8431 results for "canc*"

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• The dataset for Genomic landscapes of endometrioid and mucinous ovarian cancers and morphologically similar tumor types

  The dataset for “Genomic landscapes of endometrioid and mucinous ovarian cancers and morphologically similar tumor types” includes 288 bam files from whole genome, whole exome and targeted next-generation sequencing on the Illumina HiSeq2500 and MiSeq. The samples analyzed include tumor and normal tissue samples from patients with cancer.

  Dataset EGAD50000000744

• The dataset of Detection and characterization of lung cancer using cell-free DNA fragmentomes on NovaSeq 6000 at 1-2x coverage

  The dataset contains 364 CRAM files from whole-genome next-generation sequencing on the Illumina NovaSeq 6000 at 1- 2x coverage. These are plasma samples from healthy individuals and patients with cancer.

  Dataset EGAD50000001961

• Analysis of Somatic Mutations from Liquid Biopsy-Derived cfDNA in Advanced Cancers

  This study aims to identify somatic mutations and characterize potential neoantigens for immunotherapeutic targeting using paired whole-exome sequencing (WES) of germline DNA and plasma-derived circulating cell-free DNA (cfDNA) from patients with advanced-stage cancers.

  Study EGAS50000001169

• Analysis of Somatic Mutations and Neoantigen Landscape from Liquid Biopsy-Derived cfDNA in Advanced Cancers

  This study involves paired whole-exome sequencing (WES) of germline DNA and plasma-derived circulating cell-free DNA (cfDNA) from patients with advanced-stage cancers, aiming to identify somatic mutations and characterize potential neoantigens for immunotherapeutic targeting.

  Dac EGAC50000000717

• Validation of cfDNA fragmentome analyses for early detection of liver cancer

  Study EGAS00001008111

• Methylation data for Genomic landscapes of endometrioid and mucinous ovarian cancers and morphologically similar tumor types

  Study EGAS00001008175

• A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

  Abstract: Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cancer cell trajectory regionalizes tumours into distinct tissue niches and manifests in a conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to jointly map cancer cell states and clones in situ, we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

  Dataset EGAD00001015516

• Analysis of Somatic Mutations and Neoantigen Landscape from Liquid Biopsy-Derived cfDNA in Advanced Cancers

  This study involves paired whole-exome sequencing (WES) of germline DNA and plasma-derived circulating cell-free DNA (cfDNA) from patients with advanced-stage cancers, aiming to identify somatic mutations and characterize potential neoantigens for immunotherapeutic targeting.

  Dataset EGAD50000001671

• Exome (FrTu, cfDNA and normal), transcriptome (FrTu) and single-cell (PBLs) sequencing of metastatic cancer patients

  Whole Exome Sequencing of tumor biopsy DNA (FrTu), cfDNA and matching normal PBMC DNA; total RNA-Seq of FrTu samples; and scRNA- and scTCR-seq of PBLs from metastatic cancer patients included in Garcia-Garijo et al., 2026.

  Dataset EGAD50000001672

• PARADIGM: Combined ctDNA and serum PSA for dynamic monitoring of metastatic prostate cancer starting first-line treatment

  The prognosis of newly-diagnosed metastatic prostate cancer is highly variable. The PARADIGM prospective cohort study evaluated predictors of survival in blood collected at the start of each of the first six treatment cycles from 114 high-volume metastatic patients starting androgen deprivation therapy combined with docetaxel or an androgen receptor pathway inhibitor.

  Study EGAS50000001357