Serum exosomal lncRNA XIST is a potential non-invasive biomarker to diagnose recurrence of triple-negative breast cancer

Exosomal lncRNAs secreted by cancer cells can serve as potential biomarkers in the diagnosis and prognosis of various tumours. Here, we are committed to explore the diagnostic and prognostic value of serum exosomal XIST secreted by tumour cells to predict recurrence in patients with triple-negative breast cancer (TNBC). Significant increments in XIST and exo-XIST from tumour tissues and blood serum were found in reoccurring TNBC patients by comparison with non-recurrences. Levels of serum exo-XIST were only significantly increased in TNBC recurrence and no association with other clinicopathological parameters. Additionally, serum exo-XIST levels could be served as an assessment of change in the load of triple-negative breast cancer. Expressions of exo-XIST were markedly decreased after resection of the primary breast tumours and obviously elevated at the time of recurrence. Finally, an obvious association was identified between serum exo-XIST levels and a poorer overall survival (OS) in TNBC patients. Levels of serum exo-XIST may serve as a diagnostic and prognostic biomarker to predict the recurrent TNBC-loading status.     

Biomarkers for early detection of breast cancer: what, when, and where?

Early detection of breast cancer reduces the suffering and cost to society associated with the disease. A sensitive assay to identify biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. The earlier and more accurate the diagnostic biomarker can predict disease onset, the more valuable it becomes. Here, a brief review of existing and emerging approaches for breast cancer biomarker identification and analysis is presented. Those biomarkers found in biological fluids, blood in particular, apparently hold the best promise for fast development of screening assays. Autoantibodies and abnormal tumor-specific DNA methylation found in cell-free plasma DNA may provide the best opportunity for constructing multiplexed and highly redundant tests, which will be sufficiently specific and sensitive for early detection of breast cancer. It is expected that technologies developed for breast cancer detection will be useful for other types of cancer.     

Challenges to developing proteomic-based breast cancer diagnostics

Over the past decade, multiple genetic and histological approaches have accelerated development of new breast cancer diagnostics and treatment paradigms. Multiple distinct genetic subtypes of breast cancers have been defined, and this has progressively led toward more personalized medicine in regard to treatment options. There still remains a deficiency in the development of molecular diagnostic assays that can be used for breast cancer detection and pretherapy clinical decisions. In particular, the type of cancer-specific biomarker typified by a serum or tissue-derived protein. Progress in this regard has been minimal, especially in comparison to the rapid advancements in genetic and histological assays for breast cancers. In this review, some potential reasons for this large gap in developing protein biomarkers will be discussed, as well as new strategies for improving these approaches. Improvements in the study design of protein biomarker discovery strategies in relation to the genetic subtypes and histology of breast cancers is also emphasized. The current successes in use of genetic and histological assays for breast cancer diagnostics are summarized, and in that context, the current limitations of the types of breast cancer-related clinical samples available for protein biomarker assay development are discussed. Based on these limitations, research strategies emphasizing identification of glycoprotein biomarkers in blood and MALDI mass spectrometry imaging of tissues are described.     

The advancement of biomarker-based diagnostic tools for ovarian, breast, and pancreatic cancer through the use of urine as an analytical biofluid

Despite considerable advancements, the development of effective cancer screening tools based on serum biomarker measurements has thus far failed to achieve a meaningful clinical impact. The incremental progress observed over the course of serum biomarker development suggests that further refinements based on novel approaches may yet result in a breakthrough. The use of urine as an analytical biofluid for biomarker development may represent such an approach. The unique characteristics of urine including a high level of stability, ease of sampling, and an inactive and low-complexity testing matrix offer several potential advantages over the use of serum. A number of recent reports have demonstrated the utility of urine in the identification of novel cancer biomarkers and also the improved performance of biomarkers previously evaluated in serum. In this review, advancements related to the use of urine biomarkers within the settings of ovarian, breast, and pancreatic cancer are presented and discussed. Findings regarding the identification of specific urine biomarkers for each disease are highlighted along with comparative analyses of urine and serum biomarkers as diagnostic tools.     

Down‐regulation of uPA and uPAR by 3,3′‐diindolylmethane contributes to the inhibition of cell growth and migration of breast cancer cells

3,3′‐Diindolylmethane (DIM) is a known anti‐tumor agent against breast and other cancers; however, its exact mechanism of action remains unclear. The urokinase plasminogen activator (uPA) and its receptor (uPAR) system are involved in the degradation of basement membrane and extracellular matrix, leading to tumor cell invasion and metastasis. Since uPA‐uPAR system is highly activated in aggressive breast cancer, we hypothesized that the biological activity of B‐DIM could be mediated via inactivation of uPA‐uPAR system. We found that B‐DIM treatment as well as silencing of uPA‐uPAR led to the inhibition of cell growth and motility of MDA‐MB‐231 cells, which was in part due to inhibition of VEGF and MMP‐9. Moreover, silencing of uPA‐uPAR led to decreased sensitivity of these cells to B‐DIM indicating an important role of uPA‐uPAR in B‐DIM‐mediated inhibition of cell growth and migration. We also found similar effects of B‐DIM on MCF‐7, cells expressing low levels of uPA‐uPAR, which was due to direct down‐regulation of MMP‐9 and VEGF, independent of uPA‐uPAR system. Interestingly, over‐expression of uPA‐uPAR in MCF‐7 cells attenuated the inhibitory effects of B‐DIM. Our results, therefore, suggest that B‐DIM down‐regulates uPA‐uPAR in aggressive breast cancers but in the absence of uPA‐uPAR, B‐DIM can directly inhibit VEGF and MMP‐9 leading to the inhibition of cell growth and migration of breast cancer cells. J. Cell. Biochem. 108: 916–925, 2009. © 2009 Wiley‐Liss, Inc.     

AGR3 in Breast Cancer: Prognostic Impact and Suitable Serum-Based Biomarker for Early Cancer Detection

Blood-based early detection of breast cancer has recently gained novel momentum, as liquid biopsy diagnostics is a fast emerging field. In this study, we aimed to identify secreted proteins which are up-regulated both in tumour tissue and serum samples of breast cancer patients compared to normal tissue and sera. Based on two independent tissue cohorts (n = 75 and n = 229) and one serum cohort (n = 80) of human breast cancer and healthy serum samples, we characterised AGR3 as a novel potential biomarker both for breast cancer prognosis and early breast cancer detection from blood. AGR3 expression in breast tumours is significantly associated with oestrogen receptor α (P<0.001) and lower tumour grade (P<0.01). Interestingly, AGR3 protein expression correlates with unfavourable outcome in low (G1) and intermediate (G2) grade breast tumours (multivariate hazard ratio: 2.186, 95% CI: 1.008-4.740, P<0.05) indicating an independent prognostic impact. In sera analysed by ELISA technique, AGR3 protein concentration was significantly (P<0.001) elevated in samples from breast cancer patients (n = 40, mainly low stage tumours) compared to healthy controls (n = 40). To develop a suitable biomarker panel for early breast cancer detection, we measured AGR2 protein in human serum samples in parallel. The combined AGR3/AGR2 biomarker panel achieved a sensitivity of 64.5% and a specificity of 89.5% as shown by receiver operating characteristic (ROC) curve statistics. Thus our data clearly show the potential usability of AGR3 and AGR2 as biomarkers for blood-based early detection of human breast cancer.     

A transformation in the mechanism by which the urokinase receptor signals provides a selection advantage for estrogen receptor-expressing breast cancer cells in the absence of estrogen

Binding of urokinase-type plasminogen activator (uPA) to its receptor, uPAR, in estrogen receptor-α (ERα) expressing breast cancer cells, transiently activates ERK downstream of FAK, Src family kinases, and H-Ras. Herein, we show that when uPAR is over-expressed, in two separate ERα-positive breast cancer cell lines, ERK activation occurs autonomously of uPA and is sustained. Autonomous ERK activation by uPAR requires H-Ras and Rac1. A mutated form of uPAR, which does not bind vitronectin (uPAR-W32A), failed to induce autonomous ERK activation. Expression of human uPAR or mouse uPAR but not uPAR-W32A in MCF-7 cells provided a selection advantage when these cells were deprived of estrogen in cell culture for two weeks. Similarly, MCF-7 cells that express mouse uPAR formed xenografts in SCID mice that survived and increased in volume in the absence of estrogen supplementation, probably reflecting the pro-survival activity of phospho-ERK. Autonomous uPAR signaling to ERK was sensitive to the EGFR tyrosine kinase inhibitors, Erlotinib and Gefitinib. The transition in uPAR signaling from uPA-dependent and transient to autonomous and sustained is reminiscent of the transformation in ErbB2/HER2 signaling observed when this gene is amplified in breast cancer. uPAR over-expression may provide a pathway for escape of breast cancer cells from ERα-targeting therapeutics.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

A serum glycomics approach to breast cancer biomarkers

Because the glycosylation of proteins is known to change in tumor cells during the development of breast cancer, a glycomics approach is used here to find relevant biomarkers of breast cancer. These glycosylation changes are known to correlate with increasing tumor burden and poor prognosis. Current antibody-based immunochemical tests for cancer biomarkers of ovarian (CA125), breast (CA27.29 or CA15-3), pancreatic, gastric, colonic, and carcinoma (CA19-9) target highly glycosylated mucin proteins. However, these tests lack the specificity and sensitivity for use in early detection. This glycomics approach to find glycan biomarkers of breast cancer involves chemically cleaving oligosaccharides (glycans) from glycosylated proteins that are shed or secreted by breast cancer tumor cell lines. The resulting free glycan species are analyzed by MALDI-FT-ICR MS. Further structural analysis of the glycans can be performed in FTMS through the use of tandem mass spectrometry with infrared multiphoton dissociation. Glycan profiles were generated for each cell line and compared. These methods were then used to analyze sera obtained from a mouse model of breast cancer and a small number of serum samples obtained from human patients diagnosed with breast cancer or patients with no known history of breast cancer. In addition to the glycosylation changes detected in mice as mouse mammary tumors developed, glycosylation profiles were found to be sufficiently different to distinguish patients with cancer from those without. Although the small number of patient samples analyzed so far is inadequate to make any legitimate claims at this time, these promising but very preliminary results suggest that glycan profiles may contain distinct glycan biomarkers that may correspond to glycan "signatures of cancer."     

Urokinase plasminogen activator/urokinase-specific surface receptor expression and matrix invasion by breast cancer cells requires constitutive p38alpha mitogen-activated protein kinase activity

Overexpression of urokinase plasminogen activator (uPA) and its receptor (uPAR) has been well documented in a wide variety of tumor cells. In breast cancer, expression of uPA/uPAR is essential for tumor cell invasion and metastasis. However, the mechanism responsible for uPA/uPAR expression in cancer cells remains unclear. In the studies reported here, we show that endogenous p38 MAPK activity correlates well with breast carcinoma cell invasiveness. Treatment of highly invasive BT549 cells with a specific p38 MAPK inhibitor SB203580 diminished both uPA/uPAR mRNA and protein expression and abrogated the ability of these cells to invade matrigel, suggesting that p38 MAPK signaling pathway is involved in the regulation of uPA/uPAR expression and breast cancer cell invasion. We also demonstrated that SB203580-induced reduction in uPA/uPAR mRNA expression resulted from the de- stabilization of uPA and uPAR mRNA. Finally, by selectively inhibiting p38alpha or p38beta MAPK isoforms, we demonstrate that p38alpha, rather than p38beta, MAPK activity is essential for uPA/uPAR expression. These studies suggest that p38alpha MAPK signaling pathway is important for the maintenance of breast cancer invasive phenotype by promoting the stabilities of uPA and uPAR mRNA.     

Urokinase Plasminogen Activator Receptor (uPAR) and Plasminogen Activator Inhibitor-1 (PAI-1) Are Potential Predictive Biomarkers in Early Stage Oral Squamous Cell Carcinomas (OSCC)

Oral squamous cell carcinoma (OSCC) is often associated with metastatic disease and a poor 5 year survival rate. Patients diagnosed with small tumours generally have a more favourable outcome, but some of these small tumours are aggressive and lead to early death. To avoid harmful overtreatment of patients with favourable prognosis, there is a need for predictive biomarkers that can be used for treatment stratification. In this study we assessed the possibility to use components of the plasminogen activator (PA) system as prognostic markers for OSCC outcome and compared this to the commonly used biomarker Ki-67. A tissue-micro-array (TMA) based immunohistochemical analysis of primary tumour tissue obtained from a North Norwegian cohort of 115 patients diagnosed with OSCC was conducted. The expression of the biomarkers was compared with clinicopathological variables and disease specific death. The statistical analyses revealed that low expression of uPAR (p = 0.031) and PAI-1 (p = 0.021) in the tumour cells was significantly associated with low disease specific death in patients with small tumours and no lymph node metastasis (T1N0). The commonly used biomarker, Ki-67, was not associated with disease specific death in any of the groups of patients analysed. The conclusion is that uPAR and PAI-1 are potential predictive biomarkers in early stage tumours and that this warrants further studies on a larger cohort of patients.     

A serum protein profile predictive of the resistance to neoadjuvant chemotherapy in advanced breast cancers

Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies.     

Markers of small cell lung cancer

Lung cancer is the number one cause of cancer death; however, no specific serum biomarker is available till date for detection of early lung cancer. Despite good initial response to chemotherapy, small-cell lung cancer (SCLC) has a poor prognosis. Therefore, it is important to identify molecular markers that might influence survival and may serve as potential therapeutic targets. The review aims to summarize the current knowledge of serum biomarkers in SCLC to improve diagnostic efficiency in the detection of tumor progression in lung cancer. The current knowledge on the known serum cytokines and tumor biomarkers of SCLC is emphasized. Recent findings in the search for novel diagnostic and therapeutic molecular markers using the emerging genomic technology for detecting lung cancer are also described. It is believed that implementing these new research techniques will facilitate and improve early detection, prognostication and better treatment of SCLC.     

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.     

Biomarker discovery using a comparative omics approach in a mouse model developing heterogeneous mammary cancer subtypes

Identification of biomarkers for early breast cancer detection in blood is a challenging task, since breast cancer is a heterogeneous disease with a wide range of tumor subtypes. This is envisioned to result in differences in serum protein levels. The p53(R270H/+) WAPCre mouse model is unique in that these mice spontaneously develop both ER- and ER+ tumors, in proportions comparable to humans. Therefore, these mice provide a well-suited model system to identify human relevant biomarkers for early breast cancer detection that are additionally specific for different tumor subtypes. Mammary gland tumors were obtained from p53(R270H/+) WAPCre mice and cellular origin, ER, and HER2 status were characterized. We compared gene expression profiles for tumors with different characteristics versus control tissue, and determined genes differentially expressed across tumor subtypes. By using literature data (Gene Ontology, UniProt, and Human Plasma Proteome), we further identified protein candidate biomarkers for blood-based detection of breast cancer. Functional overrepresentation analysis (using Gene Ontology, MSigDB, BioGPS, Cancer GeneSigDB, and proteomics literature data) showed enrichment for several processes relevant for human breast cancer. Finally, Human Protein Atlas data were used to obtain a prioritized list of 16 potential biomarkers that should facilitate further studies on blood-based breast cancer detection in humans.     

REG4 Is Highly Expressed in Mucinous Ovarian Cancer: A Potential Novel Serum Biomarker

Preoperative diagnostics of ovarian neoplasms rely on ultrasound imaging and the serum biomarkers CA125 and HE4. However, these markers may be elevated in non-neoplastic conditions and may fail to identify most non-serous epithelial cancer subtypes. The objective of this study was to identify histotype-specific serum biomarkers for mucinous ovarian cancer. The candidate genes with mucinous histotype specific expression profile were identified from publicly available gene-expression databases and further in silico data mining was performed utilizing the MediSapiens database. Candidate biomarker validation was done using qRT-PCR, western blotting and immunohistochemical staining of tumor tissue microarrays. The expression level of the candidate gene in serum was compared to the serum CA125 and HE4 levels in a patient cohort of prospectively collected advanced ovarian cancer. Database searches identified REG4 as a potential biomarker with specificity for the mucinous ovarian cancer subtype. The specific expression within epithelial ovarian tumors was further confirmed by mRNA analysis. Immunohistochemical staining of ovarian tumor tissue arrays showed distinctive cytoplasmic expression pattern only in mucinous carcinomas and suggested differential expression between benign and malignant mucinous neoplasms. Finally, an ELISA based serum biomarker assay demonstrated increased expression only in patients with mucinous ovarian cancer. This study identifies REG4 as a potential serum biomarker for histotype-specific detection of mucinous ovarian cancer and suggests serum REG4 measurement as a non-invasive diagnostic tool for postoperative follow-up of patients with mucinous ovarian cancer.