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4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

ABSTRACT: Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in-house training and a multi-center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano-LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step-down to develop a predictor for tamoxifen treatment outcome. The 4-protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin-fixed paraffin-embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER-positive breast cancer. IHC further showed that PDCD4 is an independent marker.

SUBMITTER: De Marchi T

PROVIDER: S-EPMC5528925 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

De Marchi Tommaso T Liu Ning Qing NQ Stingl Cristoph C Timmermans Mieke A MA Smid Marcel M Look Maxime P MP Tjoa Mila M Braakman Rene B H RB Opdam Mark M Linn Sabine C SC Sweep Fred C G J FC Span Paul N PN Kliffen Mike M Luider Theo M TM Foekens John A JA Martens John W M JW Umar Arzu A

Molecular oncology 20150807 1

Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, ...[more]

PMID: 26285647

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Project description:PurposeRecent data have shown that the expression levels of long noncoding RNAs (lncRNAs) are associated with tamoxifen sensitivity in estrogen receptor (ER)-positive breast cancer. Herein, we constructed an lncRNA-based model to predict disease outcomes of ER-positive breast cancer patients treated with tamoxifen.MethodsLncRNA expression information was acquired from Gene Expression Omnibus by re-mapping pre-existing microarrays of patients with ER-positive breast cancer treated with tamoxifen. The distant metastasis-free survival (DMFS) predictive signature was subsequently built based on a Cox proportional hazard regression model in discover cohort patients, which was further evaluated in another independent validation dataset.ResultsSix lncRNAs were found to be associated with DMFS in the discover cohort, which were used to construct a tamoxifen efficacy-related lncRNA signature (TLS). There were 133 and 362 patients with TLS high- and low-risk signatures in the discover cohort. Both univariate and multivariate analysis demonstrated that TLS was associated with DMFS. TLS high-risk patients had worse outcomes than low-risk patients, with a hazard ratio of 4.04 (95% confidence interval, 2.83-5.77; p<0.001). Both subgroup analysis and receiver operating characteristic analysis indicated that TLS performed better in lymph node-negative, luminal B, 21-gene recurrence score high-risk, and 70-gene prognosis signature high-risk patients. Moreover, in a comparison of the 21-gene recurrence score and 70-gene prognosis signature, TLS showed a similar area under receiver operating characteristic curve in all patients. Gene Set Enrichment Analysis indicated that TLS high-risk patients showed different gene expression patterns related to the cell cycle and nucleotide metabolism from those of low-risk patients.ConclusionThis six-lncRNA signature was associated with disease outcome in ER-positive breast cancer patients treated with tamoxifen, which is comparable to previous messenger RNA signatures and requires further clinical evaluation.

Project description:BackgroundLymph node (LN) status is the most important prognostic variable used to guide ER positive (+) breast cancer treatment. While a positive nodal status is traditionally associated with a poor prognosis, a subset of these patients respond well to treatment and achieve long-term survival. Several gene signatures have been established as a means of predicting outcome of breast cancer patients, but the development and indication for use of these assays varies. Here we compare the capacity of two approved gene signatures and a third novel signature to predict outcome in distinct LN negative (-) and LN+ populations. We also examine biological differences between tumours associated with LN- and LN+ disease.MethodsGene expression data from publically available data sets was used to compare the ability of Oncotype DX and Prosigna to predict Distant Metastasis Free Survival (DMFS) using an in silico platform. A novel gene signature (Ellen) was developed by including patients with both LN- and LN+ disease and using Prediction Analysis of Microarrays (PAM) software. Gene Set Enrichment Analysis (GSEA) was used to determine biological pathways associated with patient outcome in both LN- and LN+ tumors.ResultsThe Oncotype DX gene signature, which only used LN- patients during development, significantly predicted outcome in LN- patients, but not LN+ patients. The Prosigna gene signature, which included both LN- and LN+ patients during development, predicted outcome in both LN- and LN+ patient groups. Ellen was also able to predict outcome in both LN- and LN+ patient groups. GSEA suggested that epigenetic modification may be related to poor outcome in LN- disease, whereas immune response may be related to good outcome in LN+ disease.ConclusionsWe demonstrate the importance of incorporating lymph node status during the development of prognostic gene signatures. Ellen may be a useful tool to predict outcome of patients regardless of lymph node status, or for those with unknown lymph node status. Finally we present candidate biological processes, unique to LN- and LN+ disease, that may indicate risk of relapse.

Project description:Tamoxifen is the treatment of choice in estrogen receptor alpha breast cancer patients that are eligible for adjuvant endocrine therapy. However, ?50% of ER?-positive tumors exhibit intrinsic or rapidly acquire resistance to endocrine treatment. Unfortunately, prediction of de novo resistance to endocrine therapy and/or assessment of relapse likelihood remain difficult. While several mechanisms regulating the acquisition and the maintenance of endocrine resistance have been reported, there are several aspects of this phenomenon that need to be further elucidated. Altered metabolic fate of tamoxifen within patients and emergence of tamoxifen-resistant clones, driven by evolution of the disease phenotype during treatment, appear as the most compelling hypotheses so far. In addition, tamoxifen was reported to induce pluripotency in breast cancer cell lines, in vitro. In this context, we have performed a whole transcriptome analysis of an ER?-positive (T47D) and a triple-negative breast cancer cell line (MDA-MB-231), exposed to tamoxifen for a short time frame (hours), in order to identify how early pluripotency-related effects of tamoxifen may occur. Our ultimate goal was to identify whether the transcriptional actions of tamoxifen related to induction of pluripotency are mediated through specific ER-dependent or independent mechanisms. We report that even as early as 3 hours after the exposure of breast cancer cells to tamoxifen, a subset of ER?-dependent genes associated with developmental processes and pluripotency are induced and this is accompanied by specific phenotypic changes (expression of pluripotency-related proteins). Furthermore we report an association between the increased expression of pluripotency-related genes in ER?-positive breast cancer tissues samples and disease relapse after tamoxifen therapy. Finally we describe that in a small group of ER?-positive breast cancer patients, with disease relapse after surgery and tamoxifen treatment, ALDH1A1 (a marker of pluripotency in epithelial cancers which is absent in normal breast tissue) is increased in relapsing tumors, with a concurrent modification of its intra-cellular localization. Our data could be of value in the discrimination of patients susceptible to develop tamoxifen resistance and in the selection of optimized patient-tailored therapies.

Dataset Information

4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

Publications

4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

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