Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcriptional profiles of CD8+ T cells from peripheral blood of melanoma patients before and after anti-PD1 therapy

ABSTRACT: RNA-Seq analysis was used to study the profile of CD8 t cells from melanoma patients before and after treatment to understand if we can detect transcriptional changes in peripheral blood

ORGANISM(S): Homo sapiens

PROVIDER: GSE96578 | GEO | 2017/04/23

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Proteomic profiling to predict therapeutic response to anti-PD1 therapy in advanced melanoma

Project description:Purpose Antibody-blockade of PD-1 shows durable responses, but so far, few biomarkers accurately predict if patients with metastatic melanoma will respond. Experimental design We analyzed baseline serum samples (n=56) and tumor cell cultures (n=8), from melanoma patients treated with anti-PD1 therapy. Two approaches concentrating low abundant proteins in serum and two different mass spectrometry (MS) methods were applied: depletion of high abundant proteins with shotgun-MS, and N-glycosite enrichment combined with SWATH-MS. Tumor cell cultures were processed as whole cell lysates with subsequent shotgun-MS. Results Focusing on the non-responder proteome, we identified pathways such as inflammatory processes, cell adhesion and migration, scavenger receptor activity, RAGE receptor binding, platelet aggregation, neutrophil degranulation and integrin signaling to be upregulated. Analyzing the tumor cell proteome revealed a distinctive immunophenotype with CD antigens highly overexpressed in non-responders. Validation of a subset of these markers, performed by immunofluorescence staining of a tissue microarray, revealed a higher mean of positive cells in the tumor microenvironment of non-responders. Validation of serum proteome markers on a second baseline cohort by using a timsTOF for ion mobility MS, revealed significant differences between responder and non-responder for the proteins ORM2, SERPINA1, EFEMP1, and VASN. TCGA survival analysis demonstrated prognostic significance for multiple proteins of our signature correlating with lower overall survival in melanoma patients. Based on this study, we suggest a protein-panel for responsiveness to PD-1 blockade consisting of 14 serum and 33 tumor cell markers that will be the basis for further research.

2023-08-18 | PXD027525 | Pride

Single-cell characterization of anti-LAG3+anti-PD1 treatment in melanoma patients

Project description:We evaluated blood samples from 6 patients with metastatic melanoma treated with anti-LAG3+anti-PD1 (160+480 mg) in a phase I trial (NCT01968109) using single-cell RNA and T cell receptor (TCR) sequencing (scRNA+TCRαβ-seq, 10X 5') combined with other multiomics profiling (flow, cytokine, TCRb-seq) from a larger cohort of 40 patients. This data set include three time points, including baseline, 1 month, and 3 month. The sorting is CD45+.

2023-02-03 | E-MTAB-12733 | biostudies-arrayexpress

Proteomic profiling to predict therapeutic response to anti-PD1 therapy in advanced melanoma – SWATH MS dataset - glycopeptides

Project description:MS-based proteomics based on data-independent acquisition / SWATH MS was used to study the response of melanoma patients to anti-PD1 therapy. Human serum samples were collected from patients at the start of their therapy and later categorized into responders and non-responders based on clinical outcome. Samples were subjected to glycocapture and glycopeptides were quantified by SWATH MS followed by analysis using mapDIA to protein levels.

2023-08-21 | PXD027933 | Pride

Proteomic profiling to predict therapeutic response to anti-PD1 therapy in advanced melanoma – Orbitrap Elite dataset depleted serum

Project description:MS-based proteomics based on data-dependent acquisition / shotgun MS was used to study the response of melanoma patients to anti-PD1 therapy. Human serum samples were collected from patients at the start of their therapy and later categorized into responders and non-responders based on clinical outcome. Samples were subjected to immunodepletion and proteins were identified by bottom-up proteomics.

2023-08-18 | PXD027849 | Pride

Mice melanoma treated by anti-PD1 and intra-tumor microbiome

Project description:To investigate the contribution of intratumor microbiome in cancer immunotherapy, we applied microbiome Eudoraea in combination with anti-PD-1 antibody in mouse model of B16F10 melanoma. We then performed gene expression profiling analysis using data obtained from RNA-seq of 4 different treatments .

2024-06-01 | GSE205896 | GEO

CD8+ T cells in peripheral blood lymphocytes and tumor-infiltrating lymphocytes of melanoma patients

Project description:CD8+ T cells are the primary target of immune checkpoint inhibitor (ICI) therapy in the treatment of melanoma. ICI therapy only benefits a subset of patients and complicating this issue is a reliable prediction method that does not require invasive biopsies. In the hope of remedying this challenge, we conducted single-cell transcriptomic analyses of CD8+ T cells in peripheral blood lymphocytes (CD8-mPBLs) and, importantly, tumor-infiltrating lymphocytes (CD8-mTILs) from 8 patients with metastatic melanoma.

2021-11-16 | GSE138720 | GEO

Robust immunoscore model to predict the response to anti-PD1 therapy in melanoma.

Project description:This study aimed to construct immune-related predictors to identify responders to anti-PD1 therapy of melanoma through CIBERSORT algorithm. Using the least absolute shrinkage and selection operator (LASSO) logistic regression, we constructed an immunoscore consisting of 8 immune subsets to predict the anti-PD1 response. This score achieved an overall accuracy of AUC = 0.77, 0.80 and 0.73 in the training cohort, validation cohort and on-anti-PD1 cohort, respectively. Patients with high immunoscores had significantly higher objective response rates (ORRs) than did those with low immunoscores (ORR: 53.8% vs 17.7%, P < 0.001 for entire pre-anti-PD1 cohort; 42.1% vs 15.1%, P = 0.022 for on-anti-PD1 cohort; 66.7% vs 16.7%, P = 0.038 for neoadjuvant anti-PD1 cohort). Prolonged survival trends were observed in high-immunoscore group (1-year PFS: 42.4% vs 14.3%, P = 0.059; 3-year OS: 41.5% vs 31.6%, P = 0.057). Furthermore, we found that high-immunoscore group exhibited higher fractions of tumor-infiltrating lymphocytes and an increased IFN-γ response. Analysis of the results of the GSEA indicated a significant enrichment of antitumor immunity pathways in the high-immunoscore group. Therefore, this study indicated that we constructed a robust immunoscore model to predict the anti-PD1 response of metastatic melanoma and the neoadjuvant anti-PD1 response of resectable melanoma.

| S-EPMC6932919 | biostudies-literature

10X single-cell RNASeq profiling of human CD8+ T cells from advanced metastatic melanoma tumor samples and paired peripheral blood

Project description:The ability to monitor anti-tumor CD8+ T cell responses in the blood has tremendous therapeutic potential. However, tracking tumor antigen-specific CD8+ T cells in the blood is challenging due to their small number and limited reagents to track these cells. We asked whether paired single-cell RNA and T cell receptor (TCR) sequencing could be used to detect and characterize “tumor matching” (TM) CD8+ T cells in the blood of advanced melanoma patients using the TCR as a molecular barcode. By leveraging the transcriptome, we characterized these cells, and identified candidate cell surface markers for to enrich this population. These data show that the TCR can be used to identify tumor-relevant cells for characterization, reveal unique transcriptional properties of TM cells, and develop marker panels for tracking and analysis of these cells.

2021-01-13 | GSE159251 | GEO

Molecular changes induced in melanoma cells by stromal cells and anti-BRAF therapy.

Project description:Tumor-associated stromal cells can enable cancer cells to become insensitive to therapy. They can promote aggressive phenotype in cancer cells, which become less responsive to drugs such as BRAF inhibitors (BRAFi) used to treat melanomas. To clarify potential mechanism behind stromal influence on melanoma, we analyzed gene expression in Melmet 5 melanoma cells grown as mono-cultures or co-cultures with lung fibroblasts with/without BRAFi. We have shown that Melmet 5 growing as co-cultures gained a de-differentiated, invasive transcriptional state, which is known to be linked to BRAFi-resistance. The transcriptional changes induced by BRAFi were much larger in Melmet 5 mono-cultures compared to co-cultures, indicating a much dampened transcriptional response to BRAFi in melanoma under the influence of fibroblasts. We conclude that interaction with the stromal cells stimulate melanoma cell transition to the invasive de-differentiated phenotype, leading to a worse response to BRAF inhibitors. Total RNA was isolated from Melmet 5 cell line growing as mono- or co-cultures with fibroblasts for 72 hours and treated with BRAFi for the last 24 hours.

2015-08-01 | E-GEOD-67637 | biostudies-arrayexpress

scRNA-Seq of peripheral CD8 T cells from patients with metastatic melanoma taking ICI therapy.

Project description:Patients with metastatic melanoma have been treated with the new class of drug named immune checkpoint inhibitors. Single-cell RNA-sequencing was performed in circulating CD8+ T cells isolated by cell sorting from 20 patients who were followed for 6 months. Among them, 11 responded to therapy, whilst 9 did not. Overall, the data obtained by sc-RNA seq showed the presence of MAIT cells, that had higher levels in those who showed a better response to treatment.

2021-02-05 | GSE166181 | 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