Project description:This dataset contains log2(TPM + 1) for 271 tumor samples profiled by RNA-seq for the subset of genes used for validation of the NMF cluster assignments.
Project description:This dataset contains processed single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) collected from six hepatocellular carcinoma (HCC) patients, before and after curative therapy. Samples were profiled using the 10x Genomics Chromium platform. The dataset includes gene expression matrices and associated metadata for 12 samples (6 pre-therapy, 6 post-therapy). These data support the analysis of peripheral immune cell dynamics in response to tumor removal
Project description:This dataset contains bulk RNA sequencing data from primary tumor biopsy samples of patients with prostate adenocarcinoma. TPM-normalized expression values are provided along with sample metadata. The data were generated to support transcriptomic profiling of human prostate tumors and are related to a companion single-cell RNA-seq dataset from the same cohort.
Project description:High-grade serous ovarian cancer (HGSOC) is thought to originate from the fallopian tube (FT), highlighting the importance of studying molecular features across tumor samples. This dataset contains bulk RNA sequencing data from tumor samples of HGSOC patients. The data can be used for driver mutations and copy number alterations (CNAs) profiling and analysis of gene expression patterns, including regions relevant to HLA typing. This submission contains bulk RNA sequencing data associated with this study.
Project description:Pancreatic ductal adenocarcinoma (PDAC) exhibits profound molecular heterogeneity and poor prognosis, necessitating novel tailored therapies. The basal and classical subtypes - driven by glycolysis versus lipid metabolism - have distinct prognostic implications. We mapped PDAC molecular subtype heterogeneity, capturing spatially-resolved gene expression signatures and generating a comprehensive high-resolution dataset of 42,035 spatial spots. Subtype assignments were validated via multiplex immunofluorescence and quantitative analyses in patient-derived organoids. Our analysis resolved cancer cell signatures, deconvoluted intra-tumoral heterogeneity, and delineated a classical-to-basal trajectory. We identified metabolically ‘hot’, high-grade tumor niches characterized by concurrent enrichment of glycolysis and lipogenesis across both subtypes, nominating them as subtype-agnostic therapeutic targets. Preclinical models demonstrated that despite the basal subtype’s glycolysis dependence, both classical and basal tumors are susceptible to glycolysis inhibition. This work challenges the dogma of subtype-specific therapeutic silos and demonstrates highly adaptable energetic niches as reservoirs to drive tumor progression.
Project description:Pancreatic ductal adenocarcinoma (PDAC) exhibits profound molecular heterogeneity and poor prognosis, necessitating novel tailored therapies. The basal and classical subtypes - driven by glycolysis versus lipid metabolism - have distinct prognostic implications. We mapped PDAC molecular subtype heterogeneity, capturing spatially-resolved gene expression signatures and generating a comprehensive high-resolution dataset of 42,035 spatial spots. Subtype assignments were validated via multiplex immunofluorescence and quantitative analyses in patient-derived organoids. Our analysis resolved cancer cell signatures, deconvoluted intra-tumoral heterogeneity, and delineated a classical-to-basal trajectory. We identified metabolically ‘hot’, high-grade tumor niches characterized by concurrent enrichment of glycolysis and lipogenesis across both subtypes, nominating them as subtype-agnostic therapeutic targets. Preclinical models demonstrated that despite the basal subtype’s glycolysis dependence, both classical and basal tumors are susceptible to glycolysis inhibition. This work challenges the dogma of subtype-specific therapeutic silos and demonstrates highly adaptable energetic niches as reservoirs to drive tumor progression.
Project description:Pancreatic ductal adenocarcinoma (PDAC) exhibits profound molecular heterogeneity and poor prognosis, necessitating novel tailored therapies. The basal and classical subtypes - driven by glycolysis versus lipid metabolism - have distinct prognostic implications. We mapped PDAC molecular subtype heterogeneity, capturing spatially-resolved gene expression signatures and generating a comprehensive high-resolution dataset of 42,035 spatial spots. Subtype assignments were validated via multiplex immunofluorescence and quantitative analyses in patient-derived organoids. Our analysis resolved cancer cell signatures, deconvoluted intra-tumoral heterogeneity, and delineated a classical-to-basal trajectory. We identified metabolically ‘hot’, high-grade tumor niches characterized by concurrent enrichment of glycolysis and lipogenesis across both subtypes, nominating them as subtype-agnostic therapeutic targets. Preclinical models demonstrated that despite the basal subtype’s glycolysis dependence, both classical and basal tumors are susceptible to glycolysis inhibition. This work challenges the dogma of subtype-specific therapeutic silos and demonstrates highly adaptable energetic niches as reservoirs to drive tumor progression.
Project description:To investigate the molecular determinants of response to therapy, we profiled 26 genetically diverse murine models of mammary cancer using bulk RNA sequencing. Tumors were harvested from mice that were either untreated or treated with immunotherapy (anti-PD-1 and anti-CTLA-4), chemotherapy (Carboplatin + Paclitaxel), EGFR inhibition (Erlotinib), or MEK inhibition (Trametinib) for 7 days. Each model represents a distinct transcriptional subtype and genetic background, enabling the assessment of intertumoral heterogeneity in treatment response. This dataset includes RNA-seq from 2-9 biological replicates per model per condition. Tumor samples were collected prior to or during treatment response assessment, and are accompanied by corresponding survival from similarly-treated tumors. All sequencing was performed on polyA-selected RNA using Illumina platforms.
Project description:This dataset contains counts for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).