Oncoproteomics: current trends and future perspectives

Plasma exosomal protein PLG and SERPINA1 in colorectal cancer diagnosis and coagulation abnormalities

PURPOSE

Early diagnosis of colorectal cancer (CRC) is critical to patient prognosis; however, there is lack of non-invasive biomarkers that are extremely sensitive and specific for early screening and diagnosis. Exosomes are a novel tool applied to the diagnosis and treatment of cancer. Changes in plasma exosomal proteins have a certain relationship with the development of various diseases including tumors. Here, we aimed to find exosomal biomarkers for early diagnosis of CRC.

METHODS

Exosomes obtained by ultracentrifugation from CRC patients and healthy donors were characterized by transmission electron microscopy (TEM), qNano and western blotting. Proteomic and functional enrichment analyses confirmed differences in the specific expression of exosomal proteins in plasma between CRC patients and healthy donors. Western blotting with enzyme-linked immunosorbent assay (ELISA) was used to verify the difference proteins. Statistical methods were used to analyze the relationship between protein levels and CRC.

RESULTS

The expression levels of serpin peptidase inhibitor clade A member 1 (SERPINA1) and fibrinogen (PLG) in CRC patients were significantly higher than those in healthy groups. Receptor operating characteristic (ROC) curves analysis was superior to CEA and CA19-9 for the diagnosis of colorectal cancer and early-stage colorectal cancer. The two were related to TNM staging and coagulation, and the difference was statistically significant.

CONCLUSION

The results of this study have potential value in advancing the clinical diagnosis of colorectal cancer.

The issues and challenges with cancer biomarkers

A biomarker is a measurable indicator used to distinguish precisely/objectively either normal biological state/pathological condition/response to a specific therapeutic intervention. The use of novel molecular biomarkers within evidence-based medicine may improve the diagnosis/treatment of disease, improve health outcomes, and reduce the disease's socio-economic impact. Presently cancer biomarkers are the backbone of therapy, with greater efficacy and better survival rates. Cancer biomarkers are extensively used to treat cancer and monitor the disease's progress, drug response, relapses, and drug resistance. The highest percent of all biomarkers explored are in the domain of cancer. Extensive research using various methods/tissues is carried out for identifying biomarkers for early detection, which has been mostly unsuccessful. The quantitative/qualitative detection of various biomarkers in different tissues should ideally be done in accordance with qualification rules laid down by the Early Detection Research Network (EDRN), Program for the Assessment of Clinical Cancer Tests (PACCT), and National Academy of Clinical Biochemistry. Many biomarkers are presently under investigation, but lacunae lie in the biomarker's sensitivity and specificity. An ideal biomarker should be quantifiable, reliable, of considerable high/low expression, correlate with the outcome progression, cost-effective, and consistent across gender and ethnic groups. Further, we also highlight that these biomarkers' application remains questionable in childhood malignancies due to the lack of reference values in the pediatric population. The development of a cancer biomarker stands very challenging due to its complexity and sensitivity/resistance to the therapy. In past decades, the cross-talks between molecular pathways have been targeted to study the nature of cancer. To generate sensitive and specific biomarkers representing the pathogenesis of specific cancer, predicting the treatment responses and outcomes would necessitate inclusion of multiple biomarkers.

The implementation of omics technologies in cancer microbiome research

Whilst the interplay between host genetics and the environment plays a pivotal role in the aetiopathogenesis of cancer, there are other key contributors of importance as well. One such factor of central and growing interest is the contribution of the microbiota to cancer. Even though the field is only a few years old, investigation of the 'cancer microbiome' has already led to major advances in knowledge of the basic biology of cancer risk and progression, opened novel avenues for biomarkers and diagnostics, and given a better understanding of mechanisms underlying response to therapy. Recent developments in microbial DNA sequencing techniques (and the bioinformatics required for analysis of these datasets) have allowed much more in-depth profiling of the structure of microbial communities than was previously possible. However, for more complete assessment of the functional implications of microbial changes, there is a growing recognition of the importance of the integration of microbial profiling with other omics modalities, with metabonomics (metabolite profiling) and proteomics (protein profiling) both gaining particular recent attention. In this review, we give an overview of some of the key scientific techniques being used to unravel the role of the cancer microbiome. We have aimed to highlight practical aspects related to sample collection and preparation, choice of the modality of analysis, and examples of where different omics technologies have been complementary to each other to highlight the significance of the cancer microbiome.

Proteomic identification of potential biomarkers for cervical squamous cell carcinoma and human papillomavirus infection

It is known that high-risk human papillomavirus infection is the main etiological factor in cervical carcinogenesis. However, human papillomavirus screening is not sufficient for early diagnosis. In this study, we aimed to identify potential biomarkers common to cervical carcinoma and human papillomavirus infection by proteomics for human papillomavirus-based early diagnosis and prognosis. To this end, we collected 76 cases of fresh cervical tissues and 116 cases of paraffin-embedded tissue slices, diagnosed as cervical squamous cell carcinoma, cervical intraepithelial neoplasia II-III, or normal cervix from ethnic Uighur and Han women. Human papillomavirus infection by eight oncogenic human papillomavirus types was detected in tissue DNA samples using a quantitative polymerase chain reaction. The protein profile of cervical specimens from human papillomavirus 16-positive squamous cell carcinoma and human papillomavirus-negative normal controls was analyzed by proteomics and bioinformatics. The expression of candidate proteins was further determined by quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry. We identified 67 proteins that were differentially expressed in human papillomavirus 16-positive squamous cell carcinoma compared to normal cervix. The quantitative reverse transcriptase-polymerase chain reaction analysis verified the upregulation of ASAH1, PCBP2, DDX5, MCM5, TAGLN2, hnRNPA1, ENO1, TYPH, CYC, and MCM4 in squamous cell carcinoma compared to normal cervix ( p < 0.05). In addition, the transcription of PCBP2, MCM5, hnRNPA1, TYPH, and CYC was also significantly increased in cervical intraepithelial neoplasia II-III compared to normal cervix. Immunohistochemistry staining further confirmed the overexpression of PCBP2, hnRNPA1, ASAH1, and DDX5 in squamous cell carcinoma and cervical intraepithelial neoplasia II-III compared to normal controls ( p < 0.05). Our data suggest that the expression of ASAH1, PCBP2, DDX5, and hnRNPA1, and possibly MCM4, MCM5, CYC, ENO1, and TYPH, is upregulated during cervical carcinogenesis and potentially associated with human papillomavirus infection. Further validation studies of the profile will contribute to establishing auxiliary diagnostic markers for human papillomavirus-based cancer prognosis.

Serum mass profile signature as a biomarker of early lung cancer

OBJECTIVES

Circulating molecular biomarkers of lung cancer may allow the pre-selection of candidates for computed tomography screening or increase its efficacy. We aimed to identify features of serum mass profile distinguishing individuals with early lung cancer from healthy participants of the lung cancer screening program.

METHODS

Blood samples were collected during a low-dose computed tomography (LD-CT) screening program performed by one institution (Medical University of Gdansk, Poland). MALDI-ToF mass spectrometry was used to characterize the low-molecular-weight (1000-14,000Da) serum fraction. The analysis comprised 95 patients with early stage lung cancer (including 30 screen-detected cases) and a matched group of 285 healthy controls. The cases were split into two independent cohorts (discovery and validation), analyzed separately 6 months apart.

RESULTS

Several molecular components of serum (putatively components of endogenous peptidome) discriminating patients with early lung cancer from controls were identified in a discovery cohort. This allowed building an effective cancer classifier as a model tuned to maximize negative predictive value, with an area under the curve (AUC) of 0.88, a negative predictive value of 100%, and a positive predictive value of 48%. However, the classifier performed worse in a validation cohort including independent sample sets (AUC 0.73, NPV 88% and PPV 30%).

CONCLUSIONS

We developed a serum mass profile-based signature identifying patients with early lung cancer. Although this marker has insufficient value as a stand-alone preselecting tool for LD-CT screening, its potential clinical usefulness in evaluation of indeterminate pulmonary nodules deserves further investigation.

Partial-Body Irradiation in Patients with Prostate Cancer Treated with IMRT Has Little Effect on the Composition of Serum Proteome

Partial body irradiation during cancer radiotherapy (RT) induces a response of irradiated tissues that could be observed at the level of serum proteome. Here we aimed to characterize the response to RT in group of patients treated because of prostate cancer. Five consecutive blood samples were collected before, during, and after the end of RT in a group of 126 patients who received definitive treatment with a maximum dose of 76 Gy. Serum peptidome, which was profiled in the 2000–16,000 Da range using MALDI-MS. Serum proteins were identified and quantified using the shotgun LC-MS/MS approach. The majority of changes in serum peptidome were detected between pre-treatment samples and samples collected after 3–4 weeks of RT (~25% of registered peptides changed their abundances significantly), yet the intensity of observed changes was not correlated significantly with the degree of acute radiation toxicity or the volume of irradiated tissues. Furthermore, there were a few serum proteins identified, the abundances of which were different in pre-RT and post-RT samples, including immunity and inflammation-related factors. Observed effects were apparently weaker than in comparable groups of head and neck cancer patients in spite of similar radiation doses and volumes of irradiated tissues in both groups. We concluded that changes observed at the level of serum proteome were low for this cohort of prostate cancer patients, although the specific components involved are associated with immunity and inflammation, and reflect the characteristic acute response of the human body to radiation.

Targeted proteomics for biomarker discovery and validation of hepatocellular carcinoma in hepatitis C infected patients

Hepatocellular carcinoma (HCC)-related mortality is high because early detection modalities are hampered by inaccuracy, expense and inherent procedural risks. Thus there is an urgent need for minimally invasive, highly specific and sensitive biomarkers that enable early disease detection when therapeutic intervention remains practical. Successful therapeutic intervention is predicated on the ability to detect the cancer early. Similar unmet medical needs abound in most fields of medicine and require novel methodological approaches. Proteomic profiling of body fluids presents a sensitive diagnostic tool for early cancer detection. Here we describe such a strategy of comparative proteomics to identify potential serum-based biomarkers to distinguish high-risk chronic hepatitis C virus infected patients from HCC patients. In order to compensate for the extraordinary dynamic range in serum proteins, enrichment methods that compress the dynamic range without surrendering proteome complexity can help minimize the problems associated with many depletion methods. The enriched serum can be resolved using 2D-difference in-gel electrophoresis and the spots showing statistically significant changes selected for identification by liquid chromatography-tandem mass spectrometry. Subsequent quantitative verification and validation of these candidate biomarkers represent an obligatory and rate-limiting process that is greatly enabled by selected reaction monitoring (SRM). SRM is a tandem mass spectrometry method suitable for identification and quantitation of target peptides within complex mixtures independent on peptide-specific antibodies. Ultimately, multiplexed SRM and dynamic multiple reaction monitoring can be utilized for the simultaneous analysis of a biomarker panel derived from support vector machine learning approaches, which allows monitoring a specific disease state such as early HCC. Overall, this approach yields high probability biomarkers for clinical validation in large patient cohorts and represents a strategy extensible to many diseases.

Colorectal cancer biomarker discovery and validation using LC-MS/MS-based proteomics in blood: truth or dare?

Globally, colorectal cancer (CRC) is the third most common malignant neoplasm. However, highly sensitive, specific, noninvasive tests that allow CRC diagnosis at an early stage are still needed. As circulatory blood reflects the physiological status of an individual and/or the disease status for several disorders, efforts have been undertaken to identify candidate diagnostic CRC markers in plasma and serum. In this review, the challenges, bottlenecks and promising properties of mass spectrometry (MS)-based proteomics in blood are discussed. More specifically, important aspects in clinical design, sample retrieval, sample preparation, and MS analysis are presented. The recent developments in targeted MS approaches in plasma or serum are highlighted as well.

A role for mitogen kinase kinase 3 in pulmonary inflammation validated from a proteomic approach

Proteomics is a powerful tool to ascertain which proteins are differentially expressed in the context of disease. We have used this approach on inflammatory cells obtained from patients with asthma to ascertain whether novel drugs targets could be illuminated and to investigate the role of any such target in a range of in vitro and in vivo models of inflammation. A proteomic study was undertaken using peripheral blood mononuclear cells from mild asthmatic subjects compared with healthy subjects. The analysis revealed an increased expression of the intracellular kinase, mitogen activated protein kinase (MKK3), and the function of this protein was investigated further in preclinical models of inflammation using MKK3 knockout mice. We describe a 3.65 fold increase in the expression of MKK3 in CD8(+) T lymphocytes obtained from subjects with asthma compared with healthy subjects using a proteomic approach which we have confirmed in CD8(+), but not in CD4(+) T lymphocytes or human bronchial epithelial cells from asthmatic patients using a Western blot technique. In wild type mice, bacterial lipopolysaccharide (LPS) caused a significant increase in MKK3 expression and significantly reduced airway neutrophilia in MKK3(-/-) mice (median, 25, 75% percentile; wild/LPS; 5.3 (0.7-9.9) × 10(5) cells/mL vs MKK3(-/-)/LPS; 0 (0-1.9) × 10(5) cells/mL, P < 0.05). In contrast, eosinophilia in sensitized wild type mice challenged with allergen (0.5 (0.16-0.65) × 10(5) cells/mL) was significantly increased in MKK3(-/-) mice (2.2 (0.9-3.5) × 10(5) cells/mL, P < 0.05). Our results suggest that asthma is associated with MKK3 over-expression in CD8(+) cells. We have also demonstrated that MKK3 may be critical for airway neutrophilia, but not eosinophilia, suggesting that this may be a target worthy of further consideration in the context of diseases associated with neutrophil activation such as severe asthma and COPD.

Identification of annexin II as a novel secretory biomarker for breast cancer

Early prediction of metastatic breast cancer is important for improvement of prognosis and survival rate. The present study aimed to identify secreted protein biomarkers for detection of invasive breast cancer. To this end, we performed a comparative proteomic analysis by a combination of 2DE and MALDI-TOF MS analysis of conditioned media from invasive H-Ras MCF10A human breast epithelial cells and noninvasive MCF10A and N-Ras MCF10A cells. We identified a list of 25 proteins that were strongly detected in media of H-Ras MCF10A and focused on annexin II, which was shown to be involved in cell motility. Invasive triple-negative human breast carcinoma cells, Hs578T, and MDA-MB-231, showed increased levels of annexin II in media, demonstrating that secretion of annexin II correlated well with the invasive phenotype of cells. We demonstrated a crucial role of annexin II in breast cell invasion/migration and actin cytoskeleton reorganization required for filopodia formation. Annexin II levels in the plasma samples and breast cancer tissues of breast cancer patients were significantly higher than those of normal groups, providing a clinical relevance to our in vitro findings. Taken together, we identified annexin II as a novel secretory biomarker candidate for invasive breast cancer, especially estrogen receptor-negative breast cancer.

Current status and advances in quantitative proteomic mass spectrometry

The accurate quantitation of proteins and peptides in complex biological systems is one of the most challenging areas of proteomics. Mass spectrometry-based approaches have forged significant in-roads allowing accurate and sensitive quantitation and the ability to multiplex vastly complex samples through the application of robust bioinformatic tools. These relative and absolute quantitative measures using label-free, tags, or stable isotope labelling have their own strengths and limitations. The continuous development of these methods is vital for increasing reproducibility in the rapidly expanding application of quantitative proteomics in biomarker discovery and validation. This paper provides a critical overview of the primary mass spectrometry-based quantitative approaches and the current status of quantitative proteomics in biomedical research.

Protein profiling of blood samples from patients with hereditary leiomyomatosis and renal cell cancer by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Hereditary leiomyomatosis and renal cell cancer (HLRCC) is an extremely rare syndrome with autosomal dominant inheritance. HLRCC is characterized by a predisposition to leiomyomas of the skin and the uterus as well as renal cell carcinoma. The disease-related gene has been identified as fumarate hydratase (fumarase, FH), which encodes an enzyme involved in the mitochondrial tricarboxylic acid cycle. Protein profiling may give some insight into the molecular pathways of HLRCC. Therefore, we performed protein profiling of blood samples from HLRCC patients, their family members, and healthy volunteers, using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) coupled with IMAC-Cu chips. For hierarchical clustering analysis, we used the 45 peaks that revealed significant differences in single-marker analysis over the range from 1500 to 15,000 m/z. Heat map analysis based on the results of clustering distinguished the HLRCC kindred from non-HLRCC subjects with a sensitivity of 94% and a specificity of 90%. SELDI-TOF MS profiling of blood samples can be applied to identify patients with HLRCC and to assess specific molecular mechanisms involved in this condition.

Phosphoproteomics and lung cancer research

Massive evidence suggests that genetic abnormalities contribute to the development of lung cancer. These molecular abnormalities may serve as diagnostic, prognostic and predictive biomarkers for this deadly disease. It is imperative to search these biomarkers in different tumorigenesis pathways so as to provide the most appropriate therapy for each individual patient with lung malignancy. Phosphoproteomics is a promising technology for the identification of biomarkers and novel therapeutic targets for cancer. Thousands of proteins interact via physical and chemical association. Moreover, some proteins can covalently modify other proteins post-translationally. These post-translational modifications ultimately give rise to the emergent functions of cells in sequence, space and time. Phosphoproteomics clinical researches imply the comprehensive analysis of the proteins that are expressed in cells or tissues and can be employed at different stages. In addition, understanding the functions of phosphorylated proteins requires the study of proteomes as linked systems rather than collections of individual protein molecules. In fact, proteomics approaches coupled with affinity chromatography strategies followed by mass spectrometry have been used to elucidate relevant biological questions. This article will discuss the relevant clues of post-translational modifications, phosphorylated proteins, and useful proteomics approaches to identify molecular cancer signatures. The recent progress in phosphoproteomics research in lung cancer will be also discussed.

Detection of bladder cancer using proteomic profiling of urine sediments

We used protein expression profiles to develop a classification rule for the detection and prognostic assessment of bladder cancer in voided urine samples. Using the Ciphergen PBS II ProteinChip Reader, we analyzed the protein profiles of 18 pairs of samples of bladder tumor and adjacent urothelium tissue, a training set of 85 voided urine samples (32 controls and 53 bladder cancer), and a blinded testing set of 68 voided urine samples (33 controls and 35 bladder cancer). Using t-tests, we identified 473 peaks showing significant differential expression across different categories of paired bladder tumor and adjacent urothelial samples compared to normal urothelium. Then the intensities of those 473 peaks were examined in a training set of voided urine samples. Using this approach, we identified 41 protein peaks that were differentially expressed in both sets of samples. The expression pattern of the 41 protein peaks was used to classify the voided urine samples as malignant or benign. This approach yielded a sensitivity and specificity of 59% and 90%, respectively, on the training set and 80% and 100%, respectively, on the testing set. The proteomic classification rule performed with similar accuracy in low- and high-grade bladder carcinomas. In addition, we used hierarchical clustering with all 473 protein peaks on 65 benign voided urine samples, 88 samples from patients with clinically evident bladder cancer, and 127 samples from patients with a history of bladder cancer to classify the samples into Cluster A or B. The tumors in Cluster B were characterized by clinically aggressive behavior with significantly shorter metastasis-free and disease-specific survival.

Application of Proteomics in Chinese Medicine Research

Proteomics technologies can be applied to simultaneously study the function, organization, diversity, and dynamic variety of a cell or a whole tissue. The integrative approach of proteomics is in line with the holistic concept and practices of traditional Chinese medicine (TCM). In this review, the technologies of proteomics, their adoption leverages the depth and breadth of TCM research are introduced. This article presents some examples to illustrate the use of proteomics technologies in the study of pharmacological effects and their action mechanisms relevant to TCM. Proteomics technologies could be used to screen the target molecules of the TCM actions, identify new bioactive components, and elucidate the underlying mechanisms of their effects. With proteomics approaches, it was found that the Siwu decoction could regulate the protein expression of the bone marrow of blood (Xue) deficient mice, including some proteins and enzymes involved in the hemopoiesis system. Ganoderma lucidum spores might promote the survival and axon regeneration of injured spinal motor neurons in rats by regulating the expression levels of proteins involved in the energy and tissue regeneration system. Polygonatum zanlanscianense Pamp exhibited cytotoxicity towards human myeloblast leukemia HL-60 cells through multiple apoptosis-including pathways. Panax ginseng might be beneficial to patients suffering from diabetes mellitus and its complications by alleviating inflammation. Taken together with a discussion on the challenges and perspectives, this paper provides an overview of the recent developments of proteomics technologies in TCM research, and contends that proteomics will play an important role in the modernization and internationalization of TCM.

The association of a distinct plasma proteomic profile with the cervical high-grade squamous intraepithelial lesion of Uyghur women: a 2D liquid-phase chromatography/mass spectrometry study

OBJECTIVE

To identify plasma protein biomarkers of cervical high-grade squamous intraepithelial lesion (HSIL) of Uyghur women by proteomics approach.

METHODS

Plasma protein samples of Uyghur women with HSIL and chronic cervicitis were analyzed with 2D HPLC followed by detection of target proteins with Linear Trap Quadrupole Mass Spectrometer (LTQ MS/MS).

RESULTS

We detected three upregulated and one downregulated protein peaks representing protein constituents distinguishing HSIL from controls by 2D HPLC, identified 31 target proteins by LTQ MS/MS. Further confirmed analysis with online software IPA® 8.7 and ELISA assay showed APOA1 and mTOR as potential biomarkers.

CONCLUSIONS

A distinct plasma proteomic profile may be associated with HSIL of Uyghur women.

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.

The proteome of normal pancreatic juice

OBJECTIVES

The aims of this study were to characterize the proteome of normal pancreatic juice, to analyze the effect of secretin on the normal proteome, and to compare these results with published data from patients with pancreatic cancer.

METHODS

Paired pancreatic fluid specimens (before and after intravenous secretin stimulation) were obtained during endoscopic pancreatography from 3 patients without significant pancreatic pathology. Proteins were identified and quantified by mass spectrometry-based protein quantification technology. The human RefSeq (NCBI) database was used to compare the data in samples from patients without pancreatic disease with published data from 3 patients with pancreatic cancer.

RESULTS

A total of 285 proteins were identified in normal pancreatic juice. Ninety had sufficient amino acid sequences identified to characterize the protein with a high level of confidence. All 90 proteins were present before and after secretin administration but with altered relative concentrations, usually by 1 to 2 folds, after stimulation. Comparison with 170 published pancreatic cancer proteins yielded an overlap of only 42 proteins.

CONCLUSIONS

Normal pancreatic juice contains multiple proteins related to many biological processes. Secretin alters the concentration but not the spectrum of these proteins. The pancreatic juice proteome of patients without pancreatic disease and that of patients with pancreatic cancer differ markedly.

Reproducibility of mass spectrometry based protein profiles for diagnosis of ovarian cancer across clinical studies: A systematic review

The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Identification of isocitrate dehydrogenase 1 as a potential diagnostic and prognostic biomarker for non-small cell lung cancer by proteomic analysis

Lung cancer is the leading cause of cancer-related death in the world. To explore tumor biomarkers for clinical application, two-dimensional fluorescence difference gel electrophoresis and subsequent MALDI-TOF/TOF mass spectrometry were performed to identify proteins differentially expressed in 12 pairs of lung squamous cell tumors and their corresponding normal tissues. A total of 28 nonredundant proteins were identified with significant alteration in lung tumors. The up-regulation of isocitrate dehydrogenase 1 (IDH1), superoxide dismutase 2, 14-3-3ε, and receptor of activated protein kinase C1 and the down-regulation of peroxiredoxin 2 in tumors were validated by RT-PCR and Western blot analysis in independent 15 pairs of samples. Increased IDH1 expression was further verified by the immunohistochemical study in extended 73 squamous cell carcinoma and 64 adenocarcinoma clinical samples. A correlation between IDH1 expression and poor overall survival of non-small cell lung cancer (NSCLC) patients was observed. Furthermore, ELISA analysis showed that the plasma level of IDH1 was significantly elevated in NSCLC patients compared with benign lung disease patients and healthy individuals. In addition, knockdown of IDH1 by RNA interference suppressed the proliferation of NSCLC cell line and decreased the growth of xenograft tumors in vivo. These observations suggested that IDH1, as a protein promoting tumor growth, could be used as a plasma biomarker for diagnosis and a histochemical biomarker for prognosis prediction of NSCLC.

Microscale functional cytomics for studying hematologic cancers

An important problem in translational cancer research is our limited ability to functionally characterize behaviors of primary patient cancer cells and associated stromal cell types, and relate mechanistic understanding to therapy selection. Functional analyses of primary samples face at least 3 major challenges: limited availability of primary samples for testing, paucity of functional information extracted from samples, and lack of functional methods accessible to many researchers. We developed a microscale cell culture platform that overcomes these limitations, especially for hematologic cancers. A key feature of the platform is the ability to compartmentalize small populations of adherent and nonadherent cells in controlled microenvironments that can better reflect physiological conditions and enable cell-cell interaction studies. Custom image analysis was developed to measure cell viability and protein subcellular localizations in single cells to provide insights into heterogeneity of cellular responses. We validated our platform by assessing viability and nuclear translocations of NF-κB and STAT3 in multiple myeloma cells exposed to different conditions, including cocultured bone marrow stromal cells. We further assessed its utility by analyzing NF-κB activation in a primary chronic lymphocytic leukemia patient sample. Our platform can be applied to myriad biological questions, enabling high-content functional cytomics of primary hematologic malignancies.

Effect of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry on identifying biomarkers of laryngeal carcinoma

The aim is to study the serum protein fingerprint of patients with laryngeal carcinoma (LC) and to screen for protein molecules closely related to LC during the onset and progression of the disease with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Serum samples from 68 patients with LC and 117 non-cancer control samples (75 healthy volunteers and 42 Vocal fold polyps). Q10 protein chips and PBSII-C protein chips reader (Ciphergen Biosystems Inc.) were used. The protein fingerprint expression of all the Serum samples and the resulting profiles between cancer and non-cancer groups were analyzed with Biomarker Wizard system. A group of proteomic peaks were detected. Three differently expressed potential biomarkers were identified with the relative molecular weights of 5,915, 6,440 and 9,190 Da. Among the three peaks, the one with m/z 6,440 was down-regulated, and the other two peaks with m/z 5,915 and 9,190 were up-regulated in LC. This diagnostic model could distinguish LC patients from controls with a sensitivity of 92.1% and a specificity of 91.9%. Moreover, blind test data showed a sensitivity of 86.7% and a specificity of 89.1%. The data suggested that SELDI technology could be used to screen proteins with altered expression levels in the serum of LC patients. These protein peaks were considered as specific serum biomarkers of LC and have the potential value for further investigation.

Proteomics research on muscle-invasive bladder transitional cell carcinoma

Background

Aimed to facilitate candidate biomarkers selection and improve network-based multi-target therapy, we perform comparative proteomics research on muscle-invasive bladder transitional cell carcinoma. Laser capture microdissection was used to harvest purified muscle-invasive bladder cancer cells and normal urothelial cells from 4 paired samples. Two-dimensional liquid chromatography tandem mass spectrometry was used to identify the proteome expression profile. The differential proteins were further analyzed using bioinformatics tools and compared with the published literature.

Results

A total of 885/890 proteins commonly appeared in 4 paired samples. 295/337 of the 488/493 proteins that specific expressed in tumor/normal cells own gene ontology (GO) cellular component annotation. Compared with the entire list of the international protein index (IPI), there are 42/45 GO terms exhibited as enriched and 9/5 exhibited as depleted, respectively. Several pathways exhibit significantly changes between cancer and normal cells, mainly including spliceosome, endocytosis, oxidative phosphorylation, etc. Finally, descriptive statistics show that the PI Distribution of candidate biomarkers have certain regularity.

Conclusions

The present study identified the proteome expression profile of muscle-invasive bladder cancer cells and normal urothelial cells, providing information for subcellular pattern research of cancer and offer candidate proteins for biomarker panel and network-based multi-target therapy.

Protein phosphorylation analysis in archival clinical cancer samples by shotgun and targeted proteomics approaches

Protein phosphorylation affects most eukaryotic cellular processes and its deregulation is considered a hallmark of cancer and other diseases. Phosphoproteomics may enable monitoring of altered signaling pathways as a means of stratifying tumors and facilitating the discovery of new drugs. Unfortunately, the development of molecular tests for clinical use is constrained by the limited availability of fresh frozen, clinically annotated samples. Here we report phosphopeptide analysis in human archival formalin-fixed, paraffin-embedded (FFPE) cancer samples based on immobilized metal affinity chromatography followed by liquid chromatography coupled with tandem mass spectrometry and selected reaction monitoring techniques. Our results indicate the equivalence of detectable phosphorylation rates in archival FFPE and fresh frozen tissues. Moreover, we demonstrate the applicability of targeted assays for phosphopeptide analysis in clinical archival FFPE samples, using an experimental workflow suitable for processing and analyzing large sample series. This work paves the way for the application of shotgun and targeted phosphoproteomics approaches in clinically relevant studies using archival clinical samples.

New serum biomarkers for detection of esophageal carcinoma using Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

PURPOSE

To screen for the potential protein biomarkers in serum for the diagnosis of esophageal carcinoma (EC) using proteomic fingerprint technology.

METHODS

Proteomic fingerprint technology combining magnetic beads with MALDI-TOF-MS was used to profile and compare the serum proteins from 78 patients with EC and 95 healthy blood donors. Proteomic patterns associated with EC were identified by Biomarker Patterns Software. Model of biomarkers was constructed and evaluated using the Biomarker Patterns Software.

RESULTS

A total of 60 discriminating m/z peaks were identified that were related to EC (P < 0.01). The model of biomarkers constructed by the Biomarker Patterns Software based on the four biomarkers (2049.6, 3936.5, 5339.9, and 13748.8 Da) generated excellent separation between the EC and control groups. The sensitivity was 92.5% and the specificity was 88%. Blind test data indicated a sensitivity of 89.5% and a specificity of 84.4%.

CONCLUSIONS

Biomarkers for EC can be discovered in serum by MALDI-TOF-MS combining the use of magnetic beads. The pattern of combined markers provides a powerful and reliable diagnostic method for EC with a high sensitivity and specificity.

Evaluation of changes in serum protein profiles during neoadjuvant chemotherapy in HER2-positive breast cancer using an LC-MALDI-TOF/MS procedure

Comparison of protein profiles of sera acquired before and after preoperative chemotherapy for breast cancer may reveal tumor markers that could be used to monitor tumor response. In this study, we analyzed pre- and post-chemotherapy protein profiles of sera from 39 HER2-postive breast cancer patients (n=78 samples) who received 6 months of preoperative chemotherapy using LC-MALDI-TOF/MS technology. We detected qualitative and quantitative differences in pair-wise comparison of pre- and post chemotherapy samples that were different in patients who achieved pathological complete response (pCR, n=21) compared with those with residual disease (n=18). We identified 2329 and 3152 peaks as differentially expressed in the pre-chemotherapy samples of the responders and non-responders. Comparison of matching pre- and post-chemotherapy samples identified 34 (32 decreased, two increased) and 304 peaks (157 decreased, 147 increased) that significantly changed (p<0.01, false discovery rate ≤ 20%) after treatment in responders and non-responders, respectively. The top 11 most significantly altered peptide peaks with the greatest change in intensity were positively identified. These corresponded to eight proteins including α-2-macroglobulin, complement 3, hemopexin, and serum amyloid P in the responder group and chains C and A of apolipoprotein A-I, hemopexin precursor, complement C, and amyloid P component in the non-responding groups. All proteins decreased after therapy, except chain C apolipoprotein A and hemopexin precursor that increased. These results suggest that changes in serum protein levels occur in response to chemotherapy and these changes partly appear different in patients who are highly sensitive to chemotherapy compared with those with lesser response.

MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of colorectal cancer

The aim of present study is to study the serum protein fingerprint of patients with colorectal cancer (CRC) and to screen protein molecules that are closely related to colorectal cancer during the onset and progression of the disease with Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Serum samples from 144 patients with CRC and 120 healthy volunteers were adopted in present study. Weak cation exchange (WCX) magnetic beads and PBSII-C protein chips reader (Ciphergen Biosystems Ins.) were used. The protein fingerprint expression of all the Serum samples and the resulted profiles between cancer and normal groups were analyzed with Biomarker Wizard system. Several proteomic peaks were detected and four potential biomarkers with different expression profiles were identified with their relative molecular weights of 2870.7 Da, 3084 Da, 9180.5 Da, and 13748.8 Da, respectively. Among the four proteins, two proteins with m/z 2870.7 and 3084 were down-regulated, and the other two with m/z 9180.5 and 13748.8 were up-regulated in serum samples from CRC patients. The present diagnostic model could distinguish CRC from healthy controls with the sensitivity of 92.85% and the specificity of 91.25%. Blind test data indicated a sensitivity of 86.95% and a specificity of 85%. The result suggested that MALDI technology could be used to screen critical proteins with differential expression in the serum of CRC patients. These differentially regulated proteins were considered as potential biomarkers for the patients with CRC in the serum and of the potential value for further investigation.

Identification of novel biomarker candidates by proteomic analysis of cerebrospinal fluid from patients with moyamoya disease using SELDI-TOF-MS

Background

Moyamoya disease (MMD) is an uncommon cerebrovascular condition with unknown etiology characterized by slowly progressive stenosis or occlusion of the bilateral internal carotid arteries associated with an abnormal vascular network. MMD is a major cause of stroke, specifically in the younger population. Diagnosis is based on only radiological features as no other clinical data are available. The purpose of this study was to identify novel biomarker candidate proteins differentially expressed in the cerebrospinal fluid (CSF) of patients with MMD using proteomic analysis.

Methods

For detection of biomarkers, CSF samples were obtained from 20 patients with MMD and 12 control patients. Mass spectral data were generated by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with an anion exchange chip in three different buffer conditions. After expression difference mapping was undertaken using the obtained protein profiles, a comparative analysis was performed.

Results

A statistically significant number of proteins (34) were recognized as single biomarker candidate proteins which were differentially detected in the CSF of patients with MMD, compared to the control patients (p < 0.05). All peak intensity profiles of the biomarker candidates underwent classification and regression tree (CART) analysis to produce prediction models. Two important biomarkers could successfully classify the patients with MMD and control patients.

Conclusions

In this study, several novel biomarker candidate proteins differentially expressed in the CSF of patients with MMD were identified by a recently developed proteomic approach. This is a pilot study of CSF proteomics for MMD using SELDI technology. These biomarker candidates have the potential to shed light on the underlying pathogenesis of MMD.

Mass spectrometry-based clinical proteomics: head-and-neck cancer biomarkers and drug-targets discovery

Mass spectrometry (MS)-based proteomics is a rapidly developing technology for both qualitative and quantitative analyses of proteins, and investigations into protein posttranslational modifications, subcellular localization, and interactions. Recent advancements in MS have made tremendous impact on the throughput and comprehensiveness of cancer proteomics, paving the way to unraveling deregulated cellular pathway networks in human malignancies. In turn, this knowledge is rapidly being translated into the discovery of novel potential cancer markers (PCMs) and targets for molecular therapeutics. Head-and-neck cancer is one of the most morbid human malignancies with an overall poor prognosis and severely compromised quality of life. Early detection and novel therapeutic strategies are urgently needed for more effective disease management. The characterizations of protein profiles of head-and-neck cancers and non-malignant tissues, with unprecedented sensitivity and precision, are providing technology platforms for identification of novel PCMs and drug targets. Importantly, low-abundance proteins are being identified and characterized, not only from the tumor tissues, but also from bodily fluids (plasma, saliva, and urine) in a high-throughput and unbiased manner. This review is a critical appraisal of recent advances in MS-based proteomic technologies and platforms for facilitating the discovery of biomarkers and novel drug targets in head-and-neck cancer. A major challenge in the discovery and verification of these cancer biomarkers is the typically limited availability of well-characterized and adequately stored clinical samples in tumor and sera banks, collected using recommended procedures, and with detailed information on clinical, pathological parameters, and follow-up. Most biomarker discovery studies use limited number of clinical samples and verification of cancer markers in large number of samples is beyond the scope of a single laboratory. The validation of these potential markers in large sample cohorts in multicentric studies is needed for their translation from the bench to the bedside.

Role of 14-3-3 proteins in tumorigenesis and tumor progression

14-3-3 proteins are a family of acidic proteins that are predominantly localized in the cytoplasm. They have been known to play a role in tumorigenesis and tumor progression by regulating tumor cell growth, survival, proliferation, migration, and apoptosis. Of all 14-3-3 proteins, 14-3-3σ has tumor-suppressor activity, while other members have tumor-promoting activity. In this paper, we briefly review the role of 14-3-3 proteins in tumor pathogenesis.

A novel preprocessing method using Hilbert Huang Transform for MALDI-TOF and SELDI-TOF mass spectrometry data

Motivation

Mass spectrometry is a high throughput, fast, and accurate method of protein analysis. Using the peaks detected in spectra, we can compare a normal group with a disease group. However, the spectrum is complicated by scale shifting and is also full of noise. Such shifting makes the spectra non-stationary and need to align before comparison. Consequently, the preprocessing of the mass data plays an important role during the analysis process. Noises in mass spectrometry data come in lots of different aspects and frequencies. A powerful data preprocessing method is needed for removing large amount of noises in mass spectrometry data.

Results

Hilbert-Huang Transformation is a non-stationary transformation used in signal processing. We provide a novel algorithm for preprocessing that can deal with MALDI and SELDI spectra. We use the Hilbert-Huang Transformation to decompose the spectrum and filter-out the very high frequencies and very low frequencies signal. We think the noise in mass spectrometry comes from many sources and some of the noises can be removed by analysis of signal frequence domain. Since the protein in the spectrum is expected to be a unique peak, its frequence domain should be in the middle part of frequence domain and will not be removed. The results show that HHT, when used for preprocessing, is generally better than other preprocessing methods. The approach not only is able to detect peaks successfully, but HHT has the advantage of denoising spectra efficiently, especially when the data is complex. The drawback of HHT is that this approach takes much longer for the processing than the wavlet and traditional methods. However, the processing time is still manageable and is worth the wait to obtain high quality data.

Proteomic strategies and challenges in tumor metastasis research

The rapidly evolving field of proteomics offers new approaches to understanding the pathogenesis of cancer and metastatic disease. Although numerous tumor markers have been identified with different genomic methods in the past, most are either not specific or sensitive enough to be used in routine clinical setting. The rationale for proteomic profiling is based on the fact that proteins represent the dynamic state of the cells, reflecting pathophysiological changes in the disease more accurately than genomic and epigenetic alterations. Emerging proteomic techniques allow simultaneous assessment of a large number of proteins at one time. The study of protein profiles in complex systems, such as plasma, serum or tissues of cancer patients is likely to become valuable for monitoring the response of patients during treatment or for detecting recurrence of the disease.

Serum amyloid A is elevated in the serum of lung cancer patients with poor prognosis

Background:

Lung cancer is known as the top cancer killer in most developed countries. However, there is currently no promising diagnostic or prognostic biomarker for lung cancer. This study aims to discover non-invasive differential markers in the serum of lung cancer patients, to determine the protein identity of the candidate biomarker(s), and to investigate any clinical implication of the biomarker(s) concerned.

Methods:

Blood specimens were collected from 154 pre-operative patients with lung cancer and 35 healthy blood donors with no evidence of lung cancer. Fractionated serum samples were processed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (MS). Candidate biomarker was identified using sodium dodecyl sulphate polyacrylamide gel electrophoresis and tryptic digestion followed by tandem MS fragmentation analysis, which was subsequently validated with immunoassay.

Results:

A differential protein with m/z 11.6 kDa was detected and identified as an isoform of human serum amyloid A (SAA). It was significantly increased by 1822% in lung cancer patients when compared with the healthy controls, which gave an area under the receiver operator characteristic curve of 0.88. In addition, the protein was also significantly elevated by 77% in lung cancer patients with survival <5 years when compared with patients with survival ⩾5 years.

Conclusion:

There are several functions of the SAA protein, described in the context of inflammation, that are compatible with the mechanism of tumour invasion and metastasis. Our study not only detected increased SAA level in the serum of lung cancer patients but also identified that elevated SAA level may be a non-invasive biomarker useful for the prediction of lung cancer prognosis.

Loss of collapsin response mediator Protein1, as detected by iTRAQ analysis, promotes invasion of human gliomas expressing mutant EGFRvIII

Glioblastoma multiforme (GBM) is the most common and lethal primary human brain tumor. GBMs are characterized by a variety of genetic alterations, among which oncogenic mutations of epidermal growth factor receptor (EGFRvIII) is most common. GBMs harboring EGFRvIII have increased proliferation and invasive characteristics versus those expressing wild-type (wt) EGFR. To identify the molecular basis of this increased tumorgenic phenotype, we used iTRAQ-labeling differential proteomic analysis. Among several differentially expressed proteins, we selected CRMP1, a protein implicated in cellular invasion that was markedly decreased in GBMs expressing EGFRvIII, for further study. The differential expression of CRMP1 was confirmed in a panel of human GBM cell lines and operative specimens that express wtEGFR or mutant EGFRvIII by quantitative real-time PCR, Western blot, and immunohistochemical analysis. In human GBM samples, decreased expression of CRMP1 correlated with EGFRvIII positivity. Knockdown of CRMP1 by siRNA resulted in increased invasion of wtEGFR expressing human GBM cells (U87 and U373) to those found in isogenic GBM cells. Exogenous expression of EGFRvIII in these wtEGFR-expressing GBM cells promoted their ability to invade and was accompanied by decreased expression of CRMP1. Rescuing CRMP1 expression decreased invasion of the EGFRvIII-expressing GBM cells by tilting the balance between Rac and Rho. Collectively, these results show that the loss of CRMP1 contribute to the increased invasive phenotype of human GBMs expressing mutant EGFRvIII.

Application of proteomics in the study of digestive system tumors

Proteomics is the study of all proteins present in a given organism, tissue, cell, and even organelle. Two-dimensional gel electrophoresis and mass spectrography are two core technologies in proteomics. Proteomic studies can help us understand the life as a whole. As a new platform for tumor research, proteomics is of important significance for early diagnosis, biomarker discovery, therapy and discovery of novel drug targets for esophageal carcinoma, gastric cancer, liver cancer, pancreatic cancer and colorectal cancer. Thus, it has potential broad application prospects.

Targeting stem cells-clinical implications for cancer therapy

Cancer stem cells (CSC), also called tumor initiating cells (TIC), are considered to be the origin of replicating malignant tumor cells in a variety of human cancers. Their presence in the tumor may herald malignancy potential, mediate resistance to conventional chemotherapy or radiotherapy, and confer poor survival outcomes. Thus, CSC may serve as critical cellular targets for treatment. The ability to therapeutically target CSC hinges upon identifying their unique cell surface markers and the underlying survival signaling pathways. While accumulating evidence suggests cell-surface antigens (such as CD44, CD133) as CSC markers for several tumor tissues, emerging clinical needs exist for the identification of new markers to completely separate CSC from normal stem cells. Recent studies have demonstrated the critical role of the tumor suppressor PTEN/PI3 kinase pathway in regulating TIC in leukemia, brain, and intestinal tissues. The successful eradication of tumors by therapies targeting CSC will require an in-depth understanding of the molecular mechanisms governing CSC self renewal, differentiation, and escape from conventional therapy. Here we review recent progress from brain tumor and intestinal stem cell research with a focus on the PTEN-Akt-Wnt pathway, and how the components of CSC pathways may serve as biomarkers for diagnosis, prognosis, and therapeutics.

Genomic and oncoproteomic advances in detection and treatment of colorectal cancer

Aims

We will examine the latest advances in genomic and proteomic laboratory technology. Through an extensive literature review we aim to critically appraise those studies which have utilized these latest technologies and ascertain their potential to identify clinically useful biomarkers.

Methods

An extensive review of the literature was carried out in both online medical journals and through the Royal College of Surgeons in Ireland library.

Results

Laboratory technology has advanced in the fields of genomics and oncoproteomics. Gene expression profiling with DNA microarray technology has allowed us to begin genetic profiling of colorectal cancer tissue. The response to chemotherapy can differ amongst individual tumors. For the first time researchers have begun to isolate and identify the genes responsible. New laboratory techniques allow us to isolate proteins preferentially expressed in colorectal cancer tissue. This could potentially lead to identification of a clinically useful protein biomarker in colorectal cancer screening and treatment.

Conclusion

If a set of discriminating genes could be used for characterization and prediction of chemotherapeutic response, an individualized tailored therapeutic regime could become the standard of care for those undergoing systemic treatment for colorectal cancer. New laboratory techniques of protein identification may eventually allow identification of a clinically useful biomarker that could be used for screening and treatment. At present however, both expression of different gene signatures and isolation of various protein peaks has been limited by study size. Independent multi-centre correlation of results with larger sample sizes is needed to allow translation into clinical practice.