Assessment of lectin and HILIC based enrichment protocols for characterization of serum glycoproteins by mass spectrometry

Protein glycosylation is a common post-translational modification that is involved in many biological processes, including cell adhesion, protein-protein and receptor-ligand interactions. The glycoproteome constitutes a source for identification of disease biomarkers since altered protein glycosylation profiles are associated with certain human ailments. Glycoprotein analysis by mass spectrometry of biological samples, such as blood serum, is hampered by sample complexity and the low concentration of the potentially informative glycopeptides and -proteins. We assessed the utility of lectin-based and HILIC-based affinity enrichment techniques, alone or in combination, for preparation of glycoproteins and glycopeptides for subsequent analysis by MALDI and ESI mass spectrometry. The methods were successfully applied to human serum samples and a total of 86 N-glycosylation sites in 45 proteins were identified using a mixture of three immobilized lectins for consecutive glycoprotein enrichment and glycopeptide enrichment. The combination of lectin affinity enrichment of glycoproteins and subsequent HILIC enrichment of tryptic glycopeptides identified 81 N-glycosylation sites in 44 proteins. A total of 63 glycosylation sites in 38 proteins were identified by both methods, demonstrating distinct differences and complementarity. Serial application of custom-made microcolumns of mixed, immobilized lectins proved efficient for recovery and analysis of glycopeptides from serum samples of breast cancer patients and healthy individuals to assess glycosylation site frequencies.     

Serum protein profiling to identify biomarkers for small renal cell carcinoma

Diagnostic biomarkers for early detection of renal cell carcinoma (RCC) are in great need. In the present study, we compared the serum protein profiles of patients with small RCC to those of healthy individuals to identify the differentially expressed proteins with potential to serve as biomarkers. Serum samples were collected from 10 patients with small RCC and 10 healthy individuals. The serum protein expression profiles were analyzed by two-dimensional (2-D) gel electrophoresis. Twenty-seven proteins with differences in expression levels between RCC patients and healthy volunteers were identified. Of these, 19 were expressed at different levels and eight were expressed in serum from the RCC group, but not from the control group. Six differentially expressed proteins identified by using mass spectrometry included coagulation factor XIII B, complement C3 and its precursor, misato homolog 1 (isoform CRA_b), hemopexin, and alpha-1-B-glycoprotein. Some of these serum proteins are known regulators of tumor progression in human malignancies. In conclusion, we successfully applied 2-D gel electrophoresis and identified six serum proteins differentially expressed between patients with small RCC and healthy volunteers. These proteins may provide novel biomarkers for early detection and diagnosis of human RCC.     

Different techniques for urinary protein analysis of normal and lung cancer patients

Many components in urine are useful in clinical diagnosis and urinary proteins are known as important components to define many diseases such as proteinuria, kidney, bladder and urinary tract diseases. In this study, we focused on the comparison of different sample preparation methods for isolating urinary proteins prior to protein analysis of pooled healthy and lung cancer patient samples. Selective method was used for preliminary investigation of some putative urinary protein markers. Urine samples were passed first through a gel filtration column (PD-10 desalting column) to remove high salts and subsequently concentrated. Remaining interferences were removed by ultrafiltration or four precipitation methods. The analysis of urinary proteins by high-performance liquid chromatography and sodium dodecyl sulfate-polyacrylamide gel electrophoresis showed many similarities in profiles among preparation methods and a few profiles were different between normal and lung cancer patients. In contrast, the results of two-dimensional gel electrophoresis (2-DE) showed more distinctly different protein patterns. Our finding showed that the sequential preparation of urinary proteins by gel filtration and ultrafiltration could retain most urinary proteins which demonstrated the highest protein spots on 2-D gels and able to identify preliminary urinary protein markers related to cancer. Although sequential preparation of urine samples by gel filtration and protein precipitation resulted in low amounts of proteins on 2-D gels, high Mr proteins were easily detected. Therefore, there are alternative choices for urine sample preparation for studying the urinary proteome and identifying urinary protein markers important for further preclinical diagnostic and therapeutic applications.     

Proteomic analysis of differential protein expression in platelets of septic patients

Sepsis is one of the major health problems all over the world. Early diagnostic of sepsis is an attractive strategy to decrease the mortality of septic patients. However, an effective biomarker that fulfills all the necessary requirements for the accurate characterization of sepsis is still unavailable until now. In this study, the 2-DE technique followed by mass spectrometry and a database search was used for searching and identifying the differential expressed proteins in platelets between septic patients and paired healthy controls. Platelet 2-DE profiles of septic patients and paired healthy controls with high resolution and reproducibility were obtained. Differential platelet 2-DE profiles between septic patients and paired healthy controls were established. Differential protein spots between normal healthy volunteers and septic patients from platelet 2-DE profiles were identified by 2-DE followed with mass spectrometry and a database search. Five proteins with increased expression were identified between septic patients and healthy controls from platelet samples. These up-expressed proteins were EF-hand calcium-binding domain-containing protein 7, actin, interleukin-1β, glycoprotein IX, and glycoprotein IIB. Sepsis induces a complex regulation of platelet protein changes. Our study highlights the important role of these differential expressed proteins in sepsis, which deserve further research as potential candidates for therapeutic strategies. Furthermore, our research is beneficial for the future developments of sepsis diagnosis and therapy.     

Protein microarrays using liquid phase fractionation of cell lysates

We describe an approach in which protein microarrays are produced using a two-dimensional (2-D) liquid phase fractionation of cell lysates. The method involves a pI-based fractionation using chromatofocusing in the first dimension followed by nonporous reversed-phase high-performance liquid chromatography (HPLC) of each pI fraction in the second dimension. This allows fractionation of cellular proteins in the liquid phase that could then be arrayed on nitrocellulose slides and used to study humoral response in cancer. Protein microarrays have been used to identify potential serum biomarkers for prostate cancer. It is shown that specific fractions are immunoreactive against prostate cancer serum but not against serum from healthy individuals. These proteins could serve as sero-diagnostic markers for prostate cancer. Importantly, this method allows for use of post-translationally modified proteins as baits for detection of humoral response. Proteins eliciting an immune response are identified using the molecular mass and peptide sequence data obtained using mass spectrometric analysis of the liquid fractions. The fractionation of proteins in the liquid phase make this method amenable to automation.     

Diagnostic application of serum proteomic patterns in gastric cancer patients by ProteinChip surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is one of thecurrently used techniques to identify biomarkers for cancers. This study was planned to establish a system to accurately distinguish gastric cancer patients by using SELDI-TOF-MS. A total of 100 serum samples obtained from 60 individuals with gastric cancer and 40 healthy individuals were screened. Protein expression profiles were expressed on CM10 ProteinChip arrays and analyzed. Peak intensities were analyzed with the Biomarker Wizard software to identify peaks showing significantly different intensities between normal and cancer groups. Classification analysis and construction of decision trees were done with the Biomarker Pattern software 5.0. Seventeen protein peaks showed significant differences between the two groups. The decision tree which gave the highest discrimination included four peaks at mass 5,919, 8,583, 10,286, and 13,758 as splitters. The sensitivity and specificity for classification of the decision tree were 96.7% (58/60) and 97.5% (39/40), respectively. When the protein biomarker pattern was tested on a blinded test set, it yielded a sensitivity of 93.3% (28/30) and a specificity of 90% (18/20). These results suggest that serum protein profiling by the SELDI system may distinguish gastric cancer patients from healthy controls with relatively high sensitivity and specificity.     

Comparative proteomical and metalloproteomical analyses of human plasma from patients with laryngeal cancer

Laryngeal cancer is a significant disease worldwide, which presents an increasing incidence. Two contrasting ideas of the immune system role during cancer development are accepted: (1) it fights tumor cells, and (2) it aids tumor progression. Thus, there is no clear understanding about the immune response in laryngeal cancer. Furthermore, since tobacco is the main cause of laryngeal cancer and it contains various carcinogenic components, including metallic elements, these may play a role on cancer development. Plasmas of patients with laryngeal cancer and of healthy smokers were evaluated by 2D gel electrophoresis and mass spectrometry. Proteins were detected on every gel around pH 4.0–10.0 from molecular mass of 10–60 kDa. Few differences were found among cancer and control patients. However, three spots gathered between pI 7.3 and 7.6 with different molecular masses appeared exclusively in cancer profiles. From ten spots identified, six correspond to immune system components, including the three differential ones. The latter were observed only in cancer patients. The presence of several trace elements in the identified proteins was determined by inductively coupled plasma mass spectrometry, where chromium was increased in all proteins analyzed from patients with cancer. This study reinforces the importance of the immune response as target in the understanding and treatment of laryngeal cancer and the possibility that chromium is important in the carcinogenic progress.     

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."     

Esophageal cancer metabolite biomarkers detected by LC-MS and NMR methods

Background

Esophageal adenocarcinoma (EAC) is a rarely curable disease and is rapidly rising worldwide in incidence. Barret''s esophagus (BE) and high-grade dysplasia (HGD) are considered major risk factors for invasive adenocarcinoma. In the current study, unbiased global metabolic profiling methods were applied to serum samples from patients with EAC, BE and HGD, and healthy individuals, in order to identify metabolite based biomarkers associated with the early stages of EAC with the goal of improving prognostication.

Methodology/Principal Findings

Serum metabolite profiles from patients with EAC (n = 67), BE (n = 3), HGD (n = 9) and healthy volunteers (n = 34) were obtained using high performance liquid chromatography-mass spectrometry (LC-MS) methods. Twelve metabolites differed significantly (p<0.05) between EAC patients and healthy controls. A partial least-squares discriminant analysis (PLS-DA) model had good accuracy with the area under the receiver operative characteristic curve (AUROC) of 0.82. However, when the results of LC-MS were combined with 8 metabolites detected by nuclear magnetic resonance (NMR) in a previous study, the combination of NMR and MS detected metabolites provided a much superior performance, with AUROC = 0.95. Further, mean values of 12 of these metabolites varied consistently from healthy controls to the high-risk individuals (BE and HGD patients) and EAC subjects. Altered metabolic pathways including a number of amino acid pathways and energy metabolism were identified based on altered levels of numerous metabolites.

Conclusions/Significance

Metabolic profiles derived from the combination of LC-MS and NMR methods readily distinguish EAC patients and potentially promise important routes to understanding the carcinogenesis and detecting the cancer. Differences in the metabolic profiles between high-risk individuals and the EAC indicate the possibility of identifying the patients at risk much earlier to the development of the cancer.     

Investigation of tissue inhibitor of metalloproteinases 1 in plasma from colorectal cancer patients and blood donors by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Early detection of colorectal cancer (CRC) improves patient survival. Plasma tissue inhibitor of metalloproteinases 1 (TIMP-1) measurements by enzyme-linked immunosorbent assay (ELISA) have been suggested as a new method for the early detection of CRC. To further investigate the nature of TIMP-1 in plasma, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF MS) was used. TIMP-1 measurements of plasma from 16 healthy donors and 14 CRC patients were performed using TIMP-1 monoclonal antibody in SELDI TOF MS and ELISA. SELDI TOF MS applying an antibody to TIMP-1 revealed that human plasma TIMP-1 has a mass of 25.1 kDa and exhibits several isoforms. Both methods showed increased plasma TIMP-1 values for cancer patients as compared to healthy individuals. The p values for the separation of the groups were 0.0019 for ELISA and <0.0001 for SELDI TOF MS. CRC did not fundamentally affect the appearance of TIMP-1 as evaluated by SELDI TOF MS.     

尿液蛋白富集方法的比较

人尿液中蛋白含量低,在进行质谱分析时易被高丰度蛋白掩盖。因此,发展高效和高选择性的富集方法,是实现尿蛋白标记物深度覆盖的必要前提。探究不同实验方法对尿液蛋白富集和尿蛋白质组的影响尤为重要。本研究采用超滤法、硝酸纤维素膜富集法和饱和硫酸铵沉淀法,等体积各处理5例健康志愿者和膀胱癌患者10 mL尿液样本,富集尿液蛋白,SDS-PAGE分离尿蛋白,比较不同方法纯化的效率;通过质谱分析,比较不同纯化方法的肽段鉴定效果,确定针对尿液蛋白质组蛋白的最佳富集方法。相对于超滤和硝酸纤维素膜富集法,饱和硫酸铵沉淀法成功地应用于健康人尿蛋白的富集和质谱检测,在保证回收蛋白质量的前提下,可减少高丰度白蛋白的干扰,富集更多低丰度蛋白,提高了质谱鉴定的灵敏度。综上所述,饱和硫酸铵提取尿蛋白的效果较好,该方法具有大规模处理尿液、提高蛋白质组学筛选临床诊断标记物研究的应用潜力。     

A classification method based on principal components of SELDI spectra to diagnose of lung adenocarcinoma

Purpose

Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.

Results

The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.

Conclusions and Clinical Relevance

The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.     

A supervised approach for identifying discriminating genotype patterns and its application to breast cancer data

MOTIVATION: Large-scale association studies, investigating the genetic determinants of a phenotype of interest, are producing increasing amounts of genomic variation data on human cohorts. A fundamental challenge in these studies is the detection of genotypic patterns that discriminate individuals exhibiting the phenotype under study from individuals that do not possess it. The difficulty stems from the large number of single nucleotide polymorphism (SNP) combinations that have to be tested. The discrimination problem becomes even more involved when additional high-throughput data, such as gene expression data, are available for the same cohort. RESULTS: We have developed a graph theoretic approach for identifying discriminating patterns (DPs) for a given phenotype in a genotyped population. The method is based on representing the SNP data as a bipartite graph of individuals and their SNP states, and identifying fully connected subgraphs of this graph that relate individuals enriched for a given phenotypic group. The method can handle additional data types such as expression profiles of the genotyped population. It is reminiscent of biclustering approaches with the crucial difference that its search process is guided by the phenotype under consideration in a supervised manner. We tested our approach in simulations and on real data. In simulations, our method was able to retrieve planted patterns with high success rate. We then applied our approach to a dataset of 72 breast cancer patients with available gene expression profiles, genotyped over 695 SNPs. We detected several DPs that were highly significant with respect to various clinical phenotypes, and investigated the groups of patients and the groups of genes they defined. We found the patient groups to be highly enriched for other phenotypes and to display expression coherency among their profiles. The gene groups displayed functional coherency and involved genes with known role in cancer, providing additional support to their involvement. AVAILABILITY: The program is available upon request.     

An informatics-assisted label-free approach for personalized tissue membrane proteomics: case study on colorectal cancer

We developed a multiplexed label-free quantification strategy, which integrates an efficient gel-assisted digestion protocol, high-performance liquid chromatography tandem MS analysis, and a bioinformatics alignment method to determine personalized proteomic profiles for membrane proteins in human tissues. This strategy provided accurate (6% error) and reproducible (34% relative S.D.) quantification of three independently purified membrane fractions from the same human colorectal cancer (CRC) tissue. Using CRC as a model, we constructed the personalized membrane protein atlas of paired tumor and adjacent normal tissues from 28 patients with different stages of CRC. Without fractionation, this strategy confidently quantified 856 proteins (≥2 unique peptides) across different patients, including the first and robust detection (Mascot score: 22,074) of the well-documented CRC marker, carcinoembryonic antigen 5 by a discovery-type proteomics approach. Further validation of a panel of proteins, annexin A4, neutrophils defensin A1, and claudin 3, confirmed differential expression levels and high occurrences (48-70%) in 60 CRC patients. The most significant discovery is the overexpression of stomatin-like 2 (STOML2) for early diagnostic and prognostic potential. Increased expression of STOML2 was associated with decreased CRC-related survival; the mean survival period was 34.77 ± 2.03 months in patients with high STOML2 expression, whereas 53.67 ± 3.46 months was obtained for patients with low STOML2 expression. Further analysis by ELISA verified that plasma concentrations of STOML2 in early-stage CRC patients were elevated as compared with those of healthy individuals (p < 0.001), suggesting that STOML2 may be a noninvasive serological biomarker for early CRC diagnosis. The overall sensitivity of STOML2 for CRC detection was 71%, which increased to 87% when combined with CEA measurements. This study demonstrated a sensitive, label-free strategy for differential analysis of tissue membrane proteome, which may provide a roadmap for the subsequent identification of molecular target candidates of multiple cancer types.     

Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral,breast and pancreatic cancer-specific profiles

Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening.     

Detection of Cervical Cancer Biomarker Patterns in Blood Plasma and Urine by Differential Scanning Calorimetry and Mass Spectrometry

Improved methods for the accurate identification of both the presence and severity of cervical intraepithelial neoplasia (CIN) and extent of spread of invasive carcinomas of the cervix (IC) are needed. Differential scanning calorimetry (DSC) has recently been shown to detect specific changes in the thermal behavior of blood plasma proteins in several diseases. This methodology is being explored to provide a complementary approach for screening of cervical disease. The present study evaluated the utility of DSC in differentiating between healthy controls, increasing severity of CIN and early and advanced IC. Significant discrimination was apparent relative to the extent of disease with no clear effect of demographic factors such as age, ethnicity, smoking status and parity. Of most clinical relevance, there was strong differentiation of CIN from healthy controls and IC, and amongst patients with IC between FIGO Stage I and advanced cancer. The observed disease-specific changes in DSC profiles (thermograms) were hypothesized to reflect differential expression of disease biomarkers that subsequently bound to and affected the thermal behavior of the most abundant plasma proteins. The effect of interacting biomarkers can be inferred from the modulation of thermograms but cannot be directly identified by DSC. To investigate the nature of the proposed interactions, mass spectrometry (MS) analyses were employed. Quantitative assessment of the low molecular weight protein fragments of plasma and urine samples revealed a small list of peptides whose abundance was correlated with the extent of cervical disease, with the most striking plasma peptidome data supporting the interactome theory of peptide portioning to abundant plasma proteins. The combined DSC and MS approach in this study was successful in identifying unique biomarker signatures for cervical cancer and demonstrated the utility of DSC plasma profiles as a complementary diagnostic tool to evaluate cervical cancer health.     

Differential seminal plasma proteome signatures of acute lymphoblastic leukemia survivors

With advances in therapeutic methods, there is a high survival rate among leukemia patients, of an extent more than 80%. However, chemotherapeutic drugs used to treat these patients have adverse effects on their overall health profile including fertility. The primary aim of this study was to identify differentially expressed proteins in seminal plasma of acute lymphoblastic leukemia (ALL) survivors compared to age-matched healthy controls, which can provide molecular basis of idiopathic infertility in such survivors. Differential proteome profiling was performed by 2D–differential in-gel electrophoresis, protein spots were identified by mass spectrometry and selective differentially expressed proteins (DEPs) were validated by western blotting and ELISA method. Out of eight DEPs identified, five proteins (isocitrate dehydrogenase 1, semenogelin 1, lactoferrin, prolactin-inducible protein, and human serum albumin) were upregulated and three (pepsinogen, prostate specific antigen and prostatic acid phosphatase) were downregulated. Expression profiles of these proteins are suggestive of reduction in semen quality in ALL survivors and can further be explored to determine their fertility status.     

Proteomic analysis of gingival crevicular fluid for discovery of novel periodontal disease markers

The protein composition of gingival crevicular fluid (GCF) may reflect the pathophysiology of periodontal diseases. A standard GCF proteomic pattern of healthy individuals would serve as a reference to identify biomarkers of periodontal diseases by proteome analyses. However, protein profiles of GCF obtained from apparently healthy individuals have not been well explored. As a step toward detection of proteomic biomarkers for periodontal diseases, we applied both gel-based and gel-free methods to analyze GCF obtained from healthy subjects as compared with supragingival saliva. To ensure optimized protein extraction from GCF, a novel protocol was developed. The proteins in GCF were extracted with high yield by urea buffer combined with ultrafiltration and the intensity of spots with supragingival saliva and GCF was compared using agarose two-dimensional electrophoresis. Eight protein spots were found to be significantly more intense in GCF. They included superoxide dismutase 1 (SOD1), apolipoprotein A-I (ApoA-I), and dermcidin (DCD). Moreover, GCF proteins from healthy subjects were broken down into small peptide fragments and then analyzed directly by LC-MS/MS analysis. A total of 327 proteins including ApoA-I, SOD1, and DCD were identified in GCF. These results may serve as reference for future proteomic studies searching for GCF biomarkers of periodontal diseases.     

Cancer therapy monitoring in xenografts by quantitative analysis of circulating tumor DNA

Context: Circulating tumor DNA (ctDNA) is a promising biomarker in cancer. Materials and methods: We generated xenograft models of cancer and detected ctDNA in plasma by qRCR targeting human AluJ sequences. Results: Our assay reached single cell sensitivity in vitro and a correlation between ctDNA amount and tumor size was observed in vivo. Treatment with a mitogen activated protein kinase kinase (MEK)-inhibitor (BAY 869766) reduced ctDNA levels. Using this assay, we also confirmed that high levels of cell-free DNA are found in cancer patients compared to healthy individuals. Discussion and conclusion: We show that ctDNA may be useful biomarker for monitoring tumor growth and treatment response.