Search Results - EGA European Genome-Phenome Archive

Analysis of genes related to the oncogenesis, progression or effectiveness of drug therapies of urological tumors .

To clarify the oncogenesis and the progression mechanism of urological tumors, we continuously collect tumor samples of our patients with high quality. Then we used them to comprehensive analyses such as genome sequencing or epi-genome sequencing. Through these analyses, we aim for elucidating the oncogenesis or for developing newly treatment strategies of each cancer.

Study JGAS000303

Interactions between the tumor and the systemic response of breast cancer patients

Breast cancer (BC) research has largely focused on the molecular properties of the tumor proper. However, to understand how cancer progresses and ultimately modulates patient outcome, we also require an understanding of the molecular changes in the patient systemic response (SR). Toward this end, we generated and analyzed RNA profiles from tumor and matched blood samples in 173 BC patients. We designed a system (MIxT) to explore and link tightly co-expressed gene sets (modules) across matched tissues. The gene composition of modules, and their expression, largely vary across tissues. Distinct patterns of expression in the SR are predominantly detected in highly immunogenic BC subtypes. We also find that the expression pattern of a module in one tissue is correlated to the expression pattern of a module in the other tissue in a fashion that depends on subtype. For example, systemic immunosuppression is detected in basalL patients in a fashion that is proportional to the level of expression of several tumor modules highlighting immune evasion mechanisms active in these particular cancers.

Study EGAS00001001804

MethCORR: DNA Methylation-based Characterization, Classification and Prognostication of Colorectal Cancer using Archival Formalin-fixed, Paraffin-embedded Tissue

Transcriptional characterization and classification has potential to resolve the inter-tumor heterogeneity of colorectal cancer (CRC) and improve patient management. Yet, robust transcriptional profiling is difficult using formalin-fixed, paraffin-embedded (FFPE) samples, which complicates testing in clinical and archival material. We present MethCORR, an approach that allows uniform molecular characterization and classification of fresh-frozen and FFPE samples. MethCORR identifies genome-wide correlations between RNA expression and DNA methylation in fresh-frozen samples. This information is used to infer gene expression information in FFPE samples from their DNA methylation profiles. MethCORR was applied to methylation profiles from 877 fresh-frozen and FFPE samples and comparative analysis identified the same two major subtypes in four independent cohorts. Furthermore, subtype-specific prognostic biomarkers that better predicted relapse-free survival (HR=2.66, 95% CI [1.67-4.22], P<0.001) than TNM staging and MSI status were identified and validated using DNA methylation-specific PCR assays. The MethCORR approach is general, and may be similarly successful for other cancer types.

Study EGAS00001004293

Endocrine therapy reprogramming of breast cancer facilitates metastatic escape via upregulation of P-Rex1/Rac1 signalling

The estrogen receptor (ER) drives growth in ~70% of breast cancers, and endocrine therapies prevent recurrence. However, 30% of patients recur, with many fatal cases occurring late (after >5 years), presenting a challenge due to their often non-proliferative nature and limited options. To study this, we generated slow-growing resistant cells that form small primary tumours but readily metastasize. Single-cell RNA sequencing (scRNAseq) revealed that endocrine therapy reprograms these cells, notably upregulating the Rac1 signalling component P-Rex1. We found in clinical cohorts that P-Rex1 in high in ER+ breast cancer. Intravital imaging demonstrated that Rac1 signalling is active in ER+ cells following endocrine therapy. Targeting the Rac1 pathway with small molecule inhibitors (NSC23766, R-ketorolac) reduced survival and motility in resistant cells, inhibited in vivo Rac1 activity, and reduced tumour burden when combined with tamoxifen in a drug-refractory patient-derived xenograft. This work identifies the P-Rex1/Rac1 axis, as a potential therapeutic target for late recurring ER+ breast cancer.

Study EGAS00001008353

Genomic Characterization CS-MATCH-0007 Arm N

The CS-MATCH-0007 protocol is part of a collaboration between the Center for Cancer Genomics (CCG) and the Division of Cancer Treatment and Diagnosis (DCTD) to perform whole-exome sequencing, RNA sequencing and if possible, whole-genome, methylation and miRNA sequencing using pre-and post-treatment tumor biopsy specimens from patients enrolled on a treatment arm of the NCI-MATCH clinical trial (EAY131). The goal of this study is to identify the molecular basis for response and resistance to targeted therapies that are matched to specific genomic alterations found in their cancers. Arm N is one of the treatment sub-protocols within the NCI-MATCH Clinical Trial (EAY131) where patients with PTEN mutation, or deletion with PTEN expression on IHC, are treated with the drug GSK2636771. This subprotocol is one of the treatment arms included in the CS-MATCH-0007 protocol and will provide specimens for the program including DNA from tumor tissue and whole blood.

Study phs002151

Adjuvant nivolumab in resected esophageal or gastroesophageal junction cancer: exploratory biomarker analyses from CheckMate 577

Adjuvant nivolumab demonstrated a significant improvement in disease-free survival (DFS; primary endpoint) versus placebo in patients with resected esophageal or gastroesophageal junction cancer and residual pathologic disease following neoadjuvant chemoradiotherapy (CRT) in the phase III CheckMate 577 study, leading to global approval and widespread adoption. Exploratory biomarker analyses were performed using whole-exome sequencing, RNA sequencing, and immunohistochemistry. Hazard ratios for DFS favored nivolumab versus placebo in patients with higher inflammation and proliferation gene expression signature scores; lower M2 macrophage, endothelial, and stromal GES scores; and higher densities of CD3+/CD8- T cells and natural killer cells. Assessments of pre-CRT and post-CRT tumor tissue found post-CRT increases in programmed death ligand 1 combined positive score in 51% of patients, which appeared to be associated with greater DFS benefit. Through these analyses, we identified patient subpopulations that appear to derive improved DFS benefit from adjuvant nivolumab in this setting.

Study EGAS50000001663

Next gen seq of eye cancers (2019-08-14)

Retinoblastoma (RB), the commonest eye cancer in children was the first cancer for which a genetic cause was identified: the Rb1 gene is a tumour suppressor gene that is mutated in RB. The Rb1 gene defect alone does not predict the clinical outcome. We propose to study other possible mechanisms: 1. Stepwise further mutations occur in RB, increasing its carcinogenesis. We will sequence the whole genome in RB tissue, and relate the different genes expressed to the treatments used. 2. Extracellular matric proteins contribute to a tumour permissive environment for RB to continue to grow. This includes Samll Leucine Rich Proteoglycans (SLRP), a family of 15 secreted extracellular matrix proteins involved in eye development. 3. Cancer stem cells (CSC), a subpopulation of treatment resistant cells, drive RB tumours, and whether these stem cells can be manipulated for new therapies. The aim of this study is to assist finding targeted diagnostic techniques and treatments for RB. . This dataset contains all the data available for this study on 2019-08-14.

Dataset EGAD00001005251

Cellular Profiling Identifies Targetable T Cell Phenotypes in Lymphocytic Variant Hypereosinophilic Syndrome

Lymphocytic variant hypereosinophilic syndrome (LHES) is driven by aberrant Th2‑skewed lymphocyte clones that promote tissue eosinophilia. We performed multimodal single‑cell RNA‑sequencing (scRNA‑seq), cell‑surface protein (ADT) profiling, and paired αβ‑TCR sequencing on peripheral‑blood mononuclear cells (PBMCs) from two clinically characterized LHES patients and one idiopathic HES control. One LHES patient was sequenced at two timepoints, pre-JAK inhibitor treatment and post-JAK inhibitor treatment. Single‑cell regulatory‑network inference (SCENIC), CellChat ligand–receptor modelling, and scRNA variant calling were integrated with flow‑cytometric validation and 5,000× amplicon sequencing. Two clonally expanded CD3‑CD4⁺ central‑/effector‑memory T‑cell clusters were observed exclusively in LHES. These cells showed Th2‑associated regulons (GATA3, BATF, MAF), high expression of CCR4, CD52, PTGDR2 and IL13, and down‑regulation of CD247 (CD3ζ), a mechanism experimentally confirmed to abolish surface CD3/TCR. Autocrine IL‑7 signaling, predicted by CellChat, provides an antigen‑independent proliferative cue. Therapeutically actionable targets (CCR4, CD52, IL‑5, IL‑7, TNF) were prioritized; one refractory patient achieved complete clinical and cytologic remission after mogamulizumab (anti‑CCR4) therapy. Longitudinal sequencing revealed clonal evolution with acquisition of a STAT3 p.G618R mutation under JAK‑inhibition, highlighting a resistance mechanism. Raw scRNA‑seq (5′ cDNA, VDJ, ADT) data, and sample‑level metadata will be distributed through dbGaP. These resources provide the first high‑resolution immune atlas of LHES and a framework for rational, precision immunotherapy in eosinophilic disorders.

Study phs004041

Mutational patterns and regulatory networks in epigenetic subgroups of meningioma (H033)

DNA methylation patterns delineate clinically relevant subgroups of meningioma. We previously established the six meningioma methylation classes (MC) benign-1, 2, 3, intermediate-A, B and malignant. Here, we set out to identify subgroup-specific mutational patterns and pathway regulation. Whole-genome-sequencing was performed on 62 samples across all MCs and WHO grades from 62 patients with matched blood control, including 40 sporadic and 22 radiation-induced (Mrad) meningiomas. RNA sequencing was added for 18 cases and chromatin-immunoprecipitation for the enhancer mark H3K27ac followed by sequencing (ChIP-seq) for 16 samples. Besides the known mutations in meningioma, structural alterations were found to contribute to the spectrum of mechanisms inactivating NF2 in sporadic meningioma similar to previous reports for Mrad. Aberrations of DMD were found to be enriched in MCs with NF2 mutations and DMD was among the most differentially upregulated genes in NF2 mutant compared to NF2 wild-type cases. The mutational signature AC3 was detected in both sporadic meningioma and Mrad, but distributed across the genome in sporadic cases and enriched near genomic breakpoints in Mrad. In general, the malignant MC presented a significantly higher exposure to AC3 and higher genomic instability beyond the mutational load than the other MCs. Pathway analysis of the ChIP-seq clusters revealed that the FOXM1 is most differentially activated in high-grade cases, along with super enhancer recruitment near HOX genes and respective upregulation of expression. This data further elucidate the biological mechanisms differentiating meningiomas of different WHO grade and epigenetic subgroups and suggest leveraging the genomic instability as novel therapeutic targets.

Study EGAS00001003481

RNA sequencing of high-risk paediatric cancers for identifying T-cell infiltration signatures

Transcriptome profiling of 121 high-risk paediatric cancer samples for identifying T-cell infiltration signatures using poly-A capture by Truseq and sequenced on NextSeq 500

Dataset EGAD00001010920

BCSA_Exome+SmartSeq3

Multiple regions were cut out of each tumor extracted form breast cancer patients and two experiments were run: - Whole exome sequencing on each of the regions plus an adjacent normal tissue sample - Smart-Seq3 Single cell RNA sequencing on EPCAM+ CD45- sorted cells from different tumor regions

Dataset EGAD00001009781

Dataset for cancer_of_unknown_primary-RNA

Rare cancer sequencing data of 58 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

Dataset EGAD00001008857

ecDNA copy number heterogeneity

Neuroblastoma exhibits significant inter- and intra-tumour genetic heterogeneity and varying clinical outcomes. Here, we used DNA and RNA sequencing from the same single cells in cell lines and neuroblastoma patients to investigate these effects. We reveal extensive intercellular ecDNA copy number heterogeneity. We also provide direct evidence of how this heterogeneity influences the expression of cargo genes, including MYCN and its downstream targets, and the overall transcriptional state of neuroblastoma cells.

Study EGAS50000000509

RNA sequencing of untreated head and neck cancer patient-derived xenografts (PDXs) and matched patient tumor tissue.

RNA sequencing was performed on untreated patient tumor tissue and matched patient-derived xenograft (PDX) models of primary head and neck squamous cell carcinoma (HNSCC). The aim of this analysis was to characterize the transcriptional landscape of the tumors in the in vivo setting. 117 total samples were sequenced with paired reads on 10 lanes from different flowcells. They were loaded as 2400 separate files to maintain the data as it was received from BGI - each sample is therefore composed of 5 separate EGAN with 4 files each, adding a 0.1 - .. - 0.5 to each BioSample ID to generate sample aliases. All R1 (and R2, separately) files referring to a BioSample ID should be concatenated to obtain fastq files corresponding to single patients or PDXs.

Dataset EGAD50000002278

A Clone's Genomic Stability as a Biomarker of Its DNA-Damage Resilience

A main problem in the treatment of advanced cancers, including gastric cancers and glioblastoma, is the incertitude at which we predict how individual patients will respond to DNA-damaging agents, especially on the long run. Knowing the mechanism behind a patient's response, or the lack thereof, will help us depart from the oversimplified “more-is-better” and “one-size-fits-all” principles according to which DNA-damaging agents are administered. This will improve clinical outcome by allowing us to pinpoint those who would respond better and longer to lower doses than to higher doses of DNA-damaging agents. Under the assumption that the success of DNA-damaging therapy increases with the proliferation rate of a relatively homogeneous tumor population, there was little reason to assume anything other than monotonic dose-response relations. With the recent paradigm shift that most cancers are in fact DNA mosaic products of ongoing evolution, comes the urgency to reconsider these fundamental principles behind DNA-damaging therapy administration. As the developers of one of the first DNA deconvolution methods and with access to technologies to profile the transcriptomes of up to 10,000 cells simultaneously, we are equipped to embark on first personalized dose-finding strategies for DNA-damaging therapies. We will test the potential of the very long-term legacy that DNA-damage entails on a cell “genomic instability” as new biomarker of DNA-damage response. Our preliminary studies showed that, for most cancer types, DNA-damaging agents change a clone's genomic instability and that clones succumb to a limit in the amount of genomic instability they can tolerate. In particular, our results showed that patients with intermediate genomic instability have a very poor outcome and that this relation is only evident among treatment-naive patients, but not among patients treated with DNA-damaging agents. Further they show that we can measure genomic instability per clone and that clones with extreme genomic instability typically don't grow large. Our hypothesis that genomic instability, rather than proliferation rate, determines how sensitive a tumor is to DNA-damaging agents on the long-term, is founded on two unexpected findings. Patients with extremely high genomic instability per tumor clone have an exceptionally good outcome. Aim 1 will integrate exome- and single cell RNA-Seq (scRNA-Seq) data to characterize clones and to measure how much genomic instability they can tolerate. Low genomic instability is associated with reduced benefit from DNA-damaging agents. Aim 2 will use comet assays and treatment history to quantify DNA damage per clone, relating it to the clones' ability to tolerate DNA damage and to changes in the genomic instability of therapy-surviving clones.

Study phs003762

11936 results for "cancer rna-seq"

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