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• Genome and transcriptome sequence data from a peritoneal mesothelioma patient

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

  Dataset EGAD00001001310

• RNAseq of ribosomal footprints

  Cancer genes can affect ribosomal RNA processing and this can underlie their essentiality to cells, making them cell-essential in the same way as ribosomal genes themselves. We want to confirm this, in order to understand the results of our CRISPR drop-out screens.NOTE FROM BESPOKE TEAM: Run a single read 1 (forward read) of 30 bases, then an index 1 read as normal. This would fit a 50cycle kit

  Dataset EGAD00001001930

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001002030

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001002048

• Genome_wide_CRISPR_Cas9_screening_of_Human_Organoids

  In this study we will exploit the bacterial clustered regularly interspaced short palindromic repeats (CRISPR) system to perform a genome-wide single-guide RNA (sgRNA) library to screen for genes required for proliferation and survival in human organoids.

  Study EGAS00001001932

• Dataset developed for use with EOSC4Cancer of synthetic colorectal cancer tumor/normal pairs.

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. The NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) was utilized to synthetize these 10 pairs of tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) were learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aimed to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient was down-sampled randomly, representing 50% germline variants of a given patient. These were mixed with the other 50% in silico germline variants that were modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learnt from a real CRC patient was added to the germline variant profile used for creating the normal synthetic WGS data of the same patient, consisting of the variants for tumor sample. Neither copy number profile nor structural variation profile was introduced into the tumor synthetic WGS data. Tumor content and ploidy were assumed to be 100% and 2, respectively. For mapping/variant detection, the Sarek pipeline v3.1.2 (https://nf-co.re/sarek/3.1.2) was used, specifically: 1. BWA v0.7.17-r1188 for read mapping 2. GATK v4.3.0.0 for pre-processing BAM file (including markduplicates and recalibration). 2. Mutect2 (GATK v4.3.0.0) for somatic variant calling 3. Strelka2 v2.9.10 for germline and somatic variant calling Metadata information of 10 CRC patients used for the generation of synthetic normal and tumor WGS data: Patient_id Tumor_barcode Normal_barcode Age Sex Tissue Cancer SIM007 SIM007_T SIM007_N 71 F Rectal Primary CRC SIM008 SIM008_T SIM008_N 45 F Colon Neuroendocrine Metastasis CRC SIM010 SIM010_T SIM010_N 62 M Colon Metastasis CRC SIM011 SIM011_T SIM011_N 55 M Colon Neuroendocrine Metastasis CRC SIM012 SIM012_T SIM012_N 57 M Rectal Metastasis CRC SIM013 SIM013_T SIM013_N 69 M Colon Metastasis CRC SIM014 SIM014_T SIM014_N 68 M Colon Neuroendocrine primary CRC SIM015 SIM015_T SIM015_N 58 F Colon Primary CRC SIM016 SIM016_T SIM016_N 49 M Colon/Rectal Primary CRC SIM017 SIM017_T SIM017_N 78 M Colon Neuroendocrine primary CRC

  Dataset EGAD50000000564

• Recurrent intra-tumour heterogeneity is a hallmark of metastatic prostate cancer

  The evolution of cancers from low grade to metastatic disease is a major determinant of cancer mortality. Cancer evolution involves a complex interplay between cell intrinsic genetics and transcriptional alterations and the extrinsic microenvironment. To define the key mechanisms underpinning metastatic development, we focused on the most lethal form of prostate cancer, metastatic castration-resistant prostate cancer, and employed single-cell multi-omics and whole-genome sequencing to deeply profile 34 metastatic lesions from 9 patients obtained from rapid autopsy. We found that intra-tumour heterogeneity is an indicator of key evolutionary processes, characterised by recurrent tumour populations acting as critical functional components of the tumour ecosystem, irrespective of clonal and microenvironmental backgrounds. Unexpectedly, microenvironments across metastatic sites played a limited role while clonal evolution primarily promoted transcriptional noise. Intra-patient functional convergence of tumour ecosystems was observed across metastatic sites, pointing towards system-level selection pressures that drive the heterogeneity landscape of mCRPC. Our findings reveal functional evolutionary convergence of metastatic disease into units of intra-tumour heterogeneity identifying critical determinants of metastatic progression for therapeutic targeting. Note: matched WGS for some tumour single-cell experiments, as well as germline WGS for patients CA0027, CA0034, CA0035, CA0043, and CA0076, can be found in EGAS00001006598.

  Study EGAS50000001312

• Shedding light over COVID-19 susceptibility and severity

  “One of the most remarkable features of SARS-CoV-2 infection is the variation in consequence ranging from asymptomatic to life-threatening” state the authors of the majestic Nature paper released in accelerated pre-view on July 2021. This variation in consequences represent not only an issue of burning medical relevance, but also a scientific and intellectual challenge for scientists around the globe. Researchers are known to be easily triggered by a challenge, and they recently demonstrated to be even better at double their efforts to focus on an urgent societal need. In this work led and coordinated by the COVID-19 Host Genetics Initiative (COVID-19 HGI), researchers gathered data from 49,562 cases and two million controls from 46 studies performed worldwide. The majority of the data has been deposited at the EGA, where 29 datasets have been collected under the study number EGAS00001005304. Data gathered from diverse sources conferred a huge robustness to the results. Genetic data from different ancestry origin were included in the analyses, even though data from patients with European origin was highly predominant (approx. 75%). Separated analysis of the different ancestries gave rise to relevant differences in the results, highlighting how important it is to take this aspect in consideration for future studies. Data from the individual studies was compiled into three categories according to COVID-19 severity for a meta-analysis: (a) critically ill cases (b) moderate or severe cases (c) all cases. They were paired with non-COVID-19-reported controls. All three analysis resulted in a total of 13 genetic loci associated with COVID-19 severity. Out of those, 9 loci associated specifically with critical illness, while 6 (a few overlapped) associated with moderated illness. Some genomic position matched previous findings. Interestingly, working with such an enormous amount of data allowed the depth to discriminate association with disease susceptibility vs severity, an issue of large medical relevance. Several loci mapped into biological relevant genes, like TYK2, PPP1R15A and FOXP4, previously implicated in immune response and/or lung functions. Researchers participating in the COVID-19 Host Genetics Initiative have worked in an unprecedented time-tight collaboration, prioritizing data sharing and collective results to tackle the pandemic affecting the world. The scientific community won’t miss any of the lessons to learn from this, and the EGA is ready to keep working to enable safe data sharing and fruitful collaborations.

  Blog covid-19-susceptibility-and-severity

• The evolutionary steps from primary to metastatic prostate cancer are largely uncharted, and the ability to use DNA present in body fluids as correlates of aggregate metastatic status is under-examined. We reconstructed phylogenies in ten prostate cancer patients with fatal disease using deep targeted sequencing of the prostate, adjacent and distant organs, as well as plasma, serum, and cerebrospinal fluid at various time points. A total of 163 samples are studied.

  The evolutionary steps from primary tumor to metastasis in prostate cancer are largely uncharted, and the ability to use serum, plasma, and cerebrospinal fluid as a correlate of aggregate metastatic tumor genomic status has not been tested. We used deep targeted sequencing to reconstruct tumor evolution in ten prostate cancer patients with fatal disease encompassing examination of the prostate and adjacent and distant organs, as well as plasma, serum, and cerebrospinal fluid at various time points. We show that there is substantial evolution from a common ancestor within the prostate that results in branching to multiple lineages which form an intermixed multi-clonal primary tumor mass. After the occurrence of key driver aberrations, one of these lineages will metastasize to multiple sites in a sequential fashion. These metastatic sites are then susceptible to being populated by cells from other intra-prostatic lineages or from other metastases. Genomic representation of metastases in body fluids is not uniform. Cerebrospinal fluid analysis can detect lineages not detected in circulating DNA, suggesting possible clinical utility.

  Study EGAS00001003848

• Long-term organoid culture of a small intestinal neuroendocrine tumor rna-seq

  Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are rare and highly heterogeneous neoplasms whose incidence has markedly increased over the last decades. A grading system based on the tumor cells' proliferation index predicts high-risk for G3 NETs. However, low-to-intermediate grade (G1/G2) NETs have an unpredictable clinical course that varies from indolent to highly malignant. Cultures of human cancer cells enable to perform functional perturbation analyses that are instrumental to enhance our understanding of cancer biology. To date, no tractable and reliable long-term culture of G1/G2 NET has been reported to permit disease modeling and pharmacological screens. Here, we report of the first long-term culture of a G2 metastatic small intestinal NET that preserves the main genetic drivers of the tumor and retains expression patterns of the endocrine cell lineage. Replicating the tissue, this long-term culture showed a low proliferation index, and yet it could be propagated continuously without dramatic changes in the karyotype. The model was readily available for pharmacological screens using targeted agents and as expected, showed low tumorigenic capacity in vivo. Overall, this is the first long-term culture of NETs to faithfully recapitulate many aspects of the original neuroendocrine tumor.

  Study EGAS00001007108