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• Chordoma Sequencing Project Whole Genome

  This is a continuation of the Chordoma Sequencing Project. All cancers arise due to somatically acquired abnormalities in DNA sequence. Systematic sequencing of cancer genomes allows acquisition of complete catalogues of all classes of somatic mutation present in cancer. These mutation catalogues will allow identification of the somatically mutated cancer genes that are operative and characterise patterns of somatic mutation that may reflect previous exogenous and endogenous mutagenic exposures. In this application, we aim to perform whole genome sequencing on 10 chordoma matched genome pairs. RNA Sequencing/Methylation and SNP6 and an additional sequencing of three cancer cell lines will be added to this work.

  Dataset EGAD00001000721

• Genomic Signatures Reveal DNA Damage Response Deficiency in Brain Metastases of Colorectal Cancer

  Brain metastasis (BM) of colorectal cancer (CRC) is rare but lethal and lacks effective therapies or a good understanding of its genomic landscapes. We conduct an analysis of whole-exome sequencing (WES, Illumina HiSeq 2500 sequencing platform) on 11 patient-matched BMs, primary CRC tumours, and adjacent normal tissues; and whole-genome sequencing (WGS, Illumina HiSeq X Ten platform) on 8 patient-matched BMs, primary CRC tumors, and adjacent normal tissues to uncover the whole-genome mutational landscape of colorectal cancer with brain metastasis.

  Dataset EGAD00001005030

• RNA-seq dataset of patient and healthy donors

  RNA-seq dataset on a patient (P) presenting with defects of immunity and three healthy donors (C1, C5, C6). This dataset contains raw and processed files from RNA-seq transcriptome analysis performed according to the Smart-seq2. There are 24 single-read (50 bp) fastq files, 6 per patient/donor consisting of 2 cell types and 3 replicates per cell type. There is one count matrix file generated using featureCounts against Ensembl v98 gene models.

  Dataset EGAD00001008371

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val < 0.05).

  Dataset EGAD00001001356

• Genomic landscape and molecularly-informed therapy in thymic carcinoma and other advanced thymic epithelial tumors (H021, HIPO)

  The molecular landscape of malignant thymic epithelial tumors (TET) is largely unknown. To better understand and target these clinically challenging entities, we characterize 81 patients harboring 46 thymus carcinomas (TC), 8 neuroendocrine tumors of the thymus (NET) and 27 thymomas using WGS/WES, transcriptome and methylome analysis. TC harbor different molecular alterations compared to thymoma and NET and have higher tumor mutational burden than thymomas. 14/76 TET patients show (likely) pathogenic germline alterations. Composite biomarker analysis indicates homologous repair deficiency in a subset of patients. We identify immunologically hot and cold TC. Cold TC show significantly shorter overall survival. 71/81 patients receive molecularly-informed therapy recommendations, which are applied in 28/71 cases leading to a DCR of 60% and an ORR of 24% (both n=25). 8/23 patients reach PFS ratios > 1.3, seven of them having TC.

  Study EGAS00001006408

• Molecular origins of mpMRI visibility

  Multiparametric magnetic resonance imaging (mpMRI) has transformed the landscape of prostate cancer (PCa) management. Prostate Imaging Reporting and Data System (PI-RADSv2) scores reflect function and correlate phenotypic features with tumor aggressiveness, is an independent predictor of biochemical recurrence, and may distinguish low-risk tumors requiring no treatment from high-risk tumors requiring aggressive management. We hypothesize that differences in functional mpMRI of prostate tumors reflect the genetic and epigenetic heterogeneity of PCa. We profiled the genomes and transcriptomes of 40 Gleason score 3+4 tumors, of which 20 tumors were mpMRI invisible (not PI-RADSv2 3-5) and 20 tumors were visible (PI-RADSv2 5). Genomes of visible tumors were more unstable than those of invisible tumors. We identified 62 enriched transcripts, including numerous snoRNAs, Relaxin 1 and SCHLAP1, in visible tumors. These data suggest a confluence of aggressive pathological and microenvironmental phenomenon in PI-RADSv2 5 tumors reminiscent of nimbosus, while linking the molecular profiles of tumor aggressiveness with their functional features on mpMRI.

  Study EGAS00001003179

• A compendium of mutational signatures due to environmental exposures

  Whole-genome-sequencing (WGS) of human tumours has revealed distinct mutation patterns that hint at the causative origins of cancer. We examined mutational-signatures in 324 WGS of human induced pluripotent stem cells (iPSCs) following exposure to known or suspected environmental carcinogens. 79 agents were tested with or without metabolic activation at concentrations that produced measurable cytotoxicity; 41 yielded characteristic substitution mutational signatures. Some exhibit similarity with signatures found in human tumours. Additionally, 6 agents produced double-substitution signatures and 8 produced indel signatures. Investigating mutation asymmetries across genome topography reveals fully functional mismatch and transcription-coupled repair pathways in iPSCs. Primary adducts induced by environmental carcinogens can be resolved by disparate repair/replicative pathways, resulting in an assortment of signature outcomes even for a single mutagen. This compendium of experimentally-induced mutational-signatures permits further exploration of roles of environmental agents in cancer aetiology, and underscores how human stem cell DNA is directly vulnerable to environmental agents.

  Dataset EGAD00001004583

• Tumor Profiler Project - AML scDNA data

  The dataset contains single-cell DNA seq data from 21 patients with Acute Myeloid Leukemia (AML). Samples were collected from bone marrow or blood. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format.

  Dataset EGAD50000000824

• Tumor Profiler Project - AML scRNA data

  The dataset contains single-cell RNA seq data from 23 patients with Acute Myeloid Leukemia (AML). Samples were collected from bone marrow or blood. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format.

  Dataset EGAD50000000823

• Tumor Profiler Project - AML bulk transcriptomics data

  The dataset contains bulk transcriptomics data from 23 patients with Acute Myeloid Leukemia (AML). Samples were collected from bone marrow or blood. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format.

  Dataset EGAD50000000822