9337 results for "cancer rna-seq"

in 56.99 milliseconds.

• Novel driver variants in the histone 3.3 genes, H3F3A and H3F3B, define bone and cartilage cancer sub-types

  A selection of human cancers harbours somatic driver mutations in genes encoding histones, most notably childhood brain tumours with K27M substitutions of the histone 3.3 gene, H3F3A. We performed whole genome sequencing of the benign cartilage tumour, chondroblastoma, and targeted sequencing of histone 3.3 genes, H3F3A and H3F3B, in seven further skeletal tumour types. We identified an exceptionally high prevalence of novel histone 3.3 driver mutations at glycine 34 and at lysine 36. Histone 3.3 gene mutations were found in 91% in giant cell tumours of bone (48/53), mainly H3F3A G34W variants, and in 92% of chondroblastoma (73/79), predominantly K36M mutations in H3F3B. H3F3B is paralogous to the cancer gene H3F3A. However, H3F3B driver variants have not previously been reported in human cancer. Our observation demonstrate remarkable tumour-specificity of mutations, with respect to which histone 3.3 gene and residue is mutated, indicating that the advantage these mutations confer is tumour dependent. Moreover, tumour-specific mutation of H3F3A and H3F3B suggests, that although both genes encode identical proteins, they are likely non-redundant and employed differentially during skeletal development.

  Dataset EGAD00001000646

• 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

• 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

• The dataset for Detection and characterization of lung cancer using cell-free DNA fragmentomes

  The dataset for Detection and characterization of lung cancer using cell-free DNA fragmentomes includes 872 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 EGAD00001007796

• Massive parallel RNA sequencing of highly purified mesenchymal cells derived from bone marrow specimens of 4 Shwachman-Diamond syndrome cases

  Highly purified mesenchymal cells (CD45-/7AAD-/CD235a-/CD31-/CD271+/CD105+) were prospectively FACS-isolated from bone marrow specimens of 4 Shwachman-Diamond syndrome (SDS) cases. This data set, comprising 4 SDS cases, is used as complement to 45 low-risk myelodysplastic syndrome (LRMDS) and 3 Diamond-Blackfan anemia (DBA) cases to demonstrate aberrant inflammatory signaling as a common mechanism in pre-leukemia syndromes to induce genotoxic stress in hematopoietic stem cells. In addition this data set is used to determine different overlapping gene expression signatures in pre-leukemia syndromes compared to gene expression profiles of highly purified mesenchymal cells of healthy donors.

  Dataset EGAD00001002660

• Massive parallel RNA sequencing of highly purified mesenchymal cells derived from bone marrow specimens of 3 Diamond-Blackfan anemia cases

  Highly purified mesenchymal cells (CD45-/7AAD-/CD235a-/CD31-/CD271+/CD105+) were prospectively FACS-isolated from bone marrow specimens of 3 Diamond-Blackfan anemia (DBA) cases. This data set, comprising 3 DBA cases, is used as complement to 45 low-risk myelodysplastic syndrome (LRMDS) and 4 Shwachman-Diamond syndrome (SDS) cases to demonstrate aberrant inflammatory signaling as a common mechanism in pre-leukemia syndromes to induce genotoxic stress in hematopoietic stem cells. In addition, this data set is used to determine different overlapping gene expression signatures in pre-leukemia syndromes compared to gene expression profiles of highly purified mesenchymal cells of healthy donors.

  Dataset EGAD00001002661

• Target-capture sequencing of FFPE tumor samples from patients diagnosed with NKTL

  211 NKTL FFPE specimens were screened for somatic mutations using deep targeted capture sequencing. FFPE rolls or slides were extracted using QIAamp DNA FFPE Tissue kit (QIAGEN). The FFPE genomic DNA was treated with NEBNext FFPE DNA Repair Mix and assessed by Quant-it PicoGreen dsDNA Assay Kit (Invitrogen). The library was generated from 10-200 ng DNA with SureSelectXT Low Input Target Enrichment System for Illumina Paired-End Sequencing Library (Agilent Technologies) according to manufacturer’s instructions. RNA based probe was designed with SureDesign (Agilent Technologies) to target-capture 140 genes. Next, the captured libraries were pooled in equimolar concentration and sequenced on Illumina Novaseq 6000 platform with SP or S1 chip.

  Dataset EGAD00001005303

• T-VEC CBCL scRNA

  We are presenting raw and processed data of our study where we analyze fine-needle aspirate (FNA) samples of primary cutaneous B-cell lymphoma patients undergoing oncolytic virotherapy (https://clinicaltrials.gov/ct2/show/NCT03458117). We are uploading single cell RNA-sequencing and immune repertoire profiling data of 29 FNA samples from four pCBCL patients. The four patients have three different subtypes of primary cutaneous B-cell lymphoma: pCDLBCL-LT, pCFCL and pCMZL. The samples are taken at different time points following T-VEC injection (from baseline up to 91 days after injection). We are uploading the sequences in BAM format and the outputs of the cellranger pipeline (count matrices and filtered VDJ contigs) in CSV format.

  Dataset EGAD00001006829

• MPM cell lines

  RNA-sequencing data for 12 MPM cell lines treated with 0.1% DMSO or 1 µM palbociclib for 9-10 days. Experiment was performed in duplicates for sensitive cells (MPM08, MPM21, MPM38, MPM57, MPM59, Meso11, Meso13, Meso34 and Meso56) and only once for resistant cells (MPM31, MPM34 and MPM36) except for Meso11 which was done in triplicates. For Meso11, Meso13, Meso34 and Meso56, a drug washout of 48 hours was also performed. 4 MPM cell lines (MPM31, MPM34, MPM59 and MPM66) were also analyzed in untreated condition. 2 FASTQ files for each sample (n=56) (paired)

  Dataset EGAD00001008741

• Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens

  In this study we performed whole genome sequencing on matched tumor-normal CD138+ bone marrow mononuclear cells from 60 patients with newly diagnosed multiple myeloma treated with daratumumab, carfilzomib, lenalidomide, and dexamethasone (NCT03290950-MANHATTAN trial). In addition, we performed 5’-single-cell RNA-sequencing (10X Genomics) coupled with V(D)J sequencing and capture of the surface protein markers (TotalSeq-C, Biolegend) of the CD138- bone marrow mononuclear cells to interrogate the composition of the immune microenvironment at baseline and after eight cycles of induction therapy in 22 patients with newly diagnosed multiple myeloma. Samples were multiplexed using hashtag oligo.

  Dataset EGAD00001011132

• Use_of_deep_sequencing_to_detect_clonal_mutations_in_sun_exposed_human_epidermis___whole_genome

  Deep sequencing of two skin biopsies to study the landscape of somatic mutations in human adult tissues.

  Study EGAS00001000860

• FWO_project_G_0687_12_X10_WGS

  Profiling subclonal architecture and phylogeny in tumors by whole-genome sequence data mining and single-cell genome sequencing

  Study EGAS00001001145

• Psoriatic_arthritis

  This is a pilot single cell mRNA sequencing experiment to study immune cells in psoriatic arthritis.

  Study EGAS00001002104

• Benchmarking_CRISPR_Whole_genome_Drop_out_Screen___B_S

  This study is a benchmarking exercise to explore potential source of variation between different CRISPR drop out libraries.

  Study EGAS00001002931

• Kidney Cancer Heterogeneity and Evolution Revealed by Multi-Region Exome Sequencing

  Paired end Illumina sequencing of whole exomes of multiple tumour regions.

  Dataset EGAD00001000734

• GEL WGS Comparison

  These samples are to be analysed with the CGP Developed cancer panel and the results will be compared with WGS data from 4 different comercial providers.

  Dataset EGAD00001000872

• Colon cancer ctDNA

  Samples prepared using Safe-SeqS technology. All samples ran on an Illumina MiSeq instrument. Fastq files for read 1 and the index read present (R and I respectively).

  Dataset EGAD00001003579

• Spatial Maps of Prostate Cancer Transcriptomes

  A total of 14 samples that has been analyzed with the Spatial Transcriptomics method. H&E stain can be sent if requested.

  Dataset EGAD00001004121

• DNA sequencing for gastric cancer ascites

  Whole-exome sequencing (WES) was performed on a total of 34 PC specimens, with 15 cases having matched gDNAs extracted from blood.

  Dataset EGAD00001004364

• Illumina_RNA_T-XEN_Validation_Cohort

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer sample of a validation cohort of 60 PDX

  Dataset EGAD00001002883

• Illumina_RNA_MET-CELL_Passages

  RNAseq on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample at early/late passages

  Dataset EGAD00001002884

• Colorectal cancer methylation profiling

  4 AC samples, each with adenoma, carcinoma and normal colon tissue (12 samples in total) were analysed on the Infinium MethylationEPIC BeadChip for copy number alteration analyses.

  Dataset EGAD00010001691

• Exome and Transcriptome for gynecologic cancer gene-drug analysis

  Total of 180 gynecologic tumor specimens were subjected for targeted-exome and/or whole-transcriptome sequencing.

  Dataset EGAD00001005331

• Exome and Transcriptome for gastric cancer gene-drug analysis

  Newly generated 52 gastric tumor specimens were subjected for targeted-exome and/or whole-transcriptome sequencing

  Dataset EGAD00001005766

• Sequence of breast cancer bone metastases PDX from WES

  Exome sequencing analyses obtained from 11 samples of PDX engrafted with bone metastases, match primary tumors and/or metastases and normal tissus.

  Dataset EGAD00001006071

• Lung cancers from admixed Latin American populations (2)

  Raw data from cancer panel sequencing of lung adenocarcinomas from admixed Latin American populations, predominantly samples without known oncogene mutations (n=532).

  Dataset EGAD00001006571

• Panel sequencing data of IMPACT2 patients

  Patient samples were sequenced by Foundation Medicine, Inc. (Cambridge, MA), using FoundationOne CDx, a comprehensive NGS-based in vitro diagnostic device designed to capture cancer genes.

  Dataset EGAD00001006887

• ICGC Korean Breast Cancer Project

  A comprehensive characterisation and analysis of human breast cancers through whole-genome sequencing.

  Dataset EGAD00001001322

• bam files Targeted BS

  Data from the paper Context-specific Effects of TGFβ/SMAD3 in Cancer Are Modulated by the Epigenome. Tufegdzic et al, Cell Reports 2015

  Dataset EGAD00001001667

• Illumina_WGS_MET-CELL-XEN

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

  Dataset EGAD00001002086

• Illumina_WGS_T-CELL-XEN

  Whole-genome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002090

• Illumina_WGS_T-XEN-CELL

  Whole-genome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002092

• Illumina_WXS_MET-CELL-XEN

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

  Dataset EGAD00001002097

• Illumina_WXS_T-CELL-XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002101

• Illumina_WXS_T-XEN-CELL

  Whole-exome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002103

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_001 for Triple negative breast cancer patient-derived xenograft SA609X7XB03505

  Dataset EGAD00001011219

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0148_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0148_001 for Triple negative breast cancer patient-derived xenograft SA535X4XB02498

  Dataset EGAD00001011221

• Colorectal organoids and tumour tissue

  Whole genome sequencing of single cell derived organoids from normal colon tissue and colorectal cancer.

  Dataset EGAD00001001447

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03663

  Dataset EGAD00001011229

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03434

  Dataset EGAD00001011231

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03617

  Dataset EGAD00001011232

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_001 for Triple negative breast cancer patient-derived xenograft SA535X9XB03616

  Dataset EGAD00001011233

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0195_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0195_001 for Triple negative breast cancer patient-derived xenograft SA535X8XB03664

  Dataset EGAD00001011234

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_002 for Triple negative breast cancer patient-derived xenograft SA535X10XB03696

  Dataset EGAD00001011235

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0208_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0208_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03776

  Dataset EGAD00001011237

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_001 for Triple negative breast cancer sample SA1035X6XB03216

  Dataset EGAD00001011241

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0151_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0151_001 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03425

  Dataset EGAD00001011247

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_002 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03631

  Dataset EGAD00001011249

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0014_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0014_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011251

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0015_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0015_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011252

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0020_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0020_002 for Triple negative breast cancer patient-derived xenograft SA604X8XB02164

  Dataset EGAD00001011254

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_002 for Triple negative breast cancer sample SA609X5XB03231

  Dataset EGAD00001011213

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_002 for Triple negative breast cancer patient-derived xenograft SA609X5XB03223

  Dataset EGAD00001011214

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_001 for Triple negative breast cancer patient-derived xenograft SA609X6XB03401

  Dataset EGAD00001011215

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03304

  Dataset EGAD00001011225

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03448

  Dataset EGAD00001011226

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015283

• Genome and transcriptome sequence data from a CNS sarcoma tumor patient

  Genome and transcriptome sequence data from a CNS sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015268

• Genome and transcriptome sequence data from a ocular melanoma tumor patient

  Genome and transcriptome sequence data from a ocular Melanoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015269

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

  Genome and transcriptome sequence data from a aggressive fibromatosis patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015279

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015286

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

  Genome and transcriptome sequence data from a aggressive fibromatosis patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015298

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

  Genome and transcriptome sequence data from a aggressive fibromatosis patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015299

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015305

• Genome and transcriptome sequence data from a metastatic osteosarcoma tumor patient

  Genome and transcriptome sequence data from a metastatic osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015306

• Genome and transcriptome sequence data from a synovial sarcoma tumor patient

  Genome and transcriptome sequence data from a synovial sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015313

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015315

• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

  Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015318

• Genome and transcriptome sequence data from a anaplastic astrocytoma tumor patient

  Genome and transcriptome sequence data from a anaplastic astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015319

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015324

• Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015329

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015330

• Genome and transcriptome sequence data from a pIlomyxoid astrocytoma tumor patient

  Genome and transcriptome sequence data from a pIlomyxoid astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015333

• Genome and transcriptome sequence data from a wilms tumor tumor patient

  Genome and transcriptome sequence data from a wilms tumor patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015334

• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

  Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015335

• Genome and transcriptome sequence data from a infantile fibrosarcoma tumor patient

  Genome and transcriptome sequence data from a infantile fibrosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015263

• Genome and transcriptome sequence data from a relapsed osteosarcoma tumor patient

  Genome and transcriptome sequence data from a relapsed osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015311

• Angiopredict Whole genome Shallow Sequencing

  Shallow sequencing of metastatic colorectal cancer samples for the Angiopredict and Nobev cohorts described in: van Dijk et al., JCO, in revision

  Dataset EGAD00001003990

• Common Fund (CF) Genotype-Tissue Expression Project (GTEx)

  Lay Description The aim of the Genotype-Tissue Expression (GTEx) Project is to increase our understanding of how changes in our genes affect human health and disease with the ultimate goal of improving health care for future generations. GTEx will create a database that researchers can use to study how inherited changes in genes lead to common diseases. GTEx researchers are studying genes in different tissues obtained from many different people. The GTEx project also includes a study of the GTEx donor consent process - this study will help ensure that the consent process and other aspects of the project effectively address the concerns and expectations of participants in the study. GTEx is a pioneering project that uses state-of-the-art protocols for obtaining and storing a large range of organs and tissues, and for testing them in the lab. Until now, no project has analyzed genetic variation and expression in as many tissues from the same person in such a large population as planned for GTEx. Scientific Description Understanding the role of variation in the human genome is crucial to elucidating genetic contributions to human health and disease. Despite the results of genome-wide association studies (GWAS) documenting strong statistical associations between genetic variation and human traits, the functional role for most of these variants is largely unexplained. Nearly 90% of these GWAS-implicated sites lie outside of protein-coding sequences, suggesting that these variants might regulate gene expression. The goal of the Genotype-Tissue Expression (GTEx) project is to establish a resource database and tissue biobank in which to study the relationship between genetic variation and gene expression and other molecular phenotypes in reference/non-diseased tissues. The ultimate resource will include approximately 960 post-mortem donors with several dozen tissues from each, a resource large enough to study both cis- and trans- gene expression quantitative trait loci (eQTLs). Some tissue will also be banked for additional molecular analyses. To request biosamples, please see the Biobank page for more information. GTEx was initially funded as a 2-year pilot project by the NIH Common Fund (CF) in 2010, and has been scaled up after demonstration of feasibility. The project will collect and analyze RNA levels in many different human tissues and each donor will be characterized for germline genetic variation through dense genotyping arrays and sequencing of either whole exomes or whole genomes. By treating RNA expression levels as quantitative traits, eQTLs will be identified as sites containing genetic variation that correlate with changes in RNA expression. Such eQTLs have been associated with 4%-12% of expressed human genes, and with common complex human diseases, including obesity, atherosclerosis, type 2 diabetes, Crohn's disease, and asthma. Additionally, few studies have examined the tissue specificity of eQTLs. A subset of banked tissue samples will also be analyzed for other molecular phenotypes, such as DNA methylation, DNaseI hypersensitivity sites, and proteomics. The GTEx project will thus serve as a resource database and tissue bank for many future studies, especially for understanding the functional basis of inherited susceptibility to disease. All GTEx releases since Version 5 follow the NIH Genomic Data Sharing (GDS) Policy whereby there are no restrictions on use or publication after release. Additional information about the GTEx Data Release and Publication policy can be found on the GTEx Data Portal at www.gtexportal.org.

  Study phs000424

• Shallow whole genome sequencing

  This dataset contains a collection of tumour samples from high grade serous ovarian carcinoma patients with recurrent disease collected near the point of diagnosis as well as tumour samples collected after patient relapse upon or some time after study entry. The majority of diagnosis samples were preserved in neutral buffered formalin whereas the majority of post-relapse samples were preserved in universal molecular fixative (UMFIX, Sakura Finetek USA, Inc). DNA was extracted and whole genome sequencing libraries were prepared either using the TruSeq DNA Nano kit (Illumina) or the ThruPLEX DNA-Seq Kit (Takara Bio), which were respectively sequenced at low depth (~0.1-0.5X) on illumina HiSeq 2500 and HiSeq 4000 sequencing platforms. Sequenced reads were aligned to the GRCh37 reference genome (release hs37d5).

  Dataset EGAD00001011049

• 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

• Genome, epigenome and RNA sequences of monozygotic twins discordant for multiple sclerosis

  Monozygotic (MZ) twins have been widely employed for dissection of the relative contributions of genetics and environment in disease. In multiple sclerosis (MS), an autoimmune demyelinating disease that commonly causes neurodegeneration and disability in young adults, disease discordance in MZ twins has been interpreted to indicate environmental importance in pathogenesis. However, genetic and epigenetic differences between MZ twins have been described, challenging the accepted experimental paradigm in disambiguating effects of nature and nurture. Here, we report the genome sequences of one MS-discordant MZ twin pair and messenger RNA (mRNA) transcriptome and epigenome sequences of CD4+ lymphocytes from three MS-discordant, MZ twin pairs. No reproducible differences were detected between co-twins among ~3.6 million single nucleotide polymorphisms (SNPs) or ~0.2 million insertion-deletion polymorphisms (indels). Nor were any reproducible differences observed between siblings of the three twin pairs in HLA haplotypes, confirmed MS-susceptibility SNPs, copy number variations, mRNA and genomic SNP and indel genotypes, or expression of ~19,000 genes in CD4+ T cells. Only two to 176 differences in methylation of ~2 million CpG sites were detected between siblings of the three twin pairs, in contrast to ~800 differences in methylation between T cells of unrelated individuals and several thousand differences between tissues or normal and cancerous tissues. In the first systematic effort to estimate sequence variation among MZ co-twins, we did not find evidence for genetic, epigenetic or transcriptome differences that explained disease discordance. These are the first female, twin and autoimmune disease genome sequences reported.

  Study phs000239

• Evolution and clinical impact of genetic epistasis within EGFR-mutant lung cancers

  The current understanding of tumorigenesis is largely centered on a monogenic driver oncogene model. This paradigm is incompatible with the prevailing clinical experience in most solid malignancies: monotherapy with a drug directed against an individual oncogenic driver typically results in incomplete clinical responses and eventual tumor progression1-7. By profiling the somatic genetic alterations present in over 2,000 cases of lung cancer, the leading cause of cancer mortality worldwide, we show that combinations of functional genetic alterations, i.e. genetic collectives dominate the landscape of advanced-stage disease. We highlight this polygenic landscape and evolution of advanced-stage non-small cell lung cancer (NSCLC) through the spatial-temporal genomic profiling of 7 distinct tumor biopsy specimens and 6 plasma specimens obtained from an EGFR-mutant NSCLC patient at (1) initial diagnosis of early-stage disease, (2) metastatic progression, (3) sequential treatment and resistance to 2 EGFR inhibitors, (4) death. The comprehensive genomic analysis of this case, coupled with circulating free (cf) tumor DNA profiling of additional advanced-stage EGFR-mutant NSCLC clinical cohorts with associated treatment responses uncovered features of evolutionary selection for multiple concurrent gene alterations: including the presence of EGFR inhibitor-sensitive (EGFRL858R;EGFRexon19del) or inhibitor-resistant (EGFRT790M;EGFRC797S) forms of oncogenic EGFR along with cell cycle gene alterations (e.g. in CDK4/6, CCNE1, RB1) and activating alterations in WNT/β-catenin and PI3K pathway genes, which our data suggest can cooperatively impart non-redundant functions to limit EGFR targeted therapy response and/or promote tumor progression. Moreover, evidence of an unanticipated parallel evolution of both EGFR T790M and two distinct forms of oncogenic PIK3CA was observed. Our study provides a large-scale clinical and genetic dataset of advanced-stage EGFR-mutant NSCLC, a rationale for specific polytherapy strategies such as EGFR and CDK4/6 inhibitor co-treatment to potentially enhance clinical outcomes, and prompts a re-evaluation of the prevailing paradigm of monogenic-based molecular stratification for targeted therapy. Instead, our findings highlight an alternative model of genetic collectives that operate through epistasis to drive lung cancer progression and therapy resistance.

  Study EGAS00001002604

• Chromatin profiles classify castration-resistant prostate cancers suggesting therapeutic targets

  Untreated prostate cancers rely on androgen receptor (AR) signaling for growth and survival, forming the basis for the initial efficacy of androgen deprivation therapy (ADT). Yet, the disease can relapse and progress to a lethal stage termed castration-resistant prostate cancer (CRPC). Reactivation of AR signaling represents the most common driver of CRPC growth and next generation AR signaling inhibitors (ARSIs) are now used in combination with ADT as a first line therapy. However, ARSIs can result in selective pressure generating AR-independent tumors. The transition from AR-dependence frequently accompanies a change in phenotype resembling developmental trans-differentiation or ‘lineage plasticity’. Neuroendocrine prostate cancer, which lacks a defined pathologic classification, is the most studied type of lineage plasticity. However, most AR-null tumors do not exhibit neuroendocrine features and are classified as ‘double-negative prostate cancer’, the drivers of which are poorly defined. Lineage plasticity studies in CRPC are limited by the lack of genetically defined patient-derived models that recapitulate the disease spectrum. To address this, we developed a biobank of organoids generated from patient biopsies to study the landscape of metastatic CRPC and allow for functional validation assays. Proteins called transcription factors (TFs) are drivers of tumor lineage plasticity. To identify the key TFs that drive the growth of AR-independent tumors, we integrated epigenetic and transcriptomic data generated from CRPC models. By presenting a map of the chromatin accessibility and transcriptomic landscape of CRPC using a diverse biobank of organoids, PDXs and cell lines that recapitulate the heterogeneity of metastatic prostate cancer, we validate CRPC-AR and CRPC-NE subtypes and reveal two subtypes of AR-negative/low samples as well as their respective masterTFs. Additional analysis revealed a model in which YAP, TAZ, TEAD and AP-1 function together and drive oncogenic growth in CRPC-SCL samples. Overall, we show here how an approach to stratify CRPC patients into four subtypes using their transcriptomic signatures can potentially inform upon appropriate clinical decisions.

  Study EGAS00001006059

• Gut Microbiome and Types of Colorectal Polyps

  Colorectal cancer is a heterogeneous disease arising from at least two precursors-the conventional adenoma and the serrated polyp. This dataset was used to test the relationship of the gut microbiota to specific colorectal polyp types. We included samples from two independent study populations based at colonoscopy clinics: the Centers for Disease Control and Prevention (CDC) Study of In-home Tests for Colorectal Cancer (SIT), and the New York University (NYU) Human Microbiome and Colorectal Tumor study. Gut microbiota were assessed in 667 colonoscopy-screened adults by 16S rRNA gene sequencing of stool samples, of which 540 were included in our analysis. Participants were categorized as conventional adenoma cases, serrated polyp cases, or polyp-free controls. CA cases were further classified as proximal or distal and as non-advanced or advanced. Serrated polyp cases were further classified as hyperplastic polyp or sessile serrated adenoma. Our results show associations between gut microbiome composition and presence of conventional adenomas, including reduced diversity and alterations in taxon abundance.

  Study phs001381

• Scalable Whole-Exome Sequencing of Cell-Free DNA Reveals High Concordance with Metastatic Tumors

  We performed whole genome sequencing and whole-exome sequencing of cell-free DNA (cfDNA) and whole-exome sequencing of matched tumor biopsies and germline DNA from patients with metastatic cancer. Using ichorCNA, a software tool that quantifies tumor content in ultra-low pass whole genome sequencing (~0.1x) of cfDNA without prior genomic characterization of the tumor, we show genome-wide concordance between cfDNA and tumor biopsies and detectability of high tumor fractions (>0.1) in the cfDNA of many patients with metastatic cancer. We then established that whole-exome sequencing of cfDNA can enable comprehensive profiling of tumors from blood, with high tumor-content cfDNA samples demonstrating concordance of clonal somatic mutations, copy number alterations, mutational signatures, and neoantigens with matched tumor biopsies. This study introduces a new method to identify patients that could be eligible for tumor profiling from cfDNA and sheds light on the concordance between metastatic tissue and blood biopsies.

  Study phs001417

• Single molecule molecular inversion probe capture developed using the CIViC database

  This study evaluated the use of the Clinical Interpretations of Variants in Cancer database (CIViC) to develop an Open-sourced CIViC Annotation Pipeline (OpenCAP). OpenCAP is a resource that allows users to develop a clinical capture panel that can be linked back to clinical summaries within the CIViC database. This method for capture panel development and variant annotation was tested using 27 cancer samples from 5 tumor subtypes with original exome or genome sequencing. Specifically, using OpenCAP, 111 variants were identified as relevant for the capture panel and 2,027 smMIPs were designed to target these variants. The smMIPs panel design was employed on all samples to validate the capture panel design. When compared to original sequencing, the CIViC smMIPs capture panel demonstrated a 95% sensitivity for variant detection (n = 61 variants). Additionally, the CIViC smMIPs capture panel identified 182 additional clinically relevant variants that had been missed on original sequencing.

  Study phs001890

• A Phase II Clinical Trial of the PARP Inhibitor Talazoparib in BRCA1 and BRCA2 Wild-Type Patients

  The Talazoparib Beyond BRCA (TBB), a phase II proof-of-concept clinical trial, was designed to test the efficacy of talazoparib, a potent, orally bioavailable PARP inhibitor, with an established Phase II recommended dose, in the treatment of advanced BRCA wildtype (WT), HER2-negative breast cancer and other solid tumors with homologous recombination (HR) deficiency. Eligible patients had advanced HER2-negative breast cancer or other solid tumors with a germline or somatic mutation in a gene linked to the HR pathway. Patients identified as harboring a deleterious or suspected deleterious germline or somatic mutation in the HR pathway were treated with single agent talazoparib until disease progression. Primary and metastatic tumor samples were sequenced with panel sequencing and exome sequencing to detect commonly mutated genes and those associated with DNA repair deficiency. In addition, ctDNA was sequenced at baseline and progression. Fastq files for tumor and ctDNA sequencing are deposited. 

  Study phs002803

• The Genomic Landscape of Interval Colorectal Cancers

  Interval colorectal cancers (CRC), cancers diagnosed after a screening/surveillance exam in which no cancer is detected, and before the date of the next recommended exam, reflect an unprecedented challenge in CRC detection and prevention. To better understand this poorly characterized CRC variant, we examined the clinical and mutational characteristics of interval CRCs in comparison to screen-detected CRCs. We used data from the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer randomized controlled trial (RCT) of flexible sigmoidoscopy (FS) screening, which enrolled 154,900 men and women aged 55 to 74 years between 1993 and 2001. Whole Exome Sequencing (WES) was performed on 323 samples from 168 participants (data available through dbGaP). We found colorectal cancers diagnosed between screening exams (interval cancers) had significantly increased risk of death than screen detected cancers although no significant molecular differences were observed between screen-detected and interval CRC at the whole exome sequencing level.

  Study phs003093

• Hi-C Profiling of Solid Tumor Samples

  Structural variants, such as translocations, inversions, duplications, and deletions, have long been recognized as mutations that can contribute to the initiation and progression of cancer. This can occur through a variety of mechanisms, including changes in gene dosage and the generation of fusion genes. In recent years, it is becoming increasingly clear that these events also can alter how genomes are organized in 3D space in the nucleus and contribute to changes in enhancer-promoter regulation. However, it remains poorly understand how often 3D genome structural changes occur in cancers, what genes are affected by these events, and in which tumor types. This study is part of ongoing work to better characterize 3D genome structural changes across a variety of tumor types to better understand where and when 3D genome structure changes as the result of structural variation in cancer genomes and to characterize the genes and tumor types affected.

  Study phs003227

• EuCANCan: EUropean-CANadian Cancer network

  The EUropean-CANadian Cancer network (EuCANCan) is a project aiming to develop a federated network of aligned and interoperable infrastructures for the homogeneous analysis, management and sharing of genomic oncology data for Personalized Medicine. This initiative, funded by the European Commission and the Canadian Institutes of Health and coordinated by the Barcelona Supercomputing Center (BSC), is expected to process and provide the scientific community with around 30-35 thousand patient samples from different types of cancer, coming from different nodes involved in this project, during the four years of the project. Data generated as part of primary studies, often in small hospitals or research centers, is hardly reused by other projects or groups. EuCANCan is proposed to foster the homogenisation and discoverability of cancer samples from very different sources for the whole community. These efforts will help paving the way to future standardised and global genomics projects, specially for those participant countries that eventually integrate the methodology and infrastructure generated during the project. “The more data we have access to the more and better discoveries. We attempt to overcome the legal and technical limitations in order to generate, gather and exploit bigger datasets as well as answer more ambitious questions”. These were some of the words Dr. David Torrents, main coordinator of the project, shared with a regional radio during the kick-off meeting of the project, on February 11th, 2019. The EUCANCan network will be composed of reference nodes in Amsterdam, Barcelona, Berlin, Heidelberg, Paris and Toronto which have established strong research and clinical programs in the field of genomic oncology. The Centre for Genomic Regulation (CRG), specifically the European Genome-phenome Archive (EGA), is among the leading institutions. Its exhaustive experience in biomedical data archival and secure distribution is considered key for crucial developments within the project. It is also notable the recent inclusion of EUCANCan in the list of GA4GH driver projects. Further information and details can be found in the following links: InsideHPC BSC official description Techweek (Spanish)

  Blog eucancan

• Whole-exome sequencing reveals the origin and evolution of Hepato-Cholangiocarcinoma

  Hepatocellular-cholangiocarcinoma (H-ChC) is a rare subtype of liver cancer with clinicopathological features of both hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). To date, molecular mechanisms underlying the co-existence of HCC and iCCA components in a single tumor remain elusive. Here we show that H-ChC samples contain substantial private mutations from WES analyses, ranging from 33.1% to 86.4%, indicative of substantive intratumor heterogeneity (ITH). However, on the other hand, numerous ubiquitous mutations shared by HCC and iCCA suggest the monoclonal origin of H-ChC. Mutated genes identify herein e.g. VCAN, ACVR2A and FCGBP are speculated to contribute to distinct differentiation of HCC and iCCA within H-ChC. Moreover, immunohistochemistry demonstrate that EpCAM is highly expressed in 80% of H-ChC implying the stemness of such liver cancer. In summary, our data highlight the monoclonal origin and stemness of H-ChC, as well as substantial intratumoral heterogeneity.

  Study EGAS00001002783

• Origins and functional consequence of somatic mitochondrial DNA mutations

  Recent sequencing studies have extensively explored the somatic alterations present in the nuclear genomes of cancers. Although mitochondria control energy metabolism and apoptosis, the origins and impact of cancer-associated mutations in mitochondrial DNA (mtDNA) are unclear. Here, we analysed somatic alterations in mtDNA from 1,675 tumors across 31 histologies. We identified 1,907 somatic substitutions, which exhibited dramatic replicative strand bias, predominantly C>T and A>G on the mitochondrial heavy strand. This strand-asymmetric signature differs from those found in nuclear cancer genomes but matches the inferred germline process shaping primate mtDNA sequence content. Numbers of mtDNA mutations showed considerable heterogeneity across tumor types. Missense mutations were selectively neutral and often gradually drifted towards homoplasmy over time. In contrast, mutations resulting in protein truncation undergo negative selection and were almost exclusively heteroplasmic. Our findings indicate that the endogenous mutational mechanism has far greater impact than any other external mutagens in mitochondria, and is fundamentally linked to mtDNA replication.

  Study EGAS00001000968

• Comparing nodal versus bony metastatic spread using tumour phylogenies

  The role of lymph node metastases in distant prostate cancer dissemination and lethality is ill defined. Patients with metastases restricted to lymph nodes have a better prognosis than those with distant metastatic spread, suggesting the possibility of distinct aetiologies. To explore this, we traced patterns of cancer dissemination using tumour phylogenies inferred from genome-wide copy-number profiling of 48 samples across 3 patients with lymph node metastatic disease and 3 patients with osseous metastatic disease. Our results show that metastatic cells in regional lymph nodes originate from evolutionary advanced extra-prostatic tumour cells rather than less advanced central tumour cell populations. In contrast, osseous metastases do not exhibit such a constrained developmental lineage, arising from either intra or extra-prostatic tumour cell populations, at early and late stages in the evolution of the primary. Collectively, this comparison suggests that lymph node metastases may not be an intermediate developmental step for distant osseous metastases, but rather represent a distinct metastatic lineage.

  Study EGAS00001001801

• A novel TP53-KPNA3 translocation defines a de novo treatment-resistant clone in osteosarcoma

  Osteosarcoma is the most common primary bone cancer. It can be cured by aggressive surgery and chemotherapy, but outcomes for metastatic or chemoresistant disease remain dismal. Cancer sequencing studies have shown that the p53 pathway is dysregulated in nearly every case, often by translocation; however, no studies of osteosarcoma evolution or intratumor heterogeneity have been done to date. We studied a patient with chemoresistant, metastatic disease over the course of three years. We performed exome sequencing on germline DNA and DNA collected from tumor at three separate timepoints. We compared variant calls and variant allele frequencies between different samples. We identified subclonal driver mutations in several different genes in the primary tumor sample and found that one particular subclone dominated subsequent tumor samples at relapse. This clone was marked by a novel TP53-KPNA3 translocation and loss of the opposite-strand wild-type TP53 allele. Future research must focus on functional significance of such clones and strategies to eliminate them.

  Study EGAS00001001805

• Impact of mutational profiles on response of primary oestrogen receptor-positive breast cancers to oestrogen deprivation

  Presurgical studies allow study of the relationship between mutations and response of estrogen receptor positive (ER+) breast cancer to aromatase inhibitors (AIs) but have been limited to small biopsies. Here in Phase I of this study, we perform exome sequencing on baseline, surgical core-cuts and blood from 60 patients (40 AI treated, 20 Controls). In poor responders (based on Ki67 change) we find significantly more somatic mutations than good responders. Subclones exclusive to baseline or surgical cores   occur in approximately 30% of tumours. In Phase II we combine targeted sequencing on another 28 treated patients with Phase I. We find six genes frequently mutated: PIK3CA, TP53, CDH1, MLL3, ABCA13 and FLG with 71% concordance between paired cores. TP53 mutations are associated with poor response. We conclude that multiple biopsies are essential for confident mutational profiling of ER+ breast cancer and TP53 mutations are associated with resistance to oestrogen deprivation therapy.

  Study EGAS00001001940

• A biobank of patient-derived pediatric brain tumor models

  Brain tumors are the leading cause of cancer-related death in children. Genomic studies have provided insights into molecular subgroups and oncogenic drivers of pediatric brain tumors that may lead to novel therapeutic strategies. To evaluate new treatments, better preclinical models adequately reflecting the biological heterogeneity are needed. Through the Children’s Oncology Group ACNS02B3 study, we have generated and comprehensively characterized 30 patient-derived orthotopic xenograft models and 7 cell lines representing 14 molecular subgroups of pediatric brain tumors. Patient-derived orthotopic xenograft models were found to be representative of the human tumors they were derived from in terms of histology, immunohistochemistry, gene expression, DNA methylation, copy number, and mutational profiles. In vivo drug sensitivity of targeted therapeutics was associated with distinct molecular tumor subgroups and specific genetic alterations. These models and their molecular characterization provide an unprecedented resource for the cancer community to study key oncogenic drivers and to evaluate novel treatment strategies.

  Study EGAS00001002536

• Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection

  Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. We Here we developed and validated a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyzed whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a newly developed bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.

  Study EGAS00001003206

• The CCA Genome Core is responsible for archiving the sequencing data generated by the Cancer Centre Amsterdam (CCA).

  Dac EGAC00001000181

• Myeloma_Follow_up_Pilot

  Targeted capture of exonic and intronic regions of interest for the study of genomic alterations in multiple myeloma.

  Study EGAS00001000743