Project description:Pre- and post-treatment tumor biopsies from 8 patient with metastatic uveal melanoma patients were analysed by single cell RNAseq. Transcriptomic profiling of myeloid populations including macrophages, monocytes, and dendritic cells was carried out. M2 macrophage subsets were identified at baseline and treatment-induced M2 to M1 macrophage reprogramming was characterised.
Project description:Targeted (amplicon) reduced representation bisulfite sequencing (RRBS) data for HGSOC PH039 PDXs at baseline and treated with cycles of 100 mg/kg niraparib for 21 days for four treatment rounds.
Project description:CTCF ChIP-seq of 39 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011059 (dataset).
Project description:In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection.
Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-ML
For more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.
Project description:H3K27ac ChIP-seq of 79 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). In addition, 4 samples derived from CD34+ cord blood cells of healthy donors were included. Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011060 (dataset).
Project description:Infinium 450K is a hybridization array designed for the human genome, but the relative conservation between the macaque and human genomes makes its use in macaques feasible. We used the Infinium450K array to assay twelve Cynomolgus macaque muscle biopsies and compared it to Reduced Representation Bisulphite Sequencing (RRBS) data generated on the same samples. Muscle biopsies were performed on eleven adult male cynomologus macaques
Project description:This single cell RNA-seq experiment was performed to quantify DLL3 expression in circulating tumor cells in small cell lung cancer patients to predict response to tarlatamab treatment. CTCs enriched from the blood of three SCLC patients prior or post tarlatamab treatment using the CTC-iChip followed by magnetic depletion of RBCs were processed with the 10x Genomics Chromium platform (Chromium GEM-X Single Cell 3' Kit v4) and sequenced on a NextSeq 2000 system. Corresponding EGA study number: EGAS50000001401, EGA dataset number: EGAD50000002035
Project description:Using 1 melanocyte and 6 melanoma cell line (3 pair of primary and metastatic), we generated base-resolution DNA methylation maps to document DNA methylation drivers of melanoma metastasis. Here we generated single-nucleotide resoultion DNA methylation map of a total of 7 cell lines using Reduced Representation Bisulfite Sequencing (RRBS)
Project description:We report RNA-sequencing data of 286 platelet RNA samples isolated from non-small cell lung cancer patients (NSCLC) that were at baseline for nivolumab immunotherapy. This dataset was employed to test the hypothesis that platelet RNA at baseline of nivolumab treatment predicts immune responses.