Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Clonal differences underlie variable responses to sequential and prolonged treatment

ABSTRACT: Cancer cells exhibit dramatic differences in gene expression at the single-cell level which can predict whether they become resistant to treatment. Treatment perpetuates this heterogeneity, resulting in a diversity of cell states among resistant clones. However, it remains unclear whether these differences lead to distinct responses when another treatment is applied or the same treatment is continued. In this study, we combined single-cell RNA-sequencing with barcoding to track resistant clones through prolonged and sequential treatments. We found that cells within the same clone have similar gene expression states after multiple rounds of treatment. Moreover, we demonstrated that individual clones have distinct and differing fates, including growth, survival, or death, when subjected to a second treatment or when the first treatment is continued. By identifying gene expression states that predict clone survival, this work provides a foundation for selecting optimal therapies that target the most aggressive resistant clones within a tumor.

ORGANISM(S): Homo sapiens

PROVIDER: GSE253739 | GEO | 2024/01/23

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Pre-existing cell states predict resistance to multiple treatments

Project description:Pre-existing differences between individual cancer cells can predict which cells will become resistant upon the application of treatment. This understanding has been furthered by novel methods in DNA barcoding that allow tracking of clones and their cell states during treatment. However, previous studies using these techniques have been limited in their scope, focusing on how single cell-states lead to resistance to a single treatment. In this study, we performed multi-treatment, high-throughput clonal tracking and single-cell RNA-sequencing to trace rare clones through the development of resistance to many different treatments in parallel with the goal of identifying cell states associated with multi-treatment resistance. We found that clones that will go on to develop resistance to one treatment had an increased chance of separately developing resistance to other treatments with diverse mechanisms of action. Additionally, we identified high CD44 expression in treatment-naive cells as a predictor of future resistance to multiple different treatments. Furthermore, for cells within the same treatment condition, we found that differences in gene expression states prior to treatment can lead cells to follow divergent paths towards their ultimate resistance fate. This work provides a framework for extracting targetable gene expression states from complex resistance dynamics across multiple treatments to eliminate multi-treatment resistance.

2024-10-14 | GSE279162 | GEO

Gene expression profiling of Nup98HoxA9 expressing ES cells

2016-01-04 | GSE67967 | GEO

Testing for gene expression differences between chromosomally stable and unstable isogenic clones

Project description:An irradiated but chromosomally stable CHO clone RT210B was used a reference to find differences in gene expression compared to radiation-induced chromosomally unstable isogenic clones. Equal quantities of two unstable different unstable clones were mixed prior to RNA extraction. Each mixture was tested twice versus the stable reference clone. Two different two clone mixtures were tested against the same chromosomally stable clone Keywords: repeat sample

2004-08-01 | GSE1626 | GEO

Acquired resistance of Jurkat cells to TNF-related apoptosis-inducing ligand (TRAIL)

Project description:Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has been shown to be a potent inducer of apoptosis in various cancer cell lines and primary cancer cells. However, in clinical trials administration of recombinant TRAIL or TRAIL death receptor agonists did not show sufficient efficacy for treatment of tested malignant disorders compared to standard chemotherapy. Acquired resistance of cancer cells to TRAIL and to other “death receptor” ligands may explain not only the inability of TRAIL and TRAIL “death receptor” agonists to achieve the clearance of cancer cells in vivo but also the escape of cancer cells from immune cell – mediated killing. Selective pressure of TRAIL on TRAIL-sensitive Jurkat T-lymphoblastic leukemia cells provided several TRAIL resistant Jurkat cell line clones (TR1, TR2, TR3). We showed that acquired TRAIL resistance was associated with complex disruption of both extrinsic and intrinsic apoptotic pathways manifested by acquired multi-drug resistant phenotype of TR1, TR2, and TR3 clones. To identify changes associated with TRAIL resistance of Jurkat cells we performed genome-wide gene expression analysis of TRAIL-resistant clones (TR) and compared to WT Jurkat cells. We identified significantly increased expression (1.33-fold change with adjusted p < 0.05) of 73 genes in TR1 clone, 53 genes in TR2 clone and 8 genes in TR3 clone, and significant decrease in expression (0.75-fold change with adjusted p < 0.05) of 174 genes in TR1 clone, 36 genes in TR2 clone and 28 genes in TR3 clone. There was an overlap of only 2 significantly overexpressed (midkine / MDK and zinc finger and BTB domain containing 16 gene / ZBTB16 / PLZF) and 4 downregulated genes (YAP1, IGJ, EIF1AY, RPS4Y1) in all three TR clones.

2016-06-15 | GSE38743 | GEO

Optimal Anti-EGFR Treatment of mCRC Patients With Low-Frequency RAS Mutation

Project description:The present hypothesis is that anti-EGFR agents are active in tumors with low-level RAS mutation when the majority of tumor cells is still sensitive. While response rate may be high and may reflect sensitivity to anti-EGFR agents, PFS is anticipated to be shorter than in RAS wild-type patients due to the faster development of resistance when sensitive cells are eradicated and when the RAS-mutant anti-EGFR resistant clones become predominant. The characteristics of low-level RAS mutant tumors would be: * Objective response rate (ORR) high (reflecting the sensitive clone) * Progression-free survival (PFS) short (reflecting the more rapid outgrowth of RAS mutant clones)

| 2312021 | ecrin-mdr-crc

Gene expression profiling of Nup98HoxA9 expressing ES cells

Project description:To know whether the Nup98-HoxA9 affects global gene expression, we performed DNA microarray analysis using the following four clones: two independent Nup98-HoxA9 ES clones (clone#1 and clone#9), parental ES cells, and HoxA9-Ct ES clone. Two independent Nup98-HoxA9 ES clones (clone#1 and clone#9), and HoxA9-Ct ES clone, was compared with parental ES cells (reference sample).

2016-01-04 | E-GEOD-67967 | biostudies-arrayexpress

Epigenome analysis of parental and ABT-199 resistant AML cell lines

Project description:Genome wide DNA methylation profiling of parental, ABT-199 resistant population, and ABT-199 resistant clones from 3 acute myelogenous leukemia (AML) cell lines (MOLM-13, MV-4-11, OCI-AMLO2). The Illumina Infinium MethylationEPIC Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 850,000 CpGs from AML cell line samples. Samples include 4 ABT-199 sensitive parental MOLM-13 population samples, 4 ABT-199 resistant MOLM-13 population samples, 3 ABT-199 resistant MOLM-13 S2 clone samples, 3 ABT-199 resistant MOLM-13 S5 clone samples, 4 ABT-199 resistant MOLM-13 S6 clone samples, 3 ABT-199 sensitive parental MV-4-11 population samples, 3 ABT-199 resistant MV-4-11 population samples, 3 ABT-199 sensitive parental OCI-AML2 population samples, 3 ABT-199 resistant OCI-AML2 population samples, 3 ABT-199 resistant OCI-AML2 S1 clone samples, 3 ABT-199 resistant OCI-AML2 S2 clone samples, 3 ABT-199 resistant OCI-AML2 S8 clone samples.

2022-01-12 | GSE193254 | GEO

RAS Mutations in ctDNA and Anti-EGFR reINTROduction in mCRC (RASINTRO)

Project description:Some data have suggested a clinical survival benefit related to the reintroduction of anti-EGFRs therapy in patients with metastatic colorectal cancer (mCRC). Based on resistance mechanisms related to the development of resistant clones, the investigators could assume that patients who benefited most from the reintroduction of anti-EGFRs were those who, through interval chemotherapy, had no longer mutated RAS clone in plasma that appeared during the progression with the first anti-EGFR treatment. Conversely, those who did not benefit from this therapy were probably patients who had mutated RAS clones circulating at the time of reintroduction of anti-EGFRs. To support this hypothesis, investigators propose to evaluate the correlation between the eventual presence of RAS mutations in circulating blood and the efficacy of an anti-EGFR therapy reintroduction in patients with mCRC.

| 2252506 | ecrin-mdr-crc

Transcription profiling of human cell lines sensitive and resistant to gemcitabine

Project description:KBwt is a KB parental cell; KBHURs is a hydroxyurea resistant KB clone; KBGem is a gemcitabine resistant KB clone Experiment Overall Design: We extracted the RNA from KBwt, KBHURs and KBGem clones, and conducted hybridization with HU95 v2 Chip.

2008-06-12 | E-GEOD-3344 | biostudies-arrayexpress

LFQP of lysates from wild-type, RAB18-, TBC1D20-, RAB3GAP1- and RAB3GAP2-null RPE1 cells

Project description:These data are from two wild-type RPE1 clones (WT11, WT20), a RAB18-null clone, a TBC1D20-null clone, a RAB3GAP1-null clone and a RAB3GAP2-null clone, each analysed in triplicate.

2024-01-26 | PXD016326 | Pride

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