42b6c35b-7688-47f2-891f-c782bf7a8be6 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001009684 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:A pan-cancer cohort of 1031 patients resistant to systemic therapies or with no approved therapeutic options. It includes whole-exome sequencing of 569 tumor and matched-normal samples, and transcriptome sequencing of 947 tumor samples. Biopsies were taken at entry into precision medicine trials, often after diagnosed resistance. Comprehensive clinical information is available for all patients and include patient age at biospy, tumor primary site and histological subtype, biopsy site, treatments received prior to biopsy, blood assessment results at biopsy, metastatic site at biopsy, and survival time from the biopsy date.
| EGAS00001006648 | EGA
Project description:Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells from patient biopsies. Here, we comprehensively characterize a pan-cancer cohort of 150 malignant serous effusion (MSE) samples at the cellular, molecular, and functional level. We find that MSE-derived cancer cells retain the genomic and transcriptomic profiles of their corresponding primary tumors, validating their use as a patient-relevant model system for solid tumor biology. Integrative analyses reveal that baseline gene expression patterns relate to global ex vivo drug sensitivity, while high-throughput drug-induced transcriptional changes in MSE samples are indicative of drug mode of action and acquired treatment resistance. A case study exemplifies the added value of multi-modal MSE profiling for patients who lack genetically stratified treatment options. In summary, our study provides a functional multi-omics view on a pan-cancer solid tumor cohort and underlines the feasibility and utility of MSE-based precision oncology.
2024-10-04 | PXD052582 | Pride
Project description:Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells. These patients frequently develop malignant serous effusions (MSE). The value of MSE-based tumor cell characterization for guiding precision oncology is, however, currently unclear. Here, we present a comprehensive characterization of a pan-cancer cohort of 150 MSE samples at the cellular, molecular, and functional level. Our integrative analysis reveals dynamic cellular heterogeneity in MSE, and uncovers links between tumor driver mutations and ex vivo growth patterns. Strong concordance between genomic and transcriptional profiles of MSE and their corresponding solid tumors validates their use as a model system for solid tumor biology. We link baseline gene expression patterns to global ex vivo drug sensitivity, and demonstrate that drug-induced transcriptional changes in MSE are highly indicative of compound mode of action. Two case studies exemplify the utility of our approach in investigating acquired resistance to targeted therapy and identifying treatment options for relapsed solid tumors. In summary, our study provides a functional multi-omics view on a pan-cancer MSE cohort and underlines the utility of MSE-based precision oncology.
2024-08-26 | GSE240952 | GEO
Project description:Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells. These patients frequently develop malignant serous effusions (MSE). The value of MSE-based tumor cell characterization for guiding precision oncology is, however, currently unclear. Here, we present a comprehensive characterization of a pan-cancer cohort of 150 MSE samples at the cellular, molecular, and functional level. Our integrative analysis reveals dynamic cellular heterogeneity in MSE, and uncovers links between tumor driver mutations and ex vivo growth patterns. Strong concordance between genomic and transcriptional profiles of MSE and their corresponding solid tumors validates their use as a model system for solid tumor biology. We link baseline gene expression patterns to global ex vivo drug sensitivity, and demonstrate that drug-induced transcriptional changes in MSE are highly indicative of compound mode of action. Two case studies exemplify the utility of our approach in investigating acquired resistance to targeted therapy and identifying treatment options for relapsed solid tumors. In summary, our study provides a functional multi-omics view on a pan-cancer MSE cohort and underlines the utility of MSE-based precision oncology.
2024-08-26 | GSE240951 | GEO
Project description:Histological grading is the key factors affecting the prognosis of patients and instructive in guiding treatment and assessing recurrence in non-functional pancreatic neuroendocrine tumor (NF-Pan-NET). Approximately one-third of patients without copy number variation (CNV) alteration and the prognosis of these patients are better than that of patients with CNV alteration. Tumor classification based on CNV also showed significant value in evaluating prognosis. However, the difference between CNV and histological grading is unclear. Here, at single cell level, we analyzed the heterogeneity of tumor cells according to two classification criteria, genomic instability (including CNV alteration and tumor mutation burden) and histological grading. We found that the classification basis on genomic instability of the bulk tissues was better than histological grading in distinguishing tumor cells at scRNA-seq. We revealed that the activated core pathways of tumor cells were significantly different under different histological gradings and genomic instability patterns. In particular, patients with liver metastases had specific activation pathways. Deciphering the differences of the tumor microenvironment through single-cell sequencing, we found that tip cells, lymphatic endothelial cells, macrophages, CD1A+ dendritic cell, Treg, MAIT, ILC and CAFs might patriciate in the process of hepatic metastases, which will facilitate the understanding of the patterns to decode the malignant potential and of NF-Pan-NET.
2024-02-27 | GSE256136 | GEO
Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and usually lethal lung disease characterized by unknown causes and few treatment options We used microarray to determine the expression of miRNA and 17 miRNAs were differentially expressed in IPF lungs, including 10 upregulated and 7 downregulated miRNAs. Lung tissue was obtained from 4 IPF patients with histological evidence of usual interstitial pneumonia at the time of surgical lung biopsy or lung transplantation. The diagnosis of IPF was derived according to the standards accepted by the American Thoracic Society/European Respiratory Society. Histological normal lung tissues used as controls was obtained from 3 patients with primary spontaneous pneumothorax at the time of thoracoscopy with stapling of any air leak.The miRNA expression profile was determined by Affymetrix microarray, and transcriptome with Affymetrix array
2015-12-04 | E-GEOD-75647 | biostudies-arrayexpress
Project description:All patients with suspected ovarian cancer (Raised CA 125 and a complex pelvic mass in a perimenopausal woman) were radiologically staged using CT scan and a chest x-ray. Patients with evidence of intra-abdominal metastasis and/or malignant pleural effusion were approached for entry to the study. Tissue biopsy was obtained either under radiological control (core needle biopsy) or via laparoscopic surgery (punch biopsy). Patients with histologicaly confirmed epithelial ovarian cancer were randomized to receive either three cycles of carboplatin (AUC 7) or paclitaxel (175 mg/m2). Following data filtering, only 3426 genes were retained for further downstream analysis (see 'Data_after_filtering.txt' at the foot of this record). For data filtering details, please see the 'Data processing' field in any of the associated Sample records. Experiment Overall Design: Patients with suspected ovarian cancer had a biopsy obtained before the start of treatment. Each sample was divided into two; one for histological confirmation and the other was immediately frozen for RNA extraction.
2008-06-16 | E-GEOD-9455 | biostudies-arrayexpress
Project description:Background: Clinical misdiagnosis between cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) poses treatment challenges and carries risks of recurrence, metastases, and increased morbidity and mortality. Objective: We aimed to identify discriminant proteins markers for cSCC and BCC using a minimally invasive proteome sampling method called e-biopsy, employing electroporation for non-thermal cell permeabilization and machine learning. Methods: E-biopsy facilitated ex vivo proteome extraction from 21 cSCC and 21 BCC pathologically validated human cancers. LC/MS/MS profiling of 126 proteomes was followed by Machine Learning analysis to identify proteins distinguishing cSCC from BCC. For identified panel validation, we used proteomes sampled by e-biopsy from unrelated 20 cSCC and 46 BCC human cancers, and differential expression analysis of published transcriptomics. The most commonly chosen discriminant biomarker by machine learning models, cornulin, was also validated using fluorescent immunohistochemistry. Results: 192 proteomes sampled from 108 patients were analyzed. Machine Learning-based approaches resulted in a set of 11 potential biomarker proteins that can be used to construct a model with 95.2% average cross-validation accuracy, BCC precision of 93.6±14.5%, cSCC precision of 98.4±7.2%, specificity of 97.7±11.8%, and per-patient sensitivity 92.7±15.3%. Protein-protein interaction analysis revealed a novel interaction network connecting 10 of the 11 resulted proteins. Histological and transcriptomic validation confirmed cornulin as a discriminant marker significantly lower in cSCC than in BCC. Conclusions: E-biopsy combined with machine learning provides a novel approach to molecular biomarkers sampling from skin for biomarker detection and differential expression analysis between cSCC and BCC
2024-08-22 | PXD050713 | Pride
Project description:High-risk neuroblastoma is a devastating malignancy with few therapeutic options. We identify withaferin A (WA) as a natural ferroptosis inducing agent in neuroblastoma, which acts through a novel double-edged mechanism. WA targets and inactivates glutathione peroxidase 4, as well as increases intracellular labile Fe(II) upon excessive activation of heme oxygenase-1, which both independently result in ferroptosis. This double-edged mechanism results in a high efficacy of WA compared to etoposide in killing a heterogeneous panel of high-risk neuroblastoma cells, and in suppressing neuroblastoma xenografts growth and relapse rate. Nano-targeting of WA allows systemic application and suppressed tumor growth due to an enhanced targeting to the tumor site. Collectively, our data propose a novel therapeutic strategy to kill cancer cells by ferroptosis. High-risk neuroblastoma is a devastating malignancy with few therapeutic options. We identify withaferin A (WA) as a natural ferroptosis inducing agent in neuroblastoma, which acts through a novel double-edged mechanism. WA targets and inactivates glutathione peroxidase 4, as well as increases intracellular labile Fe(II) upon excessive activation of heme oxygenase-1, which both independently result in ferroptosis. This double-edged mechanism results in a high efficacy of WA compared to etoposide in killing a heterogeneous panel of high-risk neuroblastoma cells, and in suppressing neuroblastoma xenografts growth and relapse rate. Nano-targeting of WA allows systemic application and suppressed tumor growth due to an enhanced targeting to the tumor site. Collectively, our data propose a novel therapeutic strategy to kill cancer cells by ferroptosis.
2018-05-01 | GSE112384 | GEO
Project description:Intrinsic nephron cell types and tissue resident immune cells were recovered from renal biopsy specimens using a scRNAseq protocol for the Kidney Precision Medicine Project (KPMP), designed for deployment in a multicenter research network. Combined processing of 24 samples including 16 tumor-nephrectomy, 5 surveillance and 3 pre-perfusion biopsies using the scRNAseq protocol yielded 22,268 cells (4690 from surveillance biopsies, 16,491 from tumor-nephrectomies and 2834 cells from pre-perfusion biopsies) .
2020-03-03 | GSE140989 | GEO