12086 results for "cancer rna-seq"

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• The Cancer Dependency Map (DepMap)

  The Cancer Dependency Map (DepMap) aims to accelerate precision cancer medicine by creating a comprehensive map of tumor vulnerabilities. The DepMap project genomically profiles a wide range of cancer models and characterizes them for their sensitivity to genetic and compound perturbations. The data is computationally analyzed to create a systematic overview of the genetic dependencies and compound sensitivities of cancer types and the predictive biomarkers that point to patient populations most likely to benefit from targeting of these vulnerabilities. The DepMap public portal has become a comprehensive resource to identify cancer vulnerabilities by providing datasets at scale, cutting-edge computational tools, and sophisticated visualization tools that together have accelerated the discovery of novel targets and biomarkers. Please visit Depmap.org to access the public portal.

  Study phs003444

• Genetic Alterations in Benign Breast Biopsies of Subsequent Breast Cancer Patients

  Fibrocystic changes are associated with an increased risk of breast cancer (approximately 1.5-2 times that of the general population). Although the relative risk seems low, because of the high frequency of such changes, the absolute risk is significant. Genetic alterations have been found in fibrocystic changes with or without epithelial changes, suggesting that critical oncogenic events are occurring at an early stage. We investigated a unique collective of 17 breast cancer patients who, prior to the diagnosis of invasive breast cancer, underwent open surgical biopsy showing fibrocystic changes of the breast. Massive parallel sequencing targeting genes frequently mutated in breast cancer was performed on the fibrocystic breast tissue as well as the ensuing cancer tissue.

  Study EGAS00001003563

• Highlights from 2025: Our top 10 requested studies

  In 2025, as in previous years, EGA has facilitated the reuse of numerous datasets spanning a broad range of research and healthcare topics. To reflect on this activity, we would like to highlight the top 10 studies behind the most requested datasets of last year. Notably, the vast majority of these highly requested datasets are cancer-focused, particularly involving the study of immune checkpoint inhibitor therapies. Immune checkpoint inhibitors (ICIs) have become one of the most transformative advances in modern cancer therapy. These monoclonal antibodies enhance antitumor immune responses by targeting inhibitory immune checkpoint molecules. While ICIs have led to remarkably favorable and durable responses in some patients, treatment outcomes vary widely. This variability has fueled intense research efforts aimed at understanding resistance mechanisms, exploring combination therapies, and discovering predictive biomarkers to better stratify patients and guide treatment decisions. Study Title Disease 1 EGAS00001005503 Integrated genomic analyses reveal molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma Liver Cancer 2 EGAS00001005013 Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer Lung Cancer 3 EGAS00001004809 BIOKEY: A single-cell catalogue of the dynamic changes underlying Checkpoint Immunotherapy response in Early Breast Cancer Breast Cancer 4 EGAS50000000138 BIOKEY: Immune heterogeneity in small cell lung cancer and vulnerability to immune checkpoint blockade Lung Cancer 5 EGAS50000000497 Bladder cancer subtyping study across 4 atezo clinical trials Bladder Cancer 6 EGAS50000000689 MOSAIC - Multi-Omics Spatial Atlas In Cancer Cancer Atlas 7 EGAS00000000083 METABRIC (Breast cancer - genome and transcriptome) Breast Cancer 8 EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Synthetic data 9 EGAS00001002556 TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells Bladder Cancer 10 EGAS00001004353 Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade Renal Cancer The most requested dataset of the year was EGAD00001008128. It belongs to a study that analyzed 358 liver cancer patients enrolled in clinical trials and treated with the combination of atezolizumab (anti–PD-L1) and bevacizumab (anti-VEGF), atezolizumab alone, or sorafenib. The study highlights how bevacizumab enhances anti-PD-L1 effect and identifies candidate biomarkers to predict treatment response. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer-related death worldwide [1]. Because symptoms often appear late, many patients are diagnosed at advanced stages, when curative options are no longer viable. For many years, sorafenib—a multikinase inhibitor approved in 2007—was the only systemic treatment available. Although numerous clinical trials followed, it was not until a decade later that additional therapies showed meaningful clinical benefit. Among these advances, immunotherapy emerged as a promising first-line option [2]. In particular, the combination of atezolizumab and bevacizumab demonstrated improved survival in patients with advanced HCC, reshaping the treatment landscape. However, treatment efficacy remains limited by the complex and diverse hepatic microenvironments that influence immune responses, as well as by high intratumoral heterogeneity. Identifying robust predictors of treatment response is critical to enable effective patient stratification and maximize the benefits of these therapies [2]. The continued demand for these datasets highlights the interest and critical role of data reuse in deepening our understanding of cancer, accelerating the development of more effective therapies, and improving patient outcomes. By providing secure access to high-quality genomic and clinical data, EGA acts as a catalyst for biomedical progress. References Sung, Hyuna, et al. "Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries."CA: a cancer journal for clinicians 71.3 (2021): 209-249. Ladd, Alexandra D, et al.“Mechanisms of drug resistance in HCC.” Hepatology 79(4) (2024): 926-940

  Blog top-10-studies-2025

• Clonal Lineage Tracing of Primary Human Cortical Progenitors, Cell Type Profiles in the Brain Vasculature and Genotyping Data for 22q11DS and Control iPS Lines

  We sought to characterize the cell types produced by individual human cortical progenitors and describe their lineage relationships. We labeled individual human cortical cells with a massively complex lentivirus library encoding a GFP reporter gene and a molecular barcode that could be captured by standard 3' scRNA-seq methodologies. We then cultured the labeled human cells for 6 weeks in either a 1) mouse cortical astrocyte co-culture system or 2) in the mouse cortex as a xenograft. In a separate experiment, we maintained human cortical tissue slices in organotypic slice culture for 12 days. GFP+ cells were then isolated by FACS and then captured on the 10x chromium scRNA-seq platform. An additional sample was labeled by lentivirus and briefly cultured for 3 days prior to being mixed with mouse 3T3 cells in order to perform a cell mixing experiment ("barnyard") to benchmark our methodologies. We found that human individual cortical progenitors gave rise to both excitatory and inhibitory neurons as well as glia.We also sought to characterize the adult brain vasculature in both vascular malformations and non-malformed tissue. We use the 10x chromium scRNA-seq platform across 10 individuals (5 control and 5 arteriovenous malformations). We find broad disregulation across multiple cell types including the immune cells and find a specific monocyte subtype to contribute to a rupture phenotype. We also find a novel population of fibromyocytes in both conditions.Finally, we confirmed the genotype status of iPS lines that were derived from either karyotypically normal control individuals or from DiGeorge Syndrome patients carrying the 22q11.2 deletion using the Illumina Infinium Global Screening Array v3.0.

  Study phs002624

• A Sequential Window of Opportunity Trial of Anti-PD-L1/TGF-β trap (M7824) Alone and in Combination with TriAd Vaccine and N-803 for Resectable Head and Neck Squamous Cell Carcinoma not Associated with Human Papillomavirus Infection

  This submission includes the whole exome and bulk RNA sequencing associated with the correlative study of clinical specimens from a prospective clinical study of neoadjuvant bintrafusp alfa in patients with newly diagnosed, surgically resectable oral cavity or laryngeal squamous cell carcinoma. 

  Study phs002849

• Spatiotemporal Analysis of the Human Cerebellum

  We sought to characterize neural progenitors within spatially demarcated regions of the human cerebellum at a molecular level during neurogenesis. We used laser-capture microdissection to enrich for cells within the ventricular zone and subventricular zone regions of the rhombic lip, external granule cell layer and Purkinje cell layer, then performed RNA-sequencing on the enriched samples.

  Study phs001908

• Vulnerabilities of Midbrain Dopaminergic Neurons to Parkinson's Disease Revealed by Single-Cell Genomics

  We developed a novel protocol for the enrichment of midbrain DA neurons from frozen tissue. We subsequently performed single-nuclei RNA-sequencing on enriched samples from postmortem midbrain tissue from human, macaque, tree shrew, rat, and mouse. We identified vulnerable and resistant populations from these data and associated genetic enrichments and differential expression changes.

  Study phs002879

• Personalized Treatment of Sezary Syndrome through a CTLA4:CD28 Fusion

  We sequenced two tumor and one normal whole genome libraries for a Sezary syndrome patient and also sequenced RNA libraries for each tumor. We identified a novel, highly expressed CTLA4:CD28 gene fusion that is predicted to results in a leading to stimulatory T-cell signaling in the presence of an inhibitory signal.

  Study phs000773

• 10X single cell RNA- and feature barcode sequencing of 38 AML samples

  This dataset contains fastq-files from single cell 3' RNA- and feature barcode sequencing of AML samples using 10X technology (Chromium Single Cell 3ʹ Reagent Kit v3). In total, the dataset contains data from 38 AML samples from either bone marrow or peripheral blood, representing the following genetic subtypes: NPM1-mutated, myelodysplasia related, TP53-mutated, CBFB::MYH11, RUNX1::RUNX1T1, AML without class-defining mutations, and AML meeting criteria of two subtypes. The data consists of sequenced gene expression libraries from 38 samples and feature barcoding libraries covering a panel of 10-14 surface proteins for 34 samples.

  Dataset EGAD50000001577

• Paraganglioma Smart-Seq2 Single Nuclei Sequencing Data

  Paraganglioma is a rare neuroendocrine tumor that originates from chromaffin cells of the adrenal medulla or extra-adrenal paraganglia. This study aims to investigate the heterogeneity of this tumor and to identify potential biomarkers associated with its malignancy. To achieve this, we performed single-nucleus RNA sequencing of human paraganglioma samples using the SMART-Seq2 protocol.

  Study EGAS50000001184