Project description:This dataset includes RNA-seq data from ileal and colonic biopsies at time of diagnosis for treatment-naive, uncomplicated Crohn's disease (CD) patients and matched controls. This is includes 56 CD patients each for ileal and colonic tissue and for controls, 46 colonic samples and 45 ileal samples. Clinical characteristics such as development of complications, disease remission, or progression to surgery were recorded with a mean follow-up of 6 years.
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:This dataset contains single-cell RNA-seq data from treatment-naïve biopsies of nine patients diagnosed with ovarian high-grade serous carcinoma (HGSC), including four chemo-refractory and five chemo-sensitive cancers. Four of the samples (EOC204, EOC115, EOC649, EOC1127) are newly generated, while five samples have been previously published (GSE165897). The data was generated as part of the DECIDER observational trial (NCT04846933) to investigate the molecular drivers of chemo-refractory HGSC. Key findings from the study indicate that chemo-refractory HGSC is associated with reduced interferon type I (IFN-I) activity and enhanced hypoxia pathway activity, while baseline IFN-I pathway activity in chemo-naïve cancer serves as an independent prognostic factor for chemotherapy response.
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.
Project description:RNA-seq of pre-treatment and post-relapse matched samples from melanoma patients RNA-seq was performed for matched samples before treatment and after relapse from six patients that have been treated with RAF or RAF+MEK inhibitors. Submitter declares that raw sequence data will be submitted to dbGaP due to patient privacy concerns.
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:This dataset contains data-independent acquisition (DIA) proteomics data generated from K562 human leukemia cells treated with coffee extract or vehicle control. Quantitative proteomic analysis was performed to investigate molecular mechanisms underlying coffee-induced epigenetic remodeling. The proteomics dataset was integrated with histone modification profiling, ChIP-seq and RNA-seq analyses to characterize pathways associated with histone acetylation and MYC transcriptional regulation.
Project description:Thirty patients with advanced melanoma were recruited to a clinical trial investigating the therapeutic cancer vaccine, UV1, in combination with pembrolizumab. Whole exome sequencing was performed on melanoma biopsies to determine tumor mutational burden and association with response to therapy. RNA sequencing of matched tumor biopsies to evaluate gene expression profiles at baseline and post-treatment.