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• Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of anus patient

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

  Dataset EGAD00001002994

• Genome and transcriptome sequence data from a carcinosarcoma of the uterus patient

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

  Dataset EGAD00001002995

• 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 EGAD00001002997

• Autozygosity pilot - Born in Bradford (2017-05-11)

  The offspring of first cousin marriages have ~6% of their genome autozygous, i.e. homozygous identical by descent, or even more if there was further consanguinity in their ancestry. In the UK there are large populations with very high first cousin marriage rates of 20-50%. Sequencing the exomes of a sample of these individuals has the potential both to support genetic health programmes in these populations, and to provide genetic research information about rare loss of function mutations. This pilot study based on existing cohort samples from the Born In Bradford study will identify homozygous individuals for almost all variants down to an allele frequency around 1%, plus individuals carrying hundreds of new homozygous rare loss-of-function variants, and will support development of community relations and ethics for a wider study currently being designed. The data deposited in the EGA consist of low coverage whole exome sequencing on these samples. This dataset contains all the data available for this study on 2017-05-11.

  Dataset EGAD00001003329

• Targeted resequencing of Cardiomyopathies associated genes

  In this study, we present the results of a custom “pan-cardiomyopathy panel” in a molecular screening of 38 unrelated patients, 16 affected by DCM, 14 by HCM, and 8 by ARVC. The panel was designed using the Design Studio Tool (Illumina, San Diego, CA,USA). Coding regions and intron–exon boundaries of 115 genes, known to be associated with 7 DCM, HCM, and ARVC as well as channelopathies, were selected for targeted gene enrichment. For genes with multiple transcripts, all exons included in transcripts expressed in cardiac muscle were considered in the gene panel design. Total DNA was extracted from peripheral blood samples using the Wizard Genomic DNA Purification Kit (Promega, Mannheim, Germany) according to the manufacturer’s instructions, quantified, and qualitatively checked using NanoDrop 2000c (Thermo Fisher Scientific, Waltham, MA, USA). Custom targeted gene enrichment and DNA library preparation were performed using the Nextera Capture Custom Enrichment kit (Illumina) according to the manufacturer’s instructions. Targeted regions were sequenced using the Illumina MiSeq platform, generating approximately two millions of 150-bp paired-end reads for each sample (Q30 ≥90%).

  Dataset EGAD00001003359

• Leiomyosarcoma Cancer Transcriptome Sequencing

  RNA-Seq data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 78 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 75bp paired-end protocol to a minimum mean reads of 50 million.

  Dataset EGAD00001003192

• High-resolution lung adenocarcinoma expression subtypes identify tumors with dependencies on MET, CDK4, CDK6, and PD-L1

  Lung adenocarcinoma is one of the most common cancer types with various treatment modalities. However, better biomarkers to predict therapeutic response are still needed to improve precision medicine. We utilized a consensus hierarchical clustering approach on 509 LUAD cases from TCGA to identify five robust LUAD expression subtypes. We then integrated genomic (patient and cell line) and proteomic data to help define biomarkers of response to targeted therapies and immunotherapies. This approach defined subtypes with unique proteogenomic and dependency profiles.

  Study EGAS00001006461

• AfricanNeo aDNA Study

  With the largest genomic dataset to date of Bantu-speaking populations, including newly generated data of modern-day and ancient DNA from previously unsampled regions in Africa, we shed fresh light on the expansion of peoples speaking Bantu languages that started ~4000 years ago in western Africa. We have generated whole-genome sequences from 12 Late Iron Age individuals. Our comprehensive genetic analysis of the expansion of people speaking Bantu languages reveals a complex history of serial founder events, variable levels of contact with local groups, and spread-over-spread events.

  Study EGAS00001007519

• MutWPX__CRUK_Grand_Challenge_Mutographs_of_Cancer__Effects_of_Chemotherapy_on_the_Somatic_Mutational_Landscape_in_Normal_Human_Tissue___Matched_Normals

  This study is investigating the effects of chemotherapy drugs used in cancer treatment on the somatic mutational landscape in normal human tissues. The samples used in this study have been taken from rapid autopsies of patients who have undergone chemotherapy using drugs thought to cause mutations in the DNA. Structures from a variety of tissues will be dissected using LCM and whole genome sequences will be produced in order to assess mutational burdens and signatures. This study contains the samples used as matched normals for each patient to remove germline mutations.

  Study EGAS00001004957

• Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild type primary glioblastoma

  We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy.

  Study EGAS00001006989