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Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer.

ABSTRACT: Tamoxifen resistance is a major cause of death in patients with recurrent breast cancer. Current clinical factors can correctly predict therapy response in only half of the treated patients. Identification of proteins that are associated with tamoxifen resistance is a first step toward better response prediction and tailored treatment of patients. In the present study we intended to identify putative protein biomarkers indicative of tamoxifen therapy resistance in breast cancer using nano-LC coupled with FTICR MS. Comparative proteome analysis was performed on approximately 5,500 pooled tumor cells (corresponding to approximately 550 ng of protein lysate/analysis) obtained through laser capture microdissection (LCM) from two independently processed data sets (n = 24 and n = 27) containing both tamoxifen therapy-sensitive and therapy-resistant tumors. Peptides and proteins were identified by matching mass and elution time of newly acquired LC-MS features to information in previously generated accurate mass and time tag reference databases. A total of 17,263 unique peptides were identified that corresponded to 2,556 non-redundant proteins identified with > or = 2 peptides. 1,713 overlapping proteins between the two data sets were used for further analysis. Comparative proteome analysis revealed 100 putatively differentially abundant proteins between tamoxifen-sensitive and tamoxifen-resistant tumors. The presence and relative abundance for 47 differentially abundant proteins were verified by targeted nano-LC-MS/MS in a selection of unpooled, non-microdissected discovery set tumor tissue extracts. ENPP1, EIF3E, and GNB4 were significantly associated with progression-free survival upon tamoxifen treatment for recurrent disease. Differential abundance of our top discriminating protein, extracellular matrix metalloproteinase inducer, was validated by tissue microarray in an independent patient cohort (n = 156). Extracellular matrix metalloproteinase inducer levels were higher in therapy-resistant tumors and significantly associated with an earlier tumor progression following first line tamoxifen treatment (hazard ratio, 1.87; 95% confidence interval, 1.25-2.80; p = 0.002). In summary, comparative proteomics performed on laser capture microdissection-derived breast tumor cells using nano-LC-FTICR MS technology revealed a set of putative biomarkers associated with tamoxifen therapy resistance in recurrent breast cancer.

SUBMITTER: Umar A

PROVIDER: S-EPMC2690491 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer.

Umar Arzu A Kang Hyuk H Timmermans Annemieke M AM Look Maxime P MP Meijer-van Gelder Marion E ME den Bakker Michael A MA Jaitly Navdeep N Martens John W M JW Luider Theo M TM Foekens John A JA Pasa-Tolić Ljiljana L

Molecular & cellular proteomics : MCP 20090327 6

Tamoxifen resistance is a major cause of death in patients with recurrent breast cancer. Current clinical factors can correctly predict therapy response in only half of the treated patients. Identification of proteins that are associated with tamoxifen resistance is a first step toward better response prediction and tailored treatment of patients. In the present study we intended to identify putative protein biomarkers indicative of tamoxifen therapy resistance in breast cancer using nano-LC cou ...[more]

PMID: 19329653

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Project description:BackgroundEndocrine therapy is the backbone therapy in estrogen receptor α (ER)-positive breast cancer, and tamoxifen resistance is a great challenge for endocrine therapy. Tamoxifen-resistant and sensitive samples from the international public repository, the Gene Expression Omnibus (GEO) database, were used to identify therapeutic biomarkers associated with tamoxifen resistance.Materials and methodsIn this study, integrated analysis was used to identify tamoxifen resistance-associated genes. Differentially expressed genes (DEGs) were identified. Gene ontology and pathway analysis were then analyzed. Weighted correlation network analysis (WGCNA) was performed to find modules correlated with tamoxifen resistance. Protein-protein interaction (PPI) network was used to find hub genes. Genes of prognostic significance were further validated in another GEO dataset and cohort from Shanghai Ruijin Hospital using RT-PCR.ResultsA total of 441 genes were down-regulated and 123 genes were up-regulated in tamoxifen-resistant samples. Those up-regulated genes were mostly enriched in the cell cycle pathway. Then, WGCNA was performed, and the brown module was correlated with tamoxifen resistance. An overlap of 81 genes was identified between differentially expressed genes (DEGs) and genes in the brown module. These genes were also enriched in the cell cycle. Twelve hub genes were identified using PPI network, which were involved in the mitosis phase of the cell cycle. Finally, 10 of these 12 genes were validated to be up-regulated in tamoxifen-resistant patients and were associated with poor prognosis in ER-positive patients.ConclusionOur study suggested mitosis-related genes are mainly involved in tamoxifen resistance, and high expression of these genes could predict poor prognosis of patients receiving tamoxifen. These genes may be potential targets to improve efficacy of endocrine therapy in breast cancer, and inhibitors targeted these genes could be used in endocrine-resistant patients.

Project description:ObjectiveTo identify a biomarker profile associated with tumor response to chemoradiation (CRT) in locally advanced rectal cancer.BackgroundRectal cancer response to neoadjuvant CRT is variable. Whereas some patients have a minimal response, others achieve a pathologic complete response (pCR) and have no viable cancer cells in their surgical specimens. Identifying biomarkers of response will help select patients more likely to benefit from CRT.MethodsThis study includes 132 patients with locally advanced rectal cancer treated with neoadjuvant CRT followed by surgery. Tumor DNA from pretreatment tumor biopsies and control DNA from paired normal surgical specimens was screened for mutations and polymorphisms in 23 genes. Genetic biomarkers were correlated with tumor response to CRT (pCR vs non-pCR), and the association of single or combined biomarkers with tumor response was determined.ResultsThirty-three of 132 (25%) patients achieved a pCR and 99 (75%) patients had non-pCR. Three individual markers were associated with non-pCR; v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog mutation (P = 0.0145), cyclin D1 G870A (AA) polymorphism (P = 0.0138), and methylenetetrahydrofolate reductase (NAD(P)H) C677T (TT) polymorphism (P = 0.0120). Analysis of biomarker combinations revealed that none of the 27 patients with both tumor protein p53 (p53) and v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog mutations had a pCR. Further, in patients with both p53 and v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog mutations or the cyclin D1 G870A (AA) polymorphism or the methylenetetrahydrofolate reductase (NAD(P)H) C677T (TT) polymorphism (n = 52) the association with non-pCR was further strengthened; 51 of 52 (98%) of patients were non-pCR. These biomarker combinations had a validity of more than 70% and a positive predictive value of 97% to 100%, predicting that patients harboring these mutation/polymorphism profiles will not achieve a pCR.ConclusionsA specific biomarker profile is strongly associated with non-pCR to CRT and could be used to select optimal oncologic therapy in rectal cancer patients. ClinicalTrials.org Identifier: NCT00335816.

Project description:BackgroundHonokiol (HON) inhibits epidermal growth factor receptor (EGFR) signaling and increases the activity of erlotinib, an EGFR inhibitor, in human head and neck cancers. In this study, using a bioinformatics approach and in vitro experiments, we assessed the target genes of HON against breast cancer resistance to tamoxifen (TAM).Materials and methodsMicroarray data were obtained from GSE67916 and GSE85871 datasets to identify differentially expressed genes (DEGs). DEGs common between HON-treated and TAM-resistant cells were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and protein-protein interaction (PPI) networks were constructed. Selected genes were analyzed for genetic alterations, expression, prognostic value, and receiver operating characteristics (ROC). TAM-resistant MCF-7 (MCF-7 TAM-R) cells were generated and characterized for their resistance toward TAM. A combination of HON and TAM was used for cytotoxicity and gene expression analyses. Molecular docking was performed using the Molecular Operating Environment software.ResultsPPI network analysis revealed that FN1, FGFR2, and RET were the top three genes with the highest scores. A genetic alteration study of potential target genes revealed MMP16 and ERBB4 as the genes with the highest alterations among the breast cancer samples. Pathway enrichment analysis of FGFR2, RET, ERBB4, SOX2, FN1, and MMP16 showed that the genetic alterations herein were likely to impact the RTK-Ras pathway. The expression levels of RET, MMP16, and SOX2 were strongly correlated with prognostic power, with areas under the ROC curves (AUC) of 1, 0.8, and 0.8, respectively. The HON and TAM combination increased TAM cytotoxicity in MCF-7 TAM-R cells by regulating the expression of potential target genes ret, ERBB4, SOX2, and FN1, as well as the TAM resistance regulatory genes including HES1, VIM, PCNA, TP53, and CASP7. Molecular docking results indicated that HON tended to bind RET, ErbB4, and the receptor protein Notch1 ankyrin domain more robustly than its native ligand.ConclusionHON could overcome breast cancer resistance to TAM, potentially by targeting FGFR2, RET, ERBB4, MMP16, FN1, and SOX2. However, further studies are required to validate these results.

Project description:Background: Tamoxifen (TMX) is one of the most widely used drugs to treat breast cancer (BC). However, acquired drug resistance is still a major obstacle to its application, rendering it crucial to explore the mechanisms of TMX resistance in BC. This aims of this study were to identify the mechanisms of TMX resistance and construct ceRNA regulatory networks in breast cancer. Methods: GEO2R was used to screen for differentially expressed mRNAs (DEmRNAs) leading to drug resistance in BC cells. MiRTarbase and miRNet were used to predict miRNAs and lncRNAs upstream, and the competing endogenous RNA (ceRNA) regulatory network of BC cell resistance was constructed by starBase. We used the Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA) to analyze the expression and prognostic differences of genes in the ceRNA network with core axis, and qRT-PCR was used to further verify the above conclusions. Results: We found that 21 DEmRNAs were upregulated and 43 DEmRNA downregulated in drug-resistant BC cells. DEmRNAs were noticeably enriched in pathways relevant to cancer. We then constructed a protein-protein interaction (PPI) network based on the STRING database and defined 10 top-ranked hub genes among the upregulated and downregulated DEmRNAs. The 20 DEmRNAs were predicted to obtain 113 upstream miRNAs and 501 lncRNAs. Among them, 7 mRNAs, 22 lncRNAs, and 11 miRNAs were used to structure the ceRNA regulatory network of drug resistance in BC cells. 4 mRNAs, 4 lncRNAs, and 3 miRNAs were detected by GEPIA and the Kaplan-Meier plotter to be significantly associated with BC expression and prognosis. The differential expression of the genes in BC cells was confirmed by qRT-PCR. Conclusion: The ceRNA regulatory network of TMX-resistant BC was successfully constructed and confirmed. This will provide an important resource for finding therapeutic targets for TMX resistance, where the discovery of candidate conventional mechanisms can aid clinical decision-making. In addition, this resource will help discover the mechanisms behind this type of resistance.

Dataset Information

Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer.

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Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer.

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