Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

TTFields plus TMZ treatment effects on newly diagnosed Glioblastoma patients in peripheral blood

ABSTRACT: TTFields plus TMZ treatment effects on newly diagnosed Glioblastoma patients in peripheral blood

PROVIDER: PRJNA796536 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

TTFields plus TMZ treatment effects on newly diagnosed Glioblastoma patients in peripheral blood

Project description:We report the application of single-cell-RNA-based sequencing technology for high-throughput profiling of peripheral blood mononuclear cells. By sequencing in total of 193,760 PBMCs in 12 GBM patients, we generated an overview of patient peripheral immune status with 38 biologically recongnized cell subtypes. By paired comparing pre- and post- treatment samples, we find a robust post-TTFields activation of adaptive immunity via the T1IFN trajectory anchored by plasmacytoid DCs. This study provides a framework for the application of scRNAseq towards characterization of peripheral immune activation status.

2025-01-01 | GSE193525 | GEO

TTFields treatment effects on newly diagnosed Glioblastoma patients in peripheral T lymphocytes

Project description:TTFields treatment effects on newly diagnosed Glioblastoma patients in peripheral T lymphocytes

| PRJNA797422 | ENA

TTFields treatment effects on newly diagnosed Glioblastoma patients in peripheral T lymphocytes

Project description:We report the application of RNA-based sequencing technology for high-throughput profiling of T cell enriched peripheral blood mononuclear cells. By sequencing in total of 12 pairs GBM patient samples, we extracted TCRαβ V(D)J sequences from the deep RNA-seq of T cells isolated from the 24 PBMCs to determine if TTFields treatment affected TCR diversity, using the Simpson’s diversity index (DI), which is the average proportional abundance of TCR clones based on the weighted arithmetic mean. Of the 12 patients, 9 exhibited negative log fold change (logFC) of TCR DI after TTFields, indicating clonal expansion. Notably, in all but 1 patient, the top 200 most abundant clones post TTFields, which accounted for 38.1% to 100% (median 67%) of detectable clones, showed substantial expansion compared to pre-TTFields T cells, and inversely correlated with the DI. Thus, TTFields treatment is associated with adaptive immune activation as evidenced by clonal expansion of peripheral T cells.

2022-04-20 | GSE193729 | GEO

Project description:Synergic therapeutic effects of TTFields, pembrolizumab plus TMZ on newly diagnosed Glioblastoma patients in peripheral blood

| PRJNA1123300 | ENA

Transcription profiling of bone marrow plasma cells from 320 newly diagnosed multiple myeloma patients

Project description:In order to identify relevant, molecularly defined subgroups in Multiple Myeloma (MM), gene expression profiling (GEP) was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/ GMMG-HD4 trial using Affymetrix Gene Chip U133 plus 2.0 arrays. Hierarchical clustering identified 10 distinct subgroups. Bone marrow plasma cell samples were obtained from 320 newly diagnosed multiple myeloma patients included in a large multicenter, prospective, randomized phase III trial (HOVON65/GMMG-HD4). Purified myeloma plasma cells samples with a monoclonal plasma cell purity > 80% were used for analysis.

2010-06-27 | E-GEOD-19784 | biostudies-arrayexpress

Single cell analysis of newly diagnosed AML patients

Project description:Single cell analysis of newly diagnosed AML patients

| PRJNA1000621 | ENA

Single cell analysis of newly diagnosed AML patients

Project description:Acute Myeloid Leukemia (AML) is a heterogenous disease characterized by immature blasts at different maturation stage. We used single cell sequencing technique to analyze newly diagnosed AML patients.

2023-12-18 | GSE239721 | GEO

A Gene Expression-Based Risk Stratification Model for Newly Diagnosed Multiple Myeloma

Project description:We recently defined a gene expression-based signature of high-risk multiple myeloma; this predictive signature was developed with and independently validated for newly diagnosed patients treated with high dose therapy and stem cell rescue. Here we use Phase 3 clinical trial data to show that this signature also predicts short survival in relapsed disease treated with single agent bortezomib or high dose dexamethasone. In addition, a survival signature derived with relapsed myeloma samples identified newly diagnosed patients with short survival. Taken together these data suggest that a similar biology underlies poor outcome in both newly diagnosed and relapsed myeloma and provide strong evidence that the high-risk signature is a powerful tool to identify patients who are candidates for new therapeutic regimens. Keywords: Model validation

2011-08-20 | GSE8991 | GEO

A Gene Expression-Based Risk Stratification Model for Newly Diagnosed Multiple Myeloma

2011-08-19 | E-GEOD-8991 | biostudies-arrayexpress

Differential gene expression profile of newly diagnosed versus relapse acute promyelocytic leukemia

Project description:The aim of this study was to gain insight into the potential mechanism of resistance to arsenic trioxide and to identify genes that are modulated in the malignant promyelocytes at the time of relapse in APL patients. We analyzed the gene expression profile by the whole genome microarray of primary promyelocytes obtained from 8 newly diagnosed and 8 at relapse patients. Agilent one-color experiment,Organism: Human ,Agilent Whole Genome Human 4x44k (AMADID: 014850) , Labeling kit: Agilent Quick-Amp labeling Kit (p/n5190-0442) newly diagnosed versus relapse acute promyelocytic leukemia

2013-08-02 | E-GEOD-42030 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data