Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia [scRNA-Seq]

ABSTRACT: Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia [scRNA-Seq]

PROVIDER: PRJNA901407 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR22273903_1.fastq.gz	Fastqsanger.gz
	SRR22273903_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 2 of 2

Similar Datasets

Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia

Project description:Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia

| PRJNA901398 | ENA

Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia [RNA-Seq]

Project description:Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia [RNA-Seq]

| PRJNA901403 | ENA

Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia [RNA-Seq]

Project description:Ex-vivo drug sensitivity screening (DSS) only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. Here we developed a machine-learning framework to deconvolute bulk RNA expression matched with bulk drug sensitivity into cell subtype composition and cell subtype drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples more accurately than current state-of-the-art methods. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies

2022-11-16 | GSE217919 | GEO

Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia [scRNA-Seq]

2022-11-16 | GSE217922 | GEO

Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia

Project description:This SuperSeries is composed of the SubSeries listed below.

2022-11-16 | GSE217924 | GEO

A Zebrafish-Based Orthotopic Glioblastoma Xenograft Model for Preclinical Drug Screening and Clinical Drug Sensitivity Prediction

Project description:A Zebrafish-Based Orthotopic Glioblastoma Xenograft Model for Preclinical Drug Screening and Clinical Drug Sensitivity Prediction

| PRJNA615175 | ENA

Use of DNA-alkylating pyrrole-imidazole polyamides for anti-cancer drug sensitivity screening in PDAC

Project description:Use of DNA-alkylating pyrrole-imidazole polyamides for anti-cancer drug sensitivity screening in PDAC

| PRJNA858839 | ENA

Matrix-Augmented Pooling Strategy for High-Throughput Target Deconvolution Reveals Cell Type Off-targets and Responses

Project description:Target deconvolution is a crucial but costly and time-consuming task that hinder large-scale profiling for drug discovery. We present a matrix-augmented pooling strategy (MAPS) which mixes multiple drugs into samples with optimized permutation and delineates targets of each drug simultaneously with mathematical processing. We validated this strategy with thermal proteome profiling (TPP) testing of 15 drugs concurrently, increasing experimental throughput by 60x while maintaining high sensitivity and specificity. Benefiting from the lower cost and higher throughput of MAPS, we perform target deconvolution of the 15 drugs across 5 cell lines. Our profiling revealed drug-target interactions can differ vastly in targets and binding affinity across cell lines. We further validated BRAF and CSNK2A are the potential off-targets of bafetinib and abemaciclib, respectively. This work presents the largest target profiling of structurally diverse drugs across multiple cell lines to date.

2023-07-19 | PXD043882 |

Ex vivo drug sensitivity screening predicts response to temozolomide in glioblastoma patients and identifies candidate biomarkers

Project description:Ex vivo drug sensitivity screening predicts response to temozolomide in glioblastoma patients and identifies candidate biomarkers

| PRJNA971167 | ENA

Prediction of drug sensitivity

Project description:Tumor cell lines and drug-resistant counterparts. These data support the publication Gyorffy et al, Oncogene 2005 (July), Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. We contrasted the expression profiles of 13 different human tumor cell lines of gastric (EPG85-257), pancreatic (EPP85-181), colon (HT29) and breast (MCF7 and MDA-MB-231) origin and their counterparts resistant to the topoisomerase inhibitors daunorubicin, doxorubicin or mitoxantrone. We interrogated cDNA arrays with 43 000 cDNA clones ( approximately 30 000 unique genes) to study the expression pattern of these cell lines. A cell type comparison design experiment design type compares cells of different type for example different cell lines. Using regression correlation

2005-07-30 | E-GEOD-3034 | 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