14970 results for "cancer studies using rna-seq"

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• 1000 Genomes Used for Cloud Testing

  WARNING: This is a test data set. The phenotype data was not checked to match 1000 Genomes project. Use with caution.This study provides public access to a demonstration subset of 1000 genomes sequence data in dbGaP. It is intended to be used by software developers who design, implement and validate pipelines for dbGaP data. While this project distributes only public data, it uses dbGaP security mechanisms which include a project-specific security key and a decryption password. Since the data are public, the key and password for this project will be included by SRA into public demonstration packages that permit users to test and validate the security of their computational pipelines. Note that all public users of this data will be logged under the same project and account ID. The datasets attached to this project must be restricted to 1000 Genomes data identical to the project's public FTP site. To access these test data using SRA toolkit use prj_phs710EA_test.ngc.

  Study phs000710

• Single-cell transcriptional mapping reveals genetic and hierarchy-based determinants of aberrant differentiation in AML

  Therapeutic targeting of acute myeloid leukemia (AML) has in part been hampered by profound cell state heterogeneity among leukemia cells. To develop a more precise understanding of leukemia cell states in AML, we constructed a reference atlas of human hematopoiesis from 263,519 single cell transcriptomes spanning 55 cellular states. Using this atlas, we mapped over 1.2 million single-cell transcriptomes spanning 318 myeloid leukemia samples. This uncovered twelve recurrent patterns of aberrant differentiation in AML and additional leukemia cell states involving lymphoid and erythroid progenitors. Systematic mapping of genotype-to-phenotype associations revealed specific differentiation landscapes associated with >45 genetic drivers. Importantly, AML samples sharing common genetic driver alterations can have distinct cellular origins which inform the cellular identity of their disease-sustaining leukemia stem cell (LSC). Further, distinct LSC-driven hierarchies can co-exist within individual patients. Thus, precise mapping of malignant cell states provides insights into leukemogenesis, refines disease classification, and informs therapy development.

  Study EGAS50000000918

• Direct genetic transformation bypasses tumor-associated DNA methylation alterations

  Tumors represent dynamically evolving populations of mutant cells, and many advances have been made in understanding the biology of their progression. However, key unresolved questions remain about the conditions that support their initial transformation, which cannot be easily captured in patient populations but are instead modeled using transgenic cellular or animal systems. Here, we used extensive patient atlas data to define common features of the tumor DNA methylation landscape as they compare to healthy human cells and used this benchmark to screen 21 engineered human and mouse models for their ability to reproduce these patterns. Notably, we find that genetically induced cellular transformation can trigger global changes in DNA methylation levels that are consistent with extensive proliferation but rarely recapitulate the widespread de novo methylation of Polycomb targets as found in clinical samples. Our results raise pertinent questions about the relationship between genetic and epigenetic aspects of tumorigenesis and provide an important molecular reference for evaluating existing as well as newer tumor models.

  Study EGAS50000000902

• Genomic Landscape of Apical Periodontitis

  We undertook the first genome-wide association (GWAS) study of apical periodontitis, enrolling 932 patients with deep carious lesions at UTHealth School of Dentistry and the University of Pittsburgh School of Dental Medicine. We used a case-control design to compare those with apical periodontitis to those without. Genotype data was generated for all participants and imputed using the TOPMed reference panel. We performed a single-variant association testing, a subphenotype analysis, and a genetically regulated expression association analysis, and generated a polygenic risk score and performed phenome-wide association analysis. We identified eight near-genome-wide significant loci for our primary outcome, eight genes where genetically regulated expression was associated with apical periodontitis status, and 33 phecodes that were associated with the generated polygenic risk score. We also replicated three loci previously reported in the literature. Genotype, phenotype, and results from the primary association analysis will be available via dbGaP.

  Study phs003252

• Variant allele frequency changes in TP53 predict pembrolizumab response in patients with metastatic urothelial carcinoma

  Prognoses for patients with metastatic urothelial carcinoma (mUC) have strikingly improved with pembrolizumab, an immune checkpoint inhibitor (ICI), but clinical benefits are limited to a subset of patients. Therefore, a non-invasive biomarker to predict pembrolizumab response is urgently required. We retrospectively examined genomic alterations in 25 plasma circulating tumor DNA (ctDNA) samples using a targeted sequencing of 77 genes from 16 mUC patients during pembrolizumab treatment. Eleven (68.8%) patients had two or more genomic alterations, including TP53 mutations (as defined by ctDNA-positive status). The proportion of responders to pembrolizumab in the ctDNA-positive group was higher than the ctDNA-negative group (72.7% vs. 20.0%). Furthermore, among all detected genomic alterations, variant allele frequency decreases in TP53 during pembrolizumab treatment were mainly associated with therapeutic response. Collectively, we suggest that profiling of ctDNA in plasma, especially TP53, is useful for predicting and monitoring therapeutic responses to pembrolizumab in mUC patients.

  Study JGAS000622

• Delineation of the heterogeneous molecular landscape of HRS cells and their biological contributions to forming comprehensive TME ecosystems.

  The tissue architecture of classic Hodgkin Lymphoma (CHL) is unique among cancers and characterized by rare malignant Hodgkin and Reed-Sternberg cells that are co-evolving in a complex ecosystem of immune cells in the tumor microenvironment. The lack of a comprehensive systems-level interrogation has hindered the description of disease heterogeneity and clinically relevant molecular subtypes. Here, we employed an integrative, multimodal approach to characterize CHL tumors using malignant cell sequencing, spatial transcriptomics and imaging mass cytometry. We identified four molecular subtypes (CST, CN913, STB, and CN2P) each characterized by distinct clinical features, mutational patterns, malignant cell gene expression profiles, and spatial architecture of immune cell populations. Functional modeling of CSF2RB mutations, a characteristic feature of CST, revealed dysregulated oncogenic signaling and changes in tumor microenvironment crosstalk. These findings highlight the significance of multi-dimensional profiling in elucidating patterns of molecular alterations that drive immune ecosystems and underlie therapeutically exploitable vulnerabilities.

  Study EGAS00001008064

• STimage: Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data

  Spatial transcriptomics (ST) links tissue morphology with gene expression values, opening new avenues for digital pathology. Deep learning models are used to predict gene expression or classify cell types directly from images, offering significant clinical potential but still requiring improvements in interpretability and robustness. We present STimage as a comprehensive suite of models to predict spatial gene expression and classify cell types directly from standard H\&E images. STimage enhances robustness by estimating gene expression distributions and quantifying both data-driven (aleatoric) and model-based (epistemic) uncertainty using ensemble approach with foundation models. Interpretability is achieved through attribution analysis at single-cell resolution integrated with histopathological annotations, functional genes, and latent representations. We validated STimage across diverse datasets, demonstrating its performance across various platforms. STimage-predicted gene expression can stratify patient survival and predict drug response. By enabling molecular and cellular prediction from routine histology, STimage offers a powerful tool to advance digital pathology.

  Study EGAS50000001503

• Genomic analyses of gynecologic carcinosarcomas reveal frequent mutations in chromatin remodeling genes

  Malignant Mixed Müllerian Tumors, also known as carcinosarcomas, are rare tumors of gynecological origin. Here we perform whole exome analyses of 22 tumors using massively parallel sequencing to determine the mutational landscape of this tumor type. On average, we identify 43 mutations per tumor, excluding four cases with a mutator phenotype that harbored inactivating mutations in mismatch repair genes. In addition to mutations in TP53 and KRAS, we identify genetic alterations in chromatin remodeling genes, ARID1A and ARID1B, in histone methyltransferase MLL3, in histone deacetylase modifier SPOP, and in chromatin assembly factor BAZ1A, in nearly two thirds of cases. Alterations in genes with potential clinical utility are observed in more than three quarters of the cases and included members of the PI 3-kinase and homologous DNA repair pathways. These findings highlight the importance of the dysregulation of chromatin remodeling in carcinosarcoma tumorigenesis and suggest new avenues for personalized therapy.

  Study EGAS00001000941

• The spatial organization of intratumor heterogeneity andevolutionary trajectories of metastases in hepatocellular carcinoma

  Hepatocellular carcinoma (HCC) has one of the poorest survival amongst cancers. Using multi-regional sampling of nine resected HCC with different etiologies, we find that phylogenetic relationships of these sectors show diverse levels of genetic sharing spanning early to late diversification. Unlike the variegated pattern found in colorectal cancers, a large proportion of HCC display a clear Isolation-By-Distance pattern where spatially closer sectors are genetically more similar. Two resected intra-hepatic metastases showed genetic divergence occurring before and after primary tumor diversification respectively. Interestingly, metastatic tumors had much higher variability than their primary tumors, suggesting that intra-hepatic metastasis is accompanied by rapid diversification at the distant location. The presence of co-existing mutations offers the possibility of drug repositioning for HCC treatment. Taken together, these insights into intra-tumor heterogeneity allow for a comprehensive understanding of the evolutionary trajectories of HCC and suggest novel avenues for personalized therapy.

  Study EGAS00001001603

• Exome_sequencing_of_blastic_plasmacytoid_dendritic_cell_neoplasms

  Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and aggressive haematological malignancy derived from precursors of plasmacytoid dendritic cells. Due to the rarity of BPDCNs our knowledge of their molecular pathogenesis was until recently confined to observations describing reccurent chromosomal deletions involving chromosomes 5q, 12p, 13q, 6q, 15q and 9. A recent publication went on to delineate the common deleted regions using aCGH and demonstrated that these centred around known tumour suppressor genes including CDKN2A/B (9p21.3), RB1 (12p13.2-14.3), CDKN1B (13q11-q12) and IKZF1 (7p12.2).These mutations are found recurrently in several different cancers and in most cases are thought to be involved in tumour progression rather than initiation. However, the well-defined nature and cellular ontogeny of these neoplasms suggests strongly that they share one or a few characteristic mutations as has been demonstrated for other uncommon but well-defined neoplasms such as Hairy Cell Leukemia (BRAF) and ovarian Granulosa Cell tumours (FOXL2).

  Study EGAS00001000171