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• DAC for study CMMRD–associated high-grade glioma

  This Data Access Committee (DAC) is responsible for de-identified, summarized somatic variant call data derived from paired tumor–blood Whole-exome sequencing of human samples in a glioma research study. Access requests are approved without additional restrictions and are granted solely for health-related research purposes.

  Dac EGAC50000000855

• Whole Genome Sequencing for Korean Diffuse Gastric Cancer

  Fastq files for the whole genome sequencing data (Illumina HiSeq 2500; 32.6-fold) for two diffuse gastric cancers revealing the fusion breakpoints. 2102T: CTNND1-ARHGAP26 gene fusion (g.chr11:57,578,103-g.chr5:142,358,707) 354T: ANXA2-MYO9A gene fusion (g.chr15:60,656,550-g.chr15:72,157,966)

  Dataset EGAD00001003953

• Metastatic breast cancer targeted gene screen (2014-09-24)

  The samples will be sequenced for a targeted panel of cancer relevant genes (n ~ 370) and analysed for somatic mutations. This dataset contains all the data available for this study on 2014-09-24

  Dataset EGAD00001001018

• Metastatic breast cancer targeted gene screen (2017-05-11)

  The samples will be sequenced for a targeted panel of cancer relevant genes (n ~ 370) and analysed for somatic mutations. This dataset contains all the data available for this study on 2017-05-11.

  Dataset EGAD00001003330

• The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia

  RNA-seq data for ALL patients as described in "The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia"

  Dataset EGAD00001006335

• Hyperpolarized carbon-13 MRI for very early response assessment of neoadjuvant chemotherapy in breast cancer patients

  Transcriptomic data for five patients with breast cancer undergoing neoadjuvant chemotherapy and hyperpolarised 13C-MRI for early response assessment

  Dataset EGAD00001008141

• 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

• Parkinson's Disease Cognitive Genetics Consortium (PDCGC) Stage I, NeuroX Dataset

  Cognitive impairment is a common and disabling problem in Parkinson's disease (PD). Identification of genetic variants that influence the presence or severity of cognitive deficits in PD might provide a clearer understanding of the pathophysiology underlying this important nonmotor feature. We are presently undertaking a large-scale, two-stage study designed to identify genetic risk factors for cognitive impairment in PD. The study population is divided into a discovery (Stage I) and a validation (Stage II) sample of patients enrolled in the PD Cognitive Genetics Consortium (PDCGC). Each patient has undergone a detailed neurological evaluation and cognitive testing. Clinical and genetic data for the project are stored and managed at the Coordinating Center at the University of Washington and VA Puget Sound Health Care System in Seattle. Stage I of the project is now complete; 1,219 PD patients were genotyped for 249,336 variants using the NeuroX array. Participants underwent assessments of learning and memory (Hopkins Verbal Learning Test-Revised [HVLT-R]), working memory/executive function (Letter-Number Sequencing and Trail Making Test [TMT] A and B), language processing (semantic and phonemic verbal fluency), visuospatial abilities (Benton Judgment of Line Orientation [JoLO]), and global cognitive function (Montreal Cognitive Assessment). We excluded individuals who were of non-European ancestry, failed genotyping, were missing data for one or more covariates, were related to another participant in the cohort, or failed to complete at least half of the cognitive tests. After these quality control measures were implemented, 1,105 participants remained and were included in all subsequent analyses. For common variants we used linear regression to test for association between genotype and cognitive performance with adjustment for important covariates. Rare variants were analyzed using the optimal unified sequence kernel association test. The significance threshold was defined as a false discovery rate corrected P-value (PFDR) of 0.05. Eighteen common variants in 13 genomic regions exceeded the significance threshold for one of the cognitive tests. These included GBA rs2230288 (E326K; PFDR = 2.7 x 10-4) for JoLO, PARP4 rs9318600 (PFDR = 0.006) and rs9581094 (PFDR = 0.006) for HVLT-R total recall, and MTCL1 rs34877994 (PFDR = 0.01) for TMT B-A. Analysis of rare variants did not yield any significant gene regions. We have conducted the first large-scale PD cognitive genetics analysis and nominated several new putative susceptibility genes for cognitive impairment in PD. These results will require replication in independent PD cohorts, and efforts to validate the findings in PDCGC Stage II are in progress.

  Study phs001664

• Human Blastocyst 10X Single Cell RNA Sequencing

  Recent advances in human blastoids have opened new avenues for modeling early human development and implantation. One limitation of our first protocol for human blastoid generation was relatively low efficiency. We now report an optimized protocol for the efficient generation of large quantities of high-fidelity human blastoids from naive pluripotent stem cells. This enabled proteomics analysis that identified phosphosite-specific signatures potentially involved in the derivation and/or maintenance of the signaling states in human blastoids. Additionally, we uncovered endometrial stromal effects in promoting trophoblast cell survival, proliferation, and syncytialization during co-culture with blastoids and blastocysts. Side-by-side single-cell RNA sequencing revealed similarities and differences in transcriptome profiles between pre-implantation blastoids and blastocysts, as well as post-implantation cultures, and uncovered a population resembling early migratory trophoblasts during co-culture with endometrial stromal cells. Our optimized protocol will facilitate broader use of human blastoids as an accessible, perturbable, scalable, and tractable model for human blastocysts. In addition, this study is the first to use the 10X Genomics platform for human blastocysts, and we anticipate this resource will be widely used by the community as a reference for future studies.

  Study phs003122

• Accurate detection of tumor-specific fusion genes reveals strongly immunogenic personal neo-antigens

  Fusion genes arising from cancer-associated somatic mutations are a potential rich source for highly immunogenic neo-antigens. However, their exploitation as targets for personalized cancer immunotherapy is currently limited by the lack of computational tools allowing transcriptome-wide identification of unique fusion genes in an accurate and sensitive manner. Here, we present EasyFuse, a computational pipeline, to detect individual and cancer-specific fusion genes in next-generation-sequencing transcriptome data obtained from human cancer samples. Using machine learning, EasyFuse predicts personal fusion genes with high precision and sensitivity and outperforms previously described approaches as qualified by an unprecedented ground-truth dataset of >1500 verification experiments in relevant patient samples. By testing immunogenicity with autologous blood lymphocytes from cancer patients we detected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%), and for 1 of 30 (3%) of identified fusion genes, respectively. In conclusion, we demonstrate accurate detection of cancer-specific fusion genes. The high frequency of T cell responses detected in cancer patients support the relevance of private fusion genes as neo-antigens for personalized immunotherapies, especially for tumors with low point mutation burdens.

  Study EGAS00001004877