9944 results for "cancer rna-seq"

in 42.71 milliseconds.

• Genome and transcriptome sequence data from a primary unknown cancer patient

  Genome and transcriptome sequence data from a primary unknown cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002550

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002588

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002575

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002573

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002571

• Genome and transcriptome sequence data from a metastatic endometrial cancer patient

  Genome and transcriptome sequence data from a metastatic endometrial cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002568

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002598

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002603

• Genome and transcriptome sequence data from a pancreatic cancer (likely PNET) patient

  Genome and transcriptome sequence data from a pancreatic cancer (likely PNET) patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002607

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002609

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002636

• Genome and transcriptome sequence data from a metastatic rectal cancer patient

  Genome and transcriptome sequence data from a metastatic rectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002634

• Genome and transcriptome sequence data from a metastatic colon cancer patient

  Genome and transcriptome sequence data from a metastatic colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002629

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002619

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002615

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004902

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004910

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004923

• Genome and transcriptome sequence data from a locally advanced breast cancer patient

  Genome and transcriptome sequence data from a locally advanced breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004897

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004900

• Genome and transcriptome sequence data from a metastatic rectal cancer patient

  Genome and transcriptome sequence data from a metastatic rectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004918

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004915

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004914

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004913

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004912

• Genome and transcriptome sequence data from a metastatic colon cancer patient

  Genome and transcriptome sequence data from a metastatic colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004911

• Genome and transcriptome sequence data from a metastatic serous ovarian cancer patient

  Genome and transcriptome sequence data from a metastatic serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004909

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004908

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004926

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004927

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004928

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004929

• Genome and transcriptome sequence data from a metastatic ovarian cancer patient

  Genome and transcriptome sequence data from a metastatic ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004930

• Genome and transcriptome sequence data from a oligometastatic colorectal cancer patient

  Genome and transcriptome sequence data from a oligometastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004935

• The dataset for Genome-wide cell-free DNA fragmentation in patients with cancer

  The dataset for Genome-wide cell-free DNA fragmentation in patients with cancer includes 538 bam files from whole genome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include plasma samples from healthy individuals and patients with cancer.

  Dataset EGAD00001005339

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005830

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005832

• Genome and transcriptome sequence data from a metastatic unknown primary cancer patient

  Genome and transcriptome sequence data from a metastatic unknown primary cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005845

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005847

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005860

• Genome and transcriptome sequence data from a large intestine-colon cancer patient

  Genome and transcriptome sequence data from a large intestine-colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005885

• Genome and transcriptome sequence data from a metastatic adrenocortical cancer patient

  Genome and transcriptome sequence data from a metastatic adrenocortical cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005897

• WES_localized non-small cell lung cancer (540 samples)_MDACC

  Whole exome sequencing of tumors and paired adjacent uninvolved tissues from 222 early stage NSCLC patients, in order to identify genomic drivers present in early-stage non-small cell lung cancer and determine the overall tumor mutational burden in early-stage non-small cell lung cancer.

  Dataset EGAD00001005956

• Exploration of CNV’s and SNV’s in cancers with well-known genetic rearrangements: Identification of additional genetic changes in rearrangements-driven cancer

  Specific cancers are well known to have specific gene rearrangements. Representative cancer is chronic myeloid leukemia (CML) which is famous for BCR/ABL gene rearrangements. By the way, also genetic rearrangements are not infrequently found in acute leukemia, and those include CBFB/MYH11 rearrangements, PML/RARA rearrangements, AML/ETO rearrangements, and BCR/ABL rearrangements. The situation is same is for lung adenocarcinoma of non-smokers and prostate cancers, and ALK and RET gene rearrangements are recently found. However, not all these rearrangements are sufficient to develop cancer, and it is suggested that some genetic aberrations are required for the development and progression of cancer in addition to these genetic rearrangements. In this study, we have a plan to explore the existence of additional genetic changes including single nucleotide variants (SNV’s) and copy number variations (CNV’s) in cancers with specific genetic rearrangements so as to define additional genetic changes that confer full oncogenic potential to cancer cells. So as to look at SNV’s and small indels, we are going to use whole exome sequencing (WES) data. And as for CNV’s, we are going to use array-CGH data. Also, to check the functionality of these genetic changes, we are going to use whole transcriptome data (WTS). In addition, in samples with whole genome sequencing (WGS ) data, we are going to explore genetic changes in intronic regions also. In fact, we sequenced 10 AML samples which are known to have specific genetic rearrangements. Analysis of these WGS, WES and WTS data are under the way. If we have a chance to access WES, array-CGH and WTS data of other cancer samples with specific genetic rearrangements, we would be able to compare additional genetic changes required for oncogenesis in cancer cells with various genetic rearrangements. We hope this result will enlighten our understanding of cancer genetics further.

  Study EGAS00001000673

• Plasma ctDNA is a tumor tissue surrogate and enables clinical-genomic stratification of metastatic bladder cancer

  Study EGAS00001004615

• Gastric Cancer Exome Sequencing

  Gastric Cancer Exome Sequencing

  Dataset EGAD00001000046

• Cancer Exome Resequencing

  Cancer Exome Resequencing

  Dataset EGAD00001000092

• dataset for BGI bladder cancer project

  dataset for BGI bladder cancer project

  Dataset EGAD00001000758

• Colorectal cancer – unmapped reads (Mutographs)

  Colorectal cancer – unmapped reads (Mutographs)

  Dataset EGAD00001015364

• Research on the identification of cancer stem cells for peidatric and adult malignancies.

  In recent years, "cancer stem cells" have been reported to exist in many cancers. Many reports have shown that they are the main cause of cancer recurrence. The aim of this study is to test whether cancer stem cells can be identified using patient samples. We will perform omics analysis of rare pediatric cancers by integrating genomics, transcriptomics, proteomics and metabolomics, including somatic and germline mutations, to deepen our understanding of the biological characteristics of the target disease. Based on the knowledge gained and clinical information from patients, we will conduct preclinical pharmacological studies using mouse models and in vitro models to investigate the therapeutic effects of novel therapeutic agents and novel therapeutic strategies such as cellular therapies.

  Study JGAS000623

• Whole Exome Sequencing of DNA from Pre-and Post-Chemotherapy Needle Biopsies of Triple Negative and Inflammatory Breast Cancers Enrolled in the S0800 Trial

  Purpose: The purpose of this study was to assess the somatic mutation landscape of breast tumors before and after neoadjuvant chemotherapy. The data includes whole exome sequencing of pre- and post- tissues that were collected in the context of an already published clinical trial SWOG S0800 (ZA Nahleh et al. Breast Cancer Research and Treatment. 158:485-495, 2016). The trial compared the efficacy of sequential nab-paclitaxel followed or preceded by doxorubicin/cyclophosphamide anthracycline chemotherapy with or without bevacizumab as preoperative therapy for stage II-III breast cancer. Experimental Design: 29 pre-treatment biopsies and 9 matching post-treatment surgically resected cancer tissues were available for analysis and were subjected to whole exome sequencing to identify mutational patterns associated with response to neoadjuvant chemotherapy. The 9 paired samples with residual invasive cancer after therapy were also analyzed to assess changes in mutational patterns in response to therapy. No matching normal tissues were available for germline sequencing and therefore we used n=7 post-treatment breast tissues without residual cancer (i.e. complete eradication of cancer by therapy) as a normal cohort to facilitate somatic variant calling.Conclusion: These results suggest that genomic disturbances in BRCA-related DNA repair mechanisms, reflected by a dominant mutational signature 3, confer increased chemotherapy sensitivity. Cancers that survive neoadjuvant chemotherapy, frequently have alterations in cell cycle regulating genes but different genes are affected in different patients.

  Study phs001883

• Colorectal cancer cells possess an equipotent capacity to enter a developmental pausing-like state to survive chemotherapy

  Study EGAS00001004773

• OICR PANCREATIC CANCER DATASET 2

  OICR PANCREATIC CANCER DATASET 2

  Dataset EGAD00001000276

• Bladder Cancer

  WGS of 14 paired samples of Bladder Cancer patient

  Dataset EGAD00001000865

• Summary statistics for cervical cancer GWAS

  Summary statistics from GWAS meta-analysis of cervical cancer

  Dataset EGAD00001004361

• Single cells of colorectal cancer organoids

  Dataset contains 854 single cell sequenced colorectal cancer organoids.

  Dataset EGAD00001005422

• SNP data for Ovarian cancer PRS

  SNP data for Ovarian cancer PRS (cases)

  Dataset EGAD00001008143

• Patient-derived organoids identify tailored therapeutic options and determinants of plasticity in sarcomatoid urothelial bladder cancer

  Study EGAS00001007430

• Platelet RNAseq data for SLFN14 K219N patients

  Pathogenic missense variants in SLFN14, which encodes an RNA endoribonuclease protein that regulates rRNA degradation, are known to cause inherited thrombocytopenia with impaired platelet aggregation and ATP secretion. Despite rather mild laboratory defects, these patients display an obvious bleeding phenotype. The function of SLFN14 in megakaryocyte (MK) and platelet biology is unknown. This study aims to characterize the platelet transcriptome in patients with a SLFN14 K219N variant and model the disease in the immortalized megakaryocyte cell line imMKCL. Total platelet RNA was sequenced for two patients and 19 healthy controls. Differential gene expression analysis yielded a total of 2999 and 2888 significantly (|log2FC|>1, FDR<0.05) up- and downregulated genes, respectively. Remarkably, these downregulated genes were not enriched for any biological pathway while upregulated genes were enriched for pathways involved in (mitochondrial) translation and transcription with a significant upregulation of 134 ribosomal protein genes (RPG). Heterozygous and homozygous SLFN14 K219N imMKCL showed a defect in MK differentiation that aggravated during cell passaging of undifferentiated cells and in proplatelet formation. SLFN14 defective platelets and MK showed signs of rRNA degradation while this was absent in undifferentiated imMKCL cells. Upregulation of RPG through increased mTOR signaling in SLFN14 K219N MK seems to be a compensatory response for rRNA degradation. Indeed, mTOR inhibition with rapamycin resulted in further enhanced rRNA degradation in SLFN14 K219N MK. Taken together, our study indicates dysregulation of mTOR-regulated ribosomal biogenesis as the disease mechanism for SLFN14-related thrombocytopenia.

  Study EGAS00001006339

• Center for Sub-Cellular Genomics

  A cell is a highly complex system with distributed molecular physiologies in structured sub-cellular compartments whose interplay with the nuclear genome determine the functional characteristics of the cell. A classic example of distributed genomic processes is found in neurons. Learning and memory require modulation of individual synapses through RNA localization, localized translation, and localized metabolites, such as those from dendritic mitochondria. Dendrites of neurons integrate distributed synaptic signals into both electrical and nuclear transcriptional response. Dysfunction of these distributed genomic functions in neurons can result in a broad spectrum of neuropsychiatric diseases such as bipolar and depressive disorders, autism, among others. The Center for Sub-Cellular Genomics' (CSG) mission is to develop new technologies to enable the dissection of complex genomic interactions within a single cell. New technologies include methods to target DNA and RNA sampling at sub-cellular resolution in live cells; high-throughput isolation and microfluidics chemistry for sub-cellular fractions; sub-cellular resolution mass-spectrometry; and computational tools for analysis of sub-cellular genomics data. The new technologies developed by CSG will enable the broader genomics community to associate sub-cellular processes to cell function and dysfunction. These technologies will be utilized in the center to generate a comprehensive atlas of genomic signatures of sub-cellular compartments in the mammalian neuron system. The technologies and neuron-focused data obtained here will be relevant to neurodegenerative and neuropsychiatric diseases including schizophrenia, depression, autism spectrum disorder, traumatic brain injury, and Alzheimer's.

  Study phs002120

• The subclonal architecture of metastatic breast cancer: Results from a prospective community-based rapid autopsy program 'CASCADE'

  Background: Understanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. The majority of work in this area has targeted primary tumours however, and very few studies have performed comprehensive profiling of advanced disease. Evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death. We sought to study in detail these aspects of advanced breast cancers that have resulted in lethal disease. Methods and Findings: Three patients with ER-positive, HER2-negative breast cancer and one patient with triple negative breast cancer underwent rapid autopsy as part of an institutional prospective community-based rapid autopsy program. Cases represented a range of management problems in breast cancer, including late relapse after early stage disease; de novo metastatic disease; discordant disease response and disease refractory to treatment. Between 5 and 12 metastatic sites were collected at autopsy together with available primary tumours and longitudinal metastatic biopsies taken during life. Samples underwent paired tumour-normal whole exome sequencing and single nucleotide polymorphism arrays. Subclonal architectures were inferred by jointly analysing all samples from each patient. Mutations were validated using high depth amplicon sequencing.Between cases, there were significant differences in mutational burden, driver mutations, mutational processes and copy number variation. Within each case, we found dramatic heterogeneity in subclonal structure from primary to metastatic disease and between metastatic sites, such that no single lesion captured the breadth of disease. Metastatic cross seeding was found in each case and treatment drove subclonal diversification. Subclones displayed parallel evolution of treatment resistance in some cases, and apparent augmentation of key oncogenic drivers as an alternative resistance mechanism. We also observed the key role of mutational processes in subclonal evolution.Limitations of this study include the potential for bias introduced by joint analysis of formalin fixed archival specimens with fresh specimens, and the difficulties in resolving subclones with whole exome sequencing. Other alterations that could define subclones such as structural variants or epigenetic modifications were not assessed. Conclusions: This study highlights the variety of mechanisms that shape the genome of metastatic breast cancer, and the value of studying advanced disease in detail. Treatment drives significant genomic heterogeneity in breast cancers which has implications for disease monitoring and treatment selection in the personalised medicine paradigm.

  Study EGAS00001002153

• ChIP sequencing data to study therapeutic targeting of ependymoma

  This dataset contains ChIP sequencing data from 24 patients. ChIP of 5–10 mg flash-frozen primary ependymoma tumour was performed using 5 mg H3K27ac antibody per ChIP experiment. The enriched DNA has been sequenced on a Illumina HiSeq2000 instrument in paired-end mode. Up to two lanes per sample have been sequenced resulting in 70 Fastq files.

  Dataset EGAD00001003979

• Dataset for the study - Detection of cell-free DNA fragmentation and copy number alterations in cerebrospinal fluid from glioma patients

  FASTQ files from sequencing to < 0.4x depth of coverage of thirteen glioma patients. Indexed sequencing libraries were prepared using a commercially available kit (ThruPLEX-Plasma Seq, Rubicon Genomics). Libraries were pooled in equimolar amounts and sequenced on a HiSeq 4000 (Illumina) generating 150-bp paired-end reads.

  Dataset EGAD00001004425

• McGill Sperm Methylome Capture Sequencing Data

  The study includes methylC-capture sequencing (MCC-Seq) on 94 sperm DNA samples derived from both fertile and infertile individuals who were recruited from the Men’s Health Clinic at the Royal Victoria Hospital, Montreal, Quebec. All the data were generated with 100bp paired-end reads using the Illumina NovaSeq 6000 systems.

  Dataset EGAD00001005795

• scTCR-seq from brain metastasis micorenvironment and cerebrospinal fluid

  A total of 9 brain metastasis were sequenced. For 6/9 a matched cerebrospinal fluid sample, prior to surgery and in two cases after surgery (+1 month from surgery) and after treatment (+3 month) were collected. Single-cell T cell receptor clonotypes were produced using the Chromium Single Cell 5’ Library and sequenced on an Illumina NovaSeq 6000.

  Dataset EGAD00001006451

• scRNA-seq from brain metastasis micorenvironment and cerebrospinal fluid

  A total of 9 brain metastasis were sequenced. For 6/9 a matched cerebrospinal fluid sample, prior to surgery and in two cases after surgery (+1 month from surgery) and after treatment (+3 month) were collected. Single-cell gene expression was produced using the Chromium Single Cell 5’ Library and sequenced on an Illumina NovaSeq 6000.

  Dataset EGAD00001006452

• RNAseq and ATACseq data

  RNAseq and ATACseq data for the FMF patients and healthy control. The RNAseq data was sequenced on a BGI MGI G400 machine, with PE100 reads. ATAC-seq libraries were prepared with Illumina Nextera primers and sequenced on NovaSeq 6000 platform with 50bp paired-end sequencing, where each sample was sequenced to approximate 60 million reads.

  Dataset EGAD00001010304

• Epigenomic analysis of human dopaminergic neuron differentiation reveals LBX1, NHLH1 and NR2F1/2 as necessary for lineage specification

  Sample information: The 56 samples produced in this project come from the human iPSC line GM17602 (Coriell) where a tyrosine hydroxylase-T2A-mCherry reporter was inserted. A combination of epigenetic analysis (ATAC/ChIP) along transcriptomics.

  Dataset EGAD00001009288

• Whole transcriptome seq from patient samples

  Exome sequencing data from FFPE preserved patient samples and patient-derived cultures. The goal of the study is to validate patient-derived cultures. Additional goal is to understand the relationship between omics and drug response.

  Study EGAS50000000172

• Highly complex single-cell mixture of 5 individuals of low cell number

  Mixture of 5 unrelated individuals sequenced by 10x as a scATAC-seq with low cell count. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile using the De-goulash pipeline. The separation file is submitted as the processed file. The bam are submitted as unprocessed files.

  Dataset EGAD50000000479

• The genomic landscape of large and small tumors in early-onset prostate cancer patients

  Study EGAS00001000383

• Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients

  Study EGAS00001004975

• Matched Ovarian Cancer Sequencing

  Matched Ovarian Cancer Sequencing

  Dataset EGAD00001000084

• ER-, HER2-, PR- breast Cancer genome sequencing

  ER-, HER2-, PR- breast Cancer genome sequencing

  Dataset EGAD00001000088

• Breast Cancer Exome Resequencing

  Breast Cancer Exome Resequencing

  Dataset EGAD00001000093

• Cancer Genome Libraries Tests

  Cancer Genome Libraries Tests

  Dataset EGAD00001000094

• Triple Negative Breast Cancer Whole Genomes

  Triple Negative Breast Cancer Whole Genomes

  Dataset EGAD00001000141

• Exome sequencing of high-risk prostate cancer

  Exome sequencing of high-risk prostate cancer

  Dataset EGAD00001002009

• SNP array datas of Matched cancer-PNE

  SNP array datas of Matched cancer-PNE

  Dataset EGAD00010001631

• Long-read WGS of three cancer cell lines

  Long-read WGS of three cancer cell lines

  Dataset EGAD00001005184

• Whole Genome Sequencing of Gingivo-buccal Cancer: ICGC-India Project_YR03

  As a part of the ICGC, India has undertaken genomic studies on gingivobuccal cancer of the oral cavity, which is the most prevalent form of cancer among men in India. There are various known environmental (life-style) correlates of this cancer, the most important of which are tobacco chewing and HPV infection. Whole genome sequencing of paired DNA samples – isolated from the tumor tissue and from the blood of twenty five patients – have been performed to catalog germline and somatic mutations. Association between each observed genomic alteration and exposure to environmental risk factors is being explored. Detailed clinical characterization of the patients, collection of data on demographic and environmental exposures, and isolation of DNA samples from blood and tumor tissues collected from each patient are being done at the Advanced Centre for Research, Treatment and Education on Cancer, Mumbai. Whole genome sequencing is being performed at the National Institute of Biomedical Genomics, Kalyani. We have completed whole genome sequencing of paired blood and tumor DNA samples using Illumina HiSeq-2000 and 2500.

  Study EGAS00001001901

• CPC-GENE Prostate Cancer Heterogeneity Study

  Herein we describe a molecular portrait of potentially curable, Gleason 7 and intermediate risk prostate cancer based on genome-wide CNV profiles of 96 patients, and subsequent whole-genome sequencing of 28 tumours from 10 patients, using DNA quantities that are achievable in diagnostic biopsies (50 ng). We show that Gleason 7 cancer is highly heterogeneous at the SNV, CNV, and intra-chromosomal translocation levels, and is characterized by a very low number of recurrent SNVs but significant structural variation. We identified a novel recurrent MYCL1 amplification, which was strongly associated with TP53 deletion and prognostic for biochemical recurrence in this cohort. Moreover, we identified clear evidence of divergent tumour evolution in multi focal cancer and, in 2/5 cases evaluated, multiple tumours of independent clonal origin. Taken together, these data represent the first systematic evaluation of the differential genomics of potentially curable prostate cancer, and strongly suggest that a more robust understanding of the relationship between genetic heterogeneity and clinical outcomes is required to effectively develop biomarkers of prognosis based on tumour genomics.

  Study EGAS00001000549

• Mitochondrial-Nuclear Mutational Cross-Talk Drives Recurrence of Localized Prostate Cancer

  Prostate cancer remains the most prevalent non-skin cancer in men, but has a remarkably quiet mutational profile. Exome sequencing studies have revealed few recurrent somatic single nucleotide variants (SNVs). Whole-genome sequencing studies have not yet identified highly recurrent driver non-coding SNVs (ncSNVs) or genomic rearrangements (Grs). As a result, the underlying events that drive this frequent disease remain unknown. While mitochondrial mutations have been linked to several tumour types, including prostate cancer, their global frequency and impact has remained unknown. To determine if mitochondrial mutations might drive some prostate cancers, we analyzed the mitochondrial genomes of 216 tumours from patients with the most commonly diagnosed form of prostate cancer. We developed a pipeline that conservatively identified only mitochondrial SNVs (mitoSNVs) that differed in the heteroplasmy fraction as compared to their paired-normal by at least 20%. We identified several features of the mitochondrial genome that were correlated with nuclear genomic mutations, as well as features that were significantly associated with biochemical recurrence.

  Study EGAS00001001782

• Mapping the breast cancer metastatic cascade onto circulating tumour DNA using genetic and epigenetic clonal tracking

  Circulating tumour DNA (ctDNA) allows tracking the evolution of human cancers at high resolution, overcoming many limitations of tissue biopsies. However, exploiting ctDNA to determine how a patient’s cancer is evolving in order to aid clinical decisions remains difficult. This is because ctDNA is a mix of fragmented alleles, and the contribution of different cancer cell subpopulations from distinct lesions to ctDNA remains largely unknown. Profiling of ctDNA almost invariably requires prior knowledge of what genomic alterations to track. Here we leverage on a rapid autopsy programme to demonstrate that unbiased genomic characterisation of several metastatic sites and concomitant ctDNA profiling at whole-genome resolution reveals the extent to which ctDNA is representative of widespread disease. We also present a ctDNA methylation profiling method that allows tracking evolutionary changes in the cancer cell population at single-molecule resolution without prior knowledge. These results have critical implications for the use of liquid biopsies to monitor cancer evolution in humans and potentially guide treatment.

  Study EGAS00001004014

• Identification and Characterization of Cancer Mutations in Japanese LungAdenocarcinoma without Sequencing of Normal Tissue Counterparts

  We analyzed whole-exome sequencing data from 97 Japanese lung adenocarcinomapatients and identified several putative cancer-related genes and pathways. Particularly, we observed that cancer-related mutation patterns were significantly different between different ethnic groups. As previously reported, mutations in the EGFR gene were characteristic to Japanese, while those in the KRAS gene were more frequent in Caucasians. Furthermore, during the course of this analysis, we found that cancer-specific somatic mutations can be detected without sequencing normal tissue counterparts. 64% of the germline variants could be excluded using a total of 217 external Japanese exome datasets. We also show that a similar approach may beused for other three ethnic groups, although the discriminative power depends on the ethnic group. We demonstrate that the ATM gene and the PAPPA2 gene could be identified as cancer prognosis related genes. By bypassing the sequencing of normaltissue counterparts, this approach provides a useful means of not only reducing the time and cost of sequencing but also analyzing archive samples, for which normal tissue counterparts are not available.

  Study JGAS000001

• Circulating Tumor Cell Heterogeneity in Neuroendocrine Prostate Cancer by Single Cell Copy Number Analysis

  Neuroendocrine prostate cancer (NEPC) is an aggressive variant of prostate cancer that may arise de novo or develop from pre-existing prostate adenocarcinoma as a mechanism of treatment resistance. Combined loss of tumor suppressors RB1, TP53, PTEN are frequent in NEPC but also present in a subset of prostate adenocarcinomas. Most clinical and preclinical studies support a trans-differentiation process, whereby NEPC arises clonally from a prostate adenocarcinoma precursor during the course of treatment resistance. Here we highlight a case of NEPC with significant intra-patient heterogeneity observed across metastases. We further demonstrate how single-cell genomic analysis of circulating tumor cells combined with phenotypic evaluation of cellular diversity can be considered as a window into tumor heterogeneity in patients with advanced prostate cancer. We performed whole genome sequencing of circulating tumor cells (CTCs) from patients with castration resistant adenocarcinoma (n=4) and neuroendocrine prostate cancer (n=8). CTCs were collected via Epic Sciences platform and single CTC genomic data (n=215) generated via Illumina NextSeq500. 

  Study phs002462

• Testing_the_feasibility_of_genome_scale_sequencing_in_routinely_collected_FFPE_cancer_specimens_versus_matched_fresh_frozen_samples

  Testing the feasibility of genome-scale sequencing in routinely collected formalin-fixed paraffin-embedded (FFPE) cancer specimens versus matched fresh-frozen samples using whole exome and targeted exome capture prior to Illumina sequencing.

  Study EGAS00001000173

• Genome and transcriptome sequence data from a non small cell lung cancer patient

  Genome and transcriptome sequence data from a non small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001963

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001964

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001969

• Genome and transcriptome sequence data from a melanoma skin cancer - squamous cell carcinoma patient

  Genome and transcriptome sequence data from a melanoma skin cancer - squamous cell carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002018

• Genome and transcriptome sequence data from a GI primary (prev breast cancer) patient

  Genome and transcriptome sequence data from a GI primary (prev breast cancer) patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002979

• Genome and transcriptome sequence data from a low grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a low grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003020

• Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003054

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003639

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003617

• Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient

  Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003650

• Genome and transcriptome sequence data from a metastatic clear cell ovarian cancer patient

  Genome and transcriptome sequence data from a metastatic clear cell ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003672

• Genome and transcriptome sequence data from a low grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a low grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003680

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003682