Genomic profiling of imatinib resistant and sensitive K562 cells to obtain insights into imatinib resistance
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ABSTRACT:
PROVIDER: PRJEB53970 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR9891080_1.fastq.gz | Fastqsanger.gz | |||
| ERR9891080_2.fastq.gz | Fastqsanger.gz | |||
| ERR9891077_1.fastq.gz | Fastqsanger.gz | |||
| ERR9891077_2.fastq.gz | Fastqsanger.gz | |||
| ERR9891078_1.fastq.gz | Fastqsanger.gz |
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Project description:Chronic myeloid leukemia (CML) epitomizes successful targeted therapy, with 86% of patients in the chronic phase treated with tyrosine kinase inhibitors (TKIs) attaining remission. However, resistance to TKIs occurs during treatment, and patients with resistance to TKIs progress to the acute phase called Blast Crisis (BC), wherein the survival is restricted to 7-11 months. About 80 % of patients in BC are unresponsive to TKIs. This issue can be addressed by identifying a molecular signature which can predict resistance in CML-CP prior to treatment as well as by delineating the molecular mechanism underlying resistance. Herein, we report genomic analysis of CML patients and imatinib-resistant K562 cell line to achieve the same. WGS was performed on imatinib-sensitive and -resistant K562 cells. Library preparation was done by 30x WGS KAPA PCR-Free v2.1 kit, and Illumina HiSeq X sequencer was used for 2 x 150 bp paired-end sequencing. Our study identified accumulation of aberrations on chromosomes 1, 3, 7, 16 and 22 as predictive of occurrence of resistance. Further, recurrent amplification in chromosomal region 8q11.2-12.1 was detected in highly resistant K562 cells as well as CML patients. The genes present in this region were analyzed to understand molecular mechanism of imatinib resistance.
2024-01-01 | E-MTAB-11896 | biostudies-arrayexpress
Project description:Chronic myeloid leukemia is a malignant hematopoietic disorder distinguished by a presence of BCR-ABL fused oncogene with constitutive kinase activity. Although targeted therapy by tyrosine kinase inhibitors (TKI) markedly improved patient´s survival and quality of life, development of drug resistance remains a critical issue for a subset of patients. The most common mechanism of TKI resistance in CML patients is a mutation in BCR-ABL gene which makes oncogenic Bcr-Abl protein insensitive to TKI therapy. Mutation independent mechanisms of TKI resistance are less elucidated, but exosomes, extracellular vesicles excreted from normal and tumor cells were recently linked with cancer progression and drug resistance. We used an imatinib-sensitive CML cell line K562 and derived an imatinib-resistant subline K562IR by prolonged cultivation of cells in presence of imatinib. We demonstrated that exosomes isolated from K562IR cells are internalized by K562 cells and increase their survival in presence of 2µM imatinib. To characterize the exosomal cargo and to identify resistance-associated marker proteins, we performed a deep proteomic analysis of exosomes from both cell sublines using label free quantification (LFQ). In total, we identified over 3000 exosomal proteins including 31 proteins differentially abundant in exosomes derived from K562IR cells. Among the differential proteins were three massively upregulated membrane proteins in K562IR exosomes with surface localization: IFITM3, CD146, CD36. We verified the massive upregulation of the three proteins in K562IR exosomes and also in K562IR cells. Using flow cytometry, we further demonstrated potential of CD146 as cell surface marker associated with imatinib resistance in K562 cells.
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Project description:K562 cells resistant to Imatinib or not by mass spectrometer
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Project description:Inhibition of deregulated protein kinases by small molecule drugs has evolved into a major therapeutic strategy for the treatment of human malignancies. Imatinib mesylate has emerged as the leading compound to treat chronic myeloid leukemia (CML), through its inhibition of Bcr- Abl tyrosine kinases, and other cancers. However, resistance to imatinib develops frequently, particularly in late-stage disease and has necessitated the development of new BCR-ABL inhibitors. The synthesis of a new series of phenylaminopyrimidines, structurally related to imatinib showed large interest since the introduction of the nilotibin. To identify the cellular pathways affected by new synthesized compounds, we applied mass spectrometry together with stable isotope labeling by amino acids in cell culture (SILAC) for the comparative study of protein expression in K562 cells that were untreated or treated with imatinib and imatinib derivates. Further, the global proteome of the K562 cells treated with imatinib were quantitatively compared with the cells treated with the new compounds. This study enriched our knowledge about direct cellular targets of kinase selective drugs. Further the results offered important new knowledge for gaining insights into the structural effects of action of the new compounds. Samples were analyzed on a longer column (30cm) and a longer gradient (180min). Raw data files were processed with Mascot distiller 2.3. The mgf files were searched with Mascot daemon 2.3. The quantification was also done by Mascot Distiller. All data was stored in ms_lims. The manual validation of false peptide ratios was done with Rover (part of ms_lims). Fixed modifications: none. Variable modifications: acetylation of peptide N-terminus, pyroglutamate formation of N-terminal glutamine, methionine oxidation. Enzyme: trypsine with one missed cleavage allowed. Precursor mass tolerance: 10 ppm. Peptide fragment mass tolerance: 0.5 Da Quantitation method: SILAC arginine and lysine +6 Da. Overview of the 17 different analyses: B SK23 vs DMSO C Y22 vs DMSO D SK20 vs DMSO E Y18 vs DMSO I SK20 vs DMSO K Y18 vs DMSO O Y22 vs DMSO R Imatinib vs Water Z Imatinib vs Water J SK20 vs Imatinib M SK23 vs Imatinib N Y22 vs Imatinib P SK23 vs Imatinib Q Y18 vs Imatinib S Y22 vs Imatinib T SK20 vs Imatinib Y Y18 vs Imatinib
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Project description:Both imatinib-sensitive and imatinib-resistant cells were compared in the absence of imatinib. The resistant cells in the presence of imatinib were compared to the sample in the absence of imatinib. The sample from the resistant cells in the presence of imatinib was labelled in duplicate to assess false-positive rates.
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Project description:K562 cells untreated (S) and treated (S+IM) with Imatinib as well as sensitive (S+IM) and resistant (R) to imatinib were subjected to labelled quantification by iTRAQ to identify Bcr-Abl downstream signaling components and proteins modulated in resistance respectively
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Project description:Genomic profiling of K562 cells resistant to imatinib as well as imatinib resistant CML patients harboured a recurrent chromosomal amplification in 8q11.2-12.1. Gene encoding PLAG1, a transcription factor associated with cancers and chemo resistance, resides on this locus and was found to be amplified in resistant cells. Upon PLAG1 knockdown we observed a decrease in imatinib resistance, and to understand the molecular events underlying PLAG1-mediated imatinib resistance, RNA sequencing was carried out. Imatinib-resistant K562 cells were transfected with lentiviral particles containing shRNA against PLAG1 and lentiviral particles containing pLKO.1-empty vectors. Monoclonal populations were established. Total RNA sequencing was performed. For QC, RNA samples were quantified using the Qubit RNA BR Assay kit (Invitrogen, Cat# Q10211). RNA purity was checked using QIAxpert, and RNA integrity was assessed on TapeStation using High Sensitivity RNA ScreenTape® (Agilent, Cat# 5067-5579). QC passed RNA samples were subjected to the library preparation, and NEB Ultra RNA-Seq Library Prep kit protocol (Cat# E7530L) was used and sequenced on Illumina NovaSeq 6000 instrument. Transcriptomic analysis identified several genes down-regulated upon PLAG1 knockdown which were further investigated.
2024-01-01 | E-MTAB-11893 | biostudies-arrayexpress
Project description:Chronic myeloid leukemia (CML) epitomizes successful targeted therapy, with 86% of patients in the chronic phase treated with tyrosine kinase inhibitors (TKIs) attaining remission. However, resistance to TKIs occurs during treatment, and patients with resistance to TKIs progress to the acute phase called Blast Crisis (BC), wherein the survival is restricted to 7-11 months. About 80 % of patients in BC are unresponsive to TKIs. This issue can be addressed by identifying a molecular signature which can predict resistance in CML-CP prior to treatment as well as by delineating the molecular mechanism underlying resistance. Herein, we report genomic analysis of CML patients and imatinib-resistant K562 cell line to achieve the same. Thirteen CML patients (sensitive and resistant to TKIs) and 2 BMT donors (as control) were recruited for the study. DNA was isolated from an enriched CD34+ fraction for each sample as well as from K562 cells made resistant to imatinib which provided a model system for further molecular investigations. DNA was subjected to Cytoscan HD array (Affymatrix) analysis from patient samples and cell lines. Affymetrix CytoScan™ HD array (Applied Biosystems™, Cat# 901835) chip consists of 2.6 M oligonucleotide probes across the genome, including 1953K unique non-polymorphic probes and 750K bi-allelic SNP (single nucleotide polymorphism) probes. Our study identified accumulation of aberrations on chromosomes 1, 3, 7, 16 and 22 as predictive of occurrence of resistance. Further, recurrent amplification in chromosomal region 8q11.2-12.1 was detected in highly resistant K562 cells as well as CML patients. The genes present in this region were analyzed to understand molecular mechanism of imatinib resistance.
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Project description:Purpose: The goals of this study are to characterize the transcriptional dysregulation of GIST. More importantly, the transcriptome differences betweeen Imatinib-sensitive and resistant patients are compared to identify RNAs that may impact Imatinib resistance. Methods: High-throughput RNA sequencing (RNA-seq) was employed to capture the transcriptional changes in GIST compared to normal samples. RSEM was used to quantify gene expression and DESeq2 was utilized to compared expression changes between different sample groups.
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