12213 results for "cancer rna-seq"

in 21.41 milliseconds.

• Genomic Profiling of Pediatric B-cell Acute Lymphoblastic Leukemia

  Pediatric leukemia is the most common type of childhood cancer and, despite tremendous progress in treatment, remains the second leading cause of cancer deaths in children. B-cell acute lymphoblastic leukemia (B-ALL) is the most common type of pediatric leukemia. Recent studies using transcriptional profiling of large cohorts of pediatric B-ALL have shown that patients can be grouped into transcriptional subtypes that are often defined by specific driver mutations. Interestingly, many of these mutational subtypes are defined by gene fusions or rearrangements that drive altered expression of transcription factors (ETV6-RUNX1, PAX5, DUX4, TCF3-PBX1, ZNF384) or in chromatin regulatory machinery (KMT2A), suggesting that transcriptional dysregulation is a major driver of B-ALL. While the transcriptional distinctions are clear, the relationship between patients in terms of their chromatin regulatory landscape remains unclear. To address this, we profiled gene expression, open chromatin, and 3D genome folding in patient derived B-ALL samples. We identified that at the level of 3D genome organization, many chromatin interactions and looping events are shared across mutational subtypes, suggesting that shared transcriptional programs, in addition to group specific driver mutations, possibly regulate the chromatin landscape in B-ALL. Using RNA-seq and ATAC-seq data, we were able to identify "latent" signatures of expression related to non-mutant transcription factors (TFs) that also contribute to expression heterogeneity across TF subtypes. These are related to patient specific patterns of 3D genome folding, and in some cases, are predictive of disease outcomes. This study helps shed light on the chromatin landscape in B-ALL and furthers our understanding of the interactions between diverse mutant TFs that drive B-ALL and non-mutant TFs that help shape the B-ALL chromatin landscape.

  Study phs003226

• Enhancer signatures stratify and predict outcomes of non-functional pancreatic neuroendocrine tumors

  Most pancreatic neuroendocrine tumors (PNETs) do not produce symptoms of hormonal excess and are hence considered “non-functional”. Their clinical behaviors vary widely, emphasizing the need for a robust classification with prognostic power. Using enhancer maps to infer regulatory programs, we find that the large majority of non-functional PNETs fall into two major sub-types that reflect alpha and beta endocrine cell ontogeny, respectively. Tumors of the different subtypes have similar clinical presentations and histology, but express distinct lineage-specifying transcription factors, ARX or PDX1. Here we provide the raw ChIP-seq and RNA-seq data of the PNET cohort in this study, as well as ChIP-seq data of ileal carcinoids.

  Study phs001910

• GENCORD2_RNAseq

  GENCORD2 RNA-seq BAM files using BWA

  Dataset EGAD00001000425

• SF4324 Primary Wildtype GBM Female, 57.

  Single Cell RNA-Seq of Primary GBM. Gender Female, Age, 57.

  Dataset EGAD00001006017

• Aerobiology, Immunology, and Mycobacterium Tuberculosis Transmission

  We submit data from the TB Aerobiology, Immunology and Transmission (TBAIT) study examining clinical and transcriptomic characteristics associated with Tuberculosis cough aerosolization. From a prospective longitudinal cohort of 142 participants with previously untreated pulmonary Mycobacterium tuberculosis (Mtb) infection, we assessed presence of culturable Mtb aerosols using a cough aerosol sampling system (CASS), as well as other clinical, microbiologic, and immunologic characteristics. We performed whole blood transcriptional profiling (submitted here) on a convenience subset of 29 cough aerosol culture (CAC) positive participants and 29 CAC negative participants, using Illumina NextSeq RNA Sequencing (RNA-Seq).

  Study phs003727

• RNA-seq FASTQ files from newborn screening dried blood spot samples

  Paired-end RNA-seq FASTQ files from 21 newborn screening dried blood spot (DBS) samples. These DBS samples were obtained from extremely low gestional age newborns, where 10 of them were affected by a fetal inflammatory response (FIR) before birth, and 11 were unaffected. Total RNA was sequenced using an Illumina NextSeq-500 instrument. The sample preparation protocol included the depletion of rRNA and globin mRNA using the Globin Zero Gold rRNA Removal Kit from Illumina. Libraries were prepared using the NebNext Ultra TM II Directionl RNA LIbrary Prep Kit (New England Biolabs). Each sample was sequenced in 4 lanes, leading to 8 FASTQ files per sample and a total of 21x8=168 FASTQ files. There is an additional number of 8 FASTQ files corresponding to sample BS13, which was downsampled to 1/4 of its original depth (see BS13_README file for details).

  Dataset EGAD00001004991

• DRIP-seq data for Molecular Characterization of ETMRs

  This dataset contains DRIP-seq data from 2 patients. DNA–RNA hybrids were extracted from tissue derived from ETMR patient-derived xenograft (PDX) models (BT183) that were treated using topotecan or saline as described previously27. Tumours were subsequently frozen and pelleted using ultracentrifugation. DNA–RNA hybrids were extracted as described previously using the same protocol that is applied for cultured cells21. DNA was extracted using proteinase K followed by phenol–chloroform extraction and ethanol precipitation. Subsequently the DNA was fragmented using the restriction enzymes HindIII, EcoRI, BsrGI, XbaI and SspI (New England Biolabs). Digested DNA was subsequently incubated with the anti-DNA–RNA hybrid anti- body S9.6 (Merck, MABE1095) and immunoprecipitated using agarose beads. Bound DNA–RNA hybrids were eluted and incubated with pro- teinase K and cleaned with an additional phenol–chloroform–ethanol extraction. The DNA was subsequently sonicated and sequenced using a Hiseq 2000 machine with a 50-bp single-read protocol. Each treat- ment condition was performed in duplicate and both RNase H and the input was included as negative controls resulting in 10 Fastq files.

  Dataset EGAD00001006219

• Transcriptome sequencing of cancer cell lines

  This dataset contains RNA sequencing data for 675 cancer cell lines. RNA libraries were made with the TruSeq RNA Sample Preparation kit (Illumina) according to the manufacturer protocol. The libraries were sequenced on an Illumnia HiSeq 2000

  Dataset EGAD00001000725

• MESA colorectal cancer cfDNA EM-seq dataset

  This dataset contains the methylation sequencing data of 60 nonCancer and 70 colorectal cancer cfDNA samples. The methylation library is constructed by using NEBNext Enzymatic-seq Kit.

  Dataset EGAD00001009165

• Transcriptome analysis of adolescents and young adults with Acute Lymphoblastic Leukemia

  The primary purpose of the research is identification of novel chromosome translocations in adolescents and young adults (AYA) with Acute Lymphoblastic Leukemia (ALL). We searched for novel chromosome translocations in 99 philadelphia-chromosome negative ALL cases (including 76 AYA cases) by RNA-seq using next-generation sequencers, and have discovered some novel chromosome translocations.

  Study JGAS000047

• Bulk RNA and ATACseq of 2 XLP patients and 5-6 HD

  The dataset contains five samples from P1, one pre-HSCT and four post-HSCT, one sample from P2 and 5-6 HD. RNAseq reads were processed using the LUMC BIOWDL RNAseq pipeline v5.0.0 (https://github.com/biowdl/RNA-seq). Genes with log2CPM≥1 in at least 10% of all samples were retained for downstream analysis. Differential gene expression analysis was performed using the dgeAnalysis R-Shiny application (https://github.com/LUMC/dgeAnalysis/tree/v1.4.4). ATACseq reads were processed using the LUMC BIOWDL Chipseq pipeline 1.0.0-dev (https://github.com/biowdl/ChIP-seq).

  Dataset EGAD50000002072

• RNA-seq datatset of primary human thymocyte subsets

  FASTQ-files of RNA-seq experiments of 8 primary human thymocyte subsets, isolated by flow-cytometry (FACS) from neonatal thymus biopsies. All samples were sorted to be negative for the expression of the lineage surface markers CD11c, CD19, CD56, CD141 and CD303 and additionally had the following surface phenotypes: DN1 (CD4-CD8-CD7+CD1a-CD161-CD38-); DN2 (CD4-CD8-CD7+CD1a-CD161-CD38+CD3-TCRab-); DN3 (CD4-CD8-CD7+CD1a+); ISP (CD4+CD8-CD3-TCRab-CD1a+); DPE (CD4+CD8+TCRab-CD69-); DPL (CD4+CD8+TCRab+CD69+); SP4 (CD4+CD8-); SP8 (CD4-CD8+). Samples were sorted from 2-4 pediatric patients undergoing heart surgery.

  Dataset EGAD50000001599

• Single-cell RNA-seq of immune cells from human tumor fragments after 48 hour culture ex vivo (Kaptein et al., 2024)

  Single-cell RNA-seq profiling of immune cells from human ovarian carcinoma, renal carcinoma and melanoma samples after 48h of ex vivo culture using the patient-derived tumor fragment platform (5 samples total). Samples were cultured in various conditions, including: untreated, CD8-IL2v-treated, CD8-IL2v + LCKi -treated, aPD1-treated, CD8-IL2v + aPD1 -treated, Untargeted IL2-treated, aCD3-treated and aCD3 + CD8-IL2-treated . FASTQ files contain gene expression data, feature barcode antibody capture or TCR data on immune cells from all conditions combined per patient.

  Dataset EGAD50000000377

• BLUEPRINT release August 2014, RNA-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell

  RNA-Seq data for 4 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 22 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000903

• BLUEPRINT release January 2015, RNA-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell

  RNA-Seq data for 5 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 23 run(s), 5 experiment(s), 5 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001165

• BLUEPRINT release August 2015, RNA-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  RNA-Seq data for 5 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 23 run(s), 5 experiment(s), 5 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001534

• BLUEPRINT release August 2016, RNA-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  RNA-Seq data for 6 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 24 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002339

• Blueprint: A human variation panel of genetic influences on epigenomes and transcriptomes in three immune cell types, (RNA-Seq for CD4-positive, alpha-beta T cell, on genome GRCh37)

  RNA-Seq data for 212 CD4-positive, alpha-beta T cell sample(s). 212 run(s), 212 experiment(s), 212 analysis(s) on human genome GRCh37. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/blueprint_Epivar/protocols/README_rnaseq_analysis_sanger_20160816

  Dataset EGAD00001002671

• Blueprint: A human variation panel of genetic influences on epigenomes and transcriptomes in three immune cell types, (RNA-Seq for CD14-positive, CD16-negative classical monocyte, on genome GRCh37)

  RNA-Seq data for 197 CD14-positive, CD16-negative classical monocyte sample(s). 197 run(s), 197 experiment(s), 197 analysis(s) on human genome GRCh37. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/blueprint_Epivar/protocols/README_rnaseq_analysis_sanger_20160816

  Dataset EGAD00001002674

• Epigenome cohort 30 Patient DNA and RNA

  This dataset includes RNA-seq, DNA and Chip-Seq data of samples from our paper.

  Dataset EGAD00001009041

• Transcriptome profiling of three giant cell tumour of bone (GCTB) cell lines

  This dataset contains RNA-seq data of giant cell tumour of bone (GCTB) cell lines (n=3). Cell lines consist of neoplastic "stromal" cells harboring a heterozygous H3F3A p.G34W mutation. RNA-seq was performed on the BGISEQ-500 platform (PE100) and uploaded data contains fastq files, vcf files, and gene expression values (TPM). Cell lines, data generation, and data analysis are described in the following publication: Venneker et al., Histone deacetylase inhibitors as a therapeutic strategy to eliminate neoplastic “stromal” cells from giant cell tumors of bone, 2022.

  Dataset EGAD00001009074

• Paired RNA-Seq was performed on 125 samples of low grade pediatric glioma

  Paired RNA-Seq was performed on 125 samples of low grade pediatric glioma. The sequencing was done with Illumina Novaseq 6000 and the Illumina TruSeq stranded mRNA kit.

  Dataset EGAD00001010171

• RNA-Seq of Myeloid Cells in Dendritic Cell Therapy Response

  RNA libraries were prepared from 100 ng of total RNA using the Illumina Stranded Total RNA Prep with Ribo-Zero Plus kit, then sequenced (2×150 bp) on the Illumina NextSeq550. Transcript-level quantification was done with kallisto, followed by gene-level summarization (tximport) and differential expression analysis (DESeq2). Quality control included PCA, hierarchical clustering, and deconvolution with CIBERSORTx to exclude non-myeloid genes. Final analyses identified transcriptomic differences among healthy donors and three patient subgroups based on response to dendritic cell therapy.

  Dataset EGAD50000001681

• Bulk RNA-Seq Digital Gene Expression Matrix

  Raw read counts and normalized read counts for each gene

  Dataset EGAD50000002489

• PDAC organoid genomic heterogeneity

  Two primary tumor-derived PDAC organoids were subjected to SNP array, RNA-seq, and single-cell WGS

  Dataset EGAD00010002408

• SF10432 Wildtype Primary GBM Female, 50

  Single Cell-RNA Seq of Wildtype Primary GBM for Female, Age 50.

  Dataset EGAD00001006019

• RNAseq data for EGAS00001004572

  Bulk RNA-seq data of tumours in EGAS00001004572.

  Dataset EGAD00001006876

• INSPIRE RNA-seq tumors

  INSPIRE whole transcriptome sequencing of tumors

  Dataset EGAD00001006564

• RNA-seq data from 27 glioblastoma samples

  27 Fresh frozen tissue specimens were crushed by mortar and pestle, homogenized using the QIAShredder kit (Qiagen), and genomic DNA and total RNA were extracted using the AllPrep DNA/RNA Mini kit (Qiagen), according to the manufacturer’s instructions. RNA libraries were synthesized using 200 ng of total RNA using the Ilumina TruSeq Stranded RNA LT Sample Prep Kit (Illumina), and subsequently sequenced on the NextSeq550 platform to a read depth of 80 million clusters and 160 million paired end reads (75 bp X 75 bp) using V2 chemistry.

  Dataset EGAD00001008362

• Single-cell RNA seq-derived signatures define response patterns to atezolizumab + bevacizumab in advanced hepatocellular carcinoma

  FASTq files and normalised gene expression counts from bulk RNA-sequencing of pre-treatment tumour (n=39) and adjacent non-tumour tissue samples (n=13) from advanced hepatocellular carcinoma (HCC) patients treated with atezolizumab plus bevacizumab.

  Study EGAS50000000838

• RNA seq before and after cold pressor test

  The cold pressor test (CPT) is a widely used pain provocation test to investigate both pain tolerance and cardiovascular responses. Twenty-two females were phenotypically assessed before and after a CPT, and blood samples were taken for RNA-sequencing.

  Study EGAS00001006690

• Transcriptional profiles of Kleefstra syndrome (EHMT1 and EHMT2) and healthy fibroblasts

  This dataset contains FASTQ files from RNA sequencing (RNA-seq) of fibroblasts derived from a healthy donor (control), a patient with Kleefstra syndrome (KSP), and a patient carrying a pathogenic variant in EHMT2 (P2). Ribosomal RNA was removed using an RNase H–based depletion method, and strand-specific libraries were prepared for paired-end sequencing on the MGI DNBSEQ PE100 platform.

  Dataset EGAD50000002343

• 10X single-cell Multiome (RNA+ATAC) of bone marrow-derived HSPC

  10X genomics chromium single-cell ATAC+RNA (Multiome) was use to prepare single-nucleus RNA- and ATAC-seq libraries, sequenced on an Illumina NovaSeq 6000 platform. BAM files are provided. Samples from 6 donors were sequenced after sorting for primitive HSPCs in 5 libraries; each library comprises a single donor, except one with cells from 1 male and 1 female donor.

  Dataset EGAD50000002326

• RNA-seq of multiple myeloma patient samples

  RNA from CD138+ plasma cells from patients with a plasma cell dyscrasia were sequenced using the TruSeq Stranded Total RNA Ribo-zero Gold kit (Illumina). Paired-end 75bp reads were generated on a NextSeq500 or HiSeq4000 (Illumina). The dataset consists of 1 MGUS sample, 5 SMM samples, 69 newly diagnosed myeloma samples, 1 relapsed myeloma sample, 1 previously treated myeloma samples, and 4 PCL samples. Matching whole genome sequencing data are available for these samples under study EGAS00001003164.

  Dataset EGAD00001004543

• 110 "KOREAN" never-smoker female adenocarcinoma RNA-seq

  All sequencing was performed within the DNAlink (Korea) by using the Solexa sequencing technology (Illumina, San Diego, CA). mRNA was isolated from total RNA using poly-T oligo-attached magnetic beads and was fragmented with fragmentation buffer to an average size of 300 bp. The libraries were prepared by using TruSeq RNA Library Prep Kit v2 (Illumina) and were sequenced on the Illumina HiSeq2000 using the manufacturer’s recommended protocols

  Dataset EGAD00001005358

• RNA-seq of atypical 3q26 samples

  RNA was isolated using phenol-chloroform extraction followed by DNase digestion or using the Qiagen Allprep DNA/RNA kit and protocol (Qiagen, #80204). cDNA synthesis was done using the SuperScript II Reverse Transcriptase kit (Invitrogen). Quantitative real-time PCR was performed by using primers as described previously13,21 on the 7500 Fast Real-time PCR System (Applied Biosystems). Relative levels of gene expression were calculated using the ΔΔCt method

  Dataset EGAD00001006106

• RNA-sequencing of T-cell Acute Lymphoblastic Leukemia (T-ALL)

  This dataset contains raw fastq files from the RNA-Seq of 96 T-Acute Lymphoblastic Leukemia. Libraries where prepared using Agilent SureSelect XT-HS2 RNA Reagent Kit. As such, reads contain molecular barcodes that could be specifically handled using the AGeNT tool.

  Dataset EGAD00001010273

• single cell RNA-seq and ATAC-seq of human fetal forebrain tissue

  Oligodendrogenesis in the human central nervous system has been mainly observed at the second trimester of gestation, a much later developmental stage compared to mouse. Here we characterize the transcriptomic neural diversity in the human forebrain at post conceptual weeks (PCW) 8 to 10, using single-cell RNA-Seq. We find evidence of the emergence of a first wave of oligodendrocyte lineage cells as early as PCW 8, which we also confirm at the epigenomic level with single-cell ATAC-Seq. Using regulatory network inference, we predict key transcriptional events leading to the specification of oligodendrocyte precursor cells (OPCs). Moreover, by profiling the spatial expression of fifty key genes using In Situ Sequencing (ISS), we identify regions in the human ventral fetal forebrain where oligodendrogenesis first occurs. Our results indicate evolutionary conservation of the first wave of oligodendrogenesis between mouse and human and describe regulatory mechanisms involved in human OPC specification.

  Study EGAS00001006136

• CIRCLE-seq of PDAC PDO

  CIRCLE-seq was performed on a PDAC PDO to validate the presence and the structure of ecDNAs

  Study EGAS50000000194

• Epigenomics of Prostate Cancer from Cell Free Plasma

  We sought to perform epigenomic characterization of prostate cancers by profiling circulating nucleosomes isolated from patient plasma using cell free chromatin immunoprecipitation followed by sequencing (cfChIP-seq). Our major findings include identification of prostate-cancer specific regulatory elements using a novel statistical method accounting for variable tumor DNA fraction in plasma, inferring transcription factor activity based on binding motif enrichment within regulatory elements, and epigenetically phenotyping patients based on clinical and genomic features. Data available in dbGaP will include cfChIP-seq and targeted sequencing from approximately 50 patients with a known diagnosis of cancer and healthy patient controls.

  Study phs003482

• Characterization of chromatin accessibility in metastatic prostate cancer

  The development of resistance to second-generation AR-signaling inhibitors (ARSIs) and progression to metastatic castrate-resistant prostate cancer (mCRPC) represents a huge obstacle to the improvement of patients’ clinical outcomes. Lineage plasticity is a common mechanism that drives the development of mCRPC by changing chromatin conformation at regulatory elements affecting downstream gene activity. ATAC-seq is a popular technique that measures accessible chromatin regions. To better understand of potential epigenomic mechanisms of ARSI resistance in prostate cancer, in this study, we performed ATAC-seq on 70 mCRPC tissue biopsies from the SU2C/PCF West Coast Dream Team (WCDT) cohort.

  Study EGAS00001006698

• Transcriptomics of PPD and Control LCLs Exposed to Steroid Hormones

  Lymphoblastoid cell lines (LCLs) were derived from women with (n=11) and without (n=9) past postpartum depression (PPD) and compared transcriptomically in two studies. Data provided represents: 1) normalized RNA-seq expression (CPM) or 2) Ampliseq-RNA Expression (CPM).The two studies are described below.1) The first examined estradiol (E2) and progesterone (P) hormone conditions mimicking pregnancy and parturition: supraphysiologic E2+P-addback; supraphysiologic E2+P-withdrawal; and no added E2+P (Baseline). RNA-sequencing identified unique differentially expressed genes (DEGs, p2) The second exposed control and PPD LCLs to ALLO or DMSO vehicle for 60 hours and used Ampliseq to detect differentially expressed genes (DEGs, pnominal

  Study phs003820

• A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection

  Cell-free RNA (cfRNA) is a promising analyte for cancer detection. However, a comprehensive assessment of cfRNA in individuals with and without cancer has not been conducted. We performed the first transcriptome-wide characterization of cfRNA in cancer (stage III breast [n=46], lung [n=30]) and non-cancer (n=89) participants from the Circulating Cell-free Genome Atlas (NCT02889978). Of 57,820 annotated genes, 39,564 (68%) were not detected in cfRNA from non-cancer individuals. Within these low-noise regions, we identified tissue- and cancer-specific genes, defined as "dark channel biomarker" (DCB) genes, that were recurrently detected in individuals with cancer. DCB levels in plasma were correlated with tumor shedding rate and RNA expression in matched tissue, suggesting that DCBs with high expression in tumor tissue could enhance cancer detection in patients with low levels of circulating tumor DNA. Overall, cfRNA provides a unique opportunity to detect cancer, predict the tumor tissue of origin, and determine the cancer subtype.

  Study EGAS00001004704

• Elucidation of the association of the HPV integration and oropharyngeal cancer

  Whole-genome sequencing was performed using tumor and blood samples from 14 patients with HPV-associated oropharyngeal cancer. Bulk RNA sequencing was performed using tumor samples from 19 patients with HPV-associated oropharyngeal cancer and 17 patients with non-HPV-associated oropharyngeal cancer and normal tonsil samples from 2 healthy controls.

  Study JGAS000751

• PACA-CA RNASeq bam files

  Bam files for PACA-CA RNA Seq analysis, for DCC release 27

  Dataset EGAD00001003945

• CPTAC: Proteogenomic Studies of Ovarian Tumor Responses to Agents Targeting the DNA Damage Response

  There is a critical unmet need for predictors of chemo-refractory High-grade serous ovarian cancer (HGSOC). Despite over 3 decades of research on platinum responses in cancer, no predictive biomarker has been translated into clinical use. Predictors of refractory disease could spare these patients the unnecessary toxicity of a platinum-based regimen and provide a means to triage these patients to clinical trials to identify effective therapies for refractory disease. This study has approached the development of a predictor in a novel way enabling detection of a multi-analyte panel biomarker capable of detecting a subset of refractory HGSOCs with high specificity and implicating potential treatment vulnerabilities for refractory disease. We reported the first proteogenomic analysis of platinum refractoriness in HGSOC and made the novel dataset available as a resource to the community, including a ProTrack portal: http://ptrc.cptac-data-view.org/. Detailed proteogenomic profiles were generated for this study from a retrospective cohort of 242 treatment-naive HGSOCs with enriched representation of "exceptional non-responders" (i.e., patients with chemo-refractory disease). Whole genome sequence (WGS) and RNA seq FASTQ files are available through dbGaP. Proteomic data are available through the NCI Proteomic Data Commons (PDC): https://pdc.cancer.gov/pdc/.

  Study phs003152

• Molecular determinants of response to PD-L1 blockade across tumor types

  Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity was observed between indications, in particular within the PD-L1+ tumors. mUC and NSCLC were molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A was identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis revealed molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies.

  Study EGAS00001004343

• Immune Responses in Checkpoint Myocarditis Across Heart, Blood, and Tumor

  Immune checkpoint inhibitors (ICIs) are widely used in anti-cancer therapies, but they can cause morbid and potentially fatal immune-related adverse events (irAEs), such as ICI-related myocarditis (irMyocarditis). The pathogenesis of irMyocarditis and its relationship to anti-tumor immunity remain poorly understood. We sought to define immune responses in heart, tumor, and blood in irMyocarditis patients and controls by leveraging single‐cell RNA sequencing (scRNA‐seq) coupled with T-cell Receptor Sequencing (TCR-Seq). Our analysis demonstrated increased frequencies of cytotoxic T cells, inflammatory mononuclear phagocytes (MNPs), conventional dendritic cells (cDCs), and inflammatory fibroblasts in irMyocarditis heart tissue. Additionally, we revealed decreased frequencies of plasmacytoid dendritic cells, cDCs, and B lineage cells but an increased frequency of MNPs in the blood of irMyocarditis patients. Raw data for heart and blood can be found in dbGaP. All processed data and raw TCRseq data from heart tissue and tumor samples for this study can be found in GEO (Accession number GSE228597).Subject IDs with the prefix "SIC" were collected by the Severe Immunotherapy Complications (SIC) service at MGH due to suspicion of immune-related adverse events. Subject IDs with the prefix "donor" were collected by the MGH Melanoma Biobank at scheduled timepoints; this biobank systematically collects samples from patients at MGH at clinically relevant timepoints.

  Study phs003413

• Single Cell and Spatial Transcriptomic Profiling of Haemophilus ducreyi Infection

  We investigated the immune response in end-point pustules formed by Haemophilus ducreyi in volunteers using an experimental infection model. Six volunteers were infected in the skin of the upper arm at three sites with 41-85 CFU of H. ducreyi loaded on an allergy testing device; a sham-inoculated (e.g., wound) site was also performed. Five volunteers (4 men and 1 woman; 2 Asians and 3 Whites; aged 27-46 years) formed pustules 6-8 days post infection; 1 volunteer spontaneously resolved all sites. If a volunteer formed pustules, biopsies were performed to collect their pustule and wound sites. Biopsy pairs for all 5 pustule formers were subjected to single cell (sc) RNA-seq. Sequencing libraries were prepared using the 10X Genomics Single-cell 3' RNA-seq kit. Spatial transcriptomics was performed on biopsy pairs from 4 of the 5 pustule formers. Ten micron sections were placed on 10X Genomics Visium slides for fresh-frozen tissue, and libraries were prepared with the 10X Genomics Visium spatial 3' gene expression kit. All samples were sequenced on an Illumina NovaSeq 6000. Count tables were produced with CellRanger and SpaceRanger, respectively, and Seurat was used to analyze the data. We identified 13 major cell types by scRNA-seq; the proportion of some immune cell subsets was increased in pustules compared to wound sites. We also localized the major cell types within the tissue by using the scRNA-seq data to deconvolve the spatial transcriptomic data. The FASTQ files and count tables from CellRanger and SpaceRanger are provided.

  Study phs003754

• Somatic Mutations in Single Human Neurons from Patients with Congenital Neurodegenerative Diseases

  It has been hypothesized that accumulating DNA lesions in neurons contribute to aging and neurodegeneration. Despite their pathophysiological importance, the DNA lesions have not been systematically examined with effective methods. In this study, we developed a computational method for detecting genomic deletions in single-cell whole-genome sequencing (scWGS) data, and analyzed the whole genome of human single neurons. Single neurons were obtained from neurotypical individuals of various ages and from patients with three different congenital neurodegenerative diseases: Ataxia telangiectasia (AT), Cockayne syndrome (CS), and Xeroderma pigmentosum (XP). Single-neuron WGS data of AT brains are released here, and the data of other two diseases are released in a separate dbGaP study (phs001485).To understand the molecular mechanisms underlying AT, we performed single-nucleus RNA-sequencing (snRNA-seq) of postmortem cerebellar vermis and prefrontal cortex from individuals with AT and controls. To genetically confirm the diagnosis of AT, we also performed WGS on postmortem brain tissue from each individual with AT. Induced pluripotent stem cells (iPSCs) from individuals with AT and controls were differentiated into microglia, and the transcriptomes were profiled with bulk RNA-sequencing (RNA-seq).

  Study phs003005