Arzu Umar

Identification of a putative protein-profile associating with tamoxifen therapy-resistance in breast cancer

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 ∼5,500 pooled tumor cells (corresponding to ∼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 ≥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.

Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue

Mass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while inappropriate statistical methods will lead to false positive hits. All these issues have hampered the identification of reliable protein markers. A workflow, which integrates reproducible and robust sample preparation and data handling methods, is highly desirable in clinical proteomics investigations. Here we describe a label-free tissue proteomics pipeline, which encompasses laser capture microdissection (LCM) followed by nanoscale liquid chromatography and high resolution MS. This pipeline routinely identifies on average ∼10,000 peptides corresponding to ∼1,800 proteins from sub-microgram amounts of protein extracted from ∼4,000 LCM breast cancer epithelial cells. Highly reproducible abundance data were generated from different technical and biological replicates. As a proof-of-principle, comparative proteome analysis was performed on estrogen receptor α positive or negative (ER+/−) samples, and commonly known differentially expressed proteins related to ER expression in breast cancer were identified. Therefore, we show that our tissue proteomics pipeline is robust and applicable for the identification of breast cancer specific protein markers.

Antiandrogens: selective androgen receptor modulators

Antiandrogens can efficiently block androgen receptor (AR) mediated gene expression, and are therefore useful tools in the treatment of androgen dependent prostate cancer. Antiandrogens are either complete or partial inhibitors of AR activity, depending on the nature of the compound. As compared to androgens, antiandrogens induce a different AR conformation, thereby influencing the recruitment of co-regulators (coactivators and corepressors). This ligand-selective modulation of AR activity is affected by an AR mutation (Thr877Ala substitution) found in prostate cancer. In contrast to the wild-type AR, the mutant AR conformation induced by cyproterone acetate (CPA) and hydroxyflutamide (OHF) is comparable to that induced by androgens. As a consequence, this might affect recruitment of co-regulators, thereby allowing CPA and OHF to act as strong agonists on the mutant AR.

Differential modulation of androgen receptor transcriptional activity by the nuclear receptor co-repressor (N-CoR).

Antiandrogens are widely used agents in the treatment of prostate cancer, as inhibitors of AR (androgen receptor) action. Although the precise mechanism of antiandrogen action is not yet elucidated, recent studies indicate the involvement of nuclear receptor co-repressors. In the present study, the regulation of AR transcriptional activity by N-CoR (nuclear receptor co-repressor), in the presence of different ligands, has been investigated. Increasing levels of N-CoR differentially affected the transcriptional activity of AR occupied with either agonistic or antagonistic ligands. Small amounts of co-transfected N-CoR repressed CPA (cyproterone acetate)- and mifepristone (RU486)-mediated AR activity, but did not affect agonist (R1881)-induced AR activity. Larger amounts of co-transfected N-CoR repressed AR activity for all ligands, and converted the partial agonists CPA and RU486 into strong AR antagonists. In the presence of the agonist R1881, co-expression of the p160 co-activator TIF2 (transcriptional intermediary factor 2) relieved N-CoR repression up to control levels. However, in the presence of RU486 and CPA, TIF2 did not functionally compete with N-CoR, suggesting that antagonist-bound AR has a preference for N-CoR. The AR mutation T877A (Thr877-->Ala), which is frequently found in prostate cancer and affects the ligand-induced conformational change of the AR, considerably reduced the repressive action of N-CoR. The agonistic activities of CPA- and hydroxyflutamide-occupied T877A-AR were hardly affected by N-CoR, whereas TIF2 strongly enhanced their activities. These results indicate that lack of N-CoR action allows these antiandrogens to act as strong agonists on the mutant AR.

Comparative Proteome Analysis Revealing an 11-Protein Signature for Aggressive Triple-Negative Breast Cancer

Background Clinical outcome of patients with triple-negative breast cancer (TNBC) is highly variable. This study aims to identify and validate a prognostic protein signature for TNBC patients to reduce unnecessary adjuvant systemic therapy. Methods Frozen primary tumors were collected from 126 lymph node–negative and adjuvant therapy–naive TNBC patients. These samples were used for global proteome profiling in two series: an in-house training (n = 63) and a multicenter test (n = 63) set. Patients who remained free of distant metastasis for a minimum of 5 years after surgery were defined as having good prognosis. Cox regression analysis was performed to develop a prognostic signature, which was independently validated. All statistical tests were two-sided. Results An 11-protein signature was developed in the training set (median follow-up for good-prognosis patients = 117 months) and subsequently validated in the test set (median follow-up for good-prognosis patients = 108 months) showing 89.5% sensitivity (95% confidence interval [CI] = 69.2% to 98.1%), 70.5% specificity (95% CI = 61.7% to 74.2%), 56.7% positive predictive value (95% CI = 43.8% to 62.1%), and 93.9% negative predictive value (95% CI = 82.3% to 98.9%) for poor-prognosis patients. The predicted poor-prognosis patients had higher risk to develop distant metastasis than the predicted good-prognosis patients in univariate (hazard ratio [HR] = 13.15; 95% CI = 3.03 to 57.07; P = .001) and multivariable (HR = 12.45; 95% CI = 2.67 to 58.11; P = .001) analysis. Furthermore, the predicted poor-prognosis group had statistically significantly more breast cancer–specific mortality. Using our signature as guidance, more than 60% of patients would have been exempted from unnecessary adjuvant chemotherapy compared with conventional prognostic guidelines. Conclusions We report the first validated proteomic signature to assess the natural course of clinical TNBC.

Optimized nLC-MS workflow for laser capture microdissected breast cancer tissue.

Reliable sample preparation is of utmost importance for comparative proteome analysis, particularly when investigating minute amounts of clinical specimens, such as laser capture microdissected tumor tissue. In this study, we present an optimized nanoLC-MS workflow specifically for the analysis of laser capture microdissected breast cancer tissue. Analytical performance of different laser capture microdissection (LCM) functions available on the PALM system, time dependent trypsin digestion efficiency, effect of sample preparation and digestion time on peptide modification, semi-tryptic peptides and missed cleavages were evaluated. Our results show that microdissection from uncoated glass slides results in protein degradation; that protease and phosphatase inhibitors do not result in detectable improvement in number of peptides or semi-tryptic peptides; and that digestion time longer than four hours drastically reduces the number of missed cleavages, but also increases the number of unexpectedly modified peptides. Overalkylation was the most dominant side-reaction, which significantly increased overnight (P=0.05). The latter effect could almost completely be reverted by the use of a quenching agent (P=0.001). Taken together, our results show that it is of importance to carefully control sample handling steps so that reliable protein identification and quantitation can be performed within comparative proteomics studies using LCM. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Endocrine therapy resistance in estrogen receptor (ER)-positive breast cancer

Estrogen receptor (ER)-positive breast cancer represents the majority (∼70%) of all breast malignancies. In this subgroup of breast cancers, endocrine therapies are effective both in the adjuvant and recurrent settings, although resistance remains a major issue. Several high-throughput approaches have been used to elucidate mechanisms of resistance and to derive potential predictive markers or alternative therapies. In this review, we cover the state-of-the-art of endocrine-resistance biomarker discovery with regard to the latest technological developments, and discuss current opportunities and restrictions for their implementation into a clinical setting.

Laser capture microdissection applications in breast cancer proteomics

Breast cancer tissues are characterized by cellular heterogeneity, representing a mixture of, e.g., healthy epithelial ducts, invasive or in situ tumor cells, surrounding stroma, infiltrating immune cells, blood vessels, and capillaries. As a consequence, protein extracts from whole tissue lysates also represent a variety of cell types present in the tissues under examination. This, however, seriously hampers the analysis of tumor cell-specific signals, which is of interest when performing biomarker discovery-type of studies. Therefore, laser capture microdissection is a perfect tool to isolate a relatively pure population of cells of interest, such as tumor cells. In this chapter, we describe the use of the PALM MicroBeam system for laser microdissection and pressure catapulting. Protocols are provided for sectioning, staining, microdissection, sample preparation, and mass spectrometric analysis of snap frozen breast cancer tissue.

The advantage of laser-capture microdissection over whole tissue analysis in proteomic profiling studies

Laser‐capture microdissection (LCM) offers a reliable cell population enrichment tool and has been successfully coupled to MS analysis. Despite this, most proteomic studies employ whole tissue lysate (WTL) analysis in the discovery of disease biomarkers and in profiling analyses. Furthermore, the influence of tissue heterogeneity in WTL analysis, nor its impact in biomarker discovery studies have been completely elucidated. In order to address this, we compared previously obtained high resolution MS data from a cohort of 38 breast cancer tissues, of which both LCM enriched tumor epithelial cells and WTL samples were analyzed. Label‐free quantification (LFQ) analysis through MaxQuant software showed a significantly higher number of identified and quantified proteins in LCM enriched samples (3404) compared to WTLs (2837). Furthermore, WTL samples displayed a higher amount of missing data compared to LCM both at peptide and protein levels (p‐value < 0.001). 2D analysis on co‐expressed proteins revealed discrepant expression of immune system and lipid metabolisms related proteins between LCM and WTL samples. We hereby show that LCM better dissected the biology of breast tumor epithelial cells, possibly due to lower interference from surrounding tissues and highly abundant proteins. All data have been deposited in the ProteomeXchange with the dataset identifier PXD002381 (http://proteomecentral.proteomexchange.org/dataset/PXD002381).

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

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.