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• PLCG1 R707Q mutation is counter selected under targeted therapy in a patient with a hepatic angiosarcoma

  PLCG1 R707Q mutation is counter selected under targeted therapy in a patient with a hepatic angiosarcoma

  Study EGAS00001001281

• Somatic mutations of non-malignant T cells

  Somatic mutations in T cells can cause cancer but also have implications for immunological diseases and cell therapies. The mutation spectrum in non-malignant T cells is unclear. Here, we examined somatic mutations in CD4+ and CD8+ T cells from 90 patients with hematological and immunological disorders and used T cell receptor (TCR) and single-cell sequencing to link mutations with T cell expansions and phenotypes. CD8+ cells had higher mutation burden than CD4+ cells. Notably, the biggest variant allele frequency (VAF) of non-synonymous variants was higher than synonymous variants in CD8+ T cells, indicating non-random occurrence. The non-synonymous VAF in CD8+ T cells strongly correlated with the TCR frequency, but not age. We identified mutations in pathways essential for T cell function and often affected in lymphoid neoplasia. Single-cell sequencing revealed cytotoxic Temra phenotypes of mutated T cells. Our findings suggest that somatic mutations contribute to CD8+ T cell expansions without malignant transformation.

  Study EGAS50000000237

• Mechanisms of Extreme Genomic Instability at Large Transcribed Genes

  Inhibiting DNA replication leads to copy number variant (CNV) formation throughout the genome, especially at chromosome fragile sites (CFSs). We previously showed that these hotspots for genome instability reside in late-replicating domains associated with large transcribed genes. In this study, we compared aphidicolin (APH)-induced CNV and CFS frequency between wild-type cells and isogenic cells in which FHIT gene transcription was ablated. We further examined the impact of altering RNase H1 expression on CNV or CFS induction frequency and analyzed R-loop formation genome-wide in a human fibroblast cell line. Data sets include Bru-seq nascent RNA transcription, whole genome sequencing for replication timing, SNP microarray analysis, and DRIP-seq. Results suggest that large gene transcription is a determining factor in replication stress-induced genomic instability and that CFSs mainly result from transcription-dependent passage of unreplicated DNA into mitosis with low R-loop levels observed at these loci.

  Study phs002066

• Differential Transcription Start Site Usage in Brain-related Samples

  RNA-Seq is an effective method to study the transcriptome, but specialized methods are required to identify 5' ends of transcripts. Several published strategies exist for this specific purpose, but their relative merits have not been systematically analyzed. Here, we directly compare the performance of six such methods - testing five with cellular RNA as well as a novel spike-in RNA assay that helps address interpretation challenges that arise from uncertainties in annotation or RNA processing. Using a single human RNA sample, we constructed and sequenced 18 libraries with these methods and one standard, control RNA-Seq library. We find that the CAGE method performed best for mRNA and that most of its unannotated peaks are supported by evidence from other genomic methods. We then applied CAGE to eight brain-related samples and revealed sample-specific transcription start site (TSS) usage as well as a transcriptome-wide shift in TSS usage between fetal and adult brain.

  Study phs001463

• Transcriptome study of differential expression in schizophrenia

  Schizophrenia is a common and severe psychotic disorder. While some common SNPs and rare copy number variants have been identified as being significantly associated with disease risk, the biological mechanisms remain undefined. To identify gene expression abnormalities in schizophrenia, we generated whole-genome gene expression profiles using microarrays on lymphoblastoid cell lines from a total of 413 cases and 446 controls. Regression analysis identified 95 transcripts differentially expressed by affection status at a genome-wide false discovery rate of 0.05, while simultaneously controlling for confounding effects. These transcripts represented 89 genes with functions such as neurotransmission, gene regulation, cell cycle progression, differentiation, apoptosis, and immunity. The observed differential expression of extended major histocompatibility complex region genes converges with the genetic evidence from schizophrenia genome-wide association studies, which find the same region to be the most significant schizophrenia susceptibility locus. Our analysis also provides novel candidate genes for further study to assess their potential contribution to schizophrenia.

  Study phs000775

• International Cancer Proteogenome Consortium (ICPC): Proteogenomics of Early Stage Lung Adenocarcinoma in Taiwan

  Lung cancer in East Asia is characterized by a high percentage of never-smokers with early-onset adenocarcinoma, predominantly with EGFR mutations. To provide an insight into the molecular phenotype of this demographically distinct disease, we have performed a proteogenomics study on a prospectively collected cohort representing early stage lung adenocarcinoma in Taiwan. The distinct mutational profile revealed a high prevalence of the APOBEC mutational signature in early-onset females, implicating differential regulation of APOBEC enzymes and DNA damage pathways, as well as enrichment of mutational signatures congruent with environmental carcinogens over-represented in the genomic location of EGFR. We delineate the proteogenomic hallmarks of tumor progression and propose a proteomics-informed classification that resolves the EGFR mutation heterogeneity associated with survival within the early stages. Functional annotation of the molecular subtypes by protein network analysis highlights candidate biomarkers for patient stratification. Our integrative analysis reveals the molecular architecture of lung cancer in East Asia and enables the path for precision medicine.

  Study phs001954

• TONIC-Trial-cfDNA-Project

  Cell-free DNA (cfDNA) is an emerging technology to predict and monitor response to cancer treatment, including immune checkpoint blockade (ICB). However, data on cfDNA dynamics during ICB in metastatic triple negative breast cancer (mTNBC) are limited. While most applications of cfDNA involve assays that focus on mutation detection, mTNBC and multiple other cancer types are driven by copy number alterations (CNAs). We evaluate cfDNA-based copy number profile abnormality (CPA) score as a potential biomarker for monitoring ICB response in mTNBC, analyzing data from 87 patients enrolled in stage 1 and stage 2 of the TONIC-trial. We find significant concordance between cfDNA-based and tissue-based CNA profiles. Additionally, responders show a decrease in CPA scores upon ICB at week 6 (3 cycles of nivolumab). These findings underscore the potential of cfDNA-based CNA dynamics as a non-invasive biomarker for ICB early response assessment in patients with mTNBC.

  Study EGAS50000001308

• A comprehensive assessment of somatic mutation detection in cancer using whole genome sequencing

  As whole genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Using tumor-normal sample pairs from two different types of cancer, chronic lymphocytic leukemia and medulloblastoma, we conducted a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines, and even validation methods. Here we show that using PCR-free methods and increasing sequencing depth to ~100x showed benefits, as long as the tumor:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artifact-prone nature of the raw data and lack of standards for dealing with the artifacts. However, armed with the benchmark mutation set we have created, we show that many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.

  Study EGAS00001001539

• HSC_colony_many_years_post_allogeneic_bone_marrow_transplant_TGS

  This study looks at human donors and recipients of allogeneic blood & marrow marrow transplantation. Each transplant "pair" (donor & recipient) underwent the transplant procedure 10 -30 years prior to sampling - the majority for an underlying diagnosis of leukemia. We will perform whole-genome sequencing on tens to hundreds of single HSPC (haematopoietic stem and progenitor cell)-derived colonies per individual. We will infer the phylogeny of these cells (those from both donor & recipient within a pair) by analysing the somatically acquired mutations within each cell. The structure of the phylogeny will allow estimates of hitherto unknown parameters such as the number of HSCs that engraft long-term, the dynamics of the engraftment process on the HSC level, and the additional mutation burden acquired through the arduous transplant procedure. This TGS part of the study is used for assessing the contribution of clones to mature blood subsets, and better timing mutations relative to transplantation.

  Study EGAS00001005298

• COVID19 Host Genetic Initiative

  The COVID-19 host genetics initiative is a bottom-up collaborative effort that has three main goals: 1. Provide an environment to foster the sharing of resources to facilitate COVID-19 host genetics research (e.g. protocols, questionnaires). 2. Organize analytical activities across studies to identify genetic determinants of COVID-19 susceptibility and severity. 3. Provide a platform to share the results from such activities, as well as the individual-level data where possible, to benefit the broader scientific community. Here, we release the harmonized individual-level data for the subset of individuals who participated in COVID-19 HGI. Please refer to this data dictionary: https://docs.google.com/spreadsheets/d/1whu_MjShXxbdoAJMCYxZ5C-vWOcRivwfWglTcBRNZgQ/edit#gid=470747846 Please cite both Nakanishi, T el al. J Clin Invest 2021 (PMID: 34597274) and COVID-19 Host Genetics Initiative, Nature 2021 (PMID: 34237774) when you use these data for your publications.

  Study EGAS00001005304