Proteomic characterization of microdissected breast tissue environment provides a protein-level overview of malignant transformation.

Both healthy and cancerous breast tissue is heterogeneous, which is a bottleneck for proteomics-based biomarker analysis, as it obscures the cellular origin of a measured protein. We therefore aimed at obtaining a protein-level interpretation of malignant transformation through global proteome analysis of a variety of laser capture microdissected cells originating from benign and malignant breast tissues. We compared proteomic differences between these tissues, both from cells of epithelial origin and the stromal environment, and performed string analysis. Differences in protein abundances corresponded with several hallmarks of cancer, including loss of cell adhesion, transformation to a migratory phenotype, and enhanced energy metabolism. Furthermore, despite enriching for (tumor) epithelial cells, many changes to the extracellular matrix were detected in microdissected cells of epithelial origin. The stromal compartment was heterogeneous and richer in the number of fibroblast and immune cells in malignant sections, compared to benign tissue sections. Furthermore, stroma could be clearly divided into reactive and nonreactive based on extracellular matrix disassembly proteins. We conclude that proteomics analysis of both microdissected epithelium and stroma gives an additional layer of information and more detailed insight into malignant transformation.

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).

Targeted MS Assay Predicting Tamoxifen Resistance in Estrogen-Receptor-Positive Breast Cancer Tissues and Sera.

We recently reported on the development of a 4-protein-based classifier (PDCD4, CGN, G3BP2, and OCIAD1) capable of predicting outcome to tamoxifen treatment in recurrent, estrogen-receptor-positive breast cancer based on high-resolution MS data. A precise and high-throughput assay to measure these proteins in a multiplexed, targeted fashion would be favorable to measure large numbers of patient samples to move these findings toward a clinical setting. By coupling immunoprecipitation to multiple reaction monitoring (MRM) MS and stable isotope dilution, we developed a high-precision assay to measure the 4-protein signature in 38 primary breast cancer whole tissue lysates (WTLs). Furthermore, we evaluated the presence and patient stratification capabilities of our signature in an independent set of 24 matched (pre- and post-therapy) sera. We compared the performance of immuno-MRM (iMRM) with direct MRM in the absence of fractionation and shotgun proteomics in combination with label-free quantification (LFQ) on both WTL and laser capture microdissected (LCM) tissues. Measurement of the 4-proteins by iMRM showed not only higher accuracy in measuring proteotypic peptides (Spearman r: 0.74 to 0.93) when compared with MRM (Spearman r: 0.0 to 0.76) but also significantly discriminated patient groups based on treatment outcome (hazard ratio [HR]: 10.96; 95% confidence interval [CI]: 4.33 to 27.76; Log-rank P < 0.001) when compared with LCM (HR: 2.85; 95% CI: 1.24 to 6.54; Log-rank P = 0.013) and WTL (HR: 1.16; 95% CI: 0.57 to 2.33; Log-rank P = 0.680) LFQ-based predictors. Serum sample analysis by iMRM confirmed the detection of the four proteins in these samples. We hereby report that iMRM outperformed regular MRM, confirmed our previous high-resolution MS results in tumor tissues, and has shown that the 4-protein signature is measurable in serum samples.

Ferritin heavy chain in triple negative breast cancer: a favorable prognostic marker that relates to a cluster of differentiation 8 positive (CD8+) effector T-cell response.

Ferritin heavy chain (FTH1) is a 21-kDa subunit of the ferritin complex, known for its role in iron metabolism, and which has recently been identified as a favorable prognostic protein for triple negative breast cancer (TNBC) patients. Currently, it is not well understood how FTH1 contributes to an anti-tumor response. Here, we explored whether expression and cellular compartmentalization of FTH1 correlates to an effective immune response in TNBC patients. Analysis of the tumor tissue transcriptome, complemented with in silico pathway analysis, revealed that FTH1 was an integral part of an immunomodulatory network of cytokine signaling, adaptive immunity, and cell death. These findings were confirmed using mass spectrometry (MS)-derived proteomic data, and immunohistochemical staining of tissue microarrays. We observed that FTH1 is localized in both the cytoplasm and/or nucleus of cancer cells. However, high cytoplasmic (c) FTH1 was associated with favorable prognosis (Log-rank p = 0.001), whereas nuclear (n) FTH1 staining was associated with adverse prognosis (Log-rank p = 0.019). cFTH1 staining significantly correlated with total FTH1 expression in TNBC tissue samples, as measured by MS analysis (Rs = 0.473, p = 0.0007), but nFTH1 staining did not (Rs = 0.197, p = 0.1801). Notably, IFN γ-producing CD8+ effector T cells, but not CD4+ T cells, were preferentially enriched in tumors with high expression of cFTH1 (p = 0.02). Collectively, our data provide evidence toward new immune regulatory properties of FTH1 in TNBC, which may facilitate development of novel therapeutic targets.

Integrative analysis of genomics and proteomics data on clinical breast cancer tissue specimens extracted with acid guanidinium thiocyanate-phenol-chloroform.

Acid guanidinium thiocyanate, phenol, and chloroform extraction (AGPC) is a commonly used procedure to extract RNA from fresh frozen tissues and cell lines. In addition, DNA and proteins can be recovered, which makes AGPC an attractive source for integrative analysis on tissues of which little material is available, such as clinical specimens. Despite this potential, AGPC has only scarcely been used for proteomic analysis, mainly due to difficulties in extracting proteins. We have used a quantitative mass spectrometry method to show that proteins can readily be recovered from AGPC extracted tissues with high recovery and repeatability, which allows this method to be used for global proteomic profiling. Protein expression data for a selected number of clinically relevant markers, of which transcript and protein levels are known to be correlated, were in agreement with genomic and transcriptomic data obtained from the same AGPC lysate. Furthermore, global proteomic profiling successfully discriminated breast tumor tissues according to their clinical subtype. Lastly, a reference gene set of differentially expressed transcripts was strongly enriched in the differentially abundant proteins in our cohort. AGPC lysates are therefore well suited for comparative protein and integrative analyses.

Quantitative proteomic analysis of microdissected breast cancer tissues: comparison of label-free and SILAC-based quantification with shotgun, directed, and targeted MS approaches.

Quantitative proteomics plays an important role in validation of breast-cancer-related biomarkers. In this study, we systematically compared the performance of label-free quantification (LFQ) and SILAC with shotgun and directed methods for quantifying breast-cancer-related markers in microdissected tissues. We show that LFQ leads to slightly higher coefficient of variation (CV) for protein quantification (median CV = 16.3%) than SILAC quantification (median CV = 13.7%) (P < 0.0001), but LFQ method enables ∼60% more protein quantification and is also more reproducible (∼20% more proteins were quantified in all replicate samples). Furthermore, we describe a method to accurately quantify multiple proteins within one pathway, that is, "focal adhesion pathway", in trace amounts of breast cancer tissues using a SILAC-based SRM assay. Using this SILAC-based SRM assay, we precisely quantified five "focal adhesion" proteins with good quantitative precision (CV range: 2.4-5.9%) in replicate whole tissue lysate samples and replicate microdissected samples (CV range: 5.8-16.1%). Our results show that in microdissected breast cancer tissues LFQ in combination with shotgun proteomics performed the best overall and is therefore suitable for both biomarker discovery and validation in these types of specimens. The SILAC-based SRM method can be used for the development of clinically relevant protein assays in tumor biopsies.

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.

Identification of a putative protein profile associated 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 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.

High-throughput proteomics of breast carcinoma cells: a focus on FTICR-MS.

Discovery of better biomarkers for diagnosis, prognosis and therapy-response prediction is the most critical task of a scientific quest aimed at developing novel, tailormade therapies for patients with cancer. Consequently, a proteome-wide analysis, in addition to genomic studies, is an absolute requirement for a complete functional understanding of tumor biology. Ultra-sensitive, high-performance Fourier-transform ion-cyclotron resonance (FTICR) mass spectrometry (MS) currently holds an important role in fulfilling the demands of biomarker discovery. In this review, we describe the applicability of FTICR-MS for breast cancer proteomics, particularly for the analysis of complex protein mixtures obtained from a limited number of cells typically available from clinical specimens.

Phosphoserine aminotransferase 1 is associated to poor outcome on tamoxifen therapy in recurrent breast cancer.

In a previous study, we detected a significant association between phosphoserine aminotransferase 1 (PSAT1) hyper-methylation and mRNA levels to outcome to tamoxifen treatment in recurrent disease. We here aimed to study the association of PSAT1 protein levels to outcome upon tamoxifen treatment and to obtain more insight in its role in tamoxifen resistance. A cohort of ER positive, hormonal therapy naïve primary breast carcinomas was immunohistochemically (IHC) stained for PSAT1. Staining was analyzed for association with patient's time to progression (TTP) and overall response on first-line tamoxifen for recurrent disease. PSAT1 mRNA levels were also assessed by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR; n = 161) and Affymetrix GeneChip (n = 155). Association of PSAT1 to biological pathways on tamoxifen outcome were assessed by global test. PSAT1 protein and mRNA levels were significantly associated to poor outcome to tamoxifen treatment. When comparing PSAT1 protein and mRNA levels, IHC and RT-qPCR data showed a significant association. Global test results showed that cytokine and JAK-STAT signaling were associated to PSAT1 expression. We hereby report that PSAT1 protein and mRNA levels measured in ER positive primary tumors are associated with poor clinical outcome to tamoxifen.

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.

Annexin-A1 and caldesmon are associated with resistance to tamoxifen in estrogen receptor positive recurrent breast cancer.

Tamoxifen therapy resistance constitutes a major cause of death in patients with recurrent estrogen receptor (ER) positive breast cancer. Through high resolution mass spectrometry (MS), we previously generated a 4-protein predictive signature for tamoxifen therapy outcome in recurrent breast cancer. ANXA1 and CALD1, which were not included in the classifier, were however the most differentially expressed proteins. We first evaluated the clinical relevance of these markers in our MS cohort, followed by immunohistochemical (IHC) staining on an independent set of tumors incorporated in a tissue microarray (TMA) and regression analysis in relation to time to progression (TTP), clinical benefit and objective response. In order to assess which mechanisms ANXA1 and CALD1 might been involved in, we performed Ingenuity pathway analysis (IPA) on ANXA1 and CALD1 correlated proteins in our MS cohort. ANXA1 (Hazard ratio [HR] = 1.83; 95% confidence interval [CI]: 1.22-2.75; P = 0.003) and CALD1 (HR = 1.57; 95% CI: 1.04-2.36; P = 0.039) based patient stratification showed significant association to TTP, while IHC staining on TMA showed that both ANXA1 (HR = 1.82; 95% CI: 1.12-3.00; P = 0.016) and CALD1 (HR = 2.29; 95% CI: 1.40-3.75; P = 0.001) expression was associated with shorter TTP independently of traditional predictive factors. Pearson correlation analysis showed that the majority of proteins correlated to ANXA1 also correlated with CALD1. IPA indicated that ANXA1 and CALD1 were associated with ER-downregulation and NFκB signaling. We hereby report that ANXA1 and CALD1 proteins are independent markers for tamoxifen therapy outcome and are associated to fast tumor progression.

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.

Prognostic significance of nuclear expression of UMP-CMP kinase in triple negative breast cancer patients.

We have previously identified UMP-CMP kinase (CMPK1) as a prognostic marker for triple negative breast cancer (TNBC) by mass spectrometry (MS). In this study we evaluated CMPK1 association to prognosis in an independent set of samples by immunohistochemistry (IHC) and assessed biological pathways associated to its expression through gene set enrichment analysis (GSEA). A total of 461 TNBC paraffin-embedded tissues were collected from different academic hospitals in Europe, incorporated into tissue micro-arrays (TMA), and stained for CMPK1 expression. We also collected gene expression data of 60 samples, which were also present in the TMA, for GSEA correlation analysis. CMPK1 IHC staining showed both cytoplasmic and nuclear components. While cytoplasmic CMPK1 did not show any association to metastasis free survival (MFS), nuclear CMPK1 was associated to poor prognosis independently from other prognostic factors in stratified Cox regression analyses. GSEA correlation analysis of the nuclear CMPK1-stratified gene expression dataset showed a significant enrichment of extracellular matrix (ECM; positive correlation) and cell cycle (negative correlation) associated genes. We have shown here that nuclear CMPK1 is indicative of poor prognosis in TNBCs and that its expression may be related to dysregulation of ECM and cell cycle molecules.

Functional analysis of a novel androgen receptor mutation, Q902K, in an individual with partial androgen insensitivity.

Androgen insensitivity syndrome (AIS) is caused by defects in the androgen receptor (AR) that render the AR partially or completely inactive. As a result, embryonic sex differentiation is impaired. Here, we describe a novel mutation in the AR found in a patient with partial AIS. The mutation results in a substitution of a glutamine (Q) by a lysine (K) residue at position 902, Q902K. The AR Q902K mutation was investigated in vitro with respect to its functional properties. The equilibrium dissociation constants (K(d)s) of AR Q902K in the presence of either the synthetic androgen R1881 or the natural ligand DHT were slightly elevated. The R1881 dissociation rate (t(1/2)) was increased 3-fold for AR Q902K compared with wild type. Transcriptional activity was decreased to 85% of wild type, and the dose-response curve revealed that the sensitivity to hormone was decreased due to the mutation. Furthermore, the 114-kDa androgen-induced phosphorylated AR protein band was not detectable in genital skin fibroblasts. However, it could be detected in transfected CHO cells expressing the mutant receptor in the presence of 10 and 100 nm R1881. Functional interaction assays and a GST pull-down assay showed that the interaction between the NH2 and COOH terminus of AR Q902K was reduced to 50% of wild type. Furthermore, the transactivation by the coactivator TIF2 (transcriptional intermediary factor 2) was decreased 2- to 3-fold. The half-maximal response in both assays was shifted to a higher hormone concentration compared with wild type. These results indicate that residue Q902 is involved in TIF2 and NH2/COOH interaction and that the Q to K mutation results in a mild impairment of AR function, which can explain the partial AIS phenotype of the patient.

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.

Shotgun proteomics on tissue specimens extracted with Acid guanidinium-thiocyanate-phenol-chloroform.

Protein-containing organic fractions of acid guanidinium thiocyanate-phenol-chloroform-extracted tissues are an interesting source of proteins as this method is widely used for RNA extraction for gene expression analysis. However, due to difficulties in redissolving pelleted proteins from the organic phase, protein analysis has only been limitedly reported. Current shotgun mass spectrometry-based methods, however, require minute amounts of sample, and methods have been developed that allow SDS to be removed from an extraction buffer prior to protein digestion. The limited volume of starting material needed for shotgun proteomics facilitates redissolving proteins in SDS-containing buffers, allowing proteins to be readily extracted. Here we describe a protocol for an SDS-DTT-based extraction of proteins from the organic fraction of acid guanidinium-thiocyanate-phenol-chloroform-extracted tissues that remain after RNA isolation for shotgun MS analysis.

Antibody-based capture of target peptides in multiple reaction monitoring experiments.

Targeted quantitative mass spectrometry of immunoaffinity-enriched peptides, termed immuno-multiple reaction monitoring (iMRM), is a powerful method for determining the relative abundance of proteins in complex mixtures, like plasma or whole tissue. This technique combines 1,000-fold enrichment potential of antibodies for target peptides with the selectivity of multiple reaction monitoring mass spectrometry (MRM-MS). Using heavy isotope-labeled peptide counterparts as internal standards ensures high levels of precision. Further, LC-MRM-MS selectivity allows for multiplexing; antibodies recognizing different peptides can be added directly to a single mixture without subjecting to interferences common to other multiple antibody protein assays. Integrated extracted ion chromatograms (XIC) of product ions from endogenous unlabeled "light" peptide and stable isotope-labeled internal standard "heavy" peptides are used to generate a light/heavy peak area ratio. This ratio is proportional to the amount of peptide in the digestion mixture and can be used to estimate the concentration of protein in the sample.

Global proteomic characterization of microdissected estrogen receptor positive breast tumors.

We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as "training") and PXD000485 (defined as "test") that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets comprised 56 fresh frozen estrogen receptor (ER) positive primary breast tumor specimens derived from patients who received tamoxifen as first line therapy for recurrent disease. Patient groups were defined based on time to progression (TTP) after start of tamoxifen therapy (6 months cutoff): 32 good and 24 poor treatment outcome patients were comprised in the training set, respectively. The test set included 41 good and 15 poor treatment outcome patients. All specimens were subjected to laser capture microdissection (LCM) to enrich for epithelial tumor cells prior to high resolution mass spectrometric (MS) analysis. Protein identification and label-free quantification (LFQ) were performed with MaxQuant software package [3]. A total of 3109 and 4061 proteins were identified and quantified in the training and test set, respectively. We here present the first public proteomic dataset analyzing ER positive recurrent breast cancer by LCM coupled to high resolution MS.

Int6 reduction activates stromal fibroblasts to enhance transforming activity in breast epithelial cells.

BACKGROUND: The INT6 gene was first discovered as a site of integration in mouse mammary tumors by the mouse mammary tumor virus; however, INT6's role in the development of human breast cancer remains largely unknown. By gene silencing, we have previously shown that repressing INT6 promotes transforming activity in untransformed human mammary epithelial cells. In the present study, guided by microarray data of human tumors, we have discovered a role of Int6 in stromal fibroblasts. RESULTS: We searched microarray databases of human tumors to assess Int6's role in breast cancer. While INT6 expression levels, as expected, were lower in breast tumors than in adjacent normal breast tissue samples, INT6 expression levels were also substantially lower in tumor stroma. By immunohistochemistry, we determined that the low levels of INT6 mRNA observed in the microarray databases most likely occurs in stromal fibroblasts, because far fewer fibroblasts in the tumor tissue showed detectable levels of the Int6 protein. To directly investigate the effects of Int6 repression on fibroblasts, we silenced INT6 expression in immortalized human mammary fibroblasts (HMFs). When these INT6-repressed HMFs were co-cultured with breast cancer cells, the abilities of the latter to form colonies in soft agar and to invade were enhanced. We analyzed INT6-repressed HMFs and found an increase in the levels of a key carcinoma-associated fibroblast (CAF) marker, smooth muscle actin. Furthermore, like CAFs, these INT6-repressed HMFs secreted more stromal cell-derived factor 1 (SDF-1), and the addition of an SDF-1 antagonist attenuated the INT6-repressed HMFs' ability to enhance soft agar colony formation when co-cultured with cancer cells. These INT6-repressed HMFs also expressed high levels of mesenchymal markers such as vimentin and N-cadherin. Intriguingly, when mesenchymal stem cells (MSCs) were induced to form CAFs, Int6 levels were reduced. CONCLUSION: These data suggest that besides enhancing transforming activity in epithelial cells, INT6 repression can also induce fibroblasts, and possibly MSCs as well, via mesenchymal-mesenchymal transitions to promote the formation of CAFs, leading to a proinvasive microenvironment for tumorigenesis.