Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Limited Cell Fluorescence Activated Cell-sort Sequencing (LC-FACSeq) is a method for detecting rare clones in leukemia

ABSTRACT: Detecting and monitoring tumor clonal evolution in the setting of measurable residual disease is technically difficult due to 1) high sequencing background due to limited numbers of disease cells, 2) loss of tumor genetic material due to the need for high quality purified DNA, and 3) resource and bioinformatics intensive requirements of traditional next generation sequencing techniques. Limited Cell Fluorescence Activated Cell-sort Sequencing (LC-FACSeq), is a fast, reproducible, and cost effective amplicon based sequencing technique that does not require genomic material isolation. It can be used in the context of just a few hundred tumor cells and is easily generalization for monitoring clonal evolution in various different malignancies.

ORGANISM(S): Homo sapiens

PROVIDER: GSE149545 | GEO | 2020/08/10

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Predicting the evolution of metastatic disease in breast cancer: results from 50 years of autopsies

2013-11-11 | GSE46826 | GEO

Single-cell profiling of cutaneous T-cell lymphoma reveals underlying heterogeneity predicting with disease progression.

Project description:Cutaneous T cell lymphomas (CTCL), encompassing a spectrum of T-cell lymphoproliferative disorders involving the skin, have collectively increased in incidence over the last 40 years. Sézary syndrome (SS) is an aggressive form of CTCL characterized by significant presence of malignant cells in both the blood and skin. The guarded prognosis for SS reflects a lack of reliably effective therapy, due in part to an incomplete understanding of disease pathogenesis. Using single-cell sequencing of RNA, we confirm that SS is a clonal disease, but we further define a more complex model featuring distinct transcriptomic states within SS. Our analysis showed the involvement of FOXP3+ malignant T cells during clonal evolution, transitioning from FOXP3+ T cells to GATA3+ or IKZF2+ (HELIOS) tumor cells. Transcriptomic diversities in a clonal tumor can be used to predict disease stage, and we were able to characterize a gene signature that predicts disease stage with close to 80% accuracy. Supporting the critical role of FOXP3+ T cell involvement during clonal evolution, FOXP3 was found to be the most important factor to predict early disease in SS, along with another 19 genes used to predict CTCL stage. This work offers insight into the heterogeneity of SS, providing better understanding of the transcriptomic diversities within a clonal tumor, which can predict tumor stage and thereby offer guidance of therapy.

2019-03-22 | GSE122703 | GEO

Predicting the evolution of metastatic disease in breast cancer: results from 50 years of autopsies

Project description:This is a genomic analysis of breast cancer metastasis using array based CGH and is part of a large study investigating the patterns and evolution of metastases from breast cancer using autopsy material accumulated over the last 50 years from a single institution. The samples used in the genomic profiling comprise the primary breast tumour and multiple matched metastases from each patient. The data demonstrate both the clonal nature of metastatic progression and the role of clonal evolution during progression. This study comprises six patients who died of metastatic breast cancer. For some patients the breast primary tumour and lymph node metastasis was obtained from previous surgical excision, otherwise material was obtained from a resulting autopsy. Each patient set of samples involves the primary breast tumour and multiple metastases, including from lung, liver, lymph node, adrenal gland, brain etc. DNA was extracted from formalin fixed paraffin embedded (FFPE) tissue blocks and analysed for DNA copy number alterations using an Agilent aCGH platform.

2013-11-11 | E-GEOD-46826 | biostudies-arrayexpress

Clonal Evolution of Acute Myeloid Leukemia Revealed by High-Throughput Single-Cell Genomics

Project description:Clonal diversity contributes to treatment resistance and cancer recurrence. Precise delineation of clonal substructure is essential to understand the resistance mechanism, however, bulk DNA sequencing cannot accurately resolve the complex clonal architectures. Here we report the single-cell DNA sequencing of 123 acute myeloid leukemia (AML) patients and provide cell-level evidence of co-occurrence and mutual exclusivity among driver mutations. Reconstruction of tumor phylogeny uncovers linear and branching clonal evolution patterns, with the latter involving functional convergence. Single-cell DNA sequencing of xenotransplanted samples reveales clonal diversity in leukemia initiating cell populations. Simultaneous single-cell profiling of mutations and cell surface proteins provides cellular genotype-phenotype associations. Analysis of longitudinal samples visualizes the behavior of each individual clone in response to therapy, illustrating the underlying evolutionary process of therapeutic resistance and disease recurrence. Together, these data portray clonal diversity, architecture, and evolution of AML, and highlight their clinical relevance in the era of precision medicine.

2020-09-08 | GSE156934 | GEO

Single-cell RNA profiling of living cells with LC-seq

Project description:Our study reports a new technique, named ‘Live cell sequencing LC-seq’, by which RNAs can be profiled from single live cells with minimal deleterious cellular effects using a conventional electrophysiology rig. We show that subsequent physiological monitoring on the same cells is doable. Furthermore, benchmarking against single whole-cell sequencing WC-seq confirmed the validity of the LC-seq method. We also applied LC-seq to reveal the molecular changes caused by chemical treatment of a cancer cell line and molecular characteristics of neuronal subtypes in the brain.

2023-12-31 | GSE197961 | GEO

Mapping karyotypic heterogeneity in chromosomal instable murine T-cell acute lymphoblastic lymphomas using single-cell sequencing

Project description:Chromosome instability (CIN) leads to aneuploidy and copy number variations (CNVs). Even though both are hallmarks of cancer cells, aneuploidy inhibits proliferation of untransformed cells, suggesting that cancer cells have adapted to cope with CIN. The spindle assembly checkpoint (SAC) prevents CIN by monitoring chromosome attachment and sister chromatid tension in mitosis. By conditionally inactivating Mad2, an essential SAC gene, we find that SAC inactivation in T-cells or hepatocytes is remarkably well tolerated and becomes tumorigenic when placed in a p53null or p53+/- predisposed background. The resulting T-ALLs and HCCs are highly aneuploid, exhibit clonal copy number changes that are tumor specific despite ongoing CIN, indicating that CIN is a powerful driver of tumor evolution.

2017-03-20 | GSE63688 | GEO

Lineage and ecology define liver tumor evolution in response to treatment

Project description:Single-cell transcriptome-based strategy to determine the evolutionary trajectories of longitudinal tumor biopsies from liver cancer patients by measuring cellular lineage and ecology. Our study provides a framework for monitoring tumor evolution in response to therapeutic intervention.

2023-12-22 | GSE229772 | GEO

Mus musculus

Project description:Exploiting Temporal Collateral Sensitivity in Tumor Clonal Evolution

| PRJNA295303 | ENA

Single cell RNA sequencing and TCR repertoire analysis of Adult T-cell Leukemia/Lymphoma (ATLL)

Project description:We examined ATLL using archived fresh frozen tissue after the biopsy by single-cell RNA sequencing (scRNA-seq) with T-cell receptor (TCR) clonal analysis. Highly clonal tumor cells showed multiple activating pathways, suggesting dynamic evolution of the malignancy. Dissecting diverse cell types comprising the TME led to the identification of a novel subset of cancer-associated fibroblast which showed enriched epidermal growth factor receptor (EGFR)-related transcripts.

2022-03-24 | GSE195674 | GEO

Project description:Clonal Evolution

| PRJNA847150 | ENA

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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