蛋白质组学在寻找肝癌生物标记物中的研究进展

蛋白质组学技术的快速发展和广泛应用,为寻找特异性肝癌标记物提供了值得探索的有效手段。近年来在肝癌血清、组织和细胞系中,许多有潜在价值的新标记物相继被发现。     

蛋白质组学在寻找肝癌生物标记物中的研究进展

蛋白质组学技术的快速发展和广泛应用,为寻找特异性肝癌标记物提供了值得探索的有效手段。近年来在肝癌血清、组织和细胞系中,许多有潜在价值的新标记物相继被发现。     

Salivary proteomics for oral cancer biomarker discovery

PURPOSE: This study aims to explore the presence of informative protein biomarkers in the human saliva proteome and to evaluate their potential for detection of oral squamous cell carcinoma (OSCC). EXPERIMENTAL DESIGN: Whole saliva samples were collected from patients (n = 64) with OSCC and matched healthy subjects (n = 64). The proteins in pooled whole saliva samples of patients with OSCC (n = 16) and matched healthy subjects (n = 16) were profiled using shotgun proteomics based on C4 reversed-phase liquid chromatography for prefractionation, capillary reversed-phase liquid chromatography with quadruple time-of-flight mass spectrometry, and Mascot sequence database searching. Immunoassays were used for validation of the candidate biomarkers on a new group of OSCC (n = 48) and matched healthy subjects (n = 48). Receiver operating characteristic analysis was exploited to evaluate the diagnostic value of discovered candidate biomarkers for OSCC. RESULTS: Subtractive proteomics revealed several salivary proteins at differential levels between the OSCC patients and matched control subjects. Five candidate biomarkers were successfully validated using immunoassays on an independent set of OSCC patients and matched healthy subjects. The combination of these candidate biomarkers yielded a receiver operating characteristic value of 93%, sensitivity of 90%, and specificity of 83% in detecting OSCC. CONCLUSION: Patient-based saliva proteomics is a promising approach to searching for OSCC biomarkers. The discovery of these new targets may lead to a simple clinical tool for the noninvasive diagnosis of oral cancer. Long-term longitudinal studies with large populations of individuals with oral cancer and those who are at high risk of developing oral cancer are needed to validate these potential biomarkers.     

Tissue culture-based breast cancer biomarker discovery platform

Current cancer biomarkers suffer from low diagnostic sensitivity and specificity and have not yet made a major impact on reducing cancer burden. Proteomic methods based on mass spectrometry have matured significantly over the past few years and hold promise to deliver candidate markers for diagnosis, prognosis or monitoring therapeutic response. Because of the complex nature of biological fluids such as plasma, biomarker discovery efforts using proteomics have not as yet delivered any novel tumor markers. Recently, there has been a rise in the number of publications utilizing a cell culture-based model of cancer to identify novel candidate tumor markers. The secretome of cancer cell lines constitutes an important class of proteins that can act locally and systemically in the body. Secreted proteins, in addition to serving as serological markers, play a central role in physiology and pathophysiology. In this review, we focus on the proteomics of breast cancer and the different strategies to mine for biomarkers, with particular emphasis on a cell culture-based model developed in our laboratory.     

Metabolomics in diagnosis and biomarker discovery of colorectal cancer

Colorectal cancer (CRC), a major public health concern, is the second leading cause of cancer death in developed countries. There is a need for better preventive strategies to improve the patient outcome that is substantially influenced by cancer stage at the time of diagnosis. Patients with early stage colorectal have a significant higher 5-year survival rates compared to patients diagnosed at late stage. Although traditional colonoscopy remains the effective means to diagnose CRC, this approach generally suffers from poor patient compliance. Thus, it is important to develop more effective methods for early diagnosis of this disease process, also there is an urgent need for biomarkers to diagnose CRC, assess disease severity, and prognosticate course. Increasing availability of high-throughput methodologies open up new possibilities for screening new potential candidates for identifying biomarkers. Fortunately, metabolomics, the study of all metabolites produced in the body, considered most closely related to a patient’s phenotype, can provide clinically useful biomarkers applied in CRC, and may now open new avenues for diagnostics. It has a largely untapped potential in the field of oncology, through the analysis of the cancer metabolome to identify marker metabolites defined here as surrogate indicators of physiological or pathophysiological states. In this review we take a closer look at the metabolomics used within the field of colorectal cancer. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader.     

Clinical proteomics for cancer biomarker discovery and therapeutic targeting

As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the "software" of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the "wiring diagram" of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.     

Novel technologies for cancer biomarker discovery: humoral proteomics

The repertoires of serum autoantibodies differ between healthy people and cancer patients. While in healthy individuals these autoantibodies are directed against a limited number of self-proteins, in cancer patients the antibody repertoires are much further expanded with a wider range of reactivities against other proteins. Although cancer patients clearly mount humoral immune responses, they are not very effective in preventing the progression of the disease. However, the implication from the presence of these new and abnormal antibody specificities relates to their potential as novel tools for early detection before clinical manifestations. Proteomics technologies, with their unique ability to identify both tumor antigens and their cognate serum autoantibodies, hold great promise in facilitating the development of early detection kits and possibly also as conduits for the isolation of tumor antigens for immunotherapy.     

Genomic and post-genomic approaches to cancer biomarker discovery - microarray standards at last

The advent of microarray technology has enabled oncologists to investigate the expression of thousands of genes on the genetic basis of cancer. Gene-expression profiling studies have provided a molecular classification of cancer into clinically relevant subtypes, new tools to predict disease recurrence and response to different treatments. Analyzing these data can often be a quagmire, with endless discussion as to what the appropriate statistical analyses for any given experiment might be, while raising questions about the role of these techniques in clinical practice and patient management. For the analysis of data, many different methods and new computational algorithms have evolved. Here we describe state-of-the-art of gene-expression studies in clinical cancer, and consider both their current limitations and future promise.     

PICan: An integromics framework for dynamic cancer biomarker discovery

Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high‐throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.     

Metabolomics in biomarker discovery: future uses for cancer prevention

Metabolomics is the systematic study of small-molecular-weight substances in cells, tissues and/or whole organisms as influenced by multiple factors including genetics, diet, lifestyle and pharmaceutical interventions. These substances may directly or indirectly interact with molecular targets and thereby influence the risk and complications associated with various diseases, including cancer. Since the interaction between metabolites and specific targets is dynamic, knowledge regarding genetics, susceptibility factors, timing, and degree of exposure to an agent (drug or food component) is fundamental to understanding the metabolome and its potential use for predicting and preventing early phenotypic changes. The future of metabolomics rests with its ability to monitor subtle changes in the metabolome that occur prior to the detection of a gross phenotypic change reflecting disease. The integrated analysis of metabolomics and other 'omics' may provide more sensitive ways to detect changes related to disease and discover novel biomarkers. Knowledge regarding these multivariant characteristics is critical for establishing validated and predictive metabolomic models for cancer prevention. Understanding the metabolome will not only provide insights into the critical sites of regulation in health promotion, but will also assist in identifying intermediate or surrogate cancer biomarkers for establishing preemptive/preventative or therapeutic approaches for health. While unraveling the metabolome will not be simple, the societal implications are enormous.     

New technologies in cancer. Protein microarrays for biomarker discovery

The emergence of proteomic techniques and methodological approaches to study disease has kindled the quest for new biomarkers. Thus, the use of protein microarrays has surged as a powerful tool for large-scale testing of biological samples. In this mini-review, we will discuss the application of protein microarray technologies that offer unique opportunities to find novel biomarkers.     

Hepatocyte growth factor/MET in cancer progression and biomarker discovery

Signaling driven by hepatocyte growth factor (HGF) and MET receptor facilitates conspicuous biological responses such as epithelial cell migration, 3‐D morphogenesis, and survival. The dynamic migration and promotion of cell survival induced by MET activation are bases for invasion–metastasis and resistance, respectively, against targeted drugs in cancers. Recent studies indicated that MET in tumor‐derived exosomes facilitates metastatic niche formation and metastasis in malignant melanoma. In lung cancer, gene amplification‐induced MET activation and ligand‐dependent MET activation in an autocrine/paracrine manner are causes for resistance to epidermal growth factor receptor tyrosine kinase inhibitors and anaplastic lymphoma kinase inhibitors. Hepatocyte growth factor secreted in the tumor microenvironment contributes to the innate and acquired resistance to RAF inhibitors. Changes in serum/plasma HGF, soluble MET (sMET), and phospho‐MET have been confirmed to be associated with disease progression, metastasis, therapy response, and survival. Higher serum/plasma HGF levels are associated with therapy resistance and/or metastasis, while lower HGF levels are associated with progression‐free survival and overall survival after treatment with targeted drugs in lung cancer, gastric cancer, colon cancer, and malignant melanoma. Urinary sMET levels in patients with bladder cancer are higher than those in patients without bladder cancer and associated with disease progression. Some of the multi‐kinase inhibitors that target MET have received regulatory approval, whereas none of the selective HGF‐MET inhibitors have shown efficacy in phase III clinical trials. Validation of the HGF‐MET pathway as a critical driver in cancer development/progression and utilization of appropriate biomarkers are key to development and approval of HGF‐MET inhibitors for clinical use.     

Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling

Background:

Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues.

Methods:

Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis.

Results:

A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways.

Conclusions:

Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients.     

Gene expression profiling of sequential metastatic biopsies for biomarker discovery in breast cancer

The feasibility of longitudinal metastatic biopsies for gene expression profiling in breast cancer is unexplored. Dynamic changes in gene expression can potentially predict efficacy of targeted cancer drugs.Patients enrolled in a phase III trial of metastatic breast cancer with docetaxel monotherapy versus combination of docetaxel + sunitinib were offered to participate in a translational substudy comprising longitudinal fine needle aspiration biopsies and Positron Emission Tomography imaging before (T1) and two weeks after start of treatment (T2). Aspirated tumor material was used for microarray analysis, and treatment‐induced changes (T2 versus T1) in gene expression and standardized uptake values (SUV) were investigated and correlated to clinical outcome measures.Gene expression profiling yielded high‐quality data at both time points in 14/18 patients. Unsupervised clustering revealed specific patterns of changes caused by monotherapy vs. combination therapy (p = 0.021, Fisher''s exact test). A therapy‐induced reduction of known proliferation and hypoxia metagene scores was prominent in the combination arm. Changes in a previously reported hypoxia metagene score were strongly correlated to the objective responses seen by conventional radiology assessments after 6 weeks in the combination arm, Spearman''s ρ = 1 (p = 0.017) but not in monotherapy, ρ = −0.029 (p = 1). Similarly, the Predictor Analysis of Microarrays 50 (PAM50) proliferation metagene correlated to tumor changes merely in the combination arm at 6 and 12 weeks (ρ = 0.900, p = 0.083 and ρ = 1, p = 0.017 respectively). Reductions in mean SUV were a reliable early predictor of objective response in monotherapy, ρ = 0.833 (p = 0.008), but not in the combination arm ρ = −0.029 (p = 1).Gene expression profiling of longitudinal metastatic aspiration biopsies was feasible, demonstrated biological validity and provided predictive information.     

Cancer proteomics: from biomarker discovery to signal pathway profiling

In the post-genomic era of science, the field of proteomics promises the discovery of new molecular targets for therapy, biomarkers for early detection, and new endpoints for therapeutic efficacy and toxicity. Patient-specific targeted therapeutics with reduced toxicity and increased efficacy, the ultimate goal for rational drug development, can only be achieved if direct analyses of the tissue cells in the actual human malignancy are analyzed. To that end, technologies such as Laser Capture Microdissection (LCM), is providing unparalleled access to the purified diseased human cells directly from tissue specimens. However, limited availability of patient material is a challenge towards the development of new highly sensitive proteomic methodologies. Two-dimensional gel electrophoresis (2D-PAGE), still the mainstay of most proteomic analysis of disease, is being complemented, and in some instances replaced by new exciting approaches to multiparametric protein characterization. The use of rapid, high throughput mass spectrometric-based fingerprints of peptides and proteins may prove to be valuable for new molecular classification of human tumors and disease stages. Coupled with LCM, high-density protein arrays, antibody arrays, and small molecular arrays, could have a substantial impact on proteomic profiling of human malignancies. Finally, detailed real-time knowledge about the states of intracellular signaling circuitry and pathways in the normal and malignant cells before, during and after therapy will yield invaluable information about mechanism of action and efficacy of existing and novel therapeutics for the treatment of human cancer.     

Clinical proteomics and breast cancer: strategies for diagnostic and therapeutic biomarker discovery

A major challenge of breast cancer research is the identification of accurate biomarkers that improve screening, early diagnosis, prediction of aggressiveness, and prediction of therapeutic response or toxicity, as well as the identification of new molecular therapeutic targets. The new proteomic techniques promise to be valuable for identifying such tissue and serum markers. The different techniques currently applied to clinical samples of breast cancer and the most important results obtained are summarized in this review.     

Quantitative fluorescence imaging analysis for cancer biomarker discovery: application to beta-catenin in archived prostate specimens.

The surprising disparity between the number of protein-encoding genes ( approximately 30,000) in the human genome and the number of proteins ( approximately 300,000) in the human proteome has inspired the development of translational proteomics aimed at protein expression profiling of disease states. Translational proteomics, which offers the promise of early disease detection and individualized therapy, requires new methods for the analysis of clinical specimens. We have developed quantitative fluorescence imaging analysis (QFIA) for accurate, reproducible quantification of proteins in slide-mounted tissues. The method has been validated for the analysis of beta-catenin in archived prostate specimens fixed in formalin. QFIA takes advantage of the linearity of fluorescence antibody signaling for tissue epitope content, a feature validated for beta-catenin in methacarn-fixed prostate specimens analyzed by reverse-phase protein array analysis and QFIA (r = 0.97). QFIA of beta-catenin in formaldehyde-fixed tissues correlated directly with beta-catenin content (r = 0.86). Application of QFIA in a cross-sectional study of biopsies from 42 prostate cancer (PC) cases and 42 matched controls identified beta-catenin as a potential field marker for PC. Receiver operating characteristic plots revealed that beta-catenin expression in the normal-appearing acini of cancerous glands identified 42% (95% confidence intervals, 26-57%) of cancer cases, with 88% (95% confidence intervals, 80-96%) specificity. The marker may contribute to a PC biomarker panel. In conclusion, we report the development and validation of a new method for fluorescence quantification of proteins in archived tissues and its application to archived specimens for an evaluation of beta-catenin expression as a biomarker for PC.     

The blood peptidome: a higher dimension of information content for cancer biomarker discovery

The low-molecular-weight range of the circulatory proteome is termed the 'peptidome', and could be a rich source of cancer-specific diagnostic information because it is a 'recording' of the cellular and extracellular enzymatic events that take place at the level of the cancer-tissue microenvironment. This new information archive seems to mainly exist in vivo, bound to high-abundance proteins such as albumin. Measuring panels of peptidome markers might be more sensitive and specific than conventional biomarker approaches. We discuss the advantages and disadvantages of various methods for studying the peptidome.     

Investigation of the urinary metabolic variations and the application in bladder cancer biomarker discovery

Urine metabolomics have been used to identify biomarkers for clinical diseases. However, inter‐individual variations and effect factors need to be further evaluated. In our study, we explored the urine metabolome in a cohort of 203 health adults, 6 patients with benign bladder lesions, and 53 patients with bladder cancer (BCa) using liquid chromatography coupled with high resolution mass spectrometry. Inter‐individual analysis of both healthy controls and BCa patients showed that the urine metabolome was relatively stable. Further analysis indicated that sex and age affect inter‐individual variations in urine metabolome. Metabolic pathways such as tryptophan metabolism, the citrate cycle, and pantothenate and CoA biosynthesis were found to be related to sex and age. To eliminate age and sex interference, additional BCa urine metabolomic biomarkers were explored using age and sex‐matched urine samples (Test group: 44 health adults vs. 33 patients with BCa). Metabolic profiling of urine could significantly differentiate the cases with cancer from the controls and high‐grade from low‐grade BCa. A metabolite panel consisting of trans‐2‐dodecenoylcarnitine, serinyl‐valine, feruloyl‐2‐hydroxyputrescine, and 3‐hydroxynonanoyl carnitine were discovered to have good predictive ability for BCa with an area under the curve (AUC) of 0.956 (cross validation: AUC = 0.924). A panel of indolylacryloylglycine, N₂‐galacturonyl‐L‐lysine, and aspartyl‐glutamate was used to establish a robust model for high‐ and low‐grade BCa distinction with AUC of 0.937 (cross validation: AUC = 0.891). External sample (26 control vs. 20 BCa) validation verified the acceptable accuracy of these models for BCa detection. Our study showed that urinary metabolomics is a useful strategy for differential analysis and biomarker discovery.     

Prostate carcinoma tissue proteomics for biomarker discovery

BACKGROUND: The advent of the prostate-specific antigen (PSA) test has had a profound impact on the diagnosis and treatment of prostate carcinoma. However, the use of PSA levels alone for screening for prostate carcinoma was compromised by the variations in the amount of PSA produced by the benign prostatic tissue specimens. Proteins were involved in various pathways that determine the behavior of a cell. Therefore, information regarding proteins may reveal drug targets and/or markers for early detection. METHODS: The authors used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to determine the protein profiles from fresh tissues of the prostate. Laser capture microdissection was performed to isolate pure populations of cells. RESULTS: The authors identified a protein with an average m/Z of 24,782.56 +/- 107.27 that was correlated with the presence of prostate carcinoma. Furthermore, using laser capture microdissection, they demonstrated that the origin of this protein, which the authors designated PCa-24, was derived from the epithelial cells of the prostate. PCa-24 expression was detected in 16 of 17 (94%) prostate carcinoma specimens but not in paired normal cells. In addition, this protein was not expressed in any of the 12 benign prostatic hyperplasia specimens that were assayed. CONCLUSIONS: PCa-24 may be useful a marker for prostate carcinoma.