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• Transcriptional profiling of ovarian tumours and cell lines

  Purpose: Endogenous retroviruses (ERVs) play a role in a variety of biological processes, including embryogenesis and cancer. DNA methyltransferase inhibitors (DNMTi)-induced ERV expression triggers interferon responses in ovarian cancer cells via the viral sensing machinery. Baseline expression of ERVs also occurs in cancer cells, though this process is poorly understood and previously unexplored in epithelial ovarian cancer (EOC). Here, the prognostic and immunomodulatory consequences of baseline ERV expression was assessed in EOC. Experimental Design: ERV expression was assessed using EOC transcriptional data from TCGA and from an independent cohort (HH), as well as from untreated or DNMTi-treated EOC cell lines. LASSO logistic regression defined an ERV expression score to predict patient prognosis. Immunohistochemistry (IHC) was conducted on the HH cohort. Combination of DNMTi treatment with γδ T cells was tested in vitro, using EOC cell lines and patient-derived tumor cells. Results: ERV expression was found to define clinically relevant subsets of EOC patients. An ERV prognostic score was successfully generated in TCGA and validated in the independent cohort. In EOC patients from this cohort, a high ERV score was associated with better survival (log-rank p=0.0009) and correlated with infiltration of CD8+PD1+ T cells (r=0.46, p=0.0001). In vitro, baseline ERV expression dictates the level of ERV induction in response to DNMTi. Manipulation of ERV expression by DNMTi resulted in improved EOC cell killing by cytotoxic immune cells. Conclusions: These findings uncover the potential for baseline ERV expression to robustly inform EOC patient prognosis, influence tumor immune infiltration and affect anti-tumor immunity.

  Study EGAS00001004814

• Transcriptomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum

  Background; Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH.MethodsMultiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping were performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors).ResultsOverall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high ITH and were classified discordantly. Proximal tumors showed ITH related to differences in the microenvironment, while ITH within distal tumors were cancer-cell related. This subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05).ConclusionOur transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

  Study EGAS00001004668

• Analysis of recent shared ancestry in a familial cohort identifies coding and noncoding autism spectrum disorder variants

  Autism spectrum disorder (ASD) is a collection of neurodevelopmental disorders characterized by deficits in social communication and restricted, repetitive patterns of behavior or interests. ASD is highly heritable, but genetically and phenotypically heterogeneous, reducing the power to identify causative genes. We performed whole genome sequencing (WGS) in an ASD cohort of 68 individuals from 22 families enriched for recent shared ancestry. We identified an average of 3.07 million variants per genome, of which an average of 112,512 were rare. We mapped runs of homozygosity (ROHs) in affected individuals and found an average genomic homozygosity of 9.65%, consistent with expectations for multiple generations of consanguineous unions. We identified potentially pathogenic rare exonic or splice site variants in 12 known (including KMT2C, SCN1A, SPTBN1, SYNE1, ZNF292) and 12 candidate (including CHD5, GRB10, PPP1R13B) ASD genes. Furthermore, we annotated noncoding variants in ROHs with brain-specific regulatory elements and identified putative disease-causing variants within brain-specific promoters and enhancers for 5 known ASD and neurodevelopmental disease genes (ACTG1, AUTS2, CTNND2, CNTNAP4, SPTBN4). We also identified copy number variants in two known ASD and neurodevelopmental disease loci in two affected individuals. In total we identified potentially etiological variants in known ASD or neurodevelopmental disease genes for ~61% (14/23) of affected individuals. We combined WGS with homozygosity mapping and regulatory element annotations to identify candidate ASD variants. Our analyses add to the growing number of ASD genes and variants and emphasize the importance of leveraging recent shared ancestry to map disease variants in complex neurodevelopmental disorders.

  Study EGAS00001006058

• RaScALL: Rapid screening of RNA-seq in acute lymphoblastic leukaemia

  RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, a full RNA-seq Bioinformatics workflow is time-consuming and costly in a clinical setting where rapid detection and accurate reporting of clinically relevant alterations are essential. To accelerate the identification of ALL-specific alterations (including gene fusions, single nucleotide variants and focal gene deletions), we developed the rapid screening tool RaScALL, capable of identifying more than 100 prognostically significant lesions directly from raw sequencing reads. RaScALL uses the k-mer based targeted detection tool km and known ALL variant information to achieve a high degree of accuracy for reporting subtype defining genomic alterations compared to standard alignment-based pipelines. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% (164 samples) of reported cases in a 180-patient Australian study cohort and 95% (n=63) of samples in a North American validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n=12) and IKZF1 p.N159Y (n=4). Accurate detection of intragenic IKZF1 deletions resulting in aberrant transcript isoforms was also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches, ensuring accerelated risk-stratification and therapeutic triage.

  Study EGAS00001006460

• Chronic lymphocytic leukemia IGHV somatic hypermutation detection by targeted capture next-generation sequencing

  Somatic hypermutation (SHM) status of the immunoglobulin heavy variable (IGHV) gene locus plays a crucial role in determining the prognosis and treatment of patients with chronic lymphocytic leukemia (CLL). A common approach for determining SHM status is multiplex PCR of the IGH gene followed by Sanger sequencing; however, this technique is low throughput, vulnerable to failure, and does not allow multiplexing with other molecular diagnostic assays. Here we designed and validated a DNA targeted capture approach that allows the assessment of IGHV SHM status as a sub-module of a larger next-generation sequencing (NGS) assay. We developed an end-to-end assay that takes as input raw FASTQ files and outputs a report containing IGHV SHM status and V-allele usage following the guidelines of the European Research Initiative on CLL (ERIC). We validated the approach on 35 CLL patient samples, 34 of which were previously characterized using Sanger sequencing. The targeted capture NGS panel identified the SHM status of 34 of 35 CLL patients. We showed 100% sensitivity and specificity among the 33 CLL samples with both NGS and Sanger calls. Furthermore, we demonstrated that this panel can be combined with additional targeted capture panels to detect common and prognostically important CLL single nucleotide variants (SNV) and insertion/deletion (indel) variants as well as copy number variants (del17p). Thus, a targeted capture approach to IGHV SHM detection can be easily integrated into broader sequencing panels, allowing broad CLL prognostication in a single molecular assay.

  Study EGAS00001006887

• A Machine Learning Tool Integrating Circulating Cell-Free DNA Methylation with Clinical Variables for Non-Invasive Diagnosis of Liver Graft Pathology

  Liver Transplant Recipients (LTRs) with elevated liver enzymes can develop graft injury due to various etiologies, such as T-cell mediated rejection (TCMR) and NASH (NASH-LT). While liver biopsy is the gold standard method for diagnosis of graft pathology, it is invasive and is associated with risks. The goal of our study was to develop a Machine Learning (ML) tool integrating clinical variables with methylation patterns on circulating DNA in plasma as a non-invasive diagnostic tool of graft injury. We generated methylation profiles of circulating DNA in a pilot study of 43 LTRs (11 NASH-LT, 19 TCMR, and 13 Control-LT), and developed an L2 multinomial logistic regression ML approach across 101 bootstrapped models to distinguish between the graft conditions. NASH was associated with distinctive methylation patterns on genes involved in fatty acid metabolism, while methylation of platelet-derived growth factors was identified in patients with TCMR. Our ML model achieved a mean multi-classification accuracy of 0.91, with mean specificity and sensitivity of 0.94 and 0.91, respectively. The model was found to be particularly adept at detecting TCMR and Control-LT, with true positive rates (TPRs) of 95% and 90%, and areas under the curve (AUROC) of 0.992 and 0.985, respectively. For NASH-LT, the models achieved a performance with a TPR of 82% and an AUROC of 0.991. These preliminary results suggest that our newly developed ML tool, leveraging cell-free DNA methylation and clinical variables, is a promising non-invasive classifier of graft pathology.

  Study EGAS00001007171

• Clinical relevance of somatic copy-number alterations in plasma circulating tumor DNA from advanced EGFR-mutated NSCLC patients treated with osimertinib

  Background: Somatic copy-number alterations (SCNAs) are associated with drug resistance in patients with advanced epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) treated with EGFR-tyrosine kinase inhibitors (TKIs). The purpose of our study was to assess the clinical relevance and development of genome-wide SCNAs in plasma circulating tumor DNA (ctDNA) under osimertinib therapy.Methods: We included 43 patients with advanced EGFR T790M-positive lung adenocarcinoma who were treated with osimertinib after progression under previous EGFR-TKI therapy. We performed genomic profiling of ctDNA in plasma samples from each patient obtained pre-osimertinib and after patients developed resistance to osimertinib. SCNAs were detected by shallow whole-genome plasma sequencing and EGFR mutations were assessed by droplet digital PCR.Results: SCNAs in resistance-related genes (rrSCNAs) were detected in 10 out of 39 (25.6%) evaluable patients before start of osimertinib. The presence of rrSCNAs in plasma before initiation of osimertinib therapy was associated with a lower response rate to osimertinib (50% versus 86%, p=0.02) and was an independent predictor for shorter progression-free survival (adjusted HR 3.26, 95% CI 1.37-7.75, p=0.008) and overall survival (adjusted HR 2.41, 95% CI 1.09-5.34, p=0.03).Conclusions: Genomic profiling of plasma ctDNA is clinically relevant and affects the efficacy and clinical outcome of osimertinib. Our approach allows comprehensive assessment of SCNAs in plasma samples of lung adenocarcinoma patients and may help to guide genotype-specific therapeutic strategies in the future.

  Study EGAS00001004539

• Study of Addiction: Genetics and Environment (SAGE)

  This study is part of the Gene Environment Association Studies initiative (GENEVA) funded by the National Human Genome Research Institute. The overarching goal is to identify novel genetic factors that contribute to addiction through a large-scale genome-wide association study of DSM-IV alcohol dependent (and frequently illicit drug dependent) cases and non-dependent, unrelated control subjects of European and African American descent. The focus of this proposal is a case-control design of unrelated individuals for a genetic association study of addiction. Cases are defined as individuals with DSM-IV alcohol dependence (lifetime) and potentially other illicit drug dependence. In addition to the categorical diagnosis, we have data on ordinal measurements of number of DSM-IV symptoms for alcohol, nicotine, marijuana, cocaine, opiates and other drugs so that we will able to construct quantitative measurements of addiction severity over a wide range of substances. Controls are defined as individuals who have been exposed to alcohol (and possibly to other drugs), but have never met lifetime diagnosis for alcohol dependence or dependence on other illicit substances. Analyses that include refinement of the phenotype and incorporation of important demographic and environmental factors into association studies will be pursued. Cases and controls were selected from three large, complementary datasets: the Collaborative Study on the Genetics of Alcoholism (COGA), the Family Study of Cocaine Dependence (FSCD), and the Collaborative Genetic Study of Nicotine Dependence (COGEND). COGA: COGA was initiated in 1989 and is a large-scale family study that has had as its primary aim the identification of genes that contribute to alcoholism susceptibility and related characteristics. COGA is funded through the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Subjects were recruited from 7 sites across the U.S. Alcohol dependent probands were recruited from treatment facilities and assessed by personal interview. After securing permission, other family members were also assessed. A set of comparison families was drawn from the same communities as the families recruited through the alcohol dependent probands. Assessment involved a comprehensive personal interview developed for this project, the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which gathers detailed information on alcoholism related symptoms along with other drugs and psychiatric symptoms. Families with three or more first-degree relatives who were alcohol dependent were invited for more extensive testing, including neurophysiology evaluations (ERPs and EEGs) and a battery of neuropsychological assessments. Blood was obtained for genetic studies. Institutional Review Boards at all sites approved the protocols, including sharing in the NIAAA national repository. COGA has four Co-Principal Investigators Bernice Porjesz, Victor Hesselbrock, Howard Edenberg, and Laura Bierut. COGA includes nine different centers where data collection, analysis, and storage take place. The nine sites and Principal Investigators and Co-investigators are: University of Connecticut (Victor Hesselbrock); Indiana University (Howard Edenberg, John Nurnberger, Jr., Tatiana Foroud); University of Iowa (Samuel Kuperman); SUNY Downstate (Bernice Porjesz); Washington University in St. Louis (Laura Bierut, Alison Goate, John Rice); University of California at San Diego (Marc Schuckit); Howard University (Robert Taylor); Rutgers University (Jay Tischfield); Southwest Foundation (Laura Almasy). Q. Max Guo serves as the NIAAA Staff Collaborator. This national collaborative study is supported by the NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). Family Study of Cocaine Dependence (FSCD): This project was initiated in 2000 as a case-control family study of cocaine dependence funded through the National Institute on Drug Abuse (NIDA; PI: Laura Bierut). The primary goal was to increase the understanding of the familial and non-familial antecedents and consequences of cocaine dependence. Cocaine dependent individuals were systematically recruited from chemical dependency treatment units (both public and private; residential and outpatient) in the greater St. Louis metropolitan area. Community based control subjects were identified through a Missouri Driver's License Registry (maintained at Washington University for research purposes) and matched by age, race, gender, and residential zip code. As a supplement to this project, blood samples were collected for future genetic analysis and were included in the NIDA Genetics Consortium. Phenotypic data, DNA, and cell lines are in the NIDA Center for Genetics Studies. Collaborative Genetic Study of Nicotine Dependence (COGEND): COGEND was initiated in 2001 as a three-part program project grant funded through the National Cancer Institute (NCI; PI: Laura Bierut). The three projects included a study of the familial transmission of nicotine dependence, a genetic study of nicotine dependence, and a study of the relationship of nicotine dependence with nicotine metabolism. The primary goal is to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes and to integrate these findings with the family transmission and nicotine metabolism findings. The primary design is a community based case-control family study. All subjects were recruited from Detroit and St. Louis. Nicotine dependent cases and non-dependent smoking controls were identified and recruited. In addition, one sibling for each case and control subject was recruited in a subset of the sample. Over 56,000 subjects aged 25-44 years were screened by telephone, over 3,100 subjects were personally interviewed, and over 2,900 donated blood samples for genetic studies. All three studies (COGA, COGEND, FSCD) include measures of basic socio-demographic variables, including age, sex, race/ethnicity, family income, educational attainment, religious participation, and family structure. Other important covariates and/or potential moderators of genetic effects include comorbid addictions and age at initiation of use for cigarettes, alcohol and drugs. The assessments also include measures of various life stressors, such as physical and sexual abuse, which have been implicated in gene-environment interactions for several disorders. Coding for both individual variables and indices has been standardized across studies. All subjects were assessed in person by trained research assistants. Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR), was provided by the NIH GEI (U01HG004438), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, and the NIH contract "High throughput genotyping for studying the genetic contributions to human disease"(HHSN268200782096C). Note for Publications Related to Study: The Study of Addiction: Genetics and Environment (SAGE) has not yet generated publications. Below is a listing of publications related to the three studies from which the SAGE sample was selected. COGA has over 228 publications listed at www.niaaagenetics.org This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to addiction through a large-scale genome-wide association study of DSM-IV alcohol dependent (and frequently illicit drug dependent) cases and non-dependent, unrelated control subjects of European and African American descent. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000092

• Neutrophils as effector cells in resistance to infection by Mtb

  Dac EGAC00001003255

• DAC for access to array genotypes from the PREGO biobank.

  Dac EGAC00001003484