Dataset Information

Proteomic analysis of acquired tamoxifen resistance in MCF-7 cells reveals expression signatures associated with enhanced migration.

ABSTRACT:

Introduction

Acquired tamoxifen resistance involves complex signaling events that are not yet fully understood. Successful therapeutic intervention to delay the onset of hormone resistance depends critically on mechanistic elucidation of viable molecular targets associated with hormone resistance. This study was undertaken to investigate the global proteomic alterations in a tamoxifen resistant MCF-7 breast cancer cell line obtained by long term treatment of the wild type MCF-7 cell line with 4-hydroxytamoxifen (4-OH Tam).

Methods

We cultured MCF-7 cells with 4-OH Tam over a period of 12 months to obtain the resistant cell line. A gel-free, quantitative proteomic method was used to identify and quantify the proteome of the resistant cell line. Nano-flow high-performance liquid chromatography coupled to high resolution Fourier transform mass spectrometry was used to analyze fractionated peptide mixtures that were isobarically labeled from the resistant and control cell lysates. Real time quantitative PCR and Western blots were used to verify selected proteomic changes. Lentiviral vector transduction was used to generate MCF-7 cells stably expressing S100P. Online pathway analysis was performed to assess proteomic signatures in tamoxifen resistance. Survival analysis was done to evaluate clinical relevance of altered proteomic expressions.

Results

Quantitative proteomic analysis revealed a wide breadth of signaling events during transition to acquired tamoxifen resistance. A total of 629 proteins were found significantly changed with 364 up-regulated and 265 down-regulated. Collectively, these changes demonstrated the suppressed state of estrogen receptor (ER) and ER-regulated genes, activated survival signaling and increased migratory capacity of the resistant cell line. The protein S100P was found to play a critical role in conferring tamoxifen resistance and enhanced cell motility.

Conclusions

Our data demonstrate that the adaptive changes in the proteome of tamoxifen resistant breast cancer cells are characterized by down-regulated ER signaling, activation of alternative survival pathways, and enhanced cell motility through regulation of the actin cytoskeleton dynamics. Evidence also emerged that S100P mediates acquired tamoxifen resistance and migration capacity.

SUBMITTER: Zhou C

PROVIDER: S-EPMC3446379 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Publications

Proteomic analysis of acquired tamoxifen resistance in MCF-7 cells reveals expression signatures associated with enhanced migration.

Zhou Changhua C Zhong Qiu Q Rhodes Lyndsay V LV Townley Ian I Bratton Melyssa R MR Zhang Qiang Q Martin Elizabeth C EC Elliott Steven S Collins-Burow Bridgette M BM Burow Matthew E ME Wang Guangdi G

Breast cancer research : BCR 20120314 2

<h4>Introduction</h4>Acquired tamoxifen resistance involves complex signaling events that are not yet fully understood. Successful therapeutic intervention to delay the onset of hormone resistance depends critically on mechanistic elucidation of viable molecular targets associated with hormone resistance. This study was undertaken to investigate the global proteomic alterations in a tamoxifen resistant MCF-7 breast cancer cell line obtained by long term treatment of the wild type MCF-7 cell line ...[more]

PMID: 22417809

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Project description:Aromatase inhibitors, such as letrozole, have become the first-line treatment for postmenopausal women with estrogen-dependent breast cancer. However, acquired resistance remains a major clinical obstacle. Previous studies demonstrated constitutive activation of the MAPK signaling, overexpression of HER2, and down-regulation of aromatase and ER? in letrozole-resistant breast cancer cells. Given the complex signaling network involved in letrozole-refractory breast cancer and the lack of effective treatment for hormone resistance, further investigation of aromatase inhibitor resistance by a novel systems biology approach may reveal previously unconsidered molecular changes that could be utilized as therapeutic targets. This study was undertaken to characterize for the first time global proteomic alterations occurring in a letrozole-resistant cell line. A quantitative proteomic analysis of the whole cell lysates of LTLT-Ca (resistant) versus AC-1 cells (sensitive) was performed to identify significant protein expression changes. A total of 1743 proteins were identified and quantified, of which 411 were significantly up-regulated and 452 significantly down-regulated (p < 0.05, fold change > 1.20). Bioinformatics analysis revealed that acquired letrozole resistance is associated with a hormone-independent, more aggressive phenotype. LTLT-Ca cells exhibited 84% and 138% increase in migration and invasion compared with the control cells. The ROCK inhibitor partially abrogated the enhanced migration and invasion of the letrozole-resistant cells. Flow cytometric analyses also demonstrated an increase in vimentin and twist expression in letrozole-resistance cells, suggesting an onset of epithelial to mesenchymal transition (EMT). Moreover, targeted gene expression arrays confirmed a 28-fold and sixfold up-regulation of EGFR and HER2, respectively, whereas ER? and pS2 were dramatically reduced by 28-fold and 1100-fold, respectively. Taken together, our study revealed global proteomic signatures of a letrozole-resistant cell line associated with hormone independence, enhanced cell motility, EMT and the potential values of several altered proteins as novel prognostic markers or therapeutic targets for letrozole resistant breast cancer.

Dataset Information

Proteomic analysis of acquired tamoxifen resistance in MCF-7 cells reveals expression signatures associated with enhanced migration.

Introduction

Methods

Results

Conclusions

Publications

Proteomic analysis of acquired tamoxifen resistance in MCF-7 cells reveals expression signatures associated with enhanced migration.

Similar Datasets

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