_and_clonal_dynamics_in_normal_human_tissues

  Somatic mutations (burdens and signatures) and clonal dynamics in normal human tissues

  Study EGAS00001002471

• NSCCG CRC WES

  1006 Familial early onset gemrline CRC patients sequenced by the Molecular and Population Genetics group of the Institute of Cancer Research

  Dataset EGAD00001002204

• Involvement of the FGF8/FGF Receptor Signaling Pathway in the Maintenance and Progression of Fusion-Positive Rhabdomyosarcoma

  In our previous work, we used a myoblast model of fusion-positive rhabdomyosarcoma (FP-RMS) to demonstrate that FGF8, a target of the PAX3-FOXO1 (P3F) transcription factor, is essential for tumorigenicity driven by P3F. When aberrantly expressed, FGF8 can also sustain tumorigenicity in recurrent tumors that are independent of P3F. In this study, we report that FGF8, along with its receptors FGFR1 and FGFR4, is often highly expressed in FP-RMS tumors. We found that high levels of FGF8 expression in FP-RMS cells correlate with increased sensitivity to an FGFR4 inhibitor and a pan-FGFR inhibitor. Conversely, downregulating FGF8 led to a loss of sensitivity to these inhibitors. Interestingly, when FGF8 was upregulated in myoblasts, it resulted in decreased FGFR4 expression and sensitized the cells to an FGFR1 inhibitor and a pan-FGFR inhibitor. The downregulation of FGFR4 expression mediated by FGF8 could be reversed by inhibiting FGFR1, MEK, or ERK, thereby defining a signaling pathway through which FGF8 exerts its regulatory effects. Additionally, we discovered that high FGF8 expression in P3F-independent recurrent tumors is due to a rearrangement of viral long terminal repeat (LTR) sequences into the 3' untranslated region (UTR) of FGF8, leading to increased stability of FGF8 mRNA. These findings indicate that FGF8 plays an oncogenic role in FP-RMS through FGFR4 and may also exert oncogenic effects in P3F-independent relapses via FGFR1. Our study highlights the functional significance of FGF8 in FP-RMS and supports the rationale for preclinical investigations of FGFR inhibitors in treating this type of cancer.

  Study phs004009

• California Pacific Medical Center Research Breast Health Cohort

  Description of Cohort: The California Pacific Medical Center (CPMC) Breast Health Cohort is a cohort study based at CPMC and is linked to the San Francisco Mammography Registry, one of the sites of the NCI-funded Breast Cancer Screening Consortium (U01CA063740). CPMC is a community hospital in San Francisco, which has one of the highest volumes for mammography in San Francisco. Between September 2004 and June 2007, >90,000 mammograms were performed at CPMC. The CPMC breast health cohort collects demographic and risk factor data on women receiving mammography through participation in the San Francisco Mammography Registry, as part of the Breast Cancer Screening Consortium (U01CA063740). The SFMR database collects information from all sources, including a questionnaire on demographic and risk factor information, the clinical results of the breast examination, the measures of breast density by Dr. John Shepherd and the women who agreed to donate a blood sample. By merging these various sources of information we have very efficiently developed a large sample of women who have donated blood and have had a measure of mamographic density. Blood Collection: Dr. Steve Cummings is leading an effort to collect and archive blood samples from women who are receiving mammography screening. All women who are sent for a screening mammogram at CPMC are considered eligible. Since the cohort began collecting blood samples in July 2004 until June 2007, samples have been collected from over 11,000 women. Measurement of Breast Density: Dr. John Shepherd is currently measuring breast density in a large fraction of the cohort using an automated approach with single X-ray absorptiometry. Dr. Shepherd has established a link with the CPMC mammography center that allows him to collect routine digital mammography information. Using the data from the mammogram, Dr. Shepherd and his group have developed the single X-ray absorptiometry (SXA) technique for measuring density which is described in more detail below. The table demonstrates the distribution of demographic variables and some breast cancer risk factors of women who donated blood and had a breast density measurement in the CPMC breast health cohort. Nearly 80% of the participants are Caucasian and most of the women are post-menopausal with a median age of ~52. Since it will be difficult to accrue a large enough sample from each ethnic group, our study will focus only on Caucasian women. Table: Demographic variables, reproductive history and family history of breast cancer among 2962 women participating in the CPMC cohort study who contributed blood samples between 1994-1997. Variable Median/Percentage Age (Median/IQR) 52 (46-59) Ethnicity Caucasian/White 0.76 Asian/Pacific Islander 0.141 Hispanic 0.029 Mixed Race/Ethnicity 0.039 African American/Black 0.022 American Indian 0.001 Other 0.009 First degree relative with breast cancer 0.17 Age at first birth Nulliparous 0.39 Age<20 0.043 Age>40 0.032 Age<30, ≥20 0.251 Age>30, ≤40 0.282 Measurement of Breast Density in Cohort: Measurement of breast density is accomplished using an automated technique for all mammograms obtained by Dr. Shepherd using Single X-Ray absorptiometry (SXA). SXA measurement of breast density is done on approximately 30% of all screening mammograms. Below we describe the method for measurement of breast density by SXA by Dr. Shepherd's group and its validation and association with breast cancer. As we demonstrate below, breast density, as measured by SXA, is an automated, highly reproducible measure of the density of breast tissue and is associated with breast cancer risk. SXA for Quantifying Breast Density: Single x-ray absorptiometry (SXA) was initially developed for measuring bone density. SXA can determine the fraction of each of two densities simultaneously using the fact the sample is a constant thickness, the thickness in known, and the total attenuation is known. In applying this technique to breast density, we assume a two compartment model: fat and non-fatty (fibroglandular tissue). We use a reference material composed of various concentrations of two materials: one which is the same density as fat and another which is the same density as fibroglandular tissue. The reference material (phantom) is placed in the X-ray field with each mammogram. We have been able to implement this in a way that is unintrusive to the patient and technologist at CPMC. Assuming this two-compartment model and a constant known breast thickness, we can then calculate the percent density at any region of the breast based on the assumption that % pixel grey-scale is proportion to the mass fractions of breast fat and lean tissue. If reference materials (a phantom) of fat and fibroglandular tissue are imaged with the patient's breast and the reference materials have the same thickness as the patient's breast, then the breast's grey-scale values can be converted to fat/fibroglandular mass fractions by interpolating between those two references. The total percent density is found by averaging the volume fraction over all breast pixels. The phantom being used for breast density assessment at CPMC began to be used in September 2004. The phantom does not have to be manipulated by the technologist and stays attached on the mammography device during standard craniocaudal (CC) views. Thus it creates minimal to no interference with the clinical mammogram. Reproducibility of breast density measures: Traditional measures of mammographic density require some human interpretation. A human reader outlines the area perceived to be dense and a computer then calculates the percent area outlined as a percent of the entire image. Thus, while traditional mammographic density is associated with breast cancer risk, it has some limitations. In a study by Drs. Shepherd, Kerlikowske, et al., the correlation coefficient (Pearson's R) between different readers was 0.8-0.9. In contrast to the traditional mammographic density measurement, the SXA measurement is fully automated and, therefore, the reproducibility of the measurement is higher. Dr. Shepherd and colleagues have performed a replication study of SXA as a measurement of breast density. They have estimated the correlation coefficient of the SXA measurement of breast density to be >0.98. Thus, as expected for an automated measure, SXA is a highly reproducible measure of mammographic breast density. Drs. Shepherd and Kerlikowske have recently analyzed the association between breast cancer risk and breast density as measured by SXA (Shepherd et al., Cancer Epi Biomarkers and Prev, 2011, PMID: 21610220). They found that women in the highest quintile of % volumetric density had an odds ratio of 4.1 (95% CI: 2.3 - 7.2) for breast cancer risk compared to women in the lowest quintile of volumetric density. Thus volumetric density appears to be a highly reproducible, automated measure of breast cancer.

  Study phs000395

• EGAD00010000268

  Metabric breast cancer samples (Expression raw data)

  Dataset EGAD00010000268

• ParityImmune

  Blood transcriptome from women participating in the Norwegian Women and Cancer study (NOWAC)

  Dataset EGAD00010001412

• BROCA cancer panel sequencing for 15 samples

  BROCA panel sequencing using the BROCA-HR v8 and BROCA-GO v1 versions of the gene panel

  Dataset EGAD50000000035

• Tumor Normal WES dataset

  This dataset contains tumor-normal WES data in BAM files aqcuired from patients with renal cancer tumors and treated with Sunitinib

  Dataset EGAD50000001174

• Illumina_WXS_MET-CELL

  Whole-exome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002096

• EPO2_Illumina_WXS_XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample (EPO2_cohort)

  Dataset EGAD00001003363

• Organoid Derivation Project WGS: Release 2

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005955

• Organoid Derivation Project TGS: Release 2

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001004999

• Organoid Derivation Project WGS: Release 1

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005000

• Organoid Derivation Project TGS: Release 3

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005958

• Genomic Resistance Patterns to Second Generation Androgen Blockade in Paired Tumor Biopsies of Metastatic Castrate-Resistant Prostate Cancer

  We examined genetic resistance to second generation androgen targeting therapies (abiraterone acetate or enzalutamide) by analyzing whole exome sequencing of patient-matched pre-treatment and post-resistance tumors from a series of castrate-resistant prostate cancer (CRPC) patients. Abiraterone resistant tumors harbored alterations in AR and MYC, whereas patients treated with enzalutamide had acquired alterations in the cell cycle pathway. We experimentally confirmed expression of cell-cycle kinases sufficed to drive enzalutamide resistance, which was mitigated through CDK4/6 blockade. These observations link genetic resistance to specific therapeutic agents to inform strategies in genomically selected advanced CRPC.

  Study phs001447

• Radiation-Related Genomic Profile of Papillary Thyroid Cancer after the Chernobyl Accident

  The April 26, 1986 accident at the Chernobyl nuclear power plant in northern Ukraine resulted in the release of radioactive contaminants, which were deposited in the surrounding areas in Ukraine, Belarus, and Russia. The main radiation-related health effect resulting from these exposures is the increased occurrence of thyroid cancer among individuals who were children at the time of the accident or born shortly thereafter. The purpose of this study was to conduct a comprehensive genomic landscape analysis of papillary thyroid tumors arising in individuals who were exposed as children to radioactive iodine (I-131) from the Chernobyl nuclear power plant accident.

  Study phs001134

• Sensitive urothelial cancer detection via high volume urine DNA analysis

  Cystoscopy and imaging are the gold standard for urothelial cancer (UC) detection and surveillance, but cystoscopy is an uncomfortable procedure associated with adverse effects and a low diagnostic yield as only 10% of hematuria patients have UC. Urine tumor DNA (utDNA) is a non-invasive UC biomarker that has suffered from poor sensitivity for Ta stage tumors (<80%). We hypothesized that high urine volume analysis with a multi-gene panel could overcome these limitations, and developed a 100 mL utDNA laboratory platform (UroScout) that analyzes 25 commonly mutated UC genes and 8 copy number-altered loci for somatic alterations to detect UC.

  Study EGAS50000000630

• plasma DNA LINE-1 targeted bisulfite sequencing: a new non-invasive multi-cancer detection marker

  The detection of circulating tumor DNA, which allows non-invasive tumor molecular profiling and disease follow-up, promises optimal and individualized management of patients with cancer. However, detecting small fractions of tumor DNA released when the tumor burden is reduced remains a challenge. We developed a PCR-based targeted bisulfite method coupled to deep sequencing to detect methylation patterns of L1PA elements,which we named DIAMOND (for Detection of Long Interspersed Nuclear Element Altered Methylation ON plasma DNA). We used sodium bisulfite chemical conversion to achieve base-pair resolution analysis and designed a multiplexed PCR based on 8 amplicons covering L1PAs .

  Study EGAS50000000446

• scRNAseq analysis of CD8 T cells infiltrating the bladder and tumor of 4 non-muscle-invasive bladder cancer patients, before and after BCG treatment.

  We performed scRNAseq analysis of CD8 T cells infiltrating the bladder and tumor of 4 non-muscle-invasive bladder cancer patients, before and after BCG treatment. In order to identify tumor-specific T cells, we selected CD8 T cells that were enriched in the tumor of the patient. We also selected CD8 T cells appearing in the bladder after BCG treatment, with the hypothesis that BCG treatment stimulates tumor-specific CD8 T cells. Next, we cloned the TCR of these T cells in a Jurkat cell line and tested these TCR for recognition of tumor-specific antigens.

  Study EGAS50000001384

• Use_of_Deep_Sequencing_to_Dectect_Clonal_Mutations_In_Sun_Exposed_Skin_Epidermis

  This study aims to define the landscape of somatic mutations in sun exposed human skin by deep sequencing, analyse their frequency and use the data to infer the effect of mutations on proliferating cell behaviour. The frequency of each mutation will reflect the size of the clone of cells in the tissue sample. By analyzing small samples, clones with as few as 100 cells will be detectable. Allele frequency distributions for each mutation will be used to infer cell fate using published methods (Klein et al. 2010). This study will shed unprecedented light on the early clonal events that lead to the emergence of cancer.

  Study EGAS00001000515

• Inter and intra - tumor heterogeneity in Colorectal Cancer

  We used high depth massive parallel sequencing analyses to study 27 CRC (colorectal cancer) patients, with a total of 97 tumors (both primary and matched metastases) in addition to healthy control samples. We found inter-tumor concordance for coding mutations; in contrast, gene copy numbers were found to be highly discordant between primary tumors and metastases as validated by fluorescent in-situ hybridization. in a further step, we dissected a single tumor into 68 spatially defined samples and sequenced them separately. Here we identify evenly distributed coding mutations in APC and TP53 in all tumor areas, yet highly variable gene copy numbers in numerous genes.

  Study EGAS00001002150

• Use_of_Deep_Sequencing_to_Dectect_Clonal_Mutations_In_Sun_Exposed_Skin_Epidermis_PART2

  This study aims to define the landscape of somatic mutations in sun exposed human skin by deep sequencing, analyse their frequency and use the data to infer the effect of mutations on proliferating cell behaviour. The frequency of each mutation will reflect the size of the clone of cells in the tissue sample. By analyzing small samples, clones with as few as 100 cells will be detectable. Allele frequency distributions for each mutation will be used to infer cell fate using published methods (Klein et al. 2010). This study will shed unprecedented light on the early clonal events that lead to the emergence of cancer.

  Study EGAS00001000603

• Field effect of healthy and diseased livers (2019-04-08)

  Recent work in the Campbell group has revealed somatic mutations present in normal, non-cancerous human skin. A subset of the mutations conferred selective advantages to the host cells, leading to clonal expansions and raising the risk for future cancer development. Capturing such somatic mutations in normal tissue is important to advance our understanding about carcinogenesis and could provide prospective medical insights. In this project, our goal is to detect somatic mutations in normal (pre-cancerous) liver tissue. Using Laser Microdissection technology, we will dissect individual liver lobules from patient samples and submit these to sequencing. For each patient sample, we aim to sequence multiple lobules to characterise the mutagenic burden. Samples will be taken from patients with different liver disease aetiologies, including alcoholism and obesity, with a view on distinguishing the prevalent mutation types occurring in each disease context. We will perform targeted sequencing, initially using the WTSI cancer panel. Later we aim to use a novel bait set that captures both cancer genes as well as genes relevant to the non-cancerous samples (ie. genes implicated in hereditary disorders, immune sequences). . This dataset contains all the data available for this study on 2019-04-08.

  Dataset EGAD00001004941

• BAM files of targeted next-generation DNA sequencing data of 13 chordoid gliomas of the third ventricle

  BAM files of targeted next-generation DNA sequencing data of 13 chordoid gliomas of the third ventricle (2 paired tumor-normal samples and 11 tumor-only samples). Genomic DNA was extracted from formalin-fixed, paraffin-embedded blocks of tumor tissue from 13 patients with chordoid glioma of the third ventricle using the QIAamp DNA FFPE Tissue Kit (Qiagen). Genomic DNA was also extracted from leukocytes in a peripheral blood sample from one of the patients and a non-neoplastic gastric biopsy specimen from one of the patients. Capture-based next-generation DNA sequencing was performed at the University of California, San Francisco Clinical Cancer Genomics Laboratory, using an assay that targets all coding exons of approximately 500 cancer-related genes, select introns of 47 genes, and TERT promoter with a total sequencing footprint of 2.8 Mb (UCSF500 Cancer Panel). Sequencing libraries were prepared from genomic DNA, and target enrichment was performed by hybrid capture using a custom oligonucleotide library (Nimblegen SeqCap EZ Choice). Captured libraries were sequenced as paired-end 100 bp reads on an Illumina HiSeq 2500 instrument. Duplicate sequencing reads were removed computationally to allow for accurate allele frequency determination and copy number calling.

  Dataset EGAD00001003818