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• The polarity and specificity of antiviral T lymphocyte responses determine susceptibility to SARS-CoV-2 infection in cancer patients and healthy individuals

  We monitored patient's anti SARS-CoV-2 immune responses using an in vitro cross presentation assay. The goal of this study was to identify immune correlates of clinical protection against SARS-CoV-2 infection. Briefly, peripheral blood mononuclear cells of patient were divided into a monocyte and lymphocyte. Monocyte were differentiated into monocyte derived dendritic (mo-DC)cells using GM-CSF and Interferon alpha. Mo-DC were then loaded with SARS-CoV-2 culture lysates , or VeroE86 lysates. SARS-CoV-2 loaded mo-DC were then used to stimulates their autologous lymphocytes and T cell cytokine secretion was monitored in the supernatant. We discriminated patients producing IL-2 and patients producing IL-5. RNA sequencing was performed for 18 patients, to identify gene profile associated with IL-2 or IL-5 production.

  Dataset EGAD00001008538

• Haplotype-specific assembly of shattered chromosomes in oesophageal adenocarciomas

  Genomic and epigenomic sequencing of 5 oesphageal adenocarciomas with evidence of chromothripsis. Genomic sequencing includes: Pacbio circular consensus sequencing, Pacbio continuous long read sequencing, 10X linked read and Illumia HiSeq X Ten sequencing. Epigenomic sequencing includes: Hi-C chromosome capture, ATAC-seq, ChIP seq (for H3K27ac, H3K4me3, H3K27me3 and CTCF) and long read RNA sequencing. All data types have the bam files which have not undergone haplotype resolution (demarcated as unresolved) and some data types also have haplotype resolved reads (demarcated as resolved).

  Dataset EGAD00001010871

• Dataset for urologic_cancer-RNA

  Rare cancer sequencing data of 75 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008851

• Dataset for soft_tissue_tumor-EXON

  Rare cancer sequencing data of 246 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008897

• The ALT pathway generates telomere fusions that can be detected in the blood of cancer patients

  Telomere fusions (TFs) can trigger the accumulation of oncogenic alterations leading to malignant transformation and drug resistance. Despite their relevance in tumour evolution, our understanding of the patterns and consequences of TFs in human cancers remains limited. Here, we characterize the rates and spectrum of somatic TFs across >30 cancer types using whole-genome sequencing data. TFs are pervasive in human tumours with rates varying markedly across and within cancer types. In addition to end-to-end fusions, we find novel patterns of TFs that we mechanistically link to the activity of the alternative lengthening of telomeres (ALT) pathway. We show that TFs can be detected in the blood of cancer patients, which enables cancer detection with high specificity and sensitivity even for early-stage tumours and cancers of high unmet clinical need. Overall, we report a novel genomic footprint that enables characterization of the telomere maintenance mechanism of tumours and liquid biopsy analysis.

  Dataset EGAD00001012101

• RNA sequencing in primary inflammatory (TPP) macrophages following deletion of a disease-associated gene desert at chr21q22, disruption of ETS2, or treatment of ETS2-edited macrophages with a HIF1α stabiliser.

  This dataset comprises raw RNA sequencing data (FASTQs) from primary inflammatory (TPP) macrophages that were unedited (non-targeting control, NTC), edited to delete the disease-associated chr21q22 enhancer region (n=5), or edited to disrupt ETS2 with 1 of 2 independent gRNAs (n=9). We also performed RNA-sequencing in NTC or ETS2-edited TPP macrophages that were treated for 12 hours with vehicle (DMSO) or roxadustat (30 uM, n=3). Macrophages were detached using Accutase and lysed. RNA was extracted from cell lysates using an AllPrep DNA/RNA Mini Kit (Qiagen). Sequencing libraries were prepared from 10ng RNA using the SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Takara) following the manufacturer’s instructions. The quality and molarity of all libraries was assessed using a BioAnalyzer 2100 and the libraries were sequenced on a NextSeq500. Raw data are provided as 50 bp paired-end Illumina reads.

  Dataset EGAD00001011338

• 10x dataset of an obese human subject

  Biopsies from visceral adipose tissue from the omental depot (OAT) were obtained from an obese individual with participant informed consent obtained after the nature and possible consequences of the studies were explained under protocols approved by the Institutional Review Boards of the Perelman School of Medicine at the University of Pennsylvania, the Children’s Hospital of Philadelphia, or the Tel Aviv Sourasky Medical Center. The obese donor underwent bariatric surgery, the lean donor underwent cholecystectomy. OAT samples were placed in 1 mL of DMEM, and finely minced under sterile conditions before digestion in 50 mL of DMEM with 3 mg/1 mL collagenase IV (Gibco). Samples were incubated at 37°C in a rotating oven for 20-60 min. Adipocyte and stromal vascular fractions (SVF) were separated by centrifugation, and red blood cells (RBCs) were removed from the SVF by histopaque gradient (Sigma). Single-cell RNA-sequencing libraries were prepared using the Chromium platform (10x genomics), and sequenced on the MiSeq 500 or HiSeq 2500 Sequencing System (Illumina).

  Dataset EGAD00001005101

• Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics

  10 single-cell placental RNA libraries were generated using the Chromium Single Cell 3′ Reagent Kit (10X Genomics). All single-cell libraries were sequenced with a customized paired end with dual indexing (98/14/8/10-bp) format according to the recommendation by 10X Genomics. The data were aligned using the Cell Ranger Single-Cell Software Suite (version 1.0). Moreover, plasma RNA from 22 samples were extracted using the RNeasy Mini Kit (Qiagen). cDNA reverse transcription, second-strand synthesis, and RNA-sequencing (RNA-seq) library construction were performed using the Ovation RNA-seq System V2 (NuGEN) kit according to the manufacturer’s protocol. For alignment of the plasma RNA library, adaptor sequences and low-quality bases on the fragment ends (i.e., quality score < 5) were trimmed, and reads were aligned to the human reference genome (hg19) using the TopHat (v2.0.4) software. All aligned reads were deposited in bam file format.

  Dataset EGAD00001003705

• Melanoma PEACE Dataset

  Dataset for the initial melanoma PEACE paper in Cancer Discovery, March 2023.

  Dataset EGAD00001010877

• Defining T cell States Associated with Response to Checkpoint Immunotherapy in Melanoma

  Checkpoint blockade therapy using antibodies targeting CTLA4, PD1 or PDL1 have changed the way cancer patients with advanced disease are being treated, as evident by their FDA approval in a wide variety of malignancies, and their ability to induce high response rates when compared to conventional therapies (e.g. chemotherapy). However, even in melanoma, despite the high response rate, most patients are refractory to therapy or acquire resistance in 10-12 months. To identify key immunological elements coupled with response or resistance to checkpoint immunotherapy, we performed an unbiased analysis on 16,291 CD45+ immune cells from 32 patients (48 samples) treated with checkpoint therapy (anti-PD1=37; anti-PD1/CTLA4=11), using single cell transcriptomics. Initial unsupervised clustering of all CD45+ cells identified 11 clusters. When examining the association of these clusters with clinical outcome, we found that two clusters (B-cells, p=0.005; memory T-cells, p=0.03) were significantly enriched in responder lesions while 4 clusters (monocytes/macrophages, p=0.003; dendritic cells, p=0.015; exhausted CD8+ T-cells, p=0.005; and exhausted/cell-cycle lymphocytes, 1.3x10-5) are enriched in non-responder lesions. Next, by leveraging our unbiased single cell approach, we identified not only clusters but also specific markers associated with either responder lesions (PLAC8, LTB, LY9, SELL, TCF7, IGKC and CCR7) or non-responder ones (CCL3, CD38, HAVCR2, ENTPD1 and WARS), many of which were not previously reported to be associated with clinical outcome to checkpoint therapy. Due to the importance of CD8+ T-cells in controlling tumors, the significant association of T-cell states with clinical outcome, and their high abundance in melanoma tumors, we next focused our analysis on CD8+ T-cells and identified 2 main clusters CD8_G (with increased expression of genes linked to memory) and CD8_B (enriched for genes linked to cell dysfunction), that were significantly enriched in responder (CD8_G, p=1.4x10-6) and non-responder (CD8_B, p=0.005) lesions, respectively. Since cells with both states coexist in each of the responder and non-responder lesions, we decided to calculate the ratio between the number of cells in these 2 clusters and observed a significant separation between responders (CD8_G/CD8_B>1) and non-responders (CD8_G/CD8_B<1) when looking at all samples, baseline or post samples separately. Similar results were detected when looking at the expression of a single transcription factor, TCF7, in CD8 T-cells in an independent cohort (n=43) using a simple immunofluorescence assay. Additionally, we validated the identity and function of some of the newly identified cell states as well as their epigenetic landscape, and found that cells expressing the inhibitory molecules CD39 and TIM3, on top of being enriched in non-responder lesions, are likely to play a crucial role in T-cell exhaustion in melanoma. Collectively, our study provides extensive unbiased data in human tumors for discovery of predictors and therapeutic targets to checkpoint immunotherapy.

  Study phs001680