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• White blood cell and cell-free DNA analyses for detection of residual disease in gastric cancer

  Liquid biopsies are providing new opportunities for detection of residual disease in cell-free DNA (cfDNA) after surgery but may be confounded through identification of alterations arising from clonal hematopoiesis. Here, we identify circulating tumor-derived DNA alterations (ctDNA) through ultrasensitive targeted sequencing analyses of matched cfDNA and white blood cells from the same patient. We apply this approach to analyze samples from patients in the CRITICS trial, a phase III randomized controlled study of perioperative chemotherapy in patients with operable gastric cancer. After filtering alterations derived from matched white-blood cells, the presence of ctDNA predicts recurrence when analyzed within nine weeks after preoperative treatment and after surgery in patients eligible for multimodal treatment. These analyses provide a facile method for distinguishing ctDNA from other cfDNA alterations and highlight the utility of ctDNA as a predictive biomarker of patient outcome to perioperative cancer therapy and surgical resection in patients with gastric cancer.

  Study EGAS00001004114

• Utility of ctDNA to support patient selection for early phase clinical trials: The TARGET Study

  Next generation sequencing (NGS) of circulating tumour DNA (ctDNA) supports blood-based genomic profiling but is not routinely implemented in the clinic. TARGET is a molecular profiling programme with the primary aim to match patients to early phase clinical trials based on analysis of both somatic mutations and copy number alterations (CNA) across a 641 cancer-associated gene panel in a single ctDNA assay. For the first 100 TARGET patients, ctDNA data showed good concordance with matched tumour and results were turned round within a clinically acceptable timeframe for Molecular Tumour Board (MTB) review. When applying a 2.5% Variant Allele Frequency (VAF) threshold actionable mutations were identified in 41/100 patients, with 11 of these patients going onto a matched therapy. These data support the application of ctDNA in an early phase trial setting where broad genomic profiling of contemporaneous tumour material enhances patient stratification to novel therapies and provides a practical template for bringing blood-based analyses to the clinic.

  Study EGAS00001003407

• Comparison of 3 protocols for deriving pancreatic progenitors from hPSC with RNA-seq and ATAC-seq

  Several distinct differentiation protocols for deriving pancreatic progenitors (PP) from human pluripotent stem cells have been described but it remains to be shown how similar the PP are across protocols, and how well they resemble their in vivo counterparts. Here we evaluated three differentiation protocols; performed RNA and ATAC-seq on isolated PP derived with these, and compared them to fetal human pancreas populations. This enabled us to define a shared transcriptional and epigenomic signature of the PP, including several genes not previously implicated in pancreas development. Furthermore, we identified a significant and previously unappreciated cross-protocol variation of the PP through multi-omics analysis and demonstrate how such information can be applied to refine differentiation protocols for derivation of insulin-producing beta-like cells. Together, our study highlights the importance of a detailed characterization of defined cell populations derived from distinct differentiation protocols, and provides a valuable resource for exploring human pancreatic development.

  Study EGAS00001003513

• A molecular signature for IL-10-producing Th1 cells in protozoan parasitic diseases

  Control of the intracellular parasites that cause malaria and visceral leishmaniasis (VL) is dependent on the generation of pro-inflammatory, IFNγ+ Tbet+ CD4+ T (Th1) cells by infected hosts. Immunoregulatory IL-10 produced by Th1 cells serves to mitigate subsequent inflammation and related disease pathology. However, these IL-10-producing Th1 (Tr1) cells can also not only promote parasite persistence, but may impair immunity to re-infection and potential vaccine efficacy. Here, we identify molecular and phenotypic signatures that distinguish Th1 cells from Tr1 cells in both experimental VL, caused by infection of C57BL/6 mice with the human parasite Leishmania donovani, and in Plasmodium falciparum-infected humans participating in controlled human malaria infection (CHMI) studies. Such characterisations allow for the better understanding of Tr1 cell development and function in the context of these parasitic diseases, and for the identification of targets for immune modulation to improve anti-parasitic immunity.

  Study EGAS00001004454

• GATA6 expression distinguishes classical and basal-like subtypes in advanced pancreatic cancer.

  PURPOSE: To determine the impact of basal-like and classical subtypes in advanced PDAC and to explore GATA6 expression as a surrogate biomarker. EXPERIMENTAL DESIGN: Within the COMPASS trial patients proceeding to chemotherapy for advanced PDAC undergo tumour biopsy for RNA sequencing. Overall response rate (ORR) and overall survival (OS) were stratified by subtypes and according to chemotherapy received. Correlation of GATA6 with the subtypes using gene expression profiling, in situ hybridization (ISH) were explored. RESULTS: Between December 2015-May 2019, 195 patients (95%) had enough tissue for RNA sequencing; 39 (20%) were classified as basal-like and 156 (80%) as classical. RECIST response data were available for 157 patients; 29 basal-like and 128 classical where the ORR was 10% vs. 33% respectively (p=0.02). In patients with basal-like tumours treated with modified FOLFIRINOX (mFFX) (n=22) the progression rate was 60% compared to 15% in classical PDAC (p= 0.0002). Median OS in the intention to treat population (n=195) was 9.3 months for classical vs. 5.9 months for basal-like PDAC (HR 0.47 95% CI 0.32-0.69, p=0.0001). GATA6 expression by RNAseq highly correlated with the classifier (p<0.001) and ISH predicted the subtypes with sensitivity of 89% and specificity of 83%. In a multivariable analysis, GATA6 expression was prognostic (p=0.02). In exploratory analyses, basal-like tumours, could be identified by keratin 5, were more hypoxic and enriched for a T cell inflamed gene expression signature. CONCLUSIONS: The basal-like subtype is chemoresistant and can be distinguished from classical PDAC by GATA6 expression.

  Dataset EGAD00001006081

• Transcriptional profiling of tauopathies in human IPS-derived neurons (2019-08-21)

  This project is a pilot study, in collaboration with Maria Grazia Spillantini and Mariangela Iovino (Cambridge Centre for Brain Repair), to investigate the utility of IPS-derived neurons for the study of neurodegenerative disorders. Our aim is to characterise the transcriptional consequences of tauopathies using neurons derived from differentiated IPSCs as a model system. We will use IPS cells derived from six individuals, four with known mutations in the tau protein, 2 without. RNA will be extracted at Day 0 and Day 65 of differentiation by which time the neuronal tauopathy is apparent. RNA will be extracted and the transcriptome of each line characterised using RNAseq. We will then search for genes that are differentially expressed between the transcriptomes of individuals with tau mutations versus those in controls. My lab will analyse the RNAseq data, comparing both affected and controls and both time-points, to establish candidate genes. Darren Logan’s lab, along with our collaborators, will experimentally verify and further investigate these genes in additional lines and animal models. From this analysis we will generate a list of candidate genes that are differentially expressed between cases and controls. This study will not only help us understand the molecular basis of tauopathies, but also identify gene candidates for biomarkers of neurodegenerative disease. It will serve as a proof of principle for future planned studies into generating and transcriptomically analysing an allelic series of Tau mutations in IPSCs with a controlled genetic background. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-08-21.

  Dataset EGAD00001005277

• University of Miami Study on Genetics of Autism and Related Disorders (AutismDisorders)

  Autism spectrum disorders (ASD) are neurodevelopmental in origin and characterized by impairments in reciprocal social interaction and communication accompanied by restricted and repetitive patterns of interest or behavior. With improved surveillance and a broadening of the diagnostic criteria, the most recent prevalence studies suggest that ASD may affect as many as 1/166 children in the US. Treatments are limited resulting in little impact on the profound morbidity. Little is known about the etiology of ASD, but there is a strong genetic component. Genetic studies over the past decade have failed to identify definitive ASD genes, but have clearly shown that the underlying genetics are more complex than anticipated with the likelihood that multiple genes are acting independently and/or interactively. With this realization the field of ASD genetics is at a critical juncture. To move forward we must embrace new and creative paradigms to successfully dissect the genetic etiology of this disease. Specifically the current study will: Extend our ascertainment to the full range of ASD. We will increase our ASD dataset by collecting 600 families who meet clinical diagnostic criteria for the three most common ASDs: autism (AUT), Asperger disorder (ASP) and Pervasive Developmental Disorder-NOS (PDD-NOS). Phenotype assessment will be expanded to include the Social Responsiveness Scale (SRS) and detailed seizure data. Ascertainment will focus on parent-child trios. Further characterize the ASD-associated variations in the Gamma-Aminobutyric Acid (GABA) receptor (GABR) subunits. We will complete our GABR subunit variant analysis, including coding, UTR, regulatory, and conserved intronic sequence, concentrating on GABRA4 and GABRB1. We will also investigate the role of variation in other GABR subunits. All AUT specific findings will be examined in the entire ASD dataset. Identify the chromosome 19p ASD gene. Multiple genome-wide screens have provided significant evidence for linkage to Chromosome 19p and we have found evidence for significant association in the linked families within an ~3 Mb region. We will genotype 768 SNPs spaced at ~4 kb to refine the candidate region and delineate specific candidate genes for further testing. We will screen the strongest associated candidate genes for additional variants and evaluate evidence for association in the entire ASD dataset (SA5). Develop and apply the Phenotypic Homogeneity Distinction (PHD) algorithm for identification of the phenotypic signature of linkage and/or association signals. The PHD algorithm identifies phenotypic covariates that discriminate a target sample subset (e.g. the positively linked families or families carrying the associated risk allele) from its complement. The resulting PHD discriminators will define a homogeneous subset from our newly-ascertained ASD dataset to use, as a confirmatory dataset and for localization within genes under study in our other aims. Test for gene-gene interactions. To fully explain the spectrum of autism risk, we will test for interactions between genetic variations associated with ASD (such as those in GABR subunit genes). We will build upon our experience using the MDR method and extend our analyses across biological pathways using the entire ASD data set.

  Study phs000436

• Framome cancer samples

  Cancer samples for neo-open reading frame peptides that comprise the tumor framome are a rich source of neoantigens for cancer immunotherapy.

  Dataset EGAD50000000420

• Central Africa whole genome sequencing (rainforest hunter-gatherers and neighboring populations)

  High-coverage (>30X) whole genome data (FASTQ) for 29 central African rainforest hunter-gatherer individuals (from five populations) and for 20 central African rainforest hunter-gatherer neighbor individuals (from four populations).

  Dataset EGAD50000001560

• DAC for EGA study: "ctDNA residual disease analyses during perioperative nivolumab or nivolumab plus ipilimumab in resectable diffuse pleural mesothelioma"

  This DAC regulates access to the whole-exome sequence data for the study "ctDNA residual disease analyses during perioperative nivolumab or nivolumab plus ipilimumab in resectable diffuse pleural mesothelioma".

  Dac EGAC50000000706

• Genetic analysis in an inherited cardiac arrhythmia

  To identify a causative gene for an inherited cardiac arrhythmia, exome sequence analysis was conducted in 6 patients and 5 controls.

  Study JGAS000041

• Single Cell Genome Sequence for DLP+ library A95670B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 6 on DLP+ library A95670B

  Dataset EGAD00001009449

• A98168B

  Whole genome sequencing for single cells for library A98168B 1290 cells; filetype=bam

  Dataset EGAD00001007625

• A98256B

  Whole genome sequencing for single cells for library A98256B 1437 cells; filetype=bam

  Dataset EGAD00001007125

• A95703A

  Whole genome sequencing for single cells for library A95703A 740 cells; filetype=bam

  Dataset EGAD00001007612

• 10x Genomics Single Cell Gene Expression for SA535X6XB03099

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 6

  Dataset EGAD00001009116

• 10x Genomics Single Cell Gene Expression for SA535X4XB05462

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 4

  Dataset EGAD00001009115

• A95653B

  Whole genome sequencing for single cells for library A95653B 1342 cells; filetype=bam

  Dataset EGAD00001007107

• 10x Genomics Single Cell Gene Expression for SA535X6XB03101

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 6

  Dataset EGAD00001009149

• 10x Genomics Single Cell Gene Expression for SA535X5XB02891

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009155

• 10x Genomics Single Cell Gene Expression for SA535X5XB02895

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009156

• 10x Genomics Single Cell Gene Expression for SA604X9XB02425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 9

  Dataset EGAD00001009159

• 10x Genomics Single Cell Gene Expression for SA535X4XB02498

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 4

  Dataset EGAD00001009152

• A96179B

  Whole genome sequencing for single cells for library A96179B 1396 samples; filetype=bam

  Dataset EGAD00001008245

• A96180B

  Whole genome sequencing for single cells for library A96180B 1189 samples; filetype=bam

  Dataset EGAD00001008246

• A118833A

  Whole genome sequencing for single cells for library A118833A 919 cells; filetype=bam

  Dataset EGAD00001007096

• A118833B

  Whole genome sequencing for single cells for library A118833B 1147 cells; filetype=bam

  Dataset EGAD00001007097

• Amplicon Sequencing for 'Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma'

  BAMs from deep sequencing using a custom panel for the study 'Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma'

  Dataset EGAD00001015012

• 10x Genomics Single Cell Gene Expression for SA1053EX1XB01608

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1053 passage 1

  Dataset EGAD00001009123

• Hospital for Sick Children 2020 Pediatric Low-Grade Glioma RNA and Targeted DNA Sequencing

  Whole transcriptome and targeted dna sequencing (Ampliseq) of pediatric low-grade glioma samples at the Hospital for Sick Children

  Dataset EGAD00001005987

• A118862B

  Whole genome sequencing for single cells for library A118862B 1152 cells; filetype=bam

  Dataset EGAD00001007606

• A98271A

  Whole genome sequencing for single cells for library A98271A 1190 cells; filetype=bam

  Dataset EGAD00001007633

• Dataset for transcriptome sequencing of forebrain organoids from Lissencephaly patients

  This dataset contains transcriptome sequencing for 16 samples of forebrain organoids from Lissencephaly patients. The sequencing was performed on Illumina NextSeq 6000 . The sequencing was always paired.

  Dataset EGAD00001015688

• RNA-seq data from paired tumour and germline samples from mesothelioma patients for study EGAS00001005196

  RNA-seq data from paired tumour and germline samples from mesothelioma patients for study EGAS00001005196

  Dataset EGAD00001007874

• Utility of ctDNA to support patient selection for early phase clinical trials: The TARGET Study

  The TARGET Study consists of 200 BAM files, for the 100 patients discussed in the publication. Each patient has a normal control BAM file and a ctDNA BAM file.

  Dataset EGAD00001004796

• 10x Genomics Single Cell Gene Expression for SA1053BX1XB01603

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1053 passage 1

  Dataset EGAD00001009131

• 10x Genomics Single Cell Gene Expression for SA1047BX1XB01022

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1047 passage 1

  Dataset EGAD00001009146

• 10x Genomics Single Cell Gene Expression for SA1056

  10x Genomics Single Cell Gene Expression for Telomerase immortalized breast epithelium cell line 184-hTERT-22 L9 116.126

  Dataset EGAD00001009112

• 10x Genomics Single Cell Gene Expression for SA604X6XB01979

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 6

  Dataset EGAD00001009117

• 10x Genomics Single Cell Gene Expression for SA604X7XB02089

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 7

  Dataset EGAD00001009119

• 10x Genomics Single Cell Gene Expression for SA609X6XB01899

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA609 passage 6

  Dataset EGAD00001009120

• 10x Genomics Single Cell Gene Expression for SA1050DX1XB01437

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1050 passage 1

  Dataset EGAD00001009126

• 10x Genomics Single Cell Gene Expression for SA1181AX1XB02700

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1181 passage 1

  Dataset EGAD00001009135

• 10x Genomics Single Cell Gene Expression for SA1162BX1XB03004

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1162 passage 1

  Dataset EGAD00001009138

• 10x Genomics Single Cell Gene Expression for SA605X3XB01966

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA605 passage 3

  Dataset EGAD00001009142

• 10x Genomics Single Cell Gene Expression for SA1035X7XB03502

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 7

  Dataset EGAD00001009151

• 10x Genomics Single Cell Gene Expression for SA610X3XB03802

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA610 passage 3

  Dataset EGAD00001009160

• 10x Genomics Single Cell Gene Expression for SA1050EX1XB01442

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1050 passage 1

  Dataset EGAD00001009141

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03216

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009154

• Single Cell Genome Sequence for DLP+ library A95722A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 6 on DLP+ library A95722A

  Dataset EGAD00001009451