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• The genomic landscape of pediatric acute lymphoblastic leukemia

  Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer and arises from B- or T-lineage lymphocyte precursors. ALL comprises dozens of subtypes, and genomic analyses have largely been performed within these subtypes. Here we analyze pediatric ALL genomes across all subtypes, including 768 whole genomes, 1,729 exomes, and 1,889 transcriptomes in 2,754 patients. Most ALL subtypes harbor 4 or more driver alterations per sample, similar to adult cancers, despite low mutation burdens. Hyperdiploid B-ALL copy gains are likely acquired early and synchronously, with copy gains occurring before ultraviolet-induced mutations. By contrast, ultraviolet-induced mutations precede copy gains in iAMP21 B-ALL. Overall, we identified 378 putative ALL driver genes. Most driver alterations vary in prevalence across ALL subtypes, with B-ALL enriched for Ras and B-lineage-related alterations, and T-ALL enriched for PI3K, JAK, and cell cycle alterations. Most B-ALL (54.3%) and T-ALL (51.2%) samples bear at least one rare driver gene alteration (present in less than 2% of samples), including 30 putative novel cancer driver genes associated with ubiquitination, SUMOylation, non-coding, and other functions. Known or novel alterations associated with poor outcomes in specific subtypes include TBL1XR1 alterations in ETV6-RUNX1 patients, CREBBP in Ph-like-CRLF2, SETD2 in hyperdiploid, and PTEN in TAL1 patients. Intriguingly, DUX4 and KMT2A subtypes separate into CEBPA/FLT3- or NFATC4-expressing subgroups with potential clinical implications. Together, these results deepen understanding of ALL etiology and outcomes, and facilitate ALL model development.

  Study EGAS00001005250

• Singapore Adult Metabolism Study - Phase 2 (SAMS2)

  Singapore Adult Metabolism Study - Phase 2 (SAMS2) was an interventional study where hundreds of donors aged 21-45 were recruited to participate in a 16-week weight loss program. Study individuals selected (see below for more detailed selection standards) were sedentary (exercise 1 or fewer times a week), obese or overweight with a body fat mass greater than 24% and a BMI between 23-35 kg/m2. For this study, we adjusted BMI definition for Asian population, based on the WHO Consultation 2002, and the BMI cut-off is 23 kg/m2 for overweight and 27.5 kg/m2 for obese. The weight loss program included a combination of dietary interventions, structured exercise sessions, and additional physical activity performed in participants' own time. Energy and protein requirements were calculated based on each participant's weight, height, and physical activity level, with the goal of achieving a 40% calorie deficit. Participants' calorie intake was tracked using food recalls and questionnaires. Additionally, subjects attended structured exercise sessions at least three times per week, supervised by a coach. Each session consisted of 90 minutes of aerobic and strength training exercises, designed to burn approximately 500 kcal per session. To monitor daily physical activity, participants wore pedometers throughout the study. In total, the exercise sessions (500 kcal per session) and daily physical activity (targeting an additional 500 kcal) were aimed at achieving a total caloric expenditure of 2000 kcal per week. We collected clinical data and skeletal muscle biopsies from 54 overweight/obese Asian individuals before and after a 16-week lifestyle intervention, which resulted in an average ~10% weight loss, accompanied by a ~30% increase in insulin-stimulated glucose uptake. Improvements were observed in 118 of 252 clinical traits and six blood lipids. Transcriptomic analysis of paired skeletal muscle biopsies identified 505 differentially expressed genes enriched in mitochondrial function and insulin sensitivity. Thousands of muscle-specific e/sQTLs were detected pre- and post- intervention, including hundreds of lifestyle-responsive e/sQTLs. Notably, approximately 4.2% of eQTLs and 7.3% of sQTLs showed Asian specificity. Joint analysis with GWAS identified 16 putative metabolic risk genes. Our study reveals gene-by-lifestyle interactions and how lifestyle modulates gene regulation in skeletal muscle.

  Study phs004078

• 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).

  Study EGAS00001001184

• The evolution of hematopoietic cells under cancer therapy

  Chemotherapies may increase mutagenesis of healthy cells and change the selective pressures in tissues thus influencing their evolution. However, their contributions to the mutation burden and clonal expansions of healthy somatic tissues are not clear. Here, exploiting the mutational footprint of some chemotherapies, we explore their influence on the evolution of hematopoietic cells. Cells of Acute Myeloid Leukemia (AML) secondary to treatment with platinum-based drugs showed the mutational footprint of these drugs, indicating that non-malignant blood cells received chemotherapy mutations. No trace of 5-fluorouracil (5FU) mutational signature was found in AML secondary to exposure to 5FU, suggesting that cells establishing the AML were quiescent during treatment. Using the platinum-based mutational signature as a barcode, we determined that secondary AMLs began their clonal expansion after the start of the cytotoxic treatment. Its absence in clonal hematopoiesis cases is consistent with a clonal expansion that predates the exposure to the cytotoxic agent.

  Study EGAS00001005234

• Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis

  Background: Lung carcinoma-in-situ (CIS) lesions are the pre-invasive precursor to lung squamous cell carcinoma. However, only half progress to invasive cancer in three years, while a third spontaneously regress. Whether modern molecular profiling techniques can identify those pre-invasive lesions that will subsequently progress and distinguish them from those that will regress is unknown. Methods: Progressive and regressive CIS lesions were laser-captured and their genome, epigenome and transcriptome interrogated. We analysed 83 progressive lesions, 41 regressive and 33 normal epithelial control samples. DNA methylation and gene expression profiles were further validated using publicly available lung cancer data. Results: Somatic mutation burden was higher in progressive lesions than regressive CIS lesions, across base substitutions, rearrangements, and copy number changes. Driver mutations were present in both progressive and regressive CIS lesions, but were more numerous in progressive cases. Progressive and regressive CIS lesions had distinct epigenomic and transcriptional profiles, with a strong chromosomal instability signature. Gene expression, methylation and copy number profiles can all predict accurately which CIS lesions will progress to lung cancer. Conclusion: Pre-invasive CIS lesions that will subsequently progress to invasive lung cancer can be distinguished from those that will regress using molecular profiling. Progression is associated with a strong chromosomal instability signature. These findings inform the development of novel therapeutic targets.

  Dataset EGAD00001003883

• Compound heterozygous variants in LAMC3 in association with posterior periventricular nodular heterotopia

  Periventricular nodular heterotopia is a malformation of cortical development characterized by nodules of abnormally migrated neurons.

  Study EGAS00001004793

• Mid-frequency hearing loss is a characteristic clinical feature of OTOA-associated hearing loss

  In this study, we performed NGS analysis of a large Japanese hearing loss cohort, and identified 7 patients with OTOA-associated hearing loss. As novel clinical features identified in this study, the audiometric configurations of OTOA-associated hearing loss patients was found to be mid-frequency.

  Study JGAS000200

• Tumor Profiler Project - OV scDNA data additional samples

  The dataset contains 10x Chromium single-cell DNA sequencing data from 6 samples from 5 different patients with ovarian cancer. 4 samples were collected from the tumor, and 2 samples from ascites. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format. This dataset contains additional samples from patients not classified as having high-grade serous adenocarcinoma.

  Dataset EGAD50000001294

• RnaSeq data from 414 tumor samples with non muscle invasive bladder cancer

  A better understanding of the molecular landscape of non-muscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and identify potential therapeutic targets. Here, we perform a comprehensive genomic analysis of patients diagnosed with NMIBC based on whole-exome- (n=438), shallow whole-genome- (n=362), and total RNA-sequencing (n=414). This dataset contains 414 BAM files corresponding to the full total RNA-sequencing dataset.

  Dataset EGAD50000000733

• WES data from study: Molecular underpinnings of dedifferentiation and aggressiveness in chromophobe renal cell carcinoma

  The ChRCC study WES dataset contains raw whole exome sequencing data of 17 tumor and 7 adjacent normal samples from 7 UTSW patients, who have consented to depositing their genomic data to public repository. WES was performed using 75bp paired-end fragments at an average read depth > 100x on a HiSeq2500 platform (Illumina, San Diego, CA, USA). The raw data is in fastq format.

  Dataset EGAD50000000415