Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Culture media composition influences patient-derived organoids ability to predict therapeutic response in gastrointestinal cancers

ABSTRACT: Culture media composition influences patient-derived organoids ability to predict therapeutic response in gastrointestinal cancers

PROVIDER: PRJNA873279 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Culture media composition influences patient-derived organoids ability to predict therapeutic response in gastrointestinal cancers

Project description:In this study, we have generated patient-derived organoids (PDOs) from primary and metastatic lesions of gastrointestinal cancers, including pancreatic ductal adenocarcinoma, colorectal adenocarcinoma, and cholangiocarcinoma and evaluated their ability to predict donor tumor drug response. Specifically, we examined individual PDO responses to therapeutic agents with the observed clinical responses of donor patients to the same treatments and demonstrated an approximately 80% concordance rate. Importantly, we found a profound influence of culture media in PDOs phenotypes, we observed a significant difference in response to common PDAC chemotherapies, distinct morphologies, and transcriptomes between media in the same PDOs. These studies demonstrate the important role of culture media when using PDOs to inform patient care and predict response across a spectrum of GI cancers. We compared transcriptome data from pancreatic ductal adenocarcinoma patient-derived organoids in two media (WNT and PaTOM).

2022-10-12 | GSE212014 | GEO

Culture media composition influences patient-derived organoid ability to predict therapeutic responses in gastrointestinal cancers.

Project description:BACKGROUNDA patient-derived organoid (PDO) platform may serve as a promising tool for translational cancer research. In this study, we evaluated PDO's ability to predict clinical response to gastrointestinal (GI) cancers.METHODSWe generated PDOs from primary and metastatic lesions of patients with GI cancers, including pancreatic ductal adenocarcinoma, colorectal adenocarcinoma, and cholangiocarcinoma. We compared PDO response with the observed clinical response for donor patients to the same treatments.RESULTSWe report an approximately 80% concordance rate between PDO and donor tumor response. Importantly, we found a profound influence of culture media on PDO phenotype, where we showed a significant difference in response to standard-of-care chemotherapies, distinct morphologies, and transcriptomes between media within the same PDO cultures.CONCLUSIONWhile we demonstrate a high concordance rate between donor tumor and PDO, these studies also showed the important role of culture media when using PDOs to inform treatment selection and predict response across a spectrum of GI cancers.TRIAL REGISTRATIONNot applicable.FUNDINGThe Joan F. & Richard A. Abdoo Family Fund in Colorectal Cancer Research, GI Cancer program of the Mayo Clinic Cancer Center, Mayo Clinic SPORE in Pancreatic Cancer, Center of Individualized Medicine (Mayo Clinic), Department of Laboratory Medicine and Pathology (Mayo Clinic), Incyte Pharmaceuticals and Mayo Clinic Hepatobiliary SPORE, University of Minnesota-Mayo Clinic Partnership, and the Early Therapeutic program (Department of Oncology, Mayo Clinic).

| S-EPMC9746806 | biostudies-literature

Media composition influences yeast one- and two-hybrid results.

Project description:Although yeast two-hybrid experiments are commonly used to identify protein interactions, the frequent occurrence of false negatives and false positives hampers data interpretation. Using both yeast one-hybrid and two-hybrid experiments, we have identified potential sources of these problems: the media preparation protocol and the source of the yeast nitrogen base may not only impact signal range but also effect whether a result appears positive or negative. While altering media preparation may optimize signal differences for individual experiments, media preparation must be reported in detail to replicate studies and accurately compare results from different experiments.

| S-EPMC3177868 | biostudies-other

PREDICT 2

Project description:PREDICT 2

| PRJEB75460 | ENA

PREDICT 3 UK23ART

Project description:PREDICT 3 UK23ART

| PRJEB75465 | ENA

PREDICT 3 US22A

Project description:PREDICT 3 US22A

| PRJEB75463 | ENA

PREDICT 3 UK22A

Project description:PREDICT 3 UK22A

| PRJEB75464 | ENA

PREDICT 3 US21

Project description:PREDICT 3 US21

| PRJEB75462 | ENA

A Label Free Mass Spectrometry Method to Predict Endogenous Protein Complex Composition

Project description:A novel approach to predict endogenous protein complexes by separating native protein extracts with liquid chromatography, collecting fractions that are profiled by quantitative mass spectrometry. The profile data is used in clustering analyzes to make predictions about groups of proteins that form stable complexes.

2019-08-06 | PXD012601 | JPOST Repository

Transcription profiling of human tumors of breast cancer 129 patients were analyzed for the ability to predict biomarkers (ER, PR, HER2), histologic features (grade and lymphatic-vascular invasion), and stage-related information (tumor size and lymph node metastasis)

Project description:Predictors built from gene expression data accurately predict ER, PR, and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data cannot be used to predict tumor size or lymphatic-vascular invasion. Experiment Overall Design: Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers (ER, PR, HER2), histologic features (grade and lymphatic-vascular invasion), and stage-related information (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy determined by error rates of prediction and by dimensional scaling and visualization of the states under study. Models to predict lymph node metastasis were built by combinations of molecular, histologic and anatomic features.

2008-06-14 | E-GEOD-5460 | 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