Project description:We performed genome-wide DNA methylation profiling of uterin and ovarian carcinosarcoma tissues derived from patients in the Cancer Institute Hospital of Japanese Foundation for Cancer Research.
Project description:Carcinosarcoma of the uterus or ovary is a rare, biphasic tumor comprising epithelial and mesenchymal elements, and exhibits aggressive clinical features. Four molecular subtypes of carcinosarcoma (POLE, MSI, CNH, and CNL) were recently established and shown to be associated with multiple clinicopathological parameters including patient outcomes. Immune microenvironment analyses on CS samples was performed using immune cell profiling with T-cell receptor repertoire assay. Carcinoma and sarcoma elements from CS samples were also assessed separately.
Project description:Objectives. Ovarian cancer (OC) is the eighth most common cancer and the eighth most common cause of cancer-related death in women. Identification of pathogenic variants in OC tissues is important to predict treatment response. This study aim to evaluate the mutational profile of a patient cohort, negative for BRCA1/2 germinal mutations and Mismatch Repair (MMR) defects, using next generation sequencing approach on DNA from formalin-fixed paraffin-embedded (FFPE) samples. We used a custom NGS panel, targeting 34 cancer related-genes, and analyzed NGS data to identify somatic and germline mutations in Italian patients affected by primary epithelia ovarian cancer.
Project description:The diversity and heterogeneity within high-grade serous ovarian cancer (HGSC) is not well understood. Comprehensive molecular analyses were performed including high-pass whole-genome sequencing, targeted deep DNA sequencing, RNA sequencing, reverse-phase protein arrays, mass spectrometry-based proteomics and phosphoproteomics, and immune profiling on primary and metastatic sites from highly clinically annotated HGSC samples. Samples were obtained pre-treatment based on a laparoscopic triage algorithm from patients who underwent R0 tumor debulking or received neoadjuvant chemotherapy (NACT) with excellent or poor response.
Project description:Diagnosis of ovarian cancer at an early stage is the most important determinant of survival. Thus, there is a clear need for novel biomarkers to improve diagnostic and prognostics that may better inform on therapeutic strategies. We have conducted a discovery study using label-free quantitative mass spectrometry (LFQ) to identify potential biomarker candidates in urine from individual ovarian cancer patients. LFQ analyses identified 4394 proteins (16397 peptides) in urine samples (n=20), 23 of which were significantly elevated in the malignant patient group compared to patients with benign disease. To validate these changes, we used Parallel Reaction Monitoring (PRM) to investigate their abundance in an independent cohort (n=20) of patient urine samples. Seven of the ten proteins were significantly enriched in the ovarian cancer patient samples; amongst these were established ovarian cancer markers WFDC2 (HE4) and Mesothelin (MSLN), validating our approach. This is the first application of a LFQ-PRM workflow to identify and validate ovarian cancer-specific biomarkers in urine samples.
Project description:Bulk RNA sequencing of sorted A.Cali09-specific CD4+CD45RA- cells from before and seven days after trivalent seasonal influenza vaccination in healthy UK adults of 18-36 years old and over 65 years old.
Project description:This data set was generated by the UK Brain Expression Consortium and consists of gene expression data generated from post-mortem human brain samples, dissected from 10 brain regions and originating from a large cohort of neurologically and neuropathologically normal individuals. The UK Brain Expression Consortium has generated gene expression data on a large cohort of neurologically and neuropathologically normal individuals in order to better understand gene expression differences across the human brain.
Project description:This data set was generated by the UK Brain Expression Consortium and consists of gene expression data generated from post-mortem human brain samples, dissected from 10 brain regions and originating from a large cohort of neurologically and neuropathologically normal individuals. The UK Brain Expression Consortium has generated gene expression data on a large cohort of neurologically and neuropathologically normal individuals in order to better understand gene expression differences across the human brain.