7240 results for "canc*"

in 13.91 milliseconds.

• Vaccine-Expanded Plasmablast-like B Cells Are Associated with Response to Dendritic Cell Therapy in Metastatic Melanoma

  Dendritic cells (DCs), either monocyte-derived (moDCs) or naturally circulating, have been employed in cancer treatment across both metastatic and adjuvant settings. DC vaccines (DC Vax) are capable of inducing tumor-specific immune responses, yet clinical outcomes have thus far fallen short of expectations. Here, we performed a multi-omics analysis of the apheresis product, including CD14⁺ monocytes, and the final DCVax formulation. Among all the components analyzed, the most striking finding was the unexpected presence of CD19⁺ B cells within the DC Vax formulation, which distinguished Responders from Non-Responders to moDC vaccination in metastatic melanoma. The abundance of B cells in DC Vax correlated with their peripheral frequency; however, within the vaccine, these cells acquired a CD20lowCD27hiCD38hi plasmablast-like phenotype, distinct from their autologous circulating B-cell precursors. Notably, a parallel observation was made in Responders, whose pre-treatment lesions were enriched in B cells organized as Tertiary Lymphoid Structures. These results underscore the importance of B cells in both baseline immunity and DCVax formulation, hinting at unexplored mechanisms by which they may influence vaccine effectiveness.

  Study EGAS50000001177

• Genomic Rearrangements in Pediatric Cancer

  Dataset EGAD00001007701

• WGS dataset for Genomic rearrangements in Pediatric Cancer

  Dataset EGAD00001008272

• Detection of brain cancer using genome-wide cell-free DNA fragmentomes

  Diagnostic delays in patients with brain cancer are common and can impact patient outcome. Development of a blood-based assay for detection of brain cancers could accelerate brain cancer detection. In this study, we analyzed genome-wide cell-free (cfDNA) fragmentomes, including fragmentation profiles and repeat landscapes, from the plasma of individuals with (n=148) and without (n=357) brain cancer. Machine learning analyses of cfDNA fragmentome features detected brain cancer across all grade gliomas (AUC=0.90, 95% CI: 0.87-0.93) and these results were validated in an independent prospectively collected cohort. cfDNA fragmentome changes in patients with gliomas represented a combination of fragmentation profiles from glioma cells and altered white blood cell populations in the circulation. These analyses reveal the properties of cfDNA in patients with brain cancer and open new avenues for noninvasive detection of these individuals.

  Study EGAS50000000986

• The dataset for Detection of brain cancer using genome-wide cell-free DNA fragmentomes

  The dataset for “Detection of brain cancer using genome-wide cell-free DNA fragmentomes” includes 310 bam files from whole genome next-generation sequencing on the Illumina Novaseq 6000. The dataset analyzed include plasma cfDNA samples from patients with cancer, patients with neurological conditions, and healthy individuals.

  Dataset EGAD50000001445

• Data access committee for "Detection of brain cancer using genome-wide cell-free DNA fragmentomes"

  The data access committee for “ Detection of brain cancer using genome-wide cell-free DNA fragmentomes”. The DAC is comprised of Drs. Dimitrios Mathios, Jillian Phallen, Victor Velculescu, Robert Scharpf, Noushin Niknafs, and Shashikant Koul, and can be contacted at skoul3@jh.edu.

  Dac EGAC50000000605

• Sensitive neoantigen discovery by real-time mutanome-guided immunopeptidomics - WES

  Targeting cancer-specific HLA-peptide complexes is a promising immunotherapy approach, with mutated neoantigens offering high value for their immunogenicity and cancer-specificity. Selecting immunogenic targets requires personalized prioritization of cancer-specific immunopeptides. Mass spectrometry (MS)-based immunopeptidomics supports this process by directly identifying cancer-specific antigens and informing global presentation patterns to refine immunogenicity predictions. Clinical pipelines, however, must balance global depth and target sensitivity compounded by low input samples and time constraints. Here, we present NeoDiscMS, an extension of the NeoDisc pipeline, enabling personalized immunopeptidomics data acquisition. By leveraging real-time NGS-guided spectral acquisitions, NeoDiscMS maximizing sensitivity with minimal loss of global depth. NeoDiscMS improves TAA-derived peptide detection up to 20% and enhances neoantigen identification confidence compared to the clinical gold standard method. Designed for effectiveness and ease of use, it requires minimal effort for implementation. NeoDiscMS advances personalization in clinical antigen discovery by enabling more sensitive neoantigen detection while seamlessly integrating into existing workflows.

  Study EGAS50000000976

• Tumor gene project

  The study focuses on human tumor susceptibility as well as somatic cancer genomics. We use high-throughput sequencing approaches to achieve a comprehensive molecular characterization of tumors. Our goal is to better understand their genesis and management opportunities.

  Study EGAS50000000984

• GOSH_Paediatric_Tumour_23P108_WSSS_WGS_Managed_Access

  We have a case of two siblings (twins) who were born with nodular blue rashes over their body. The first twin had a large periorbital subcutaneous mass which was diagnosed as infantile undifferentiated sarcoma. This baby was too systematically unwell for chemotherapy and died at 12 days of age. The second sibling received two cycles of emergency chemotherapy however died due to disease progression and secondary haemorrhage at 2 months of age. Post-mortem normal and tumour samples have been taken. In addition to placental tissue and parental germline DNA, the goal will be to investigate intra-uterine transfer of sarcoma and the phylogenetic relationship of tumours in both twins.

  Study EGAS00001007536

• A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

  The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and non-canonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in-silico tools for the identification, prediction, and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens and high-confidence tumor-specific antigens.

  Study EGAS50000000228