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• WGS of cell-free DNA derived from plasma of patients with pediatric sarcoma and healthy controls, and lcWGS/RRBS of matched tumor tissue

  This dataset contains: i) 241 deep (median 12x) whole-genome sequencing profiles of 95 patients with Ewing sarcoma, 31 patients with other pediatric sarcomas, and 22 additional profiles from healthy controls. Sequencing was performed on a NovaSeq 6000 instrument using the NovaSeq S4 2x100 bp configuration. In addition, pilot experiments for 18 cfDNA samples were performed using Illumina HiSeq 2000/2500 machines (2x75 bp configuration). Data is provided as .fastq.gz files (2 files, .R1 and .R2, per sample). i) Low coverage whole-genome-sequencing on 43 tumor biopsy samples from patients with Ewing sarcoma with matched cfDNA samples. The samples were sequenced using a NovaSeq 6000 instrument with the NovaSeq S4 1x100 bp configuration. Data are provided as unmapped (raw) .bam files. iii) Reduced-representation bisulfite sequencing data for 38 tumor biopsy samples from patients with Ewing sarcoma with matched cfDNA samples, and 2 control samples. RRBS libraries were sequenced on Illumina HiSeq 2000/2500 machines. Data are provided as unmapped (raw) .bam files.

  Dataset EGAD00001007080

• Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

  Background The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug associated transcriptional cell states. Results We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure. Conclusions Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration and re-treatment strategies.

  Study EGAS00001007242

• A human induced pluripotent stem cell toolbox for studying sex chromosome effects-Whole exome seq

  To generate autosomaly isogenic hiPSCs with various chromosome complement, we use Klinefelter syndrome patient skin fibroblasts. These cells harbour XXY sex chromosomes, which can be lost during the reprogramming event and generate cells which are autosomaly identical to each other but differ in sex chromosome complement. We use state of the art reprogramming methods to generate panels of isogenic XXY, XX and XY iPSCs from XXY patient fibroblasts. We have performed Whole exome sequencing on Klinefelter syndrome patient's skin fibroblasts in order to check if two X chromosomes are identical and to see if we can distinguish which X chromosome is more likely to be lost during the reprogramming event in XY cell lines.

  Study EGAS50000000930

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: pancreas (2019-04-03)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (Illumina HiSeqX, 40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004893

• Small-molecule inhibitors in melanoma - Kenski / Kong - WES (2019-04-11)

  Small-molecule inhibitors targeting the most commonly activated pathway in melanoma, MAPK pathway (either alone or in combination) are already given to melanoma patients for few years, and initially reduce tumour burden dramatically, eventually melanomas become resistant and tumours progress while on treatment. Resistance to this treatment occurs by acquisition of additional mutations or other alterations that affect the mitogen-activated protein kinase (MAPK) pathway by either direct or indirect signalling. Many resistance mechanisms somehow lead to reactivation of extracellular signal-regulated kinase (ERK), thereby restoring signalling of the oncogenic BRAF/MEK/ERK pathway. In addition, PI3K pathway activation contributes to resistance to BRAF inhibition. Less frequent but equally important to the phenomenon of targeted drug resistance is the observation that B15-20% of BRAF mutant melanoma patients fail to respond to BRAF inhibition already early on treatment, owing to intrinsic resistance. These patients have little therapeutic options, unless immunotherapy can be given. To better understand the resistance mechanisms in MAPK inhibitor-treated melanoma patients and melanoma biology, our lab generated a big panel of MAPK inhibitor resistant melanoma cell lines by continuous drug exposure. The understanding of the genetic landscape and gene expression as well as cross resistance to other treatment regimens, and other aspects of melanoma biology such as phenotype switch, will allow us to better exploit new therapeutic strategies for melanoma patients. . This dataset contains all the data available for this study on 2019-04-11.

  Dataset EGAD00001004952

• WGS and WTS data of patient diagnosed with HSTCL

  WGS and WTS data of a single patient diagnosed with HSTCL. Whole-genome sequencing (WGS) was performed for the tumor-normal sample. Genomic DNA from tumor tissue was extracted with QIAamp DNA Mini Kit. The DNA for the matching normal was obtained from blood or buccal swabs and purified by Blood and Cell Culture DNA Mini kit or E.Z.N.A. Tissue DNA Kit (Omega Bio-tek) according to manufacturer’s instructions. The quantity and quality were assessed by Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen) and agarose gel electrophoresis. All sequencing libraries were prepared using TruSeq Nano DNA Library Prep Kit (Illumina). Paired-end sequencing was performed on Illumina HiSeq 4000 2x151 bp read length. Whole-transcriptome sequencing (WTS): Total RNA from snap frozen EITL tumor samples was extracted using TRIzol (Invitrogen) and purified with RNeasy Mini Kit (Qiagen) according to manufacturer’s instructions. The integrity of RNA was determined by electrophoresis using 2100 Bioanalyzer (Agilent Technologies). 500 ng of total RNA was reverse transcribed with iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA). Quantification was performed using SsoFast EvaGreen Supermix and CFX96 Real-Time PCR System (both Bio-Rad). Sequencing libraries were prepared using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero (Illumina) and WTS was performed on Illumina HiSeq 2500 with 2x101 bp read length.

  Dataset EGAD00001005229

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Colorectal_LCM (2020-02-20)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2020-02-20.

  Dataset EGAD00001005994

• RNA-seq data

  RNAseq FASTq files of 181 bulk pre-treatment and 14 post-treatment tumors from GO30140 Ph1b group A and F and 177 bulk pre-treatment tumors of IMbrave150 PhIII

  Dataset EGAD00001008128

• Spatially confined sub-tumor microenvironments in pancreatic cancer

  Intratumoral heterogeneity is a critical frontier in understanding how the tumor microenvironment (TME) propels malignant progression. Here, we deconvolute the human pancreatic TME through large-scale integration of histology-guided regional multiOMICs with clinical data and patient-derived preclinical models. We discover subTMEs, histologically definable tissue states anchored in fibroblast plasticity, with regional relationships to tumor immunity, subtypes, differentiation, and treatment response. Reactive subTMEs rich in complex but functionally coordinated fibroblast communities were immune-hot and inhabited by aggressive tumor cell phenotypes. The matrix-rich deserted subTMEs harbored less activated fibroblasts and tumor- suppressive features yet were markedly chemoprotective and enriched upon chemotherapy. SubTMEs originated in fibroblast differentiation trajectories and transitory states were notable both in single cell transcriptomics and in situ. The intratumoral co-occurrence of subTMEs produced patient-specific phenotypic and computationally predictable heterogeneity tightly linked to malignant biology. Therefore, heterogeneity within the plentiful, notorious pancreatic TME is not random but marks fundamental tissue organizational units.

  Dataset EGAD00001008155

• HELIUS cohort gut microbiome batch2

  The gut microbiota composition is unique to every individual but is shaped by common factors including diet, lifestyle, medication use, early-life determinants, living environment or genetics. Most of these factors may be influenced by ethnicity. This study explored variations in fecal microbiota composition in 6048 individuals with different ethnic backgrounds living in the same geographical area (Amsterdam, the Netherlands). The HELIUS data are owned by the Amsterdam University Medical Centers, location AMC in Amsterdam, The Netherlands. To allow sharing of microbiome data collected in HELIUS with (inter)national researchers, 16s rRNA sequence analysis has been stored at the European genome-phenome archive (EGA; accession code EGAD00001004106). This requires that access needs to be granted, also because the HELIUS data are stored with relevant phenotypical variables. Access is granted to all researchers affiliated with an internationally recognized research institution who request to use the HELIUS data within the EGA context, after having signed the data transfer agreement. Any researcher can request the data by submitting a proposal to the HELIUS Executive Board as outlined at http://www.heliusstudy.nl/en/researchers/collaboration, by email: heliuscoordinator at amsterdamumc dot nl. The HELIUS Executive Board will check proposals if they do not conflict with ethical approvals and informed consent forms of the HELIUS study.

  Dataset EGAD00001009732