Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Expression data from transplanted HCmel3 mouse melanomas relapsing to adoptive T-cell therapy in vivo

ABSTRACT: Adoptive cell therapies (ACT) with cytotoxic T-cell targeting melanocytic antigens can achieve remissions in metastatic melanoma patients, but tumours frequently relapse. To study the underlying mechanisms of resistance we have generated a genetically engineered mouse melanoma model that faithfully recapitulates tumour regression, remission and relapse as seen in patients. HCmel3 mouse melanoma cells were injected into syngneic C57/BL6 (H-2b) mice. We performed transcriptional profiling of control, relapsed and re-transplanted relapsed tumours to characterize the changes in gene expression patterns associated with the respective treament conditions. In addition, we analysed the transcription profile of ACT relapsed melanoma cultures in vitro and the transcriptional response to the inflammatory cytokine Tnf-alpha. The study aimed to identify molecular mechanisms contributing to ACT resistance to optimize therapeutic regimens in the clinic.

ORGANISM(S): Mus musculus

PROVIDER: GSE40213 | GEO | 2012/10/10

SECONDARY ACCESSION(S): PRJNA173206

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Expression data from transplanted HCmel3 mouse melanomas relapsing to adoptive T-cell therapy in vivo

Project description:Adoptive cell therapies (ACT) with cytotoxic T-cell targeting melanocytic antigens can achieve remissions in metastatic melanoma patients, but tumours frequently relapse. To study the underlying mechanisms of resistance we have generated a genetically engineered mouse melanoma model that faithfully recapitulates tumour regression, remission and relapse as seen in patients. HCmel3 mouse melanoma cells were injected into syngneic C57/BL6 (H-2b) mice. We performed transcriptional profiling of control, relapsed and re-transplanted relapsed tumours to characterize the changes in gene expression patterns associated with the respective treament conditions. In addition, we analysed the transcription profile of ACT relapsed melanoma cultures in vitro and the transcriptional response to the inflammatory cytokine Tnf-alpha. The study aimed to identify molecular mechanisms contributing to ACT resistance to optimize therapeutic regimens in the clinic. Total RNA obtained from five biological replicates (rep) of control, relapsed and re-transplanted HCmel3 mouse melanomas in vivo and two biological replicates (rep) of HCmel3 cells, HCmel3-R (relapse) cells and Tnf-alpha treated HCmel3 cells in vitro.

2012-10-10 | E-GEOD-40213 | biostudies-arrayexpress

Gene expression analysis in adoptive T cell immunotherapy (ACT)

Project description:To understand the global effector immune responses in the tumor induced by ACT, we performed a gene chip analysis using B16 melanoma-pmel-1 TCR transgenic T cells model. Gene expression was measured in the tumor from untreated or ACT mice on day 1, 3, 5 and 7. Gene expression was also measured in the tumor on day 3, 5 and 7 from ACT mice with anti-IFNg or control Rat IgG antibodies treatment.

2014-08-25 | E-GEOD-57304 | biostudies-arrayexpress

Gene expression analysis in adoptive T cell immunotherapy (ACT)

Project description:To understand the global effector immune responses in the tumor induced by ACT, we performed a gene chip analysis using B16 melanoma-pmel-1 TCR transgenic T cells model.

2014-08-25 | GSE57304 | GEO

Mutational and neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma

Project description:Purpose: Utility of immunological treatment in cancer has increased; however, many patients do not respond to treatment. Identification of robust predictive biomarkers is required to correctly stratify patients. Although clinical trials based on adoptive T cell therapy (ACT) have yielded high response rates and many durable responses in melanoma, 50-60% of the patients have no clinical benefit. Herein, we searched for predictive biomarkers to ACT in melanoma. Methods: Whole exome- and transcriptome sequencing, neoantigen prediction and immune cell signature analysis were applied to pre-treatment melanoma samples from 27 patients recruited to a clinical phase I/II trial of ACT in stage IV melanoma. All patients had previously been treated with other immunotherapies. Results: We found that clinical benefit was associated with significantly higher neoantigen load (P=0.025). High mutation and neoantigen load were significantly associated with improved progression-free and overall survival (P=8x10^-4 and P=0.001, respectively). Further, gene-expression analysis of pre-treatment biopsies showed that clinical benefit was associated with strong immune activation signatures including a high MHC-I antigen processing and presentation score. Conclusions: These results improve our understanding of clinical benefit of ACT in melanoma, which can lead to clinically useful predictive biomarkers to be used for selecting patients that benefit from these highly intensive treatment regimens.

2017-12-04 | GSE100797 | GEO

Response to tumor- infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+T- myeloid cell networks in melanoma [scTCRseq]

Project description:Adoptive cell therapy (ACT) using ex vivo–expanded tumor-infiltrating lymphocytes (TILs) can eliminate or shrink metastatic melanoma, but its long-term efficacy remains limited to a fraction of patients. Using longitudinal samples from 13 patients with metastatic melanoma treated with TIL-ACT in a phase 1 clinical study, we interrogated cellular states within the tumor microenvironment (TME) and their interactions. We performed bulk and single-cell RNA sequencing, whole-exome sequencing, and spatial proteomic analyses in pre- and post-ACT tumor tissues, finding that ACT responders exhibited higher basal tumor cell–intrinsic immunogenicity and mutational burden. Compared with nonresponders, CD8+ TILs exhibited increased cytotoxicity, exhaustion, and costimulation, whereas myeloid cells had increased type I interferon signaling in responders. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders had rich baseline intratumoral and stromal tumor–reactive T cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study identifies potential T-myeloid cell network–based biomarkers that could improve patient selection and guide the design of ACT clinical trials.

2024-02-05 | GSE222750 | GEO

Response to tumor- infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+T- myeloid cell networks in melanoma [bulk RNAseq]

2024-02-05 | GSE221253 | GEO

Response to tumor- infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+T- myeloid cell networks in melanoma [full scRNAseq]

2024-02-05 | GSE222446 | GEO

Response to tumor- infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+T- myeloid cell networks in melanoma [CD45 scRNAseq]

2024-02-05 | GSE221553 | GEO

Gene expression profiles associated with pediatric relapsed AML

Project description:In high income countries 90% of the patients achieve complete remission after induction chemotherapy. However, 30-40% of these patients suffer from relapse. These patients face a dismal prognosis, as the majority (>60%) of relapsed patients die within 5 years. As a result, outcome for pediatric acute myeloid leukemia (AML) patients remains poor and has stabilized over the past 15 years. To prevent or better treat relapse of AML is the best option to improve outcome. Despite patient specific differences, most patients do respond to initial therapy. This suggests that at relapse, mechanisms are active that cause the altered response to chemotherapy. Detailed understanding of mechanisms that cause relapse remain largely elusive. To gain insight in the molecular pathways that characterize relapsed AML, we performed genome wide gene expression profiling on paired initial diagnosis and relapsed AML samples of 23 pediatric AML patients. We used pathway analysis to find which molecular pathways are involved in altered gene expression between diagnosis and relapse samples of individual AML patients. 23 paired diagnosis and relapse bone marrow or peripheral blood samples were collected and cryo-preserved. They were later thawed and processed for hybridization to Affymetrix U133 Plus 2.0 arrays.

2015-05-07 | E-GEOD-52891 | biostudies-arrayexpress

Comprehensive profiling of myxopapillary ependymomas identifies a distinct molecular subtype with relapsing disease

Project description:Myxopapillary ependymoma is a heterogeneous disease with respect to histopathology and clinical outcome. However, there are no diagnostic markers or clinical parameters that reliably predict the progression-free survival of the respective patients. We analyzed 185 tumors that classified as myxopapillary ependymoma based on global DNA methylation. Of these, 46 samples have been previously analyzed in GSE65362, GSE90496 or GSE109381. Using k-means clustering, our series split up into two distinct subtypes: MPE-A occurred in younger patients (median age 27 years) and were significantly enriched with tumors demonstrating papillary morphology and MGMT promoter methylation. Half of these tumors could not be totally resected, and 85% relapsed within 10 years. On the other hand, MPE-B occurred at a median age of 45 years. They included a significantly higher number of tumors with an initial diagnosis of WHO grade II and tanycytic morphology. Importantly, patients within this subgroup had a significantly better outcome with a relapse rate of only 33% in 10 years.

2022-04-25 | GSE184900 | 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