Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

RNAseq analysis of USP7 knockdown in MM16 and MM27 metastatic melanoma PDX cells

ABSTRACT: RNAseq analysis of USP7 knockdown in MM16 and MM27 metastatic melanoma PDX cells

PROVIDER: PRJNA750314 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR15276646.fastq.gz	Fastqsanger.gz
	SRR15276646_1.fastq.gz	Fastqsanger.gz
	SRR15276646_2.fastq.gz	Fastqsanger.gz
	SRR15276647.fastq.gz	Fastqsanger.gz
	SRR15276647_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 36

Similar Datasets

RNAseq analysis of USP7 knockdown in MM16 and MM27 metastatic melanoma PDX cells

Project description:Purpose: Study of transcriptomic changes upon depletion of USP7 Methods: Melanoma PDX cells were transduced with lentiviral vectors expressing Scrambled(shSCR) or USP7 targeting (shUSP7) shRNA

2022-08-24 | GSE180986 | GEO

RNAseq analysis of transcriptome changes in MM27 metastatic melanoma PDX cells upon treatment with AURKA and/or MEK inhibitors

Project description:Purpose: Study the changes in the transcriptome of metastatic melanoma PDX cells upon combination treatment with AURKA and MEK inhibitors Methods: RNAseq analysis of melanoma PDX cells treated with AURKA inhibitor Alisertib (500nM) and MEK inhibitor trametinib (100nM) for 72 h

2023-07-07 | GSE207645 | GEO

RNAseq analysis of MM13 and MM2 metastatic melanoma PDX cells overexpressing TINCR

Project description:RNAseq analysis of MM13 and MM2 metastatic melanoma PDX cells overexpressing TINCR

| PRJNA517676 | ENA

Patient-derived xenograft platform for metastatic melanoma: RNA sequencing of 4 melanoma PDX samples

Project description:The therapeutic landscape of melanoma is rapidly changing. While targeted inhibitors yield significant responses, their clinical benefit is often limited by the early onset of drug resistance. This motivates the pursuit to establish more durable clinical responses, by developing combinatorial therapies. But while potential new combinatorial targets steadily increase in numbers, they cannot possibly all be tested in patients. Similarly, while genetically engineered mouse melanoma models have great merit, they do not capture the enormous genetic diversity and heterogeneity typical in human melanoma. Furthermore, whereas in vitro studies have many advantages, they lack the presence of micro-environmental factors, which can have a profound impact on tumor progression and therapy response. This prompted us to develop an in vivo model for human melanoma that allows for studying the dynamics of tumor progression and drug response, with concurrent evaluation and optimization of new treatment regimens. Here, we present a collection of patient-derived xenografts (PDX), derived from BRAFV600E, NRASQ61 or BRAFWT/NRASWT melanoma metastases. The BRAFV600E PDX melanomas were acquired both prior to treatment with the BRAF inhibitor vemurafenib and after resistance had occurred, including six matched pairs. We find that PDX resemble their human donors’ melanomas regarding biomarkers, chromosomal aberrations, RNA expression profiles, mutational spectrum and targeted drug resistance patterns. Mutations, previously identified to cause resistance to BRAF inhibitors, are captured in PDX derived from resistant melanomThis melanoma PDX platform represents a comprehensive public resource to study both fundamental and translational aspects of melanoma progression and treatment in a physiologically relevant setting.

2016-05-27 | GSE73737 | GEO

Patient-derived xenograft platform for metastatic melanoma: RNA sequencing of 95 melanoma PDX samples

Project description:Patient-derived xenograft platform for metastatic melanoma: RNA sequencing of 95 melanoma PDX samples

| PRJNA530201 | ENA

ChIP-Seq melanoma PDX TM00702

Project description:H3K4me3 ChIP-seq from melanoma PDX tumours fed with different diets

2020-05-18 | GSE125822 | GEO

Patient-derived xenograft platform for metastatic melanoma: RNA sequencing of 95 melanoma PDX samples

Project description:Although high clinical response rates are seen for immune checkpoint blockade (ICB) of metastatic melanoma, both intrinsic and acquired ICB resistance remain formidable challenges. Combination ICB shows improved clinical benefit, but is associated with severe adverse events and exceedingly high cost. Therefore, there is a dire need to stratify individual patients for their likelihood of responding to either anti-PD-1 or anti-CTLA-4 monotherapy, or the combination. Since it is conceivable that ICB responses are influenced by both tumor cell-intrinsic and stromal factors, we hypothesized that a predictive classifier ought to mirror both of these distinct features. We used a panel of melanoma patient-derived xenografts (PDX), in which human stromal cells upon transplantation are naturally replaced by their murine counterparts, to computationally subtract PDX RNA expression signals from those in patients’ melanomas. We thus derived both “Stromal immune” (SIM) and tumor cell-specific “Tumor-autonomous inflammation” (TAF) signatures. Here we report that the SIM signature predicts response to anti-CTLA-4 but not anti-PD-1 treatment, whereas the tumor TAF signature predicts response to anti-PD-1 but not anti-CTLA-4. Moreover, when used in conjunction, the signatures accurately predict response in two independent patient cohorts treated with the anti-CTLA-4 + anti-PD-1 combination. These signatures may be clinically exploited for personalized treatment advice based on the predicted benefit from either anti-CTLA-4 or anti-PD-1 monotherapy or their combination.

2020-04-27 | GSE129127 | GEO

ChIP-Seq melanoma PDX TM00702

Project description:ChIP-Seq melanoma PDX TM00702

| PRJNA517652 | ENA

RNAi knockdown of Tip60 and Usp7 in mouse 3T3-L1 adipocytes

Project description:Knockdown of Tip60 or Usp7 in differentiated 3T3-L1 adipocytes

2013-08-28 | E-TABM-1145 | biostudies-arrayexpress

siRNA-mediated knockdown of MITF and PAX3 in metastatic melanoma cell lines

Project description:The transcription factors PAX3 and MITF are required for the development of the neural crest and melanocyte lineage, and both proteins play important roles in melanoma cell growth and survival. PAX3 transcriptionally activates MITF expression during neural crest development, but the relationship between these transcription factors during melanocyte development and in melanoma cells is currently poorly understood. This study aimed to further our understanding of the interaction between transcriptional networks controlled by PAX3 and MITF by assessing the effect of siRNA-mediated knockdown of PAX3 and MITF in metastatic melanoma cell lines. The goals of this study were to determine (i) if PAX3 is required for maintaining expression of MITF in melanoma and melanocyte cell lines; (ii) whether PAX3 and MITF independently, or redundantly, influence growth and survival in melanoma cell lines; and (iii) to investigate the respective roles of PAX3 and MITF expression in melanoma cell differentiation. Microarrays were used to measure global changes in transcript expression in response to siRNA-mediated knockdown of PAX3 or MITF compared to non-targeting controls in two metastatic melanoma cells lines. RNA was isolated from two different metastatic melanoma cell lines 30 hours after one of four different treaments: (i) transfection with siRNA targeting PAX3; or (ii) transfection with siRNA targeting MITF; or (iii) or transfection with siRNA targeting luciferase (non-targeting negative control); or (iv) treatment with media only (control). Therefore, eight samples were used for gene expression profiling by using GeneChip arrays, with one replicate per cell line per treatment.

2010-01-01 | E-GEOD-16249 | 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