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• Genome-wide study of the effect of blood collection tubes on the cell-free DNA methylome

  Background The methylation pattern of cfDNA, isolated from liquid biopsies, is gaining substantial interest for diagnosis and monitoring of diseases. We have evaluated the impact of type of blood collection tube and time delay between blood draw and plasma preparation on bisulfite-based cfDNA methylation profiling. Methods 15 tubes of blood were drawn from three healthy volunteer subjects (BD Vacutainer K2E EDTA spray tubes, Streck Cell-Free DNA BCT tubes, PAXgene Blood ccfDNA tubes, Roche Cell-Free DNA Collection tubes and Biomatrica LBgard blood tubes in triplicate). Samples were either immediately processed or stored at room temperature for 24 or 72 hours before plasma preparation. DNA fragment size was evaluated by capillary electrophoresis. Reduced representation bisulfite sequencing was performed on the cell-free DNA isolated from these plasma samples. We evaluated the impact of blood tube and time delay on several quality control metrics. Results All preservation tubes performed similar on the quality metrics that were evaluated. Furthermore, a considerable increase in cfDNA concentration and the fraction of it derived from NK cells was observed after a 72-hour time delay in EDTA tubes. Conclusion The methylation pattern of cfDNA is robust and reproducible in between the different preservation tubes. EDTA tubes processed as soon as possible, preferably within 24 hours, are the most cost effective. If immediate processing is not possible, preservation tubes are valid alternatives.

  Study EGAS00001004271

• 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

• Long-read trio sequencing of unsolved patients with intellectual disability

  Long-read sequencing (LRS) has the potential to comprehensively identify all medically-relevant genome variation, including variation commonly missed by short read sequencing (SRS) approaches. To determine this potential, we performed LRS up to 40x coverage using the Pacific Biosciences Sequel instrument for 5 patient-parent trios with intellectual disability, whose aetiology remained unresolved after SRS exomes and genomes. A first systematic assessment of LRS coverage showed that ~35Mb of the genome was only accessible by LRS and not SRS. Structural variant (SV) calling yielded on average 28,292 SV calls per individual, totalling 12.9 MB of sequence. Trio-based analyses showed concordance for up to 95% of these SV calls across the genome, and 80% of the LRS SV calls were not identified by SRS. De novo mutation analysis did not identify any de novo SVs, confirming these are rare events. Because of high sequence coverage, we were also able to call single nucleotide substitutions. On average, we identified 3 million substitutions per genome, with a Mendelian inheritance concordance of up to 97%. Of these, approximately 100,000 were located in the ~35Mb of the genome that was only captured by LRS. Moreover, these variants affected the coding sequence of 64 genes, including 32 known to cause Mendelian disorders. Although no clinical diagnoses were made in these 5 patients, our data show the potential added value of LRS compared to SRS for identifying medically relevant genome variation.

  Study EGAS00001004319

• Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  Study EGAS00001002073

• Single-cell ATAC-seq analysis for COVID19 patients

  While SARS-CoV-2 infection causes mild respiratory disease in most individuals, a small group of patients develops severe COVID-19. Dysfunctional innate immune responses have been identified to contribute to differences in COVID-19 severity, but the key regulators are still unknown. Here, we present an integrative single-cell epigenetics, transcriptomic, and genetics analysis of peripheral blood mononuclear cells from hospitalized and convalescent COVID-19 patients. In classical monocytes, we identified 41.3% of significantly up-regulated genes in hospitalized COVID-19 patients potentially induced by differential chromatin accessibility. Sub-clustering and motif-enrichment analyses of monocytes reveal disease condition-specific regulation by transcription factors, such as C/EBPs and SPI1, and their targets, including a long-noncoding RNA LUCAT1, which further regulates interferon responses and is associated with the need for oxygen supply of COVID-19 patients. The interaction between C/EBPs and LUCAT1 was validated through loss-of-function experiments. Finally, we investigated genetic risk variants that exhibit allele-specific open chromatin (ASoC) in promoters/enhancers of COVID-19 patients. Integrating our data with publicly available expression quantitative trait loci and chromosomal interactions indicates that ASoC SNP rs6800484-C is associated with lower expression of CCR2, which may contributeto higher viral loads in lungs and higher risk of COVID-19 hospitalization. Altogether, our study highlights the diverse genetic and epigenetic regulators that contribute to the innate immune responses of different COVID-19 patients.

  Study EGAS00001006559

• Single-cell RNA-seq analysis for COVID19 patients

  While SARS-CoV-2 infection causes mild respiratory disease in most individuals, a small group of patients develops severe COVID-19. Dysfunctional innate immune responses have been identified to contribute to differences in COVID-19 severity, but the key regulators are still unknown. Here, we present an integrative single-cell epigenetics, transcriptomic, and genetics analysis of peripheral blood mononuclear cells from hospitalized and convalescent COVID-19 patients. In classical monocytes, we identified 41.3% of significantly up-regulated genes in hospitalized COVID-19 patients potentially induced by differential chromatin accessibility. Sub-clustering and motif-enrichment analyses of monocytes reveal disease condition-specific regulation by transcription factors, such as C/EBPs and SPI1, and their targets, including a long-noncoding RNA LUCAT1, which further regulates interferon responses and is associated with the need for oxygen supply of COVID-19 patients. The interaction between C/EBPs and LUCAT1 was validated through loss-of-function experiments. Finally, we investigated genetic risk variants that exhibit allele-specific open chromatin (ASoC) in promoters/enhancers of COVID-19 patients. Integrating our data with publicly available expression quantitative trait loci and chromosomal interactions indicates that ASoC SNP rs6800484-C is associated with lower expression of CCR2, which may contributeto higher viral loads in lungs and higher risk of COVID-19 hospitalization. Altogether, our study highlights the diverse genetic and epigenetic regulators that contribute to the innate immune responses of different COVID-19 patients.

  Study EGAS00001006560

• deep-learning-powered tissue deconvolution for cfDNA

  Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

  Study EGAS00001007213

• RNA-seq study of a Princess Margaret Cancer Centre human acute myeloid leukemia patient cohort

  The PMCC AML RNAseq dataset consists of 81 AML patient samples (clinical data in Supplemental Table 11 of manuscript), processed in two batches. These patient samples are able to engraft in the NSG (NOD.Cg PrkdcscidIl2rgtm1Wjl /SzJ) mouse model. Five patients (90543, 598, 90240, 110484, 100500) were included in both batches. Viaably frozen material from the Leukemia Tissue Bank at Princess Margaret Cancer Centre/ University Health Network were thawed by dropwise addition of X-VIVO + 50% fetal calf serum supplemented with DNase (100μg/mL final concentration, Roche). RNA was extracted from bulk peripheral blood mononuclear cells (PBMC) using the RNeasy Micro Kit (Qiagen Inc.). A paired-end 76 base-pair flow-cell lane Illumina High seq 2000 yielded an average of 240 million sequence reads aligning to genome per sample at the Genome Sciences Centre, BC Cancer Agency for cohort 1. Cohort 2 was subjected to 125 bp, paired-end RNA-sequencing on the Illumina HiSeq 2500 with an average of 50 million reads/sample at the Centre for Applied Genomics, Sick Kids Hospital.

  Study EGAS00001004792

• A combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancer

  The earlier diagnosis of cancer is one of the keys to reducing future cancer deaths. Here we describe our efforts to develop a non-invasive, blood test for the detection of pancreatic ductal adenocarcinoma. We combined blood tests for KRAS gene mutations with carefully thresholded protein biomarkers to determine whether the combination of these markers was superior to any single marker. The cohort tested included 221 patients with resectable pancreatic ductal adenocarcinoma and 185 control patients without known cancers. KRAS mutations were detected in the plasma of 66 patients (30%) and every mutation found in the plasma was identical to that subsequently found in the patients' primary tumor (100% concordance). The use of KRAS in conjunction with two thresholded protein biomarkers (CA19-9 and prolactin) increased the sensitivity to 67%. Only one of the 185 plasma samples from the control cohort were positive for any of the three markers (99.5% specificity). This combinatorial approach may prove useful for the earlier detection of many cancer types.

  Study EGAS00001002444

• T2D-GENES Consortium: San Antonio Mexican American Family Studies (SAMAFS)

  The Type 2 Diabetes (T2D) Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Consortium is a collaborative international effort to identify genes influencing susceptibility to type 2 diabetes in multiple ethnic groups using next generation sequencing. To fulfill this objective, T2D-GENES Consortium undertook two large sequencing studies, called T2D-GENES Projects 1 and 2. Project 1 has carried out whole exome sequencing of 12,940 individuals, 6,504 with T2D and 6,436 non-diabetic controls, equally divided among five continental ancestry groups: Europeans, East Asians, South Asians, Hispanic Americans, and African Americans. The goal of Project 1 is to identify all genetic variants in the complete coding regions of the genomes (i.e., whole exome) by sequencing, including rare variants. Project 2 (i.e., SAMAFS substudy 2) is a pedigree-based study designed to identify low frequency or rare variants influencing susceptibility to T2D, using whole genome sequence information from approximately 600 individuals in 20 Mexican American T2D-enriched pedigrees from San Antonio, Texas, augmented with family-based imputation into approximately 440 additional family members. The major objectives of Project 2 are to identify low frequency or rare variants in and around known common variant signals for T2D, as well as to find novel low frequency or rare variants influencing susceptibility to T2D. Both T2D-GENES Projects 1 and 2 involve the San Antonio Mexican American Family Studies (SAMAFS), which are composed of four San Antonio, Texas-based family studies: the San Antonio Family Heart Study (SAFHS), San Antonio Family Diabetes/Gallbladder Study (SAFDGS), Veterans Administration Genetic Epidemiology Study (VAGES), and Family Investigation of Nephropathy and Diabetes - San Antonio (FIND-SA) Component and its extension called the Extended FIND [E-FIND]. The SAFHS and SAFDGS began in 1991 and have followed participants with extensive clinical phenotyping related to T2D for over 20 years. The VAGES was initiated in 1994 and a large battery of T2D-related phenotypic data has been obtained from its participants. The FIND-SA began in 2000, a part of the multicenter FIND study which was designed to identify genetic determinants of diabetic kidney disease; data from its participants related to T2D were used for this project. Non-overlapping subsets of SAMAFS participants are part of the T2D-GENES Projects 1 and 2, henceforth referred to as SAMAFS substudies 1 and 2, respectively. The SAMAFS substudies 1 and 2 are part of one of the five awards funded by NIDDK under a cooperative agreement award mechanism, which is governed by the Steering Committee of the T2D-GENES Consortium. Since Project 1 relies on population based subsets of cases and controls, 491 unrelated participants are drawn from the four SAMAFS as part of the T2D-GENES Project 1 Mexican American sample (i.e., SAMAFS substudy 1). The whole exome sequencing was performed at the Broad Institute. For Project 2, 1048 individuals are drawn from two SAMAFS (SAFHS and SAFDGS), representing 20 large families for substudy 2. The substudy 2 strategy is to sequence approximately 600 individuals at an average of 50x coverage across the entire genome, then impute genome wide genotypes for about 440 additional family members. The 600 sequenced individuals are specifically chosen for their value in imputing sequence information into other family members. By studying large pedigrees, we expect to find multiple individuals carrying each genetic variant, even if this variant is very rare in the population at large. Thus, a pedigree-based approach provides an excellent opportunity for identifying rare novel variants influencing risk of T2D and quantitative variation in T2D-related phenotypes. The whole genome sequencing has been done commercially by Complete Genomics, Inc. (CGI). The available sample of 1,048 includes 5 sequenced individuals who do not belong to any of the 20 large pedigrees. The final family data of 1,043 individuals includes whole genome sequence data for 607 individuals. After quality control, 590 sequenced individuals provide data for family based imputation using Merlin linkage analysis software into approximately 440 additional family members for whom chip based genotypes are available to indicate which parental haplotype is transmitted. The complete SAMAFS data including phenotype, genotype, sequence, other T2D-related trait data utilized for Projects 1 and 2 are available. These data can readily be viewed by clicking on the substudy title shown below or in the box: "Substudies", located on the right hand side of this parent or top study page phs000847.v1.p1, titled T2D-GENES Consortium: San Antonio Mexican American Family Studies (SAMAFS). phs000849 T2D-GENES Project 1: San Antonio Mexican American Family Studies (SAMAFS), Substudy 1: Whole Exome Sequencing phs000462 T2D-GENES Project 2: San Antonio Mexican American Family Studies (SAMAFS), Substudy 2: Whole Genome Sequencing in Pedigrees

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