Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

7 paired AML Diagnosis vs Relapse patient samples

ABSTRACT: MS-based proteomics and phosphoproteomics study based on 7 paired diagnosis-first relapse AML patient samples

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Cell, Blood

DISEASE(S): Acute Leukemia

SUBMITTER: Maria Hernandez-Valladares

LAB HEAD: Frode Berven

PROVIDER: PXD018359 | Pride | 2020-06-12

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Publications

The Progression of Acute Myeloid Leukemia from First Diagnosis to Chemoresistant Relapse: A Comparison of Proteomic and Phosphoproteomic Profiles.

Aasebø Elise E Berven Frode S FS Hovland Randi R Døskeland Stein Ove SO Bruserud Øystein Ø Selheim Frode F Hernandez-Valladares Maria M

Cancers 20200604 6

Acute myeloid leukemia (AML) is an aggressive hematological malignancy. Nearly 50% of the patients who receive the most intensive treatment develop chemoresistant leukemia relapse. Although the leukemogenic events leading to relapse seem to differ between patients (i.e., regrowth from a clone detected at first diagnosis, progression from the original leukemic or preleukemic stem cells), a common characteristic of relapsed AML is increased chemoresistance. The aim of the present study was to inve ...[more]

PMID: 32512867

Similar Datasets

RNA sequencing of paired AML samples at diagnosis and relapse

Project description:RNA sequencing of paired AML samples at diagnosis and relapse

| PRJNA820018 | ENA

RNA sequencing of paired AML samples at diagnosis and relapse

Project description:The treatment landscape of AML is evolving with promising therapies entering clinical translation, yet patient responses remain heterogeneous and biomarkers for tailoring treatment are lacking. To understand how disease heterogeneity links with therapy response, we determined the leukemia cell hierarchy make-up from bulk transcriptomes of over 1000 patients through deconvolution using single-cell reference profiles of leukemia stem, progenitor, and mature cell types. Leukemia hierarchy composition was associated with functional, genomic, and clinical properties and converged into four overall classes, spanning Primitive, Mature, GMP, and Intermediate. Critically, variation in hierarchy composition along the Primitive vs GMP or Primitive vs Mature axes were associated with response to chemotherapy or drug sensitivity profiles of targeted therapies, respectively. A 7-gene biomarker derived from the Primitive vs Mature axis was predictive of patient response to 105 investigational drugs. Thus, hierarchy composition constitutes a novel framework for understanding disease biology and advancing precision medicine in AML.

2022-05-03 | GSE199451 | GEO

Diagnosis-relapse in ALL

Project description:Almost a quarter of pediatric patients with Acute Lymphoblastic Leukemia (ALL) suffer from relapses. The biological mechanisms underlying therapy response and development of relapses have remained unclear. In an attempt to better understand this phenomenon, we have analyzed 41 matched diagnosis relapse pairs of ALL patients using genomeâ??wide expression arrays (82 arrays) on purified leukemic cells. In roughly half of the patients very few differences between diagnosis and relapse samples were found (â??stable groupâ??), suggesting that mostly extra-leukemic factors (e.g., drug distribution, drug metabolism, compliance) contributed to the relapse. Therefore, we focused our further analysis on 20 samples with clear differences in gene expression (â??skewed groupâ??), reasoning that these would allow us to better study the biological mechanisms underlying relapsed ALL. After finding the differences between diagnosis and relapse pairs in this group, we identified four major gene clusters corresponding to several pathways associated with changes in cell cycle, DNA replication, recombination and repair, as well as B cell developmental genes. We also identified cancer genes commonly associated with colon carcinomas and ubiquitination to be upregulated in relapsed ALL. Thus, about half of relapses are due to selection or emergence of a clone with deregulated expression of a genes involved in pathways that regulate B cell signaling, development, cell cycle, cellular division and replication. The study contains 41 pairs of samples taken from patients at diagnosis and relapse (a total of 82 arrays).

2010-03-23 | E-GEOD-18497 | biostudies-arrayexpress

Diagnosis-relapse in ALL

Project description:Almost a quarter of pediatric patients with Acute Lymphoblastic Leukemia (ALL) suffer from relapses. The biological mechanisms underlying therapy response and development of relapses have remained unclear. In an attempt to better understand this phenomenon, we have analyzed 41 matched diagnosis relapse pairs of ALL patients using genome–wide expression arrays (82 arrays) on purified leukemic cells. In roughly half of the patients very few differences between diagnosis and relapse samples were found (“stable group”), suggesting that mostly extra-leukemic factors (e.g., drug distribution, drug metabolism, compliance) contributed to the relapse. Therefore, we focused our further analysis on 20 samples with clear differences in gene expression (“skewed group”), reasoning that these would allow us to better study the biological mechanisms underlying relapsed ALL. After finding the differences between diagnosis and relapse pairs in this group, we identified four major gene clusters corresponding to several pathways associated with changes in cell cycle, DNA replication, recombination and repair, as well as B cell developmental genes. We also identified cancer genes commonly associated with colon carcinomas and ubiquitination to be upregulated in relapsed ALL. Thus, about half of relapses are due to selection or emergence of a clone with deregulated expression of a genes involved in pathways that regulate B cell signaling, development, cell cycle, cellular division and replication.

2010-03-10 | GSE18497 | GEO

35 patients at diagnosis and relapse

Project description:35 paired samples from initial diagnosis and first marrow relapse. Genes and pathways differentiating diagnosis and relapse were identified. Potential therapeutic targets were also identified. Keywords: paired

2006-03-20 | GSE3910 | GEO

CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) of paired diagnosis and relapse specimens from acute myeloid leukemia (AML) patients treated with venetoclax plus azacitidine

Project description:To understand relapse mechanisms related to AML patients treated with venetoclax plus azacitidine therapy, we performed CITE-seq on paired diagnosis and relapse specimens from an AML patient treated with the therapy.

2020-01-24 | GSE143363 | GEO

Transcriptome analysis of AML samples at diagnosis and relapse after Hematopoietic Stem Cell Transplantation (HSCT)

Project description:Transcriptome profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.

2019-01-29 | E-MTAB-7456 | biostudies-arrayexpress

Transcriptome analysis of AML samples at diagnosis and relapse after Hematopoietic Stem Cell Transplantation (HSCT)

2019-01-31 | E-MTAB-7628 | biostudies-arrayexpress

Transcriptome analysis of AML samples at diagnosis and relapse after Hematopoietic Stem Cell Transplantation (HSCT)

2019-01-31 | E-MTAB-7630 | biostudies-arrayexpress

Genotyping analysis of AML samples at diagnosis and relapse after Hematopoietic Stem Cell Transplantation (HSCT)

Project description:Genotype profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.

2019-01-30 | E-MTAB-7631 | biostudies-arrayexpress

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