Search Results - EGA European Genome-Phenome Archive

Germline variants in patients with rare cancers - control samples WGS and WES

The control samples (mostly blood) of 351 samples (paired WGS and WES sequencing) are in this dataset. The WGS was in nearly all cases at an Illumina HighSeq X Ten with the Illumina TruSeq Nano DNA Kit. The WES mostly on Illumina HighSeq 4000 with the Agilent SureSelect V5 plus UTRs Kit.

Dataset EGAD00001010047

Genomic data used in "ACT-Discover: identifying karyotype heterogeneity in pancreatic cancer evolution using ctDNA"

Dataset EGAD00001010250

lymphoma plasma cfRNA

Dataset contains total RNA sequencing data from plasma of 65 different human donors: 30 diffuse large B-cell lymphoma (DLBCL) and 13 primary mediastinal large B-cell lymphoma (PMBCL) patients, and 22 cancer-free controls. Samples were sequenced on a NovaSeq 6000 and are provided in FASTQ format.

Dataset EGAD00001010259

Evaluation of triple negative breast cancer with heterogeneous immune infiltration

This dataset contains the raw RNA-seq data (FASTQ files) of the samples used in the study title: "Evaluation of triple negative breast cancer with heterogeneous immune infiltration". The dataset is composed of 3 patients with 6 samples per patient (3 with high TILs and 3 with low TILs).

Dataset EGAD00001010280

Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (tumour data)

There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers. This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.

Dataset EGAD00001010274

UCSF-CASCADE analysis of metastatic prostate cancer

Targeted cancer therapy inevitably selects for tumor cells that harbor some form of therapy resistance. This phenomenon is a principle reason why advanced prostate cancer, which can be treated with agents targeting androgen signaling dependency and DNA repair failure, is a lethal condition. The influence of a patient's clinical history and disease evolution on how their disseminated tumors develop resistance has been difficulty to study, because few autopsy studies have been performed in heavily treated patients with DNA-repair deficient metastatic castration-resistant prostate cancer (mCRPC). Here, we assessed how resistance to targeted cancer therapies evolved in an autopsy cohort of 54 mCRPC tumors from six men. This dataset includes targeted sequencing, exome sequencing, and RNA-seq conducted on these biopsies.

Dataset EGAD00001010275

Clonal selection after gene therapy in sickle cell disease

While gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD), the occurrence of myeloid malignancies in clinical trials has prompted concern. To interrogate potential mechanisms underlying increased cancer risk, we used hematopoietic stem cell (HSC) clonal tracking by whole genome sequencing (WGS) to map the somatic mutation and clonal landscape of 2,592 gene modified as well as unmodified single stem and progenitor cells from six SCD patients undergoing gene therapy (7-26 years old, average 12.7× depth). Pre-GT phylogenetic trees in SCD were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified. However, an increased frequency of driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) were seen in both genetically modified and unmodified cells suggested positive selection of mutant clones during gene therapy. This work sheds light on the mutation landscape and HSC clonal dynamics in gene therapy for SCD and highlights enhanced fitness of some HSCs harboring pre-existing driver mutations following gene therapy. Future studies should define the long-term fate of mutant clones including any contribution to expansions associated with myeloid neoplasms.

Dataset EGAD00001010913

Clonal selection after gene therapy in sickle cell disease

While gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD), the occurrence of myeloid malignancies in clinical trials has prompted concern. To interrogate potential mechanisms underlying increased cancer risk, we used hematopoietic stem cell (HSC) clonal tracking by whole genome sequencing (WGS) to map the somatic mutation and clonal landscape of 2,592 gene modified as well as unmodified single stem and progenitor cells from six SCD patients undergoing gene therapy (7-26 years old, average 12.7× depth). Pre-GT phylogenetic trees in SCD were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified. However, an increased frequency of driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) were seen in both genetically modified and unmodified cells suggested positive selection of mutant clones during gene therapy. This work sheds light on the mutation landscape and HSC clonal dynamics in gene therapy for SCD and highlights enhanced fitness of some HSCs harboring pre-existing driver mutations following gene therapy. Future studies should define the long-term fate of mutant clones including any contribution to expansions associated with myeloid neoplasms.

Dataset EGAD00001010914

POETIC clinical Trial Ki67 Bookend Breast Cancer Targeted Exome and RNAseq Project

Using RNAseq, we compare the 15% of poorest responders (PRs, n=177) as measured by proportional Ki67 changes after 2 weeks of neoadjuvant aromatase inhibitors to good responders (GRs, n=190) selected from the top 50% responders in the POETIC trial and matched for baseline Ki67 categories. In the POETIC trial, 4,480 postmenopausal women with primary ER+ BC were randomised 2:1 to receive either treatment with a non-steroidal AI (letrozole or anastrozole) for 2 weeks before and 2 weeks after surgery or to no perisurgical treatment. Only AI-treated patients with HER2- tumors, paired baseline and surgery Ki67 available, and baseline Ki67 immunohistochemistry (IHC) >10% (to minimise imprecision in proportional Ki67 falls) were included for selection. Data is baseline RNAseq and targeted exome DNA sequencing analysis of POETIC Good/Poor Responders to aromatase inhibitors based on change in Ki67.

Dataset EGAD00001010919

RNA sequencing of high-risk paediatric cancers for identifying T-cell infiltration signatures

Transcriptome profiling of 121 high-risk paediatric cancer samples for identifying T-cell infiltration signatures using poly-A capture by Truseq and sequenced on NextSeq 500

Dataset EGAD00001010920

RNAseq of primary mesothelioma cell lines

Mesothelioma is an aggressive cancer associated with previous exposure to asbestos and dismal prognosis. Since a pemetrexed/cisplatin combination was introduced for treatment of mesothelioma, no new first- or second-line therapies have been discovered. Thus, to better understand what drives mesothelioma carcinogenesis and to identify potential targets for therapy, in this project we aim at performing RNAseq analysis of a panel of mesothelioma cells lines.

Dataset EGAD00001010924

Cancerous Adaptive Dosing Melanoma WGS Dataset

Illumina NovaSeq 6000 30x WGS of 26 samples, each with up to 5 matched timepoints. Timepoints A,B,C,D,and E correspond to Pretreatment, Week 3, Week 6, Week 9, and Week 12 after treatment, respectively. Additional sample metadata (sample recurrence, treatment course, age, sex, comorbidities, etc.) are present in sample description.

Dataset EGAD00001010926

The dataset for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes

The dataset for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes includes 444 BAM files from whole genome next-generation sequencing on the Illumina NovaSeq 6000. The samples analyzed include plasma samples from individuals with and without cancer.

Dataset EGAD00001010931

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Germline variants in patients with rare cancers - control samples WGS and WES

Genomic data used in "ACT-Discover: identifying karyotype heterogeneity in pancreatic cancer evolution using ctDNA"

lymphoma plasma cfRNA

Evaluation of triple negative breast cancer with heterogeneous immune infiltration

Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (tumour data)

UCSF-CASCADE analysis of metastatic prostate cancer

Clonal selection after gene therapy in sickle cell disease

Clonal selection after gene therapy in sickle cell disease

POETIC clinical Trial Ki67 Bookend Breast Cancer Targeted Exome and RNAseq Project

RNA sequencing of high-risk paediatric cancers for identifying T-cell infiltration signatures

RNAseq of primary mesothelioma cell lines

Cancerous Adaptive Dosing Melanoma WGS Dataset

The dataset for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes

Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

Genome and transcriptome sequence data from a myoepithelioma patient

Genome and transcriptome sequence data from a leiomyosarcoma patient

Genome and transcriptome sequence data from a squamous cell carcinoma patient

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

Genome and transcriptome sequence data from a carcinoma of unknown primary patient

Genome and transcriptome sequence data from a pancreatic cancer patient

Genome and transcriptome sequence data from a cervical cancer patient

Genome and transcriptome sequence data from a pancreatic cancer patient

Genome and transcriptome sequence data from a endometrial adenocarcinoma patient

Genome and transcriptome sequence data from a breast ductal carcinoma patient

Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

Genome and transcriptome sequence data from a chromophobe renal cell carcinoma patient

Genome and transcriptome sequence data from a unknown primary adenocarcinoma patient

Genome and transcriptome sequence data from a breast ductal carcinoma patient

Genome and transcriptome sequence data from a uveal melanoma patient

Genome and transcriptome sequence data from a leiomyosarcoma patient

Genome and transcriptome sequence data from a metastatic cancer to the breast patient

Genome and transcriptome sequence data from a ovarian serous carcinoma patient

Genome and transcriptome sequence data from a carcinoma of unknown primary patient

Genome and transcriptome sequence data from a pancreas adenocarcinoma patient

Genome and transcriptome sequence data from a metastatic adenocarcinoma of colon patient

Genome and transcriptome sequence data from a metastatic thymic carcinoma patient

Genome and transcriptome sequence data from a metastatic melanoma patient

Genome and transcriptome sequence data from a metastatic pancreatic neuroendocrine tumour patient

Genome and transcriptome sequence data from a metastatic carcinoma of unknown primary patient

Genome and transcriptome sequence data from a melanoma patient

Genome and transcriptome sequence data from a follicular lymphoma patient

Genome and transcriptome sequence data from a thymic cancer patient

Genome and transcriptome sequence data from a pancreatic cancer patient

Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

Genome and transcriptome sequence data from a urachal adenocarcinoma patient

Genome and transcriptome sequence data from a liposarcoma patient

Genome and transcriptome sequence data from a gastrointestinal stromal tumor patient

Genome and transcriptome sequence data from a granulosa cell ovarian tumor patient

Genome and transcriptome sequence data from a metastatic triple negative breast cancer patient

Genome and transcriptome sequence data from a thymoma patient