Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes

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

ORGANISM(S): Homo sapiens

PROVIDER: GSE166999 | GEO | 2021/11/23

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes

Project description:We generated PDX models of aggressive NSCLC, which represent the DNA methylation features modelling of primary NSCLC

2021-11-24 | E-MTAB-10156 | biostudies-arrayexpress

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes [HT12v4]

Project description:Non-small cell lung cancer (NSCLC) death rates exceed the next 3 prevalent cancers combined; however, most NSCLC tumors lack actionable mutations. Recent studies of NSCLC and other cancers revealed profound proteome remodelling with prognostic impact that is not fully predicted by DNA or RNA analyses. These revelations portend proteome-based cancer classification and treatment. This will require model systems that recapitulate tumor proteomes and phenotypes. A subset (~35%) of the most aggressive NSCLC can form a patient-derived xenograft (PDX). We generated 137 PDX models of aggressive NSCLC, which represent the histological, genome, transcriptome, and DNA methylation features and proteome remodelling of primary NSCLC. The models indicate 3 lung adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, candidate targets, and in adenocarcinoma, distinct stromal immune features. The PDX resource will foster proteome-directed stratification and development of new treatments for aggressive NSCLC.

2021-11-23 | GSE166997 | GEO

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes [WG-DASL]

2021-11-23 | GSE166998 | GEO

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies distinct proteotypes associated with patient outcomes.

Project description:Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbor actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. NSCLC and other cancers display profound proteome remodeling compared to normal tissue that is not predicted by DNA or RNA analyses. Here, we generate 137 NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Proteome analysis of the PDX models reveals 3 adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, signatures of activated pathways and candidate targets, and in adenocarcinoma, stromal immune features. These findings portend proteome-based NSCLC classification and treatment and support the PDX resource as a viable model for the development of new targeted therapies.

| S-EPMC8983714 | biostudies-literature

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes

Project description:Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbour actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. NSCLC and other cancers display profound proteome remodelling compared to normal tissue that is not predicted by DNA or RNA analyses. We generated NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Here, we completed whole exome sequencing on 122 NSCLC PDXs.

| EGAS00001006068 | EGA

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes

Project description:Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes

| PRJNA702550 | ENA

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes [HT12v4]

Project description:Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes [HT12v4]

| PRJNA702553 | ENA

Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes [WG-DASL]

Project description:Integrative analysis of non-small cell lung cancer patient-derived xenografts identifies unique proteotypes associated with patient outcomes [WG-DASL]

| PRJNA702554 | ENA

Zebrafish patient-derived xenografts accurately and quickly reproduce treatment outcomes in non-small cell lung cancer patients.

Project description:Zebrafish patient-derived xenograft (zPDX) models have shown great potential in predicting the short-term treatment response in various types of tumor cases. However, few studies have used zPDX models for drug screening in non-small cell lung cancer (NSCLC). We aimed to compare the treatment responses of patients with NSCLC with those of the corresponding zPDX models. Tumor cells were obtained from pleural fluid or biopsy procedures from patients with NSCLC and injected into the perivitelline space of zebrafish larvae. Then, the same antineoplastic drugs administered to the corresponding patient were tested in the successfully constructed zPDX model, for 3 days. Responses to treatment were compared. A total of 21 patients with advanced NSCLC were enrolled in our study, and 13 corresponding zPDX models were successfully established. Based on the clinical medication of enrolled patients, we provided a corresponding drug treatment to these zebrafish embryos, including epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs), pemetrexed/platinum (AP), or docetaxel/platinum (DP) administration. The chemosensitivity consistency rate between the clinical responses and those obtained from zPDXs was 76.9% (10/13). There was a high correlation between patient responses and the corresponding zPDX drug responses. Thus, zPDX can accurately and quickly reproduce patient responses to treatment with EGFR TKIs, AP, and DP and has a considerable potential to serve as a biological platform for predicting treatment effect on patients with NSCLC.

| S-EPMC10159519 | biostudies-literature

Raman spectroscopy identifies radiation response in human non-small cell lung cancer xenografts.

Project description:External beam radiation therapy is a standard form of treatment for numerous cancers. Despite this, there are no approved methods to account for patient specific radiation sensitivity. In this report, Raman spectroscopy (RS) was used to identify radiation-induced biochemical changes in human non-small cell lung cancer xenografts. Chemometric analysis revealed unique radiation-related Raman signatures that were specific to nucleic acid, lipid, protein and carbohydrate spectral features. Among these changes was a dramatic shift in the accumulation of glycogen spectral bands for doses of 5 or 15 Gy when compared to unirradiated tumours. When spatial mapping was applied in this analysis there was considerable variability as we found substantial intra- and inter-tumour heterogeneity in the distribution of glycogen and other RS spectral features. Collectively, these data provide unique insight into the biochemical response of tumours, irradiated in vivo, and demonstrate the utility of RS for detecting distinct radiobiological responses in human tumour xenografts.

| S-EPMC4756358 | biostudies-literature

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