Dataset Information

CML proteome analysis, TripleTOF 5600

ABSTRACT: Using mass spectrometry the proteomes of CML were compared to normal tissue.

OTHER RELATED OMICS DATASETS IN: PRJNA112367PRJNA132313PRJNA208363PRJNA96045

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Cell, Blood

DISEASE(S): Chronic Myeloid Leukemia

SUBMITTER: Andrew Williamson

LAB HEAD: Anthony David Whetton

PROVIDER: PXD001504 | Pride | 2016-06-03

REPOSITORIES: Pride

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Publications

Dual targeting of p53 and c-MYC selectively eliminates leukaemic stem cells.

Abraham Sheela A SA Hopcroft Lisa E M LE Carrick Emma E Drotar Mark E ME Dunn Karen K Williamson Andrew J K AJ Korfi Koorosh K Baquero Pablo P Park Laura E LE Scott Mary T MT Pellicano Francesca F Pierce Andrew A Copland Mhairi M Nourse Craig C Grimmond Sean M SM Vetrie David D Whetton Anthony D AD Holyoake Tessa L TL

Nature 20160608 7607

Chronic myeloid leukaemia (CML) arises after transformation of a haemopoietic stem cell (HSC) by the protein-tyrosine kinase BCR-ABL. Direct inhibition of BCR-ABL kinase has revolutionized disease management, but fails to eradicate leukaemic stem cells (LSCs), which maintain CML. LSCs are independent of BCR-ABL for survival, providing a rationale for identifying and targeting kinase-independent pathways. Here we show--using proteomics, transcriptomics and network analyses--that in human LSCs, ab ...[more]

PMID: 27281222

Similar Datasets

Project description:Background MicroRNAs are important regulators of transcription in hematopoiesis. Their expression deregulations were described in association with pathogenesis of some hematological malignancies. This study provides integrated microRNA expression profiling at different phases of chronic myeloid leukemia (CML) with the aim to select CML specific miRNAs and find new possible biomarkers. The functions of in silico filtered targets are in this report annotated and discussed in relation to CML pathogenesis. Results Using microarrays we identified differential expression profiles of 49 miRNAs in CML patients at diagnosis, in hematological relapse, therapy failure, blast crisis and major molecular response. The expression deregulation of miR-150, miR-20a, miR-17, miR-19a, miR-103, miR-144, miR-155, miR-181a, miR-221 and miR-222 in CML was confirmed by real-time quantitative PCR and in silico analyses identified targeted genes of these miRNAs encoding proteins that are involved in cell cycle, growth inhibition, MAPK, ErBb, transforming growth factor beta and p53 signaling pathways that are related to CML. Validated miR-150 decreased levels were detected in patients at diagnosis, in blast crisis and 67% of hematological relapses and showed significant negative correlation with miR-150 proved target MYB and with BCR-ABL transcript level. Conclusions This study revealed microRNAs that may be related to the CML pathogenesis and may reflect transformation from chronic to accelerated phases. The obtained expression patterns in peripheral blood total leukocytes during the course of CML suggest specific miRNAs as possible biomarkers. The annotated functions of in silico filtered targets of selected miRNAs outline mechanisms whereby microRNAs may be involved in CML pathogenesis. Twenty four patient samples of total leukocytes from peripheral blood were used to prepare pools representing different CML phases for microarray analysis: diagnosis (n=5, Dg), major molecular response (n=5, MMR), therapy failure (n=5, TF), hematological relapse (n=5, Hr), and blast crisis (n=4, BC). Eleven healthy donors of age median 60 (range 45 - 78) and man/woman ratio 3/2 following CML incidence were used to create a control pool.

Project description:BACKGROUND: BCR-ABL1+ chronic myeloid leukemia (CML) is characterized by abnormal production of leukemic stem (LSC) and progenitor cells and their spread from the bone marrow into the blood resulting in extramedullary myeloproliferation. So far, little is known about specific markers and functions of LSC in CML. METHODS: We examined the phenotype and function of CD34+/CD38─/Lin─ CML LSC by a multi-parameter screen approach employing antibody-phenotyping, mRNA expression profiling, and functional studies, including LSC repopulation experiments in irradiated NOD-SCID-IL-2Rgamma-/- (NSG) mice, followed by marker-validation using diverse control-cohorts and follow-up samples of CML patients treated with imatinib. RESULTS: Of all LSC markers examined, dipeptidylpeptidase IV (DPPIV=CD26) was identified as specific and functionally relevant surface marker-enzyme on CD34+/CD38─ CML LSC. CD26 was not detected on normal CD34+/CD38─ stem cells or LSC in other hematopoietic malignancies. The percentage of CD26+ CML LSC decreased to undetectable levels during successful treatment with imatinib in all patients (p<0.001). Whereas the sorted CD26─ stem cells obtained from CML patients engrafted irradiated NSG mice with multilineage BCR-ABL1-negative hematopoiesis, CD26+ LSC engrafted NSG mice with BCR-ABL1+ cells. Functionally, CD26 was identified as target-enzyme disrupting the SDF-1alpha-CXCR4-axis by cleaving SDF-1alpha a chemotaxin for CXCR4+ stem cells. Whereas CD26 was found to inhibit SDF-1alpha-induced migration, CD26-targeting gliptins reverted this effect and blocked the mobilization of CML LSC in a stroma co-culture assay. CONCLUSIONS: CD26 is a robust biomarker of LSC and a useful tool for their quantification and isolation in patients with BCR/ABL1+ CML. Moreover, CD26 expression may explain the extramedullary spread of LSC in CML. To define specific mRNA expression patterns and to identify specific LSC markers in CML LSC, gene array analyses were performed. RNA was isolated from sorted CD34+/CD45+/CD38─ CML LSC, CD34+/CD45+/CD38+ CML progenitor cells, CML MNC, sorted CD34+/CD38─ cord blood (CB) SC, CB-derived CD34+/CD38+ progenitor cells, and CB MNC. Total RNA was extracted from sorted cells using RNeasy Micro-Kit (Qiagen) and used (100 ng total RNA) for Gene Chip analyses. Preparation of terminal-labeled cRNA, hybridization to genome-wide human PrimeView GeneChips (Affymetrix, Santa Clara, CA, USA) and scanning of arrays were carried out according to the manufacturer's protocols (https://www.affymetrix.com). Robust Multichip Average (RMA) signal extraction and normalization were performed according to http://www.bioconductor.org/ as described.18 Differences in mRNA expression levels (from multiple paired samples) were calculated as mRNA ratio of i) CML LSC versus CB SC, ii) CML LSC versus CD34+/CD38+ CML progenitors, and normal cord blood SC versus cord blood progenitors. To calculate differential gene expression between individual sample groups where appropriate, we performed a statistical comparison using the LIMMA package as described previously. Briefly, LIMMA estimates the fold change between predefined sample groups by fitting a linear model and using an empirical Bayes method to moderate the standard errors of the estimated log-fold changes for each probe set.

			Action	DRS
	5600CML_Cyto_1_MGFPeaklist.mgf	Mgf
	CML_Cyto_1.mzXML	Mzxml
	CML_Cyto_1.xml	Xml
	CML_Cyto_10.mzXML	Mzxml
	CML_Cyto_11.mzXML	Mzxml