Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE235093 | GEO | 2024/07/24

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_blood]

Project description:Merkel Cell Carcinoma (MCC) is an aggressie skin cancer with limiterd treatment and low response rates to Immune Checkpoint Blockade (ICB) therapy. We compiled the largest sc database of MCC tumor samples and matched blood to identify tumor gener modules that Predict ICB response.

2024-07-24 | GSE235091 | GEO

Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_tissue]

2024-07-24 | GSE235090 | GEO

Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [bulkRNA-seq]

Project description:Merkel Cell Carcinoma (MCC) is an aggressive skin cancer with limiterd treatment and low response rates to Immune Checkpoint Blockade (ICB) therapy. We compiled the largest sc database of MCC tumor samples and matched blood to identify tumor gener modules that Predict ICB response.

2024-07-24 | GSE235092 | GEO

Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy

Project description:Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy

| PRJNA984312 | ENA

Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_blood]

Project description:Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_blood]

| PRJNA984315 | ENA

Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_tissue]

Project description:Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_tissue]

| PRJNA984316 | ENA

Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [bulkRNA-seq]

Project description:Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [bulkRNA-seq]

| PRJNA984318 | ENA

Pre-existing skin-resident CD8 and gd T cell circuits mediate immune response in Merkel cell carcinoma and predict immunotherapy efficacy

Project description:Pre-existing skin-resident CD8 and gd T cell circuits mediate immune response in Merkel cell carcinoma and predict immunotherapy efficacy

| PRJNA1106925 | ENA

Pre-existing skin-resident CD8 and gd T cell circuits mediate immune response in Merkel cell carcinoma and predict immunotherapy efficacy

Project description:Pre-existing skin-resident CD8 and gd T cell circuits mediate immune response in Merkel cell carcinoma and predict immunotherapy efficacy

| PRJNA1106926 | ENA

Pre-existing cell states predict resistance to multiple treatments

Project description:Pre-existing differences between individual cancer cells can predict which cells will become resistant upon the application of treatment. This understanding has been furthered by novel methods in DNA barcoding that allow tracking of clones and their cell states during treatment. However, previous studies using these techniques have been limited in their scope, focusing on how single cell-states lead to resistance to a single treatment. In this study, we performed multi-treatment, high-throughput clonal tracking and single-cell RNA-sequencing to trace rare clones through the development of resistance to many different treatments in parallel with the goal of identifying cell states associated with multi-treatment resistance. We found that clones that will go on to develop resistance to one treatment had an increased chance of separately developing resistance to other treatments with diverse mechanisms of action. Additionally, we identified high CD44 expression in treatment-naive cells as a predictor of future resistance to multiple different treatments. Furthermore, for cells within the same treatment condition, we found that differences in gene expression states prior to treatment can lead cells to follow divergent paths towards their ultimate resistance fate. This work provides a framework for extracting targetable gene expression states from complex resistance dynamics across multiple treatments to eliminate multi-treatment resistance.

2024-10-14 | GSE279162 | GEO

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