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• The Genomic Landscape of Mongolian Hepatocellular Carcinoma

  Background and Hypotheses: Mongolia has the highest reported incidence of - and mortality from - hepatocellular carcinoma (HCC) in the world, which is between three and seven times higher than that observed in other high-incidence populations, such as South Korea, Thailand, and China. Although its causative factors and underlying tumor biology remain unknown, risk factors such as chronic hepatitis B virus and/or hepatitis C virus infection are highly prevalent. Moreover, Mongolia has an extraordinarily high prevalence of hepatitis delta virus (HDV), more than ten times above the global estimate, which we hypothesize as a major driver of Mongolian hepatocarcinogenesis. By performing a comprehensive and integrative genomic characterization, we aimed to identify robust molecular subclasses and driver features informative of the etiology and progression of the disease. This study, the first one of this nature ever to be conducted on a Mongolian cohort, identifies similarities to previous studies performed on other populations (which reflect general characteristics of liver carcinogenesis) as well as unique features of Mongolian HCC. Study Design and Methods: Clinical information and paired tumor/non-tumor liver tissue samples were obtained from 76 HCC patients undergoing surgery at the National Cancer Center of Mongolia. Transcriptome sequencing and whole exome sequencing were performed on all tumor and non-tumor samples, followed by bioinformatics processing and quality control. Consensus clustering and regularized Cox regression analyses were performed on transcriptome data. Driver mutations and mutational signatures were assessed. These results were compared to those from 373 HCC patients of different races and ethnicities and diverse etiologies. Furthermore, molecular subclass gene signatures from multiple previous studies were assessed for validation. Results and Conclusions: Using a transcriptomics-based unsupervised approach, we identified four molecular subclasses among Mongolian HCC cases, which were associated with different patient survival outcomes. These molecular subclasses were mapped to signatures reported in other HCC cohorts, showing significant associations consistent with survival outcomes. A supervised analysis determined risk scores strongly associated with survival, consistent with the four molecular subclasses. Mutational signature analysis using recently published COSMIC and Environmental Agents Compendia identified signatures with distinct prevalence among HDV+ and HVD- groups. Signatures differentially associated with HDV+ include mutational patterns linked to alkylating agents (such as temozolomide), tobacco chewing and exposures to 1,8-Dinitropyrene and furan, whereas HDV- appeared differentially associated with aristolochic acid II. In addition to common driver mutations (TP53, CTNNB1) frequently found in pan-cancer analysis, Mongolian HCC exhibits several unique drivers (most notably GTF2IRD2B, PNRC2, and SPTA1), the latter of which may be linked to HDV infection. These results suggest the existence of new molecular mechanisms at play in Mongolian hepatocarcinogenesis.

  Study phs002000

• MILK-Omics: Systems Biology of Human Milk and Its Links to Maternal and Infant Health

  The primary objective of the project is to generate a systems-level view of human milk in the context of healthy mothers and their term infants. The study uses biospecimens and clinical data from the Mothers and Infants LinKed for Healthy Growth Study (MILk Study), a prospective observational cohort of approximately 500 exclusively breastfeeding women and their infants who are followed to 6 months of age. The project focuses on mature milk samples collected at 1 month postpartum and infant gut microbiome samples collected at 1 and 6 months of age. Specific Aim 1 is to identify maternal genetic and clinical factors that shape human milk gene expression. Using bulk and single-cell RNA sequencing of the milk cell pellet and genotypes from maternal DNA, we will identify novel genetic determinants of the milk transcriptome and assess potential modification of these genetic associations by gestational weight gain, diet, and other clinical factors. Specific Aim 2 is to describe key features of the normative human milk biosystem and their interactions with one another. Specifically, machine learning techniques will be used to characterize interaction networks and correlational structures among the following features of human milk: transcriptomics, microbiomes, oligosaccharides, metabolomics, lipidomics, and milk macronutrient composition. Specific Aim 3 is to establish how the milk biosystem is related to variation in infant gut microbiomes and health. dbGaP will house the maternal genotype and milk transcriptomics data.The infant microbiome sequence data are associated with BioProject PRJNA1019702.

  Study phs003408

• Tumor-reactive heterotypic CD8 T cell clusters from clinical samples

  Emerging evidence suggests a correlation between CD8+ T cell-tumor cell proximity and anti-tumor immune response. However, it remains unknown whether these cells exist as functional clusters that can be isolated from clinical samples. By conventional and imaging flow cytometry, we show here that from 21/21 human melanoma metastases, we could isolate heterotypic clusters, comprising CD8+ T cells interacting with one or more tumor cells and/or antigen-presenting cells (APCs). Single-cell RNA-sequencing revealed that T cells from clusters were enriched for gene signatures associated with tumor reactivity and exhaustion. Clustered T cells exhibited increased TCR clonality indicative of expansion, while TCR-matched T cells showed more exhaustion and co-modulation when conjugated to APCs than to tumor cells. T cells that were expanded from clusters ex vivo exerted on average 9-fold increased killing activity towards autologous melanomas, which was accompanied by enhanced cytokine production. Upon adoptive cell transfer into mice, T cells from clusters showed improved patient-derived melanoma control, which was associated with increased T cell infiltration and activation. Together, these results demonstrate that tumor-reactive CD8+ T cells are enriched in functional clusters with tumor cells and/or APCs and that they can be isolated and expanded from clinical samples. Typically excluded by single cell gating in flow cytometry, these distinct heterotypic T cell clusters serve as a valuable source to decipher functional tumor-immune cell interactions, while they may also be therapeutically explored.

  Study EGAS50000000785

• RRBS sequencing of 7 tumour regions and a normal sample from a single TRACERx patient.

  Background: Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome - so-called `epialleles' - offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually. Results: We have developed a Bayesian model to infer which epialleles are present in multiple regions of the same tumour. We apply our method to reduced representation bisulfite sequencing (RRBS) data from multiple regions of one lung cancer tumour and a matched normal sample. The model borrows information from all tumour regions to leverage greater statistical power. The total number of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all automatically inferred from the data. Uncertainty as to which epiallele an observed sequencing read originated from is explicitly incorporated by marginalising over the appropriate posterior densities. The degree to which tumour samples are contaminated with normal tissue can be estimated and corrected for. By tracing the distribution of epialleles throughout the tumour we can infer the phylogenetic history of the tumour, identify epialleles that differ between normal and cancer tissue, and define a measure of global epigenetic disorder. Conclusions: Detection and comparison of epialleles within multiple tumour regions enables phylogenetic analyses, identification of differentially expressed epialleles, and provides a measure of epigenetic heterogeneity.

  Study EGAS00001002484

• The long term effects of chemotherapy on normal blood - WGS dataset

  In developed countries, ~10% of individuals are exposed to systemic chemotherapy for cancer and other diseases. Many chemotherapeutic agents act by increasing DNA damage in cancer cells, hence triggering cell death. However, there is limited understanding of the extent and consequences of collateral DNA damage to normal tissues. To investigate the impact of chemotherapy on mutation burdens and cell population structure of a normal tissue we sequenced blood cell genomes from 23 individuals, aged 3-80 years, treated with a range of chemotherapy regimens. Substantial additional mutation loads with characteristic mutational signatures were imposed by some chemotherapeutic agents, but there were differences in burden between different classes of agent, different agents of the same class and different blood cell types. Chemotherapy also induced premature changes in the cell population structure of normal blood, similar to those of normal ageing. The results constitute an initial survey of the long-term biological consequences of cytotoxic agents to which a substantial fraction of the population is exposed during the course of their disease management, raising mechanistic questions and highlighting opportunities for mitigation of adverse effects.

  Dataset EGAD00001015339

• RNA-Seq of human longitudinal whole blood samples from PCR-positive and PCR-negative adults following controlled inoculation with Influenza A H3N2 virus.

  Understanding of the transcriptomic profile of individuals in early influenza infection is limited. To investigate this, longitudinal whole-blood samples (n=178) were taken from adult participants following controlled inoculation with Influenza A H3N2 virus (sampling at baseline (0) and days 1, 2, 3, 7, 10 and 14 post-challenge). Most participants became influenza PCR-positive; a minority remained PCR-negative. Total RNA was extracted from PAXgene tubes before undergoing globin and rRNA depletion. DNA libraries were constructed using the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina. All samples were then sequenced using Illumina NovaSeq 6000.

  Dataset EGAD50000000956

• Ribosomal profiling on Epstein-Barr virus transformed B-lymphoblastoid cell lines.

  This dataset contains Ribo-seq BAM files of Epstein-Barr virus-transformed B-lymphoblastoid cell lines from six individuals (HG00114, HG00282, NA12005, NA12044, NA12717 and NA12751) of the GEUVADIS Project. For each EBV-LCL, 20 million cells were treated with 2 μg/ml harringtonine and 100 μg/ml cycloheximide. After lysing cells in lysis buffer supplemented with 100 μg/ml cycloheximide, ribosome complexes were purified by density purification. Small RNA molecules were isolated using the NucleoSpin miRNA Kit (Bioke, Leiden, Netherlands), followed by RNA PAGE gel separation. Universal linkers were added after dephosphorylation. After reverse transcription, the cDNA was circularized and, after ribosomal RNA depletion, barcoded and sequenced on Illumina NextSeq 500.

  Dataset EGAD50000000465

• Transcriptomic profiling of T cells following ABHD11 inhibition

  This dataset contains paired-end RNA sequencing data from human T cells treated with ML226, a selective ABHD11 inhibitor, and matched vehicle controls. The experiment was designed to investigate the role of ABHD11 in sterol metabolism and its impact on T cell effector function and autoimmunity. Samples were collected across biological replicates following 24-hour treatment, and RNA was extracted for high-throughput sequencing. Sequencing was performed using an Illumina platform. The dataset supports analysis of differentially expressed genes, alternative transcript usage, and pathway-level effects of ABHD11 inhibition. This dataset is part of the study titled "ABHD11 inhibition drives sterol metabolism to modulate T cell effector function and alleviate autoimmunity".

  Dataset EGAD50000001845

• Dataset for upper_gastrointestinal_tumor-RNA

  Rare cancer sequencing data of 44 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008853

• CD19 CAR-T cells from non-Hodgkin's lymphoma patients

  The samples across 17 NHL patient samples with the CD19 CAR-T treatments are sequenced in 10x genomics. Refer to the manuscript supplementary tables and "https://github.com/hwanglab/hwanglab_2021_tigitCarT" for the sequencing sample sheets, the patient clinical information, processed data, and source codes.

  Dataset EGAD00001007741