7087 results for "canc*"

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• Single-cell RNA sequencing of metastatic colorectal cancer patient-derived xenografts treated with cetuximab

  Single-cell RNA sequencing was performed on a cetuximab-sensitive metastatic colorectal cancer (mCRC) patient-derived xenograft (PDX) model. The objective of this analysis was to assess whether the transcriptional patterns identified in the single-cell RNA-seq data of matched organoids were preserved in the in vivo context.

  Study EGAS50000001459

• Single-cell RNA sequencing of metastatic colorectal cancer organoids treated with cetuximab

  Single-cell RNA sequencing was performed on metastatic colorectal cancer (mCRC) organoids derived from a cetuximab-sensitive patient-derived xenograft (PDX) model. The objective of this analysis was to investigate, at single-cell resolution, the transcriptional changes previously identified in bulk RNA-seq analyses of PDX models exposed to anti-EGFR therapeutic pressure.

  Study EGAS50000001460

• Single-cell RNA sequencing of metastatic colorectal cancer patient-derived xenografts treated with cetuximab

  Tumor implantation and expansion were performed in two 6-week-old NOD/SCID mice. Once tumors reached an average volume of approximately 700 mm³, mice were randomly assigned to one of two treatment arms: vehicle (physiological saline, intraperitoneally, twice weekly) or cetuximab (20 mg/kg, intraperitoneally, twice weekly). After 1 week of treatment, mice were sacrificed, and tumors were harvested and preserved in sCelLiVE® Buffer for downstream single-cell RNA sequencing analysis.

  Dataset EGAD50000002103

• Single-cell RNA sequencing of metastatic colorectal cancer organoids treated with cetuximab

  Organoids were seeded in 12-well plates at a density of 5 × 10⁴ cells per well, embedded in 200 µL of Matrigel diluted 1:1 with culture medium, and maintained under EGF-deprived conditions. After four days of growth, organoids were either left untreated or treated with cetuximab (20 µg/mL). Following 72 hours of treatment, organoids were harvested, dissociated, and live single cells were sorted for single-cell RNA sequencing using the 10x Genomics platform.

  Dataset EGAD50000002104

• RNAseq for 208 patients with medulloblastoma cancer (MB_COMICS cohort)

  This dataset contains 208 paired fastq files sequenced with Illumina HiSeq 2500.

  Dataset EGAD50000000600

• Berlin Neuroblastoma Dataset

  Study EGAS00001004022

• Sequence data to study genomic CNVs that drive apoptotic resistance and relapses on immune checkpoint inhibitors

  This project investigates the genomic mechanisms underlying acquired resistance to immune checkpoint inhibitors (ICIs) across tumor types, addressing the critical clinical problem of treatment relapses where the molecular basis remains poorly understood. Through integrative genomic analyses of longitudinal clinical melanomas, functional CRISPR-Cas9 screens, and human and murine melanoma models, the study demonstrates that copy-number variant (CNV)-driven subclonal evolution is a primary driver of clinical relapses. The research identifies resistance-driver genes targeted by CNVs, including pro-apoptotic gene deletions and anti-apoptotic gene amplifications, which collectively downregulate apoptotic priming in resistant cells. The findings establish that modulating the apoptotic threshold can prevent or reverse ICI resistance, positioning CNV evolution as a key driver of acquired resistance and identifying apoptotic regulation as a therapeutic target to combat resistance evolution.

  Study EGAS50000001055

• Shallow whole genome sequencing of ctDNA samples from DETECT study

  A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival from the paper "A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival", by Beddowes et al. Mol.Onc.

  Study EGAS50000000911

• Development and validation of multivariate predictors of primary endocrine resistance to tamoxifen and aromatase inhibitors in luminal breast cancer reveal drug-specific differences

  Background: Endocrine therapy is highly effective in blocking the estrogen receptor pathway in HR+/HER2- early breast cancer (EBC). However, up to 40% of patients experience relapse during or after adjuvant endocrine therapy. Here, we investigate molecular mechanisms associated with primary resistance to endocrine therapy and develop predictive models. Results: TP53 mutations were prominently associated with primary resistance to both tamoxifen (TAM) and aromatase inhibitors (AI), with AI non-responders exhibiting resistance in up to 32% of cases. Additionally, we identified distinct DNA methylation patterns in TAM and AI non-responders, with TAM non-responders showing global DNA methylation loss, associated with KRAS signaling, apical junctions and epithelial-mesenchymal transition (EMT). Conversely, we observed methylation gain in AI non-responders affecting developmental transcription factors, hypoxia and estrogen signaling. TAM or AI resistance was associated with increased methylation-inferred proportions of immune cells and decreased proportions of endothelial cells. Based on these findings and patient age, we developed the Predictive Endocrine ResistanCe Index (PERCI). PERCI stratified NR and R cases in both treatment groups and cohorts with high accuracy (ROC AUC TAM discovery 93.9%, validation 83%; AI discovery 98.6%, validation 76.9%). A simplified PERCI efficiently predicted progression-free survival in the TCGA-BRCA sub-cohort (Kaplan-Meier log-rank p-value = 0.03 between low and high PERCI groups). Conclusions: We identified genomic and epigenomic features associated with primary resistance to TMA and AI. By combining information on genomic alterations, patient age, differential methylation and tumor microenvironment (TME) composition, we developed PERCI TAM and PERCI AI as novel predictors of primary resistance, with potential additional prognostic value. Applying PERCI in a clinical setting may allow patient-specific drug selection to overcome resistance.

  Study EGAS50000001102

• Whole exome sequencing of patients with esophageal squamous cell carcinoma receiving chemoradiotherapy

  We performed multi-region whole exome sequencing (WES) of pre-treatment and recurrent esophageal cancer receiving chemoradiotherapy in order to characterize clonal evolution of treatment resistant clones. Furthermore, additional solitary pre-treatment tumor samples were subjected to WES to detect biomarkers predicting clinical outcome.

  Study JGAS000227