Gaining insights into cancer biology through exploration of the cancer secretome using proteomic and bioinformatic tools

Introduction: Tumor-associated proteins released by cancer cells and by tumor stroma cells, referred as ‘cancer secretome’, represent a valuable resource for discovery of potential cancer biomarkers. The last decade was marked by a great increase in number of studies focused on various aspects of cancer secretome including, composition and identification of components externalized by malignant cells and by the components of tumor microenvironment.

Areas covered: Here, we provide an overview of achievements in the proteomic analysis of the cancer secretome, elicited through the tumor-associated interstitial fluid recovered from malignant tissues ex vivo or the protein component of conditioned media obtained from cultured cancer cells in vitro. We summarize various bioinformatic tools and approaches and critically appraise their outcomes, focusing on problems and challenges that arise when applied for the analysis of cancer secretomic databases.

Expert commentary: Recent achievements in the omics- analysis of structural and metabolic aspects of altered cancer secretome contribute greatly to the various hallmarks of cancer including the identification of clinically significant biomarkers and potential targets for therapeutic intervention.     

miR-449a在乳腺癌组织中的表达及生物信息学分析

探讨miR-449a在乳腺癌组织中的表达及其在乳腺癌发生发展过程中的作用。利用实时荧光定量PCR检测83例乳腺癌和癌旁组织中miR-449a的相对表达量,发现miR-449a在乳腺癌组织中的表达水平高于癌旁组织,并与肿瘤组织学级别、大小、雌激素受体状态和孕激素受体状态有关(P＜0.05)。miR-449a在三阴性乳腺癌中的表达水平显著低于管腔型。使用Kaplan-Meier Plotter数据库进行生存分析,结果显示在三阴性乳腺癌中miR-449a低表达组总生存率显著低于高表达组,而在管腔B型乳腺癌中miR-449a高表达组总生存率显著降低(P＜0.05)。利用ENCORI数据库预测得到靶基因186个,通过metascape数据库进行富集分析,发现其功能涉及间充质细胞分化、细胞迁移、内分泌抵抗、粘附连接、肌动蛋白细胞骨架调节以及NOTCH、TGF-β、Wnt、PI3K-Akt等介导的信号通路。通过string数据库进行蛋白互作网络分析,并使用Cytoscape软件筛选出由NOTCH1、JAG1和cyclin D1等蛋白构成的关键子网络。应用ENCORI数据库分析miR-449a与NOTCH途径靶基因的相关性,发现miR-449a与NOTCH1在乳腺癌组织中的表达呈负相关。本研究结果表明miR-449a在乳腺癌组织中的表达具有明显的异质性,可通过影响多种信号通路参与肿瘤发展过程,调控NOTCH信号通路可能是其在乳腺癌中的重要机制。     

Increasing information from shotgun proteomic data by accounting for misassigned precursor ion masses

Although mass spectrometers are capable of providing high mass accuracy data, assignment of true monoisotopic precursor ion mass is complicated during data-dependent ion selection for LC-MS/MS analysis of complex mixtures. The complication arises when chromatographic peak widths for a given analyte exceed the time required to acquire a precursor ion mass spectrum. The result is that many measured monoisotopic masses are misassigned due to calculation from a single mass spectrum with poor ion statistics based on only a fraction of the total available ions for a given analyte. Such data in turn produces errors in automated database searches, where precursor m/z value is one search parameter. We propose here a postacquisition approach to correct misassigned monoisotopic m/z values that involves peak detection over the entire elution profile and correction of the precursor ion monoisotopic mass. As a result of using this approach to reprocess shotgun proteomic data we increased peptide sequence assignments by 10% while reducing the estimated false positive ratio from 1 to 0.2%. We also show that 4% of the salvaged identifications may be accounted for by correction of mixed tandem mass spectra resulting from fragmentation of multiple peptides simultaneously, a situation which we refer to as accidental CID.     

Unravelling the biology of chromatin in health and cancer using proteomic approaches

Introduction: Chromatin remodeling complexes play important roles in the control of genome regulation in both normal and diseased states, and are therefore critical components for the regulation of epigenetic states in cells. Given the role epigenetics plays in cancer, for example, chromatin remodeling complexes are routinely targeted for therapeutic intervention.

Areas covered: Protein mass spectrometry and proteomics are powerful technologies used to study and understand chromatin remodeling. While impressive progress has been made in this area, there remain significant challenges in the application of proteomic technologies to the study of chromatin remodeling. As parts of large multi-subunit complexes that can be heavily modified with dynamic post-translational modifications, challenges in the study of chromatin remodeling complexes include defining the content, determining the regulation, and studying the dynamics of the complexes under different cellular states.

Expert commentary: Impwortant considerations in the study of chromatin remodeling complexes include the complexity of sample preparation, the choice of proteomic methods for the analysis of samples, and data analysis challenges. Continued research in these three areas promise to yield even greater insights into the biology of chromatin remodeling and epigenetics and the dynamics of these systems in human health and cancer.     

Ovarian cancer detection by logical analysis of proteomic data

A new type of efficient and accurate proteomic ovarian cancer diagnosis systems is proposed. The system is developed using the combinatorics and optimization-based methodology of logical analysis of data (LAD) to the Ovarian Dataset 8-7-02 (http://clinicalproteomics.steem.com), which updates the one used by Petricoin et al. in The Lancet 2002, 359, 572-577. This mass spectroscopy-generated dataset contains expression profiles of 15 154 peptides defined by their mass/charge ratios (m/z) in serum of 162 ovarian cancer and 91 control cases. Several fully reproducible models using only 7-9 of the 15 154 peptides were constructed, and shown in multiple cross-validation tests (k-folding and leave-one-out) to provide sensitivities and specificities of up to 100%. A special diagnostic system for stage I ovarian cancer patients is shown to have similarly high accuracy. Other results: (i) expressions of peptides with relatively low m/z values in the dataset are shown to be better at distinguishing ovarian cancer cases from controls than those with higher m/z values; (ii) two large groups of patients with a high degree of similarities among their formal (mathematical) profiles are detected; (iii) several peptides with a blocking or promoting effect on ovarian cancer are identified.     

Comparative proteomic analysis of cysteine oxidation in colorectal cancer patients

Oxidative stress promotes damage to cellular proteins, lipids, membranes and DNA, and plays a key role in the development of cancer. Reactive oxygen species disrupt redox homeostasis and promote tumor formation by initiating aberrant activation of signaling pathways that lead to tumorigenesis. We used shotgun proteomics to identify proteins containing oxidation-sensitive cysteines in tissue specimens from colorectal cancer patients. We then compared the patterns of cysteine oxidation in the membrane fractions between the tumor and non-tumor tissues. Using nano-UPLC-MS^E proteomics, we identified 31 proteins containing 37 oxidation-sensitive cysteines. These proteins were observed with IAM-binding cysteines in non-tumoral region more than tumoral region of CRC patients. Then using the Ingenuity pathway program, we evaluated the cellular canonical networks connecting those proteins. Within the networks, proteins with multiple connections were related with organ morphology, cellular metabolism, and various disorders. We have thus identified networks of proteins whose redox status is altered by oxidative stress, perhaps leading to changes in cellular functionality that promotes tumorigenesis.     

Chemical proteomic and bioinformatic strategies for the identification and quantification of vascular antigens in cancer

One avenue towards the development of more selective anti-cancer drugs consists in the targeted delivery of bioactive molecules to the tumor environment by means of binding molecules specific to tumor-associated markers. In this context, the targeted delivery of therapeutic agents to newly-formed blood vessels (“vascular targeting”) is particularly attractive, because of the dependence of tumors on new blood vessels to sustain growth and invasion, and because of the accessibility of neo-vascular structures for therapeutic agents injected intravenously. Ligand-based vascular targeting strategies crucially rely on good-quality vascular tumor markers. Here we describe a number of established technologies for the enrichment of accessible vascular proteins based on the isolation of glycoproteins, the in vivo coating of accessible cell surfaces with colloidal silica and the in vivo perfusion with reactive ester derivatives of biotin. Label-free as well as isotopic labeling based strategies for the subsequent MS-based protein quantification are outlined. Finally, bioinformatic workflows for protein quantification are depicted aiming at assisting in the evaluation of appropriate strategies for individual projects. This review gives an overview of current chemical proteomic strategies for the enrichment and quantification of the accessible vascular proteome and helps in selecting bioinformatic strategies for data analysis and validation.     

A prostate cancer tissue specific spectral library for targeted proteomic analysis

Prostate cancer is the most common cancer in males worldwide. Mass spectrometry-based targeted proteomics has demonstrated great potential in quantifying proteins from formalin-fixed paraffin-embedded (FFPE) and (fresh) frozen biopsy tissues. Here we provide a comprehensive tissue-specific spectral library for targeted proteomic analysis of prostate tissue samples. Benign and malignant FFPE prostate tissue samples were processed into peptide samples by pressure cycling technology (PCT)-assisted sample preparation, and fractionated with high-pH reversed phase liquid chromatography (RPLC). Based on data-dependent acquisition (DDA) MS analysis using a TripleTOF 6600, we built a library containing 108,533 precursors, 84,198 peptides and 9384 unique proteins (1% FDR). The applicability of the library was demonstrated in prostate specimens.     

Systems biology and proteomic analysis of cerebral cavernous malformation

Cerebral cavernous malformations (CCM) are vascular anomalies caused by mutations in genes encoding KRIT1, OSM and PDCD10 proteins causing hemorrhagic stroke. We examine proteomic change of loss of CCM gene expression. Using human umbilical vein endothelial cells, label-free differential protein expression analysis with multidimensional liquid chromatography/tandem mass spectrometry was applied to three CCM protein knockdown cell lines and two control cell lines: ProteomeXchange identifier PXD000362. Principle component and cluster analyses were used to examine the differentially expressed proteins associated with CCM. The results from the five cell lines revealed 290 and 192 differentially expressed proteins (p < 0.005 and p < 0.001, respectively). Most commonly affected proteins were cytoskeleton-associated proteins, in particular myosin-9. Canonical genetic pathway analysis suggests that CCM may be a result of defective cell–cell interaction through dysregulation of cytoskeletal associated proteins. Conclusion: The work explores signaling pathways that may elucidate early detection and novel therapy for CCM.     

Clarifying the signal network of salvianolic acid B using proteomic assay and bioinformatic analysis

Salvianolic acid B (SB) is a natural compound with protective effect against ischemia-reperfusion heart injury. However, the signal network of SB including both direct target proteins and downstream signal-related proteins has not been clarified. In the present study, epidermal growth factor receptor (EGFR) was predicted to be the most possible direct protein target of SB by INVDOCK, a ligand-protein inverse-docking algorithm. Possible signal-related proteins of SB in H9C2 cells, including both under normal condition and under ischemia-reperfusion injury, were searched using 2-DE analysis. Totally, 14 signal-related proteins were found. Finally, signal network from EGFR to the signal-related proteins was established using bioinformatic analysis. Interestingly, 9 of the 14 signal-related proteins could be included in a network together with EGFR through direct interaction or only one intermediate partner. The signal cascade from EGFR to heat shock protein 27 (HSP27) and mitofilin (IMMT, inner membrane mitochondrial protein) might be the most important cascade. The signal network was certified by measuring the binding affinity of SB to EGFR in vitro, the effect of SB on internalization and phosphorylation of EGFR, the effect of SB on viability and proliferation of H9C2 cells, and the expression of inner membrane mitochondrial protein in the presence of EGFR inhibitor AG 1478.     

What can proteomic analyses contribute to understanding the molecular biology and clinical behavior of prostate cancer?

Identifying the proteins and their complex interactions that promote and/or sustain the aggressive malignant phenotype is essential for understanding key effectors of the molecular biology of prostate cancer. This is also essential for development of new clinical applications. A variety of proteomic techniques, ranging from mass spectrometry to new methods of multiplexing protein identification, have great potential for rapidly achieving these goals. However, in order to obtain meaningful results, these techniques must be applied within the context of our knowledge of the heterogeneity of prostate tissues and tumors, the impact of specimen processing on both the quality and quantity of proteins detected and a thorough understanding of prostate cell biology. Collaboration between the protein chemist and the prostate cell biologist will expedite progress in this important field.     

Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data

The histone octamer induced bending of DNA into the super-helix structure in nucleosome core particle, is very unique and vital for DNA packing into chromatin. We collected 48 nucleosome crystal structures from PDB and applied a multivariate analysis on the nucleosome structural data. Based on the anisotropic nature of DNA structure, a principal conformational subspace (PCS) is derived from multiple properties to represent the most significant variances of nucleosome DNA structures. The coupling of base pair-oriented parameters with sugar phosphate backbone parameters presented in principal dimensionalities reveals two main deformation modes that have supplemented the existing physical model. By using sequence alignment-based statistics, a positiondependent conformational map for the super-helical DNA path is established. The result shows that the crystal structures of nucleosome DNA have much consistency in position-specific structural variations and certain periodicity is found to exist in these variations. Thus, the positions with obvious deformation patterns along the DNA path in nucleosome core particle are relatively conservative from the perspective of statistics.     

Comprehensive proteomic analysis of Penicillium verrucosum

Mass spectrometric identification of proteins in species lacking validated sequence information is a major problem in veterinary science. In the present study, we used ochratoxin A producing Penicillium verrucosum to identify and quantitatively analyze proteins of an organism with yet no protein information available. The work presented here aimed to provide a comprehensive protein identification of P. verrucosum using shotgun proteomics. We were able to identify 3631 proteins in an “ab initio” translated database from DNA sequences of P. verrucosum. Additionally, a sequential window acquisition of all theoretical fragment‐ion spectra analysis was done to find differentially regulated proteins at two different time points of the growth curve. We compared the proteins at the beginning (day 3) and at the end of the log phase (day 12).     

Quantitative proteomic profiling of pancreatic cancer juice

Pancreatic juice is an exceptionally rich source of cancer-specific proteins shed from cancerous ductal cells into the pancreatic juice. Quantitative proteomic analysis of the proteins specific to pancreatic cancer juice has not previously been reported. We used isotope-code affinity tag (ICAT) technology and MS/MS to perform quantitative protein profiling of pancreatic juice from pancreatic cancer patients and normal controls. ICAT technology coupled with MS/MS allows the systematic study of the proteome and measures the protein abundance in pancreatic juice with the potential for development of biomarkers. A total of 105 proteins were identified and quantified in the pancreatic juice from a pancreatic cancer patient, of which 30 proteins showed abundance changes of at least twofold in pancreatic cancer juice compared to normal controls. Many of these proteins have been externally validated. This is the first comprehensive study of the pancreatic juice proteome by quantitative global protein profiling, and the study reveals numerous proteins that are shown for the first time to be associated with pancreatic cancer, providing candidates for diagnostic biomarkers. One of the identified proteins, insulin-like growth factor binding protein-2 was further validated by Western blotting to be elevated in pancreatic cancer juice and overexpressed in pancreatic cancer tissue.     

Bioinformatics analysis of mass spectrometry-based proteomics data sets

Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.     

拟南芥LEC同源基因的生物信息学分析

用生物信息学方法对拟南芥叶状子叶(LEC)基因的核苷酸序列以及推导氨基酸序列的组成、功能结构域、亲水性等进行了分析。结果表明,拟南芥的LEC蛋白为位于细胞核中的亲水性不稳定蛋白;通过对LEC蛋白的功能结构域的分析发现,LEC1和L1L蛋白中高度保守的区域即为CBF-NFYB-HMF结构域,LEC2和FUSCA3蛋白中高度保守的区域为B3结构域。     

The proteomic to biology inference,a frequently overlooked concern in the interpretation of proteomic data: A plea for functional validation

Proteomics will celebrate its 20th year in 2014. In this relatively short period of time, it has invaded most areas of biology and its use will probably continue to spread in the future. These two decades have seen a considerable increase in the speed and sensitivity of protein identification and characterization, even from complex samples. Indeed, what was a challenge twenty years ago is now little more than a daily routine. Although not completely over, the technological challenge now makes room to another challenge, which is the best possible appraisal and exploitation of proteomic data to draw the best possible conclusions from a biological point of view. The point developed in this paper is that proteomic data are almost always fragmentary. This means in turn that although better than an mRNA level, a protein level is often insufficient to draw a valid conclusion from a biological point of view, especially in a world where PTMs play such an important role. This means in turn that transformation of proteomic data into biological data requires an important intermediate layer of functional validation, i.e. not merely the confirmation of protein abundance changes by other methods, but a functional appraisal of the biological consequences of the protein level changes highlighted by the proteomic screens.     

The emerging role of ICP-MS in proteomic analysis

Quantitative proteomics and absolute determination of proteins are topics of fast growing interest, since only the quantity of proteins or changes in their abundance reflect the status and extent of changes of a given biological system. Quantification of the desired proteins has been carried out by molecule specific MS techniques, but relative quantifications are commonplace so far even resorting to stable isotope labelling techniques such as ICAT and SILAC. In the last decade the idea of using element-selective mass spectrometric detection (e.g. ICP-MS instruments) to achieve absolute quantification has been realised and ICP-MS stands now as a new tool in the field of quantitative proteomics.In this review the emerging role of ICP-MS in protein and proteomic analysis is highlighted. The potential of ICP-MS methods and strategies for screening multiple heteroatoms (e.g. S, P, Se, metals) in proteins and their mixtures and extraordinary capabilities to tackle the problem of absolute protein quantifications, via heteroatom determinations, are discussed and illustrated. New avenues are also open derived from the use of ICP-MS for precise isotope abundance measurements in polyisotopic heteroatoms. The “heteroatom (isotope)-tagged proteomics” concept is focused on the use of naturally present element tags and also extended to any protein by resorting to bioconjugation reactions (i.e. labelling sought proteins and peptides with ICP-MS detectable heteroatoms). A major point of this review is displaying the possibilities of using a “hard” ion source, the ICP, to complement well-established “soft” ion sources for mass spectrometry to tackle present proteomic analysis.     

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.