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• TRACERx100 metastatic samples

  Whole exome sequencing of 10 metastatic biopsies from four TRACERx100 patients (see EGA dataset EGAS00001002247), collected either after relapse or death. The data from these samples are initially published with Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early stage lung cancer evolution. Nature, http://dx.doi.org/10.1038/nature22364 (2017). Abstract: Earlier detection of relapse following primary surgery for non-small cell lung cancer and the characterization of emerging subclones seeding metastatic sites might offer new therapeutic approaches to limit tumor recurrence. The potential to non-invasively track tumor evolutionary dynamics in ctDNA of early-stage lung cancer is not established. Here we conduct a patient-specific approach to ctDNA profiling in the first 100 lung TRACERx (TRAcking Cancer Evolution through therapy (Rx)) study participants, including one patient co-recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release in early-stage non-small cell lung cancer and perform tumor volume limit of detection analyses. Through blinded profiling of post-operative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients destined to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastases, providing a new approach for ctDNA driven therapeutic studies.

  Dataset EGAD00001003301

• Ovarian Cancer Organoid Biobank

  WGS of ovarian cancer organoids, tumor samples and blood references. Ovarian cancer (OC) is a heterogeneous disease usually diagnosed at a late stage. Experimental in vitro models that faithfully capture the hallmarks and tumor heterogeneity of OC are limited and hard to establish. We present a novel protocol that enables efficient derivation and long-term expansion of OC organoids. Utilizing this protocol, we have established 56 organoid lines from 32 patients, representing the spectrum of ovarian neoplasms, including non-malignant borderline tumors, as well as mucinous, clear-cell, endometrioid, low- and high-grade serous carcinomas. OC organoids recapitulate histological and genomic features of the pertinent lesion from which they were derived, illustrating intra- and inter-patient heterogeneity, and can be genetically modified. We show that OC organoids can be used for drug screening assays and capture different tumor subtype responses to the gold standard platinum-based chemotherapy, including acquisition of chemoresistance in recurrent disease. Finally, OC organoids can be xenografted, enabling in vivo drug sensitivity assays. Taken together, this demonstrates their potential application for research and personalized medicine.

  Dataset EGAD00001004387

• The landscape of somatic mutation in normal colorectal epithelial cells

  The colorectal adenoma-carcinoma sequence has provided a paradigmatic framework for understanding the successive somatic genetic events and consequent clonal expansions leading to cancer. As for most cancer types, however, understanding of the earliest phases of colorectal neoplastic change, which may occur in morphologically normal tissue, is comparatively limited because of the difficulty of detecting somatic mutations in normal cells. Each colorectal crypt is a small clone of cells derived from a single recently-existing stem cell. Here, we sequenced hundreds of normal crypts from 42 individuals. Signatures of multiple mutational processes were revealed, some ubiquitous and continuous, others only found in some individuals, in some crypts or during some phases of the cell lineage from zygote to adult cell. Likely driver mutations were present in ~1% of normal colorectal crypts in middle-aged individuals, indicating that adenomas and carcinomas are rare outcomes of a pervasive process of neoplastic change across morphologically normal colorectal epithelium.

  Dataset EGAD00001004192

• Epigenome maps of time-resolved monocyte to macrophage differentiation and innate immune memory

  Innate immune memory is the phenomenon whereby innate immune cells such as monocytes or macrophages undergo functional reprogramming after exposure to microbial components such as LPS. We apply an integrated epigenomic approach to characterize the molecular events involved in LPS-induced tolerance in a time dependent manner. ChIP-seq, RNA-seq, WGBS and ATAC-seq data were generated. This analysis identified epigenetic programs in tolerance and trained macrophages, and the potential transcription factors involved. Experimental set-up Time-course in vitro culture of human monocytes. Two innate immune memory states can be induced in culture through an initial exposure of primary human monocytes to either LPS or BG for 24 hours, followed by removal of stimulus and differentiation to macrophages for an additional 5 days. Cells were collected at baseline (day 0), 1 hour, 4 hour, 24 hour and 6 days.

  Dataset EGAD00001002693

• SNV and indel calls from 8921 individuals in the British Autozygosity Populations BioResource dataset

  This includes variant calls (single nucleotide variants and small insertions/deletions) from 8086 (mostly British Pakistani/British Bangladeshi) individuals from the following studies: 1. 5236 British Pakistani/British Bangladeshi adults from East London Genes and Health (ELGH) 2. 2624 British South Asian mothers from Born in Bradford (mostly Pakistani) (BiB) 3. 1061 British South Asian adults from Birmingham (mostly Pakistani) (Birm) All of the Birmingham and most of the Born in Bradford samples were previously sequenced as part of PMID: 26940866. In the sample list file, the columns of interest to most people will be: vcf.id - sample ID from the vcf cohort - which cohort they're in sex.assigned - sex inferred from coverage on the X and Y chromosomes. Individuals for whom this did not match their reported sex have been discarded total, chrX and chrY - coverage within bait regions across all chromosomes, chrX and chrY respectively Mapping was done with bwa-mem and variant calling was carried out with GATK HaplotypeCaller. We removed variant sites for which the following was true: SNPs: "QD < 2.0 || FS > 30 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" Indels: "QD < 2.0 || FS > 30 || ReadPosRankSum < -20.0"

  Dataset EGAD00001005469

• THE GENOMIC LANDSCAPE OF ACTINIC KERATOSIS

  Actinic keratoses (AK) are lesions of epidermal keratinocyte dysplasia and are precursors for invasive cutaneous squamous cell carcinoma (CSCC). Identifying the specific genomic alterations driving progression from normal skin-AK-invasive CSCC is challenging due to the massive ultraviolet radiation-induced mutational burden characteristic at all stages of this progression. Here, we report the largest AK whole exome sequencing study to date and perform mutational signature and candidate driver gene analysis on these lesions. We demonstrate in 37 AK, from both immunosuppressed and immunocompetent patients, that there are significant similarities to CSCC in terms of mutational burden, copy number alterations, mutational signatures and patterns of driver gene mutations. We identify 44 significantly mutated AK driver genes and confirm that these genes are similarly altered in CSCC. We identify the azathioprine mutational signature in all AK from patients exposed to the drug, providing further evidence for its role in keratinocyte carcinogenesis. CSCC differ from AK in having higher levels of intra-sample heterogeneity. Alterations in signaling pathways also differ, with immune-related signaling and TGF-β signaling significantly more mutated in CSCC. Integrating our findings with independent gene expression datasets confirms that dysregulated TGF-β signaling may represent an important event in AK-CSCC progression.

  Dataset EGAD00001006894

• A95626A

  Whole genome sequencing for single cells for library A95626A 1040 cells; filetype=bam

  Dataset EGAD00001007103

• A96118A

  Whole genome sequencing for single cells for library A96118A 1193 cells; filetype=bam

  Dataset EGAD00001007113

• A110632A

  Whole genome sequencing for single cells for library A110632A 1151 cells; filetype=bam

  Dataset EGAD00001007094

• A110632B

  Whole genome sequencing for single cells for library A110632B 892 cells; filetype=bam

  Dataset EGAD00001007095