Inconvenient truth: cancer biomarker development by using proteomics

Identification of cancer protein biomarker based on cell specific peptide and its potential role in predicting tumor metastasis

The identification of tumor related membrane protein is important for both cancer diagnosis and targeted therapy. Currently, the number of ideal clinical biomarkers is still limited partially because of lacking efficient methods in biomarker discovery. Targeting peptides are generated by library screening and can recognize their cognate targets with high specificity and affinity. In addition, these functional peptides have been considered to be a valuable molecule for both imaging detection and targeting therapy in oncology. The selected peptides can be used to identify cell-surface protein biomarkers of cancer cells. In our study, the peptide (VECYLIRDNLCIY) derived from the bacteria displaying library worked as a bait to capture its binding partner and aldolase A was identified as the candidate. The results indicated that aldolase A' expression level on the cell membrane was regulated by PI3K and aldolase A located on the membrane could inhibit the aggression of tumors through mediating cell metabolic pathway. Aldolase A could work as the joint for the metabolic and signal pathways related to tumor progression. In our work, we demonstrated a promising technology for selecting and identifying binding partners for cell-specific peptides that enables discovery of new tumor biomarkers, showing great scientific study values and clinical translation potencies. SIGNIFICANCE: MS-based cancer biomarker discovery provides promising target candidates for cancer diagnosis and therapy. However, the inevitable limits make it inconvenient in the process of sample preparation and data analysis. In this way, the small molecular probes show some advantages due to their readily availability and specific binding affinity such as the aptamers screened with SELEX technology and peptides derived from displaying libraries. In the present study, aldolase A was proved to be the membrane binding partner of a specific peptidic ligand towards ZR-75-1 tumor cell. It was discovered that membrane aldolase A was more sensitive and observable than other subcellular fractions in response to cellular metabolic state alteration or glucose availability. In addition, the reduced membrane-localized aldolase A expression indicated a more aggressive tumor phenotype and was accompanied by the upregulation of MMP-2/MMP-9. The non-glycolysis activity endowed it with potential utility as a tumor diagnostic marker and therapeutic target. This work demonstrates the practicability of screened peptide in cancer biomarker discovery, facilitating the development of diagnostic tools and therapeutic approaches to cancer, and markedly improves our understanding of cancer biology.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

Urine LOX-1 and Volatilome as Promising Tools towards the Early Detection of Renal Cancer

Simple Summary

Renal cell carcinoma (RCC) is often late diagnosed at an advanced stage, worsening the prognosis of the patients. Thus, an early marker is desirable. This paper presents an innovative combined approach useful to identify, for the first time, the presence of LOX-1 protein within the urine of clear cell RCC patients. The LOX-1 protein is related to metabolic disorder-associated carcinogenesis and is shown to be quantitatively correlated to tumor grade and stage. The analysis of volatile compounds released by urine shows the diagnostic potentialities of volatilome and indicates that at least one volatile compound is correlated with both LOX-1 and cancer. In this work, we propose the potential use of a noninvasive approach that enables an early, routine ccRCC diagnosis and leads to a better management of the patients.

Abstract

Renal cell carcinoma (RCC) represents around 3% of all cancers, within which clear cell RCC (ccRCC) are the most common type (70–75%). The RCC disease regularly progresses asymptomatically and upon presentation is recurrently metastatic, therefore, an early method of detection is necessary. The identification of one or more specific biomarkers measurable in biofluids (i.e., urine) by combined approaches could surely be appropriate for this kind of cancer, especially due to easy obtainability by noninvasive method. OLR1 is a metabolic gene that encodes for the Lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1), implicated in inflammation, atherosclerosis, ROS, and metabolic disorder-associated carcinogenesis. Specifically, LOX-1 is clearly involved in tumor insurgence and progression of different human cancers. This work reports for the first time the presence of LOX-1 protein in ccRCC urine and its peculiar distribution in tumoral tissues. The urine samples headspace has also been analyzed for the presence of the volatile compounds (VOCs) by SPME-GC/MS and gas sensor array. In particular, it was found by GC/MS analysis that 2-Cyclohexen-1-one,3-methyl-6-(1-methylethyl)- correlates with LOX-1 concentration in urine. The combined approach of VOCs analysis and protein quantification could lead to promising results in terms of diagnostic and prognostic potential for ccRCC tumors.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

An Overview on G Protein-coupled Receptor-induced Signal Transduction in Acute Myeloid Leukemia

BACKGROUND

Acute Myeloid Leukemia (AML) is a genetically heterogeneous disease characterized by uncontrolled proliferation of precursor myeloid-lineage cells in the bone marrow. AML is also characterized by patients with poor long-term survival outcomes due to relapse. Many efforts have been made to understand the biological heterogeneity of AML and the challenges to develop new therapies are therefore enormous. G Protein-coupled Receptors (GPCRs) are a large attractive drug-targeted family of transmembrane proteins, and aberrant GPCR expression and GPCR-mediated signaling have been implicated in leukemogenesis of AML. This review aims to identify the molecular players of GPCR signaling, focusing on the hematopoietic system, which are involved in AML to help developing novel drug targets and therapeutic strategies.

METHODS

We undertook an exhaustive and structured search of bibliographic databases for research focusing on GPCR, GPCR signaling and expression in AML.

RESULTS AND CONCLUSION

Many scientific reports were found with compelling evidence for the involvement of aberrant GPCR expression and perturbed GPCR-mediated signaling in the development of AML. The comprehensive analysis of GPCR in AML provides potential clinical biomarkers for prognostication, disease monitoring and therapeutic guidance. It will also help to provide marker panels for monitoring in AML. We conclude that GPCR-mediated signaling is contributing to leukemogenesis of AML, and postulate that mass spectrometrybased protein profiling of primary AML cells will accelerate the discovery of potential GPCR related biomarkers for AML.

Expression of Cytosolic Phospholipase A2 (cPLA2)-Arachidonic Acid (AA)-Cyclooxygenase-2 (COX-2) Pathway Factors in Lung Cancer Patients and Its Implication in Lung Cancer Early Detection and Prognosis

Background

The aim of this study was to elucidate the involvement of cPLA2-AA-COX-2 pathway factors and their potential role in lung cancer early diagnosis and prognosis.

Material/Methods

We selected 80 lung cancer patients as the cancer group, and 30 normal patients were selected as the normal group. Serum contents of COX-2, cPLA2, COX-1, mPGES, PGE2, and PGI2 were measured, and mRNA levels of COX-2, cPLA2, COX-1, and mPGES in serum were determined. Spearman’s P-test was used to analyze the correlation between expression of PGI2 and mPGES in serum and the clinical characteristics of these lung cancer patients. The factors affecting the prognosis lung cancer were analyzed by COX regression model.

Results

The serum contents of COX-2, cPLA2, COX-1, mPGES, PGE2, and PGI2 in the cancer patient group were significantly higher (p<0.05) than in the normal group; after treatment, the serum contents of these factors were significantly decreased (p<0.05). However, distant metastasis had a significant effect on serum contents of mPGES and PGI2 (p<0.05), but not on the other factors. The mRNA levels of COX-2, cPLA2, COX-1, and mPGES in cancer patients were significantly higher than in normal patients. In addition, the 5-year survival rate of patients with high expression of mPGES and/or PGI2 was lower than that of the low expression group. Cox regression analysis showed that the expression of mPGES and PGI2 had statistical significance in predicting the prognosis of lung cancer.

Conclusions

The cPLA2-AA-COX-2 pathway is closely associated with lung cancer. These findings are important for clinical diagnosis of lung cancer.

Calreticulin as A Novel Potential Metastasis-Associated Protein in Myxoid Liposarcoma, as Revealed by Two-Dimensional Difference Gel Electrophoresis

Myxoid liposarcoma (MLS) is a mesenchymal malignancy. To identify innovate seeds for clinical applications, we examined the proteomes of primary tumor tissues from 10 patients with MLS with different statuses of postoperative metastasis. The protein expression profiles of tumor tissues were created, and proteins with differential expression associated with postoperative metastasis were identified by two-dimensional difference gel electrophoresis (2D-DIGE) and mass spectrometry. The validation was performed using specific antibodies and in vitro analyses. Using 2D-DIGE, we observed 1726 protein species and identified proteins with unique expression levels in metastatic MLS. We focused on the overexpression of calreticulin in metastatic MLS. The higher expression of calreticulin was confirmed by Western blotting, and gene silencing assays demonstrated that reduced expression of calreticulin inhibited cell growth and invasion. Our findings suggested the important roles of calreticulin in MLS metastasis and supported its potential utility as a prognostic biomarker in MLS. Further investigations of the functional properties of calreticulin and other proteins identified in this study will improve our understanding of the biology of MLS and facilitate novel clinical applications.

Interstitial Fluid in Gynecologic Tumors and Its Possible Application in the Clinical Practice

Gynecologic cancers are an important cause of worldwide mortality. The interstitium consists of solid and fluid phases, situated between the blood vessels and cells. The interstitial fluid (IF), or fluid phase, is an extracellular fluid bathing and surrounding the tissue cells. The TIF (tumor interstitial fluid) is a dynamic fluid rich in lipids, proteins and enzyme-derived substances. The molecules found in the IF may be associated with pathological changes in tissues leading to cancer growth and metastatization. Proteomic techniques have allowed an extensive study of the composition of the TIF as a source of biomarkers for gynecologic cancers. In our review, we analyze the composition of the TIF, its formation process, the sampling methods, the consequences of its accumulation and the proteomic analyses performed, that make TIF valuable for monitoring different types of cancers.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

INTRODUCTION

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

OBJECTIVES

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

METHODS

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Cancer biomarker development and two-dimensional difference gel electrophoresis (2D-DIGE)

Cancer results from the accumulation of genomic alterations. As the genome is functionally translated to the proteome and regulates tumor cell behavior, proteomics studies are expected to further the current understanding of the molecular mechanisms underlying carcinogenesis and cancer progression. Biomarkers are potential tools to classify cancers for therapy, predict responses to treatments, and support treatment-related decision-making. Biomarker development has been actively pursued in oncology by proteomic approaches. Two-dimensional difference gel electrophoresis (2D-DIGE) is a proteomics technique based on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). In 2D-DIGE, protein samples are labeled with distinct fluorescent dyes before fractionation via 2D-PAGE. 2D-DIGE offers advantages to identify biomarker candidates, including reproducibility, high sensitivity, comprehensiveness, and high throughput. 2D-DIGE has contributed to the establishment of tissue biomarkers, which potentially facilitate precision medicine. 2D-DIGE is thus expected to yield major advancements in cancer biomarker identification and development.

Plasma phospholipase A2 activity may serve as a novel diagnostic biomarker for the diagnosis of breast cancer

Previous studies have indicated that phospholipase A2 (PLA2) may be associated with tumorigenesis in human tissues. The present study aimed to investigate the association between plasma PLA2 activity and the breast cancer (BC) status of patients. Increased plasma PLA2 activity was detected in patients with breast cancer when compared with healthy controls. Plasma samples were obtained from patients with BC (n=169), patients with benign disease (BD; n=80) and healthy controls (n=81). PLA2 activity was assessed using a quantitative fluorescent assay with selective inhibitors. It was demonstrated that increased PLA2 and secretory PLA2 (sPLA2) activity was associated with tumor stage, particularly in patients with late-stage disease. Additionally, smoking, alcohol consumption, body mass index (BMI) and age of patients did not have a significant effect on PLA2 activity. Analysis of receiver operating characteristic curves revealed that plasma PLA2 and sPLA2 activities were increased in BC patients compared with healthy controls. It was concluded that plasma PLA2 activity may serve as a biomarker for patients with BC.

The levels of serine proteases in colon tissue interstitial fluid and serum serve as an indicator of colorectal cancer progression

The proteins in tissue interstitial fluids (TIFs) can spread into the blood and have been proposed as an ideal material to find blood biomarkers. The colon TIFs were collected from 8-, 13-, 18-, and 22-week Apc^Min/+, a typical mouse model of colorectal cancer (CRC), and wild-type mice. iTRAQ-based quantification proteomics was conducted to survey the TIF proteins whose abundance appeared to depend on tumor progression. A total of 46 proteins that exhibited consecutive changes in abundance were identified, including six serine proteases, chymotrypsin-like elastase 1 (CELA1), chymotrypsin-like elastase 2A (CEL2A), chymopasin, chymotrypsinogen B (CTRB1), trypsin 2 (TRY2), and trypsin 4 (TRY4). The observed increases in the abundance of serine proteases were supported in another quantitative evaluation of the individual colon TIFs using a multiple reaction monitor (MRM) assay. Importantly, the increases in the abundance of serine proteases were also verified in the corresponding sera. The quantitative verification of the serine proteases was further extended to the clinical sera, revealing significantly higher levels of CELA1, CEL2A, CTRL/chymopasin, and TRY2 in CRC patients. The receiver operating characteristic analysis illustrated that the combination of CELA1 and CTRL reached the best diagnostic performance, with 90.0% sensitivity and 80.0% specificity. Thus, the quantitative target analysis demonstrated that some serine proteases are indicative of CRC progression.

NMR Metabolomics for Stem Cell type discrimination

Cell metabolism is a key determinant factor for the pluripotency and fate commitment of Stem Cells (SCs) during development, ageing, pathological onset and progression. We derived and cultured selected subpopulations of rodent fetal, postnatal, adult Neural SCs (NSCs) and postnatal glial progenitors, Olfactory Ensheathing Cells (OECs), respectively from the subventricular zone (SVZ) and the olfactory bulb (OB). Cell lysates were analyzed by proton Nuclear Magnetic Resonance (¹H-NMR) spectroscopy leading to metabolites identification and quantitation. Subsequent multivariate analysis of NMR data by Principal Component Analysis (PCA), and Partial Least Square Discriminant Analysis (PLS-DA) allowed data reduction and cluster analysis. This strategy ensures the definition of specific features in the metabolic content of phenotypically similar SCs sharing a common developmental origin. The metabolic fingerprints for selective metabolites or for the whole spectra demonstrated enhanced peculiarities among cell types. The key result of our work is a neat divergence between OECs and the remaining NSC cells. We also show that statistically significant differences for selective metabolites characterizes NSCs of different ages. Finally, the retrived metabolome in cell cultures correlates to the physiological SC features, thus allowing an integrated bioengineering approach for biologic fingerprints able to dissect the (neural) SC molecular specificities.

Waste, Leaks, and Failures in the Biomarker Pipeline

BACKGROUND

The large, expanding literature on biomarkers is characterized by almost ubiquitous significant results, with claims about the potential importance, but few of these discovered biomarkers are used in routine clinical care.

CONTENT

The pipeline of biomarker development includes several specific stages: discovery, validation, clinical translation, evaluation, implementation (and, in the case of nonutility, deimplementation). Each of these stages can be plagued by problems that cause failures of the overall pipeline. Some problems are nonspecific challenges for all biomedical investigation, while others are specific to the peculiarities of biomarker research. Discovery suffers from poor methods and incomplete and selective reporting. External independent validation is limited. Selection for clinical translation is often shaped by nonrational choices. Evaluation is sparse and the clinical utility of many biomarkers remains unknown. The regulatory environment for biomarkers remains weak and guidelines can reach biased or divergent recommendations. Removing inefficient or even harmful biomarkers that have been entrenched in clinical care can meet with major resistance.

SUMMARY

The current biomarker pipeline is too prone to failures. Consideration of clinical needs should become a starting point for the development of biomarkers. Improvements can include the use of more stringent methodology, better reporting, larger collaborative studies, careful external independent validation, preregistration, rigorous systematic reviews and umbrella reviews, pivotal randomized trials, and implementation and deimplementation studies. Incentives should be aligned toward delivering useful biomarkers.

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).

Quantitative analysis of gene expression in fixed colorectal carcinoma samples as a method for biomarker validation

Biomarkers have been described as the future of oncology. Modern proteomics provide an invaluable tool for the near-whole proteome screening for proteins expressed differently in neoplastic vs. healthy tissues. However, in order to select the most promising biomarkers, an independent method of validation is required. The aim of the current study was to propose a methodology for the validation of biomarkers. Due to material availability the majority of large scale biomarker studies are performed using formalin-fixed paraffin-embedded (FFPE) tissues, therefore these were selected for use in the current study. A total of 10 genes were selected from what have been previously described as the most promising candidate biomarkers, and the expression levels were analyzed with reverse transcription-quantitative polymerase chain reaction (RT-qPCR) using calibrator normalized relative quantification with the efficiency correction. For 6/10 analyzed genes, the results were consistent with the proteomic data; for the remaining four genes, the results were inconclusive. The upregulation of karyopherin α 2 (KPNA2) and chromosome segregation 1-like (CSE1L) in colorectal carcinoma, in addition to downregulation of chloride channel accessory 1 (CLCA1), fatty acid binding protein 1 (FABP1), sodium channel, voltage gated, type VII α subunit (SCN7A) and solute carrier family 26 (anion exchanger), member 3 (SLC26A3) was confirmed. With the combined use of proteomic and genetic tools, it was reported, for the first time to the best of our knowledge, that SCN7A was downregulated in colorectal carcinoma at mRNA and protein levels. It had been previously suggested that the remaining five genes served an important role in colorectal carcinogenesis, however the current study provided strong evidence to support their use as biomarkers. Thus, it was concluded that combination of RT-qPCR with proteomics offers a powerful methodology for biomarker identification, which can be used to analyze FFPE samples.

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.

Investigations into the Use of a Protein Sensor Assay for Metabolite Analysis

Rapid and definitive classification of biological samples has application in industrial, agricultural, and clinical settings. Considerable effort has been given to analytical methods to address such applications over the past 50 years, with the majority of successful solutions focusing on a single molecular target. However, in many cases, a single or even a few features are insufficient for accurate characterization or classification. Serum albumin (SA) proteins are a class of cargo-carrying proteins in blood that have evolved to transport a wide variety of metabolites and peptides in mammals. These proteins have up to seven binding sites which communicate allosterically to orchestrate a complex pick-up and delivery system involving a large number of different molecules at any time. The ability of SA proteins to bind multiple molecular species in a sophisticated manner inspired the development of assays to differentiate complex biological solutions. The combination of SA and high-resolution liquid chromatography mass spectrometry (LC-MS) is showing exciting promise as a protein sensor assay (PSA) for classification of complex biological samples. In this study, the PSA has been applied to cells undergoing and recovering from mild oxidative stress. Analysis using traditional LC-MS-based metabolomics failed to differentiate samples into treatment or temporal groups, whereas samples first treated with the PSA were cleanly classified into both correct treatment and temporal groups. The success of the PSA could be attributed to selective binding of metabolites, leading to a reduction in sample complexity and a general reduction in chemical noise. Metabolites important to successful sample classification were often enriched by 100-fold or more yet displayed a wide range of affinities for SA. The end result of PSA treatment is better classification of samples with a reduction in the number of features seen overall. Together, these results demonstrate how the use of a protein-based assay before LC-MS analysis can greatly improve separation and lead to more accurate and successful tracking of the metabolic state in an organism, suggesting potential application in a wide range of fields.

Tumor Interstitial Fluid Formation, Characterization, and Clinical Implications

The interstitium, situated between the blood and lymph vessels and the cells, consists of a solid or matrix phase and a fluid phase representing the tissue microenvironment. In the present review, we focus on the interstitial fluid phase of solid tumors, the tumor interstitial fluid (TIF), i.e., the fluid bathing the tumor and stroma cells, also including immune cells. This is a component of the internal milieu of a solid tumor that has attracted regained attention. Access to this space may provide important insight into tumor development and therapy response. TIF is formed by transcapillary filtration, and since this fluid is not readily available we discuss available techniques for TIF isolation, results from subsequent characterization and implications of recent findings with respect to fluid filtration and uptake of macromolecular therapeutic agents. There appear to be local gradients in signaling substances from neoplastic tissue to plasma that may provide new understanding of tumor biology. The development of sensitive proteomic technologies has made TIF a valuable source for tumor specific proteins and biomarker candidates. Potential biomarkers will appear locally in high concentrations in tumors and may eventually be found diluted in the plasma. Access to TIF that reliably reflects the local tumor microenvironment enables identification of substances that can be used in early detection and monitoring of disease.

Serum and urine biomarkers for human renal cell carcinoma

Renal cell carcinoma (RCC) diagnosis is mostly achieved incidentally by imaging provided for unrelated clinical reasons. The surgical management of localized tumors has reported excellent results. The therapy of advanced RCC has evolved considerably over recent years with the widespread use of the so-called “targeted therapies.” The identification of molecular markers in body fluids (e.g., sera and urine), which can be used for screening, diagnosis, follow-up, and monitoring of drug-based therapy in RCC patients, is one of the most ambitious challenges in oncologic research. Although there are some promising reports about potential biomarkers in sera, there is limited available data regarding urine markers for RCC. The following review reports some of the most promising biomarkers identified in the biological fluids of RCC patients.

Molecular imaging of breast cancer: present and future directions

Medical imaging technologies have undergone explosive growth over the past few decades and now play a central role in clinical oncology. But the truly transformative power of imaging in the clinical management of cancer patients lies ahead. Today, imaging is at a crossroads, with molecularly targeted imaging agents expected to broadly expand the capabilities of conventional anatomical imaging methods. Molecular imaging will allow clinicians to not only see where a tumor is located in the body, but also to visualize the expression and activity of specific molecules (e.g., proteases and protein kinases) and biological processes (e.g., apoptosis, angiogenesis, and metastasis) that influence tumor behavior and/or response to therapy. Breast cancer, the most common cancer among women and a research area where our group is actively involved, is a very heterogeneous disease with diverse patterns of development and response to treatment. Hence, molecular imaging is expected to have a major impact on this type of cancer, leading to important improvements in diagnosis, individualized treatment, and drug development, as well as our understanding of how breast cancer arises.