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

Peptide set test: a peptide-centric strategy to infer differentially expressed proteins

MOTIVATION

The clinical translation of mass spectrometry-based proteomics has been challenging due to limited statistical power caused by large technical variability and inter-patient heterogeneity. Bottom-up proteomics provides an indirect measurement of proteins through digested peptides. This raises the question whether peptide measurements can be used directly to better distinguish differentially expressed proteins.

RESULTS

We present a novel method called the peptide set test, which detects coordinated changes in the expression of peptides originating from the same protein and compares them to the rest of the peptidome. Applying our method to data from a published spike-in experiment and simulations demonstrates improved sensitivity without compromising precision, compared to aggregation-based approaches. Additionally, applying the peptide set test to compare the tumor proteomes of tamoxifen-sensitive and tamoxifen-resistant breast cancer patients reveals significant alterations in peptide levels of collagen XII, suggesting an association between collagen XII-mediated matrix reassembly and tamoxifen resistance. Our study establishes the peptide set test as a powerful peptide-centric strategy to infer differential expression in proteomics studies.

AVAILABILITY AND IMPLEMENTATION

Peptide set test (PepSetTest) is publicly available at https://github.com/JmWangBio/PepSetTest.

Proteomic profiling and ROC analysis identify CD151 and ELAVL1 as potential therapy response markers for the antiviral drug in resistant TNBC

Triple-negative breast cancer is high heterogeneous, aggressive, and metastatic with poor prognosis. Despite of advances in targeted therapies, TNBC has been reported to cause high morbidity and mortality. A rare subpopulation within the tumor microenvironment organized into a hierarchy of cancer stem cells is responsible for therapy resistance and tumor recurrence. Repurposing of antiviral drugs for cancer treatment is gaining momentum due to reduced cost, labour, and research time, but limited due to lack of prognostic, and predictive markers. The present study investigates proteomic profiling and ROC analysis to identify CD151 and ELAVL1 as potential therapy response markers for the antiviral drug 2-thio-6-azauridine (TAU) in resistant TNBC. The stemness of MDA-MB 231 and MDA-MD 468 adherent cells was enriched by culturing them under non-adherent and non-differentiation conditions. Then, CD151⁺ subpopulation was isolated and characterized for the enrichment of stemness. This study found that CD151 has overexpressed in stemness enriched subpopulations, and also showed CD44 high and CD24 low expression along with stem cell-related transcription factors octamer-binding transcription factor 4 (OCT4) and Sex determining Y-box 2 (SOX2). This study also found that TAU induced significant cytotoxicity and genotoxicity in the CD151⁺TNBC subpopulation and inhibited their proliferation by inducing DNA damage, cell cycle arrest at the G₂M phase, and apoptosis. Further, a proteomic profiling study showed that the expression of CD151 along with ELAVL1, an RNA-binding protein, was significantly reduced with TAU treatment. KM plotter showed correlation of CD151 and ELAVL1 gene expression with a poor prognosis of TNBC. ROC analysis predicted and validated CD151 and ELAVL1 as best therapy response marker for TAU in TNBC. These findings provide new insight into repurposing antiviral drug TAU for treatment of metastatic and drug resistant TNBC.

Lycorine weakens tamoxifen resistance of breast cancer via abrogating HAGLR-mediated epigenetic suppression on VGLL4 by DNMT1

Much is known about the significance of lycorine, a natural alkaloid, in combating various types of cancer, including breast cancer (BC), but whether it participates in regulating tamoxifen (TAM) resistance and its underlying mechanism remain to be elucidated. Tamoxifen-resistant (TAMR) BC cells were first established by continuously exposed to increasing concentrations of TAM. Levels of targeted gene including HOXD antisense growth-associated lncRNA (HAGLR) and Vestigial like family member 4 (VGLL4) were analyzed by qRT-PCR and western blot, respectively. Cell proliferation ability was assessed by MTT and EdU assays. Flow cytometry was carried out to evaluate the apoptosis. VGLL4 promoter methylation was examined using methylation specific PCR (MSP). The role of HAGLR acting on the expression of VGLL4 via DNA hypermethylation was confirmed by RNA immunoprecipitation (RIP). Here, we reported that lycorine administration reduced the survival ratio of TAMR BC cells, decreased the IC₅₀ of TAM, and strengthened TAM-induced apoptosis. HAGLR, observed to be highly expressed in TAMR BC cells, was identified to be a downstream effector of lycorine, of which overexpression abolished lycorine-mediated TAMR inhibition. VGLL4 served as a target of HAGLR in regulating lycorine-mediated suppression on tamoxifen resistance of TAMR BC cells. Mechanistically, HAGLR epigenetically suppressed VGLL4 expression via DNA methyltransferase 1 (DNMT1)-mediated DNA hypermethylation. Taken together, our data highlights the pivotal role of lycorine in TAM resistance of BC, which may provide a potential agent for improving the effectiveness and efficacy of BC resistance.

Exploring the Mechanism and Suggesting Combination Therapies for HDAC Inhibitors in Androgen Receptor-Null Prostate Cancer Using Multivariate Statistical Analysis and Data Mining Techniques

Previously, we showed that novel histone deacetylase (HDAC) inhibitors, N1-hydroxy-N ⁸-(4-(pyridine-2-carbothioamido)phenyl)octanediamide (Jazz90) and [chlorido(η⁵-pentamethylcyclopentadienyl)(N1-hydroxy-N8-(4-(pyridine-2-carbothioamido-κ² N, S)phenyl)octanediamide)rhodium(III)] chloride (Jazz167), have cytostatic and anti-angiogenic effects in androgen receptor-negative prostate cancer cells and are also non-toxic in BALB/c mice. However, only univariate statistical analysis was carried out to determine the role of individual proteins. In this study, multivariate statistical analyses (MVAs) and data mining procedures were carried out with the objective of determining the molecular networks that explain the growth inhibitory potential of Jazz90 and Jazz167 in PC3 cells and to determine potential inhibitors that can be used in combination with these HDAC inhibitors. Lasso regression revealed that angiogenic factors, vascular endothelial growth factor-A (VEGF-A), and vascular endothelial growth factor receptor-2 (VEGFR-2), alongside HDAC inhibition, predicted the reduction in cell number with an adjusted R ² value of 0.99 following Jazz90 treatment, whereas VEGFR-2, acetylation of histone-3, and HDAC inhibition predicted cell number with an adjusted R ² value of 0.84 following Jazz167 treatment. These results were further followed up with ridge regression, hierarchical cluster analysis, random forest classification (RFC), and support vector machines. RFC and support vector machines also predicted the treatment groups with a 100% accuracy. MVAs also revealed that Jazz90 should be examined in combination with epithelial to mesenchymal transitioning inhibitors, such as simvastatin and olaparib, whereas Jazz167 should be examined with venetoclax or navitoclax. Future studies should also address the roles of VEGF-A and VEGFR-2 in cellular proliferation, whereas p27 function should be examined for its role in PC3 cell migration.

Dissecting the Roles of PDCD4 in Breast Cancer

The human programmed cell death 4 (PDCD4) gene was mapped at chromosome 10q24 and encodes the PDCD4 protein comprised of 469 amino acids. PDCD4 inhibits protein translation PDCD4 inhibits protein translation to suppress tumor progression, and its expression is frequently decreased in breast cancer. PDCD4 blocks translation initiation complex by binding eIF4A via MA-3 domains or by directly binding 5’ mRNA internal ribosome entry sites with an RNA binding domain to suppress breast cancer progression and proliferation. Numerous regulators and biological processes including non-coding RNAs, proteasomes, estrogen, natural compounds and inflammation control PDCD4 expression in breast cancer. Loss of PDCD4 expression is also responsible for drug resistance in breast cancer. HER2 activation downregulates PDCD4 expression by activating MAPK, AKT, and miR-21 in aromatase inhibitor-resistant breast cancer cells. Moreover, modulating the microRNA/PDCD4 axis maybe an effective strategy for overcoming chemoresistance in breast cancer. Down-regulation of PDCD4 is significantly associated with short overall survival of patients, which suggests that PDCD4 may be an independent prognostic marker for breast cancer.

Nano liquid chromatography, an updated review

Low flow chromatography has a rich history of innovation but has yet to reach widespread implementation in bioanalytical applications. Improvements in pump technology, microfluidic connections, and nano-electrospray sources for mass spectrometry have laid the groundwork for broader application, and innovation in this space has accelerated in recent years. This article reviews the instrumentation used for nano-flow liquid chromatography , the types of columns employed, and strategies for multi-dimensionality of separations, which is key to the future state of the technique to the high-throughput needs of modern bioanalysis. An update of the current applications where nano-LC is widely used, such as proteomics and metabolomics, is discussed. But the trend towards biopharmaceutical development of increasingly complex, targeted, and potent therapeutics for the safe treatment of disease drives the need for ultimate selectivity and sensitivity of our analytical platforms for targeted quantitation in a regulated space. The selectivity needs are best addressed by mass spectrometric detection, especially at high resolutions, and exquisite sensitivity is provided by nano-electrospray ionization as the technology continues to evolve into an accessible, robust, and easy to use platform.

Estrogen receptor-positive breast cancer survival prediction and analysis of resistance-related genes introduction

Background

In recent years, ER+ and HER2- breast cancer of adjuvant therapy has made great progress, including chemotherapy and endocrine therapy. We found that the responsiveness of breast cancer treatment was related to the prognosis of patients. However, reliable prognostic signatures based on ER+ and HER2- breast cancer and drug resistance-related prognostic markers have not been well confirmed, This study in amied to establish a drug resistance-related gene signature for risk stratification in ER+ and HER2- breast cancer.

Methods

We used the data from The Cancer Genoma Atlas (TCGA) breast cancer dataset and gene expression database (Gene Expression Omnibus, GEO), constructed a risk profile based on four drug resistance-related genes, and developed a nomogram to predict the survival of patients with I-III ER+ and HER2- breast cancer. At the same time, we analyzed the relationship between immune infiltration and the expression of these four genes or risk groups.

Results

Four drug resistance genes (AMIGO2, LGALS3BP, SCUBE2 and WLS) were found to be promising tools for ER+ and HER2- breast cancer risk stratification. Then, the nomogram, which combines genetic characteristics with known risk factors, produced better performance and net benefits in calibration and decision curve analysis. Similar results were validated in three separate GEO cohorts. All of these results showed that the model can be used as a prognostic classifier for clinical decision-making, individual prediction and treatment, as well as follow-up.

Analysis of differentially expressed proteins between HER2 positive and triple negative breast cancer and their prognostic significance

Both triple negative breast cancer (TNBA) and HER2-positive breast cancer lack expression of estrogen receptor alpha (ER) and progesterone receptor (PR), while human epidermal growth factor receptor 2 (HER2) in TNBC is also negative. This study aimed to identify the differentially expressed proteins (DEPs) between TNBC and HER2-positive breast cancer and to improve understanding of their role in the prognosis of breast cancer. By analyzing the breast cancer data set in The Cancer Proteome Atlas (TCPA) database, 15 DEPs between TNBC and HER2-positive breast cancer were identified. GO and pathway enrichment analysis were performed on DEPs, and the protein-protein interaction (PPI) network was constructed. The overall survival (OS) analysis of the breast cancer protein dataset in the Kaplan-Meier plotter showed that low expression of ACC1 suggested a higher OS of HER2-positive breast cancer (HR = 5.34, P < 0.05) and TNBC (HR = 2.88, P < 0.05). And TNBC patients with high TBA1B (HR = 0.16, P < 0.01) or low INPP4B (HR = 3.47, P < 0.05) expression have a better prognosis. Our research provides new insights into the prognostic indicators of TNBC and HER2-positive breast cancer, which could be further studied.

Molecular markers associated with the outcome of tamoxifen treatment in estrogen receptor-positive breast cancer patients: scoping review and in silico analysis

Tamoxifen (TMX) is used as adjuvant therapy for estrogen receptor-positive (ER+) breast cancer cases due to its affinity and inhibitory effects. However, about 30% of cases show drug resistance, resulting in recurrence and metastasis, the leading causes of death. A literature review can help to elucidate the main cellular processes involved in TMX resistance. A scoping review was performed to find clinical studies investigating the association of expression of molecular markers profiles with long-term outcomes in ER+ patients treated with TMX. In silico analysis was performed to assess the interrelationship among the selected markers, evaluating the joint involvement with the biological processes. Forty-five studies were selected according to the inclusion and exclusion criteria. After clustering and gene ontology analysis, 23 molecular markers were significantly associated, forming three clusters of strong correlation with cell cycle regulation, signal transduction of proliferative stimuli, and hormone response involved in morphogenesis and differentiation of mammary gland. Also, it was found that overexpression of markers in selected clusters is a significant indicator of poor overall survival. The proposed review offered a better understanding of independent data from the literature, revealing an integrative network of markers involved in cellular processes that could modulate the response of TMX. Analysis of these mechanisms and their molecular components could improve the effectiveness of TMX.

Survival analysis in breast cancer using proteomic data from four independent datasets

Breast cancer clinical treatment selection is based on the immunohistochemical determination of four protein biomarkers: ESR1, PGR, HER2, and MKI67. Our aim was to correlate immunohistochemical results to proteome-level technologies in measuring the expression of these markers. We also aimed to integrate available proteome-level breast cancer datasets to identify and validate new prognostic biomarker candidates. We searched studies involving breast cancer patient cohorts with published survival and proteomic information. Immunohistochemistry and proteomic technologies were compared using the Mann-Whitney test. Receiver operating characteristics (ROC) curves were generated to validate discriminative power. Cox regression and Kaplan-Meier survival analysis were calculated to assess prognostic power. False Discovery Rate was computed to correct for multiple hypothesis testing. We established a database integrating protein expression data and survival information from four independent cohorts for 1229 breast cancer patients. In all four studies combined, a total of 7342 unique proteins were identified, and 1417 of these were identified in at least three datasets. ESR1, PGR, and HER2 protein expression levels determined by RPPA or LC-MS/MS methods showed a significant correlation with the levels determined by immunohistochemistry (p < 0.0001). PGR and ESR1 levels showed a moderate correlation (correlation coefficient = 0.17, p = 0.0399). An additional panel of candidate proteins, including apoptosis-related proteins (BCL2,), adhesion markers (CDH1, CLDN3, CLDN7) and basal markers (cytokeratins), were validated as prognostic biomarkers. Finally, we expanded our previously established web tool designed to validate survival-associated biomarkers by including the proteomic datasets analyzed in this study ( https://kmplot.com/ ). In summary, large proteomic studies now provide sufficient data enabling the validation and ranking of potential protein biomarkers.

Curcumin attenuates lncRNA H19‑induced epithelial‑mesenchymal transition in&nbsp;tamoxifen‑resistant breast cancer cells

The H19 long non-coding RNA is involved in the development of tamoxifen resistance in breast cancer. However, the relationship between H19 and the metastatic potential and treatment options for tamoxifen-resistant (TAMR) breast cancer is not completely understood. Curcumin inhibits cellular proliferation, migration and invasiveness in several cancer types, including pancreatic cancer, breast cancer and chronic myeloid leukemia. The present study aimed to investigate the role of H19 in MCF-7/TAMR cell epithelial-mesenchymal transition (EMT), migration and invasiveness, and to assess the ability of curcumin to inhibit H19-mediated effects. Reverse transcription-quantitative PCR and western blot analysis were conducted to detect the gene or protein expression. Cell Counting Kit-8, wound healing and Transwell invasion assays were performed to estimate the capabilities of cell viability, invasion and migration. H19 overexpression enhanced MCF-7/TAMR cell EMT, invasion and migration by upregulating Snail. Furthermore, curcumin notably decreased the expression levels of epithelial marker E-cadherin and markedly increased the expression levels of mesenchymal marker N-cadherin in MCF-7/TAMR cells compared with the control group. In addition, following treatment with curcumin for 48 h, H19 expression was decreased in a dose-dependent manner. Moreover, curcumin treatment for 48 h significantly attenuated H19-induced alterations in N-cadherin and E-cadherin expression levels. Curcumin also prevented H19-induced invasion and migration. The present study indicated that H19 may serve as a promoting factor of EMT, invasion and migration in MCF-7/TAMR cells, suggesting that curcumin may prevent H19-associated metastasis. Therefore, curcumin may serve as a promising therapeutic drug for patients with TAMR breast cancer.

GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis

Motivation

Missingness in label-free mass spectrometry is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in free mass spectrometry data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns.

Results

Three proteomics datasets and one metabolomics dataset indicate missing values could be a mixture of abundance-dependent and abundance-independent missingness. We assess the performance of GMSimpute using simulated data (with a wide range of 80 missing patterns) and metabolomics data from the Cancer Genome Atlas breast cancer and clear cell renal cell carcinoma studies. Using Pearson correlation and normalized root mean square errors between the true and imputed abundance, we compare its performance to K-nearest neighbors’ type approaches, Random Forest, GSimp, a model-based method implemented in DanteR and minimum values. The results indicate GMSimpute provides higher accuracy in imputation and exhibits stable performance across different missing patterns. In addition, GMSimpute is able to identify the features in downstream differential expression analysis with high accuracy when applied to the Cancer Genome Atlas datasets.

Availability and implementation

GMSimpute is on CRAN: https://cran.r-project.org/web/packages/GMSimpute/index.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Mechanisms of resistance in estrogen receptor positive breast cancer: overcoming resistance to tamoxifen/aromatase inhibitors

Several mechanisms of resistance have been identified, underscoring the complex nature of estrogen receptor (ER) signaling and the many connections between this pathway and other essential signaling pathways in breast cancer cells. Many therapeutic targets of cell signaling and cell cycle pathways have met success with endocrine therapy and remain an ongoing area of investigation. This review focuses on two major pathways that have recently emerged as important opportunities for therapeutic intervention in endocrine resistant breast tumors: PI3K/AKT/mTOR cell signaling and cyclinD1/cyclin-dependent kinase 4/6 cell cycle pathways. Additionally, we highlight individual and combination strategies in current clinical trials that target these pathways and others under investigation for the treatment of ER positive breast cancer.

Proteomic alterations in early stage cervical cancer

Laser capture microdissection (LCM) allows the capture of cell types or well-defined structures in tissue. We compared in a semi-quantitative way the proteomes from an equivalent of 8,000 tumor cells from patients with squamous cell cervical cancer (SCC, n = 22) with healthy epithelial and stromal cells obtained from normal cervical tissue (n = 13). Proteins were enzymatically digested into peptides which were measured by high-resolution mass spectrometry and analyzed by “all-or-nothing” analysis, Bonferroni, and Benjamini-Hochberg correction for multiple testing. By comparing LCM cell type preparations, 31 proteins were exclusively found in early stage cervical cancer (n = 11) when compared with healthy epithelium and stroma, based on criteria that address specificity in a restrictive “all-or-nothing” way. By Bonferroni correction for multiple testing, 30 proteins were significantly up-regulated between early stage cervical cancer and healthy control, including six members of the MCM protein family. MCM proteins are involved in DNA repair and expected to be participating in the early stage of cancer. After a less stringent Benjamini-Hochberg correction for multiple testing, we found that the abundances of 319 proteins were significantly different between early stage cervical cancer and healthy controls. Four proteins were confirmed in digests of whole tissue lysates by Parallel Reaction Monitoring (PRM). Ingenuity Pathway Analysis using correction for multiple testing by permutation resulted in two networks that were differentially regulated in early stage cervical cancer compared with healthy tissue. From these networks, we learned that specific tumor mechanisms become effective during the early stage of cervical cancer.

Discovery of Tamoxifen and N-Desmethyl Tamoxifen Protein Targets in MCF-7 Cells Using Large-Scale Protein Folding and Stability Measurements

The proteins in an MCF-7 cell line were probed for tamoxifen (TAM) and n-desmethyl tamoxifen (NDT) induced stability changes using the Stability of Proteins from Rates of Oxidation (SPROX) technique in combination with two different quantitative proteomics strategies, including one based on SILAC and one based on isobaric mass tags. Over 1000 proteins were assayed for TAM- and NDT-induced protein stability changes, and a total of 163 and 200 protein hits were identified in the TAM and NDT studies, respectively. A subset of 27 high-confidence protein hits were reproducibly identified with both proteomics strategies and/or with multiple peptide probes. One-third of the high-confidence hits have previously established experimental links to the estrogen receptor, and nearly all of the high-confidence hits have established links to breast cancer. One high-confidence protein hit that has known estrogen receptor binding properties, Y-box binding protein 1 (YBX1), was further validated as a direct binding target of TAM using both the SPROX and pulse proteolysis techniques. Proteins with TAM- and/or NDT-induced expression level changes were also identified in the SILAC-SPROX experiments. These proteins with expression level changes included only a small fraction of those with TAM- and/or NDT-induced stability changes.

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.

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.

Ex vivo tumor culture systems for functional drug testing and therapy response prediction

Optimal patient stratification is of utmost importance in the era of personalized medicine. Prediction of individual treatment responses by functional ex vivo assays requires model systems derived from viable tumor samples, which should closely resemble in vivo tumor characteristics and microenvironment. This review discusses a broad spectrum of model systems, ranging from classic 2D monolayer culture techniques to more experimental ‘cancer-on-chip’ procedures. We mainly focus on organotypic tumor slices that take tumor heterogeneity and tumor–stromal interactions into account. These 3D model systems can be exploited for patient selection as well as for fundamental research. Selection of the right model system for each specific research endeavor is crucial and requires careful balancing of the pros and cons of each technology.

Selection of the right therapy for individual cancer patients is very important with the expanding number of possible treatments. How tumors respond to a therapy can be tested by treating a sample from the tumor outside the body. Various culture methods can be used to maintain this tumor sample. Each of these model systems has its own benefits and disadvantages. In this review, we discuss the advantages and drawbacks of the available model systems and how they can be used to guide personalized medicine.

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.

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.

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).

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.

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.