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Functional classification of cellular proteome profiles support the identification of drug resistance signatures in melanoma cells.

ABSTRACT: Drug resistance is a major obstacle in melanoma treatment. Recognition of specific resistance patterns, the understanding of the patho-physiology of drug resistance, and identification of remaining options for individual melanoma treatment would greatly improve therapeutic success. We performed mass spectrometry-based proteome profiling of A375 melanoma cells and HeLa cells characterized as sensitive to cisplatin in comparison to cisplatin resistant M24met and TMFI melanoma cells. Cells were fractionated into cytoplasm, nuclei and secretome and the proteome profiles classified according to Gene Ontology. The cisplatin resistant cells displayed increased expression of lysosomal as well as Ca²? ion binding and cell adherence proteins. These findings were confirmed using Lysotracker Red staining and cell adhesion assays with a panel of extracellular matrix proteins. To discriminate specific survival proteins, we selected constitutively expressed proteins of resistant M24met cells which were found expressed upon challenging the sensitive A375 cells. Using the CPL/MUW proteome database, the selected lysosomal, cell adherence and survival proteins apparently specifying resistant cells were narrowed down to 47 proteins representing a potential resistance signature. These were tested against our proteomics database comprising more than 200 different cell types/cell states for its predictive power. We provide evidence that this signature enables the automated assignment of resistance features as readout from proteome profiles of any human cell type. Proteome profiling and bioinformatic processing may thus support the understanding of drug resistance mechanism, eventually guiding patient tailored therapy.

SUBMITTER: Paulitschke V

PROVIDER: S-EPMC3733130 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Functional classification of cellular proteome profiles support the identification of drug resistance signatures in melanoma cells.

Paulitschke Verena V Haudek-Prinz Verena V Griss Johannes J Berger Walter W Mohr Thomas T Pehamberger Hubert H Kunstfeld Rainer R Gerner Christopher C

Journal of proteome research 20130619 7

Drug resistance is a major obstacle in melanoma treatment. Recognition of specific resistance patterns, the understanding of the patho-physiology of drug resistance, and identification of remaining options for individual melanoma treatment would greatly improve therapeutic success. We performed mass spectrometry-based proteome profiling of A375 melanoma cells and HeLa cells characterized as sensitive to cisplatin in comparison to cisplatin resistant M24met and TMFI melanoma cells. Cells were fra ...[more]

PMID: 23713901

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Project description:BackgroundAcute myeloid leukemia (AML) is a hematological malignancy with a dismal prognosis. For over four decades, AML has primarily been treated by cytarabine combined with an anthracycline. Although a significant proportion of patients achieve remission with this regimen, roughly 40% of children and 70% of adults relapse. Over 90% of patients with resistant or relapsed AML die within 3 years. Thus, relapsed and resistant disease following treatment with standard therapy are the most common clinical failures that occur in treating this disease. In this study, we evaluated the relationship between AML cell line global metabolomes and variation in chemosensitivity.MethodsWe performed global metabolomics on seven AML cell lines with varying chemosensitivity to cytarabine and the anthracycline doxorubicin (MV4.11, KG-1, HL-60, Kasumi-1, AML-193, ME1, THP-1) using ultra-high performance liquid chromatography - mass spectrometry (UHPLC-MS). Univariate and multivariate analyses were performed on the metabolite peak intensity values from UHPLC-MS using MetaboAnalyst to identify cellular metabolites associated with drug chemosensitivity.ResultsA total of 1,624 metabolic features were detected across the leukemic cell lines. Of these, 187 were annotated to known metabolites. With respect to doxorubicin, we observed significantly greater abundance of a carboxylic acid (1-aminocyclopropane-1-carboxylate) and several amino acids in resistant cell lines. Pathway analysis found enrichment of several amino acid biosynthesis and metabolic pathways. For cytarabine resistance, nine annotated metabolites were significantly different in resistance vs. sensitive cell lines, including D-raffinose, guanosine, inosine, guanine, aldopentose, two xenobiotics (allopurinol and 4-hydroxy-L-phenylglycine) and glucosamine/mannosamine. Pathway analysis associated these metabolites with the purine metabolic pathway.ConclusionOverall, our results demonstrate that metabolomics differences contribute toward drug resistance. In addition, it could potentially identify predictive biomarkers for chemosensitivity to various anti-leukemic drugs. Our results provide opportunity to further explore these metabolites in patient samples for association with clinical response.

Dataset Information

Functional classification of cellular proteome profiles support the identification of drug resistance signatures in melanoma cells.

Publications

Functional classification of cellular proteome profiles support the identification of drug resistance signatures in melanoma cells.

Similar Datasets

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