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• Whole exome sequencing of small cell neuroendocrine cancer of the cervix

  In order to improve treatment selection for high grade neuroendocrine carcinomas of the cervix (NECC), we performed a comparative genomic analysis between this rare tumor type and other cervical cancer types, as well as extra-cervical neuroendocrine small cell carcinomas of the lung and bladder. We performed whole exome sequencing on fresh-frozen tissue from 15 NECCs and matched normal tissue. We then identified mutations and copy number variants using standard analysis pipelines. Published mutation tables from cervical cancers and extra-cervical small cell carcinomas were used for comparative analysis. Descriptive statistical methods were used and a two-sided threshold of P < .05 was used for significance. In the NECC cohort, we detected a median of 1.7 somatic mutations per megabase (range 1.0-20.9). PIK3CA p.E545K mutations were the most frequency observed oncogenic mutation (4/15 tumors, 27%). Activating MAPK pathway mutations in KRAS (p.G12D) and GNAS (p.R201C) co-occurred in two tumors (13%). In total we identified PI3-kinase or MAPK pathway activating mutations in 67% of NECC. When compared to NECC, lung and bladder small cell carcinomas exhibited a statistically significant higher rate of coding mutations (P < .001 for lung; P = .001 for bladder). Mutation of TP53 was uncommon in NECC (13%) and was more frequent in both lung (103 of 110 tumors [94%], P < .001) and bladder (18 of 19 tumors [95%], P < .001) small cell carcinoma. These comparative genomics data suggest that NECC may be genetically more similar to common cervical cancer subtypes than to extra-cervical small cell neuroendocrine carcinomas of the lung and bladder. These results may have implications for the selection of cytotoxic and targeted therapy regimens for this rare disease.

  Study EGAS00001003142

• Development and validation of multivariate predictors of primary endocrine resistance to tamoxifen and aromatase inhibitors in luminal breast cancer reveal drug-specific differences

  Background: Endocrine therapy is highly effective in blocking the estrogen receptor pathway in HR+/HER2- early breast cancer (EBC). However, up to 40% of patients experience relapse during or after adjuvant endocrine therapy. Here, we investigate molecular mechanisms associated with primary resistance to endocrine therapy and develop predictive models. Results: TP53 mutations were prominently associated with primary resistance to both tamoxifen (TAM) and aromatase inhibitors (AI), with AI non-responders exhibiting resistance in up to 32% of cases. Additionally, we identified distinct DNA methylation patterns in TAM and AI non-responders, with TAM non-responders showing global DNA methylation loss, associated with KRAS signaling, apical junctions and epithelial-mesenchymal transition (EMT). Conversely, we observed methylation gain in AI non-responders affecting developmental transcription factors, hypoxia and estrogen signaling. TAM or AI resistance was associated with increased methylation-inferred proportions of immune cells and decreased proportions of endothelial cells. Based on these findings and patient age, we developed the Predictive Endocrine ResistanCe Index (PERCI). PERCI stratified NR and R cases in both treatment groups and cohorts with high accuracy (ROC AUC TAM discovery 93.9%, validation 83%; AI discovery 98.6%, validation 76.9%). A simplified PERCI efficiently predicted progression-free survival in the TCGA-BRCA sub-cohort (Kaplan-Meier log-rank p-value = 0.03 between low and high PERCI groups). Conclusions: We identified genomic and epigenomic features associated with primary resistance to TMA and AI. By combining information on genomic alterations, patient age, differential methylation and tumor microenvironment (TME) composition, we developed PERCI TAM and PERCI AI as novel predictors of primary resistance, with potential additional prognostic value. Applying PERCI in a clinical setting may allow patient-specific drug selection to overcome resistance.

  Study EGAS50000001102

• NextGen Consortium: GENESiPS Study: Identifying the Gene Networks of Insulin Resistance

  Variability in induced pluripotent stem cell (iPSC) lines remains a roadblock for disease modeling and regenerative medicine. Through linear mixed models we have described different sources of gene expression variability from RNA sequencing data in 317 human iPSC lines from 101 individuals. We found that ~50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele specific expression show that iPSCs retain a subject-specific gene expression pattern. Pathway enrichment and key driver analyses, based on predictive causal gene networks, found that Polycomb targets explain a significant part of the non-genetic variability present in iPSCs within and across individuals. These publically available iPSC lines and genetic datasets will be a resource to the scientific community and will open new avenues to reduce variability in iPSCs and improve their utility in disease modeling. SNP array data from individuals included in RNA-seq transcriptome profiling study of human induced pluripotent stem cells to characterize gene expression variation across individuals and within multiple iPSC lines from the same individual. Genotyping was performed on patient blood. Data availability: SNP-genotyping: dbGaP - current study RNA-seq counts: GEO - GSE79636 FASTQ files: SRA - SRP072417

  Study phs001139

• GoT2D: Genetics of Type 2 Diabetes, a study of the the genetic architecture of type 2 diabetes using low pass whole genome sequencing and high density SNP genotyping in 2,657 individuals.

  The Genetics of Type 2 Diabetes Consortium (GoT2D) is a collaboration between the University of Michigan, the Broad Institute and the Wellcome Trust Centre for Human Genetics. The overall aim is to extend upon recent efforts, such as genome-wide association studies (GWAS) and large scale meta-analyses. While they have proved successful at mapping genomic loci that influence human diseases, like type 2 diabetes, much of the heritability remains unexplained. In this study, we use next generation sequencing and genotyping technologies to query for lower frequency variants in the human genome. Thereby, allowing a deeper characterization of the spectrum of alleles associated with type 2 diabetes risk, and a better assessment of the genes that play a role in the etiology of type 2 diabetes development. We studied 1,326 T2D cases and 1,331 normoglycemic controls from Northern and Central Europe (Sweden, Finland, UK, and Germany). To efficiently characterize the entire genome sequence of each individual, we performed low-coverage (~5x) whole-genome sequencing, augmented by deep coverage (~100x) sequencing of the exome, and dense (2.5M) single nucleotide polymorphism (SNP) genotyping using the HumanOmni2.5 array. The data deposited in EGA will include all the Swedish, Finnish, UK, and German samples.

  Study EGAS00001001459

• National Human Genome Research Institute Tumor Sequencing Project (TSP) - Lung Adenocarcinoma

  The Tumor Sequencing Project (TSP) Consortium is a collaboration among participants at the Baylor College of Medicine Human Genome Sequencing Center, the Broad Institute Genome Sequencing Platform, the Dana Farber Cancer Institute, the Memorial Sloan-Kettering Cancer Center, the Genome Sequencing Center and Siteman Cancer Center at Washington University, the M.D. Anderson Cancer Center and the University of Michigan Medical Center. The TSP Part A will pilot approaches to large-scale identification of genomic changes in tumors by sequencing the exonic regions of 623 genes in 188 specimens of adenocarcinoma of the lung, as well as using high density SNP genotyping arrays for high resolution identification of changes in chromosomal copy number. The TSP Part B will pilot approaches to tumor characterization of lung adenocarcinoma samples using next-generation sequencing technologies and benchmark those results against Part A data generated with ABI3730 instruments.

  Study phs000144

• RNA sequencing of surgically removed lung adenocarcinoma afterwards treated with immune checkpoint inhibitors

  Patients with lung adenocarcinoma (LUAD) from The Cancer Genome Atlas (TCGA) database were enrolled in the training cohort to establish a prognostic signature based on the expression of tumor-specific amino acid metabolism related genes. The established signature was further investigated against an original cohort, KCC-ICI, of chemotherapy combined with immune checkpoint inhibitors. The signature consisting of nine genes, KYNU, PSPH, PPAT, MIF, GCLC, ACAD8, TYRP1, ALDH2, and HDC, could effectively stratify the OS in LUAD patients. The KCC-ICI cohort confirmed the predictive ability of the established signature in the clinical response to immune checkpoint blockade therapy, which correlated with the infiltration of immune cells (e.g., T lymphocytes and nature killer cells). We discovered that the high signature risk score patients showed a significantly lower concentration of plasma-free ??-aminobutyric acid (AABA).

  Study JGAS000675

• Genetic Analysis of Latin American Cervical Cancer

  HPV16 is the most oncogenic type of human papillomaviruses (HPV). Integration of HPV into the human genome is an important mechanism of carcinogenesis but is absent in at least 30% of HPV16+ tumors. We applied long-read whole-genome sequencing (WGS) to cervical cancer cell lines and tumors to characterize HPV16 carcinogenesis in the absence of integration. WGS of HPV16+ cervical tumor samples from Latin America revealed that 11/20 tumors with only episomal HPV (EP) had intact monomer episomes. The remaining 9 EP tumors had multimer and rearranged HPV genomes. The majority (80%) of the HPV rearrangements and deletions disrupted the E1 and E2 genes, and EP tumors overexpressed the E6 and E7 viral oncogenes, a similar profile to tumors with HPV integration. Tumors with putative multimer HPV integrations display HPV multimers and concatemers of human and viral sequences. Our data uncovered a novel mechanism for HPV16 to cause cancer without integration through aberrant episomal replication, and forming rearranged, mutated, and multimer episomes.

  Study phs002810

• Resequencing_candidate_genes_for_male_spermatogenic_impairment

  This study aims to identify novel candidate variants from human Y-chromosomal genes DAZ, BPY2 and CDY1 by resequencing the coding regions of these genes from male patients with spermatogenic impairment. The coding regions of the genes have been amplified by standard PCR, amplicon lengths range from 244 to 486 bp. A total of 61 amplicons were amplified for each of the 96 patients, totalling to approx. 25 kb per sample. Amplicons were quantified by gel electrophoresis and pooled in approx. equimolar concentrations per patient. For each of the 96 submitted samples, approx. 1 microgram of amplified DNA pool is provided in a total volume of 120 microlitres. The samples need to be indexed and libraries prepared for a PE250bp Illumina MiSeq run. 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/

  Study EGAS00001002157

• Glucose Binds and Activates NSUN2 to Promote Translation and Epidermal Differentiation

  This study was undertaken to determine the role of NSUN2 in epidermal homeostasis. Primary neonatal foreskin keratinocytes were obtained from discarded surgical samples at a California hospital. Samples were anonymous and not selected. NSUN2 was found to promote epidermal differentiation and alter translation using the methods of RNA-seq, CLIP-seq and Ribo-seq.

  Study phs003767

• The mutational landscape of normal human endometrial epithelium - Additional Samples

  All normal somatic cells are thought to acquire mutations but understanding of the rates, patterns, causes and consequences of somatic mutation in normal cells is limited. Uterine endometrium adopts multiple physiological states over a lifetime and is lined by a gland-forming epithelium. Whole genome sequencing of normal endometrial glands from women aged 19 to 81 years showed them to be clonal cell populations derived from recent common ancestors, with total mutation burdens that increase with age at ~29 base substitutions/year and which are many-fold lower than endometrial cancers. Normal endometrial glands frequently carry driver mutations in cancer genes. Driver mutation burdens increase with age and correlate negatively with parity. Phylogenetic trees of normal endometrial glands constructed using whole genome sequences indicated that clones with drivers often originate during the first decades and spread to colonise the endometrial epithelial lining. The results show that driver mutation landscapes differ between normal cell types, perhaps shaped by differences in normal tissue physiology, and suggest that the procession of neoplastic changes leading to endometrial cancer is initiated early in life.

  Dataset EGAD00001005214