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Moving cancer diagnostics from bench to bedside
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Vol.25 No.4
Moving cancer diagnostics from bench to bedside Xuewu Zhang1, Lin Li1, Dong Wei1, Yeeleng Yap2 and Feng Chen3 1
College of Light Industry and Food Sciences, South China University of Technology, 381 Wushan Road, Guangzhou 510640, China Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, Singapore 138671 3 Department of Botany, University of Hong Kong, Pokfulam Road, Hong Kong, China 2
To improve treatment and reduce the mortality from cancer, a key task is to detect the disease as early as possible. To achieve this, many new technologies have been developed for biomarker discovery and validation. This review provides an overview of omics technologies in biomarker discovery and cancer detection, and highlights recent applications and future trends in cancer diagnostics. Although the present omic methods are not ready for immediate clinical use as diagnostic tools, it can be envisaged that simple, fast, robust, portable and cost-effective clinical diagnosis systems could be available in near future, for home and bedside use. Introduction Cancer is commonly described as a genetic disease. Although we have witnessed the development of many drugs against cancer, the death rates for the most prevalent cancers have not been reduced. A key challenge in cancer medicine is to detect the disease as early as possible; however, the majority of patients are diagnosed as having cancer at a late stage. For example, 72% of lung cancer patients, 57% of colorectal cancer patients, and 34% of breast cancer patients in the US are diagnosed at late stage. If these cancers are diagnosed at an early stage, the survival rate exceeds 85% [1,2]. To realize the benefits of early diagnosis, highly sensitive and specific assays for biomarkers are needed: a biomarker is an in vivo, biologically derived molecule, which indicates the progress or status of a disease. Recent omics technologies have opened the door to discovering new biomarkers for the diagnosis, prognosis, therapeuticresponse prediction and population screening of human cancers. In this article we focus on the recent advances and future directions in omics-based cancer diagnostics. Biomarker discovery with omics technologies In the post-genome era, efforts are focused on biomarker discovery and the early diagnosis of cancer through the application of various omics technologies – transcriptomics, proteomics, metabonomics, peptidomics, glycomics, phosphoproteomics or lipidomics – on tissue samples and body fluids. Currently, the biological samples analyzed include blood, urine, sputum, saliva, nipple-aspirate fluid, breath, tear fluid, cerebrospinal fluid and tissue samples. The Corresponding authors: Zhang, X. ([email protected]); Chen, F. ([email protected]). Available online 20 February 2007. www.sciencedirect.com
diversity of components in these samples (e.g. amino acids, peptides, proteins or metabolites) can further increase the analytical complexity. The primary steps for omics-based cancer diagnosis are summarized in Figure 1, and a systematic comparison of these technologies is provided in Table 1. Transcriptomics The transcriptome is the complete set of RNA products transcribed in a given organism, and transcriptomics is the study of the transcriptome. Microarray technology is a powerful tool for transcriptomics analysis that has been used to identify biomarkers associated with some tumor types – the patterns of gene expression can be used to classify types of tumors and predict the outcome. Numerous reports have demonstrated the potential power of expression profiling for the molecular diagnosis of human cancers (reviewed in [3–7]). In particular, using large-scale meta-analysis of cancer microarrays some common cancer biomarkers have been identified. For example, TOP2A is present in 18 cancer versus normal signatures, representing ten types of cancer [8]. Similarly, seven gene pairs were identified for common cancer biomarkers (colon, melanoma, ovarian and esophageal cancers); these biomarkers could be broadly used to increase the sensitivity and accuracy of cancer diagnosis [9]. Proteomics Proteomics is the large-scale identification and functional characterization of all the expressed proteins in a given cell or tissue, including all protein isoforms and modifications. The frequently used tools for proteomic investigations include two-dimensional gel electrophoresis (2D-gel) and mass spectrometry (MS). However, 2D-gel technology has many inherent drawbacks: (i) poor resolution for less abundant proteins; (ii) the inability to detect proteins with extreme properties (small, large, hydrophobic and acidic or basic properties); and (iii) identification of the proteins is difficult, time-consuming and costly. Mass spectrometry-based proteomics technology has been considered as a promising approach for the early diagnosis of cancers. Up to now, this technology has been applied to many types of cancers for biomarker discovery and diagnosis, including ovarian, prostate, breast, bladder, renal, lung, pancreas and astroglial tumors (reviewed in [10–16]). Recently, many new strategies have been developed for cancer biomarker discovery. For example, the
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high-sensitivity identification of biomarkers from complex biological samples [19]. Metabonomics In this context, two terms are frequently used: metabolomics and metabonomics. Metabolomics refers to the measurement of all metabolite concentrations in cells and tissues. Metabonomics is the quantitative measurement of the metabolic responses of multicellular systems to pathophysiological stimuli or genetic modification. Nuclear magnetic resonance (NMR) spectroscopy and MS, both in combination with modern separation approaches, are two primary analytical methods for conducting metabonomic measurements. Currently, only limited metabonomic investigations are available in cancer biomarker discovery. For example, HPLC-based metabonomics was applied to the diagnosis of liver cancer [20]. The results showed that a subset of the identified urinary nucleosides correlate better with cancer diagnosis than a-fetoprotein (AFP), the traditional single tumor-marker. Odunsi et al. [21] used 1 H–NMR-based metabonomics for the detection of ovarian cancer, and demonstrated that the sera from patients with ovarian cancer and healthy postmenopausal women could be discriminated with 100% sensitivity and specificity. A recent study has indicated that the simultaneous determination of modified nucleosides and creatinine in urine samples of cancer patients is a useful method for clinical cancer diagnosis and therapy [22].
Figure 1. Workflow for omics-based cancer diagnostics.
combination of multi-dimensional liquid chromatography and 2D-gels has been applied to plasma proteomics of lung cancer [17]; 2D-gel coupled with matrix assisted laser desorption ionisation time-of-flight/time-of-flight (MALDITOF–TOF) was used to screen biomarker candidates in serum samples of breast cancer patients [18]; coupling 2DLC/MS/MS with automated genome-assisted spectra interpretation enables the direct, high-throughput and
Peptidomics The aim of peptidomics is the simultaneous visualization and identification of the whole peptidome of a cell or tissue, that is, all expressed peptides with their post-translational modifications. In general, there are two sources of the candidate peptidome biomarkers: one is the peptides and fragments derived from parental protein molecules; the other is the cleavage products generated, ex vivo, after blood clotting. Both can make contributions to cancer diagnostics. Villanueva et al. [23] developed an automated procedure for serum peptide profiling that uses magnetic, reverse-phase beads to capture peptides, followed by MALDI-TOF–MS analysis. The results demonstrated that a pattern of 274 peptides can be used to correctly predict
Table 1. Comparison between omics technologies for biomarker discovery Technique Transcriptomics
Proteomics
Metabonomics
Peptidomics Glycomics Phosphoproteomics Lipidomics a
Advantages Well-established technology Few genes and/or transcripts (25 000 in humans) relative to proteins Suitable for various biological samples
Suitable for various biological samples Fewer metabolites (10 000 in humans) relative to transcripts or proteins Low molecular weight Increased stability and solubility of glycoprotiens relative to unmodified protiens Sub-proteome: reduces the amount of proteins that can be analyzed Sub-metabonomics: reduces the amount of metabolites that can be measured
Disadvantages Tissue materials required
Many different approaches More proteins (>500 000 in humans) relative to transcripts or metabolites Technology is in development Environmental impacts are ignored a Proteolysis in ex vivo samples complicate the results Difficulty in glycosylation analysis, particularly structure identification Difficulty in the identification of phosphorylated proteins Technology in development
The effects of environmental factors that will change metabonomic profiles, such as diet, drugs, smoking and physical activity, are too complicated to be considered.
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96.4% of samples from patients with or without brain tumors. Recently, using highly optimized peptide extraction and MALDI-TOF–MS, Villanueva et al. [24] profiled 106 serum samples from patients with advanced prostate cancer, bladder cancer and breast cancer, and identified 61 signature peptides that provide accurate prediction between the cancerous patients and controls. Surprisingly, the peptides identified as cancer-type-specific markers proved to be products generated from enzymatic breakdown after patient blood collection. Glycomics The emerging field of glycomics has been attracting interest in the area of cancer research. The aim of glycomics is to identify and study all the glycan molecules produced by an organism, encompassing all glycoconjugates (glycolipids, glycoproteins, lipopolysaccharides, peptidoglycans and proteoglycans), whereas glycoproteomics refers only to the characterization of the glycosylation of proteins. Comparative studies of the specific carbohydrate chains of glycoproteins can provide useful information for the diagnosis, prognosis and immunotherapy of tumors [25]. For example, glycoproteomic analysis was used to discover serum markers in liver cancer [26]: a glycoprotein, Golgi protein 73 (GP73), was found to be elevated in the serum of people with hepatocellular carcinoma (HCC). Serum GP73 levels correlate with a diagnosis of HCC, with a positive predictive value equal to or greater than the currently used marker, AFP [27]. In another study, 19 glycoproteins were found to be hyperfucosylated in HCC and are potential biomarkers for HCC diagnosis [28]. MS combined with lectin-based glycoprotein capture strategies for the discovery of serum glycoprotein biomarkers has been reviewed in [29]. Recently, a novel strategy – natural glycoprotein microarray using multi-lectin fluorescence detection – was shown to be useful for the identification of potential cancer biomarkers [30]. Phosphoproteomics Phosphoproteomics is the characterization of the phosphorylation of proteins. Is it possible to mine potential cancer biomarkers for molecular diagnosis and prognosis from the tumor phosphoproteome? Preliminary studies indicated that a distinct pattern could exist in the phosphotyrosine proteome of cancer patients. For example, Lim et al. [31] reported that three proteins (vimentin, hsp70, and actin) were consistently hyperphosphorylated at tyrosine residues in breast tumors but not in normal tissues. Kim et al. [32] identified 238 phosphorylation sites in 116 proteins from the HT-29 human colon adenocarcinoma cell line, using immobilized metal-affinity chromatography combined with liquid chromatography-tandem mass spectrometry (IMAC-LC-MS/MS) analysis. However, it is noted that the identification of phosphorylated proteins remains a difficult task. Currently, there are several emerging strategies for the enrichment and quantification of phosphoproteins. Reinders and Sickmann [33] reviewed the most frequently used methods for the isolation and detection of phospho-proteins and -peptides, such as specific enrichment or separation strategies as well as the localization of the phosphorylated residues www.sciencedirect.com
by various mass spectrometric techniques. In particular, the coupling of stable-isotope labeling with amino acids in cell culture (SILAC) to MS is considered to be a powerful, simple and quantitative phosphoproteomics technology [34,35]. Lipidomics Lipidomics is the systems-level analysis and characterization of lipids and their interacting partners. Lipids have been implicated in many human diseases, including cancer. For example, the simplest phospholipid, lysophosphatidic acid (LPA), was found to be markedly elevated in the ascetic fluid of ovarian cancer patients [36]. Furthermore, levels of sphingolipids are altered in various types of cancers [37]. Recently, to facilitate the understanding of the role of lipid mediators in cancer, a liquid chromatography–electrospray ionization-mass spectrometry–mass spectrometry (LC–ESI-MS–MS) assay was developed to conduct lipidomic analysis of 27 mediators, including prostaglandins, prostacyclines, thomboxanes, dihydroprostaglandins and isoprostanes [38]. It is possible that a global analysis of lipid patterns could provide diagnostic information for particular cancers, although, at present, little research is available in this field. Bioinformatics No matter which omics technology is used in biomarker development, bioinformatics tools are required to extract the diagnostic or prognostic information from the complex data. Based on pattern recognition technologies, discriminatory patterns (a panel of gene, protein or peptide patterns) can be identified for the diagnosis of persons with and without a cancer. For transcriptomics investigations, numerous approaches are used to conduct diagnostic and prognostic predictions for cancer patients, based on gene expression profiles – for example, the recently developed independent component analysis (ICA) [39]. The performance of different algorithms has been compared by Dudoit et al. [40]. For proteomics experiments, the computational issues in the processing and classification of protein mass spectra have been reviewed in detail by Hilario et al. [41], and the performance of various methods has been evaluated by Shin and Markey et al. [42]. For metabonomic data analysis, a few studies are available, such as a principle component analysis (PCA) method that was used for the diagnosis of liver cancer [20], and the statistical total correlation spectroscopy analysis method for biomarker identification from metabolic NMR datasets [43]. There are, however, still some bioinformatics challenges in omics research. One substantial problem is the overfitting of data – when the number of parameters in a model is too great relative to the number of samples, and the outcome is that the model fits the original data but might predict poorly for independent data. Two methods are frequently used to avoid data overfitting: cross-validation (applied to the entire data-analysis process) and validation of independent datasets. Another major concern is that different bioinformatic analyses generate different predictive patterns. The main problem for these discrepancies could be due to the small number of samples [44]. Recently, Ein-Dor et al. [45] demonstrated that thousands of samples
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are needed for transcriptomic analysis to generate a robust gene list for predicting outcome in cancer. Unfortunately, no such framework is available for estimating efficient sample sizes in other omics research. Globally, the main similarity for all the omics technologies is that they all rely on analytical chemistry methods and generate complex datasets. In particular, MS technology is a common and powerful tool for all omics platforms except transcriptomics. Alternatively, in omics research the development of robust data analysis approaches is important to generate discriminatory patterns (e.g. gene, protein or metabolite). In principle, the bioinformatic tools developed for microarray analysis should be transferable to other omics, such as proteomics. In particular, the lessons learned from analysis of DNA microarray data, including clustering, compendium, and pattern-matching approaches, should be helpful for designing new methods in proteomics [46], but are limited owing to the unique attributes of proteomics data. For example, the number of interrogations in microarrays is determined pre-experimentally by the number of genes or gene-specific probes on the array, whereas the number of targets in the proteomic or metabonomic analysis of complex samples is unknown. Hence, it is difficult to estimate the degree of confidence of the findings in such omics experiments. Future prospects for cancer diagnostics Integrative use of various omics platforms for biomarker discovery It is becoming increasingly clear that, to fulfill the dream of individualized cancer care and treatment, we must move from the use of a single omic platform to the integration of multiple omic platforms and finally to systems biology. However, it is difficult to establish a direct link between genes and/or proteins and metabolites: multiple mRNAs could be formed from one gene; multiple proteins from one mRNA; multiple metabolites can be formed from one enzyme; and the same metabolite can participate in many different pathways. This complicates the interpretation and integration of the various omics data. Bernal et al. [47] developed a new experimental method, integrative functional informatics, which is based on the convergence and integration of proteomics, bioinformatics and highthroughput screening techniques, to accelerate the discovery and validation of novel biomarkers. This approach enables high-throughput testing of potential biomarkers without compromising high-specificity and sensitivity; hence it is an ideal tool for the validation of novel biomarkers [48]. Ippolito et al. [49] combined transcriptomics with metabolomics to identify features of neuroendocrine (NE) cancers associated with a poor prognosis (Figure 2). In this study, GeneChip was first used to yield a signature of 446 genes, and this signature was then used for in silico metabolic reconstructions of NE cell metabolism. Finally, these reconstructions, in turn, were used to direct GC–MS/ MS and LC–MS/MS analysis of metabolites in NE tumors and cell lines: dopa decarboxylase (DDC) was identified as a general biomarker for NE tumors, and amiloride-binding protein 1 (ABP1) as a biomarker of poor-prognosis NE tumors. Recently, Li et al. [50] used an integrative omics approach (genomics, transcriptomics and proteomics) to www.sciencedirect.com
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identify, directly, potential biomarkers for the diagnosis and therapy of lung cancer (Figure 3). Initially, 183 genes, with increases in both genomic copy number and transcript, in six lung cell lines were identified. Then, 2D-gel electrophoresis and MS was used to identify 42 overexpressed proteins in the cancer cells relative to normal cells. Based on the comparison between the 183 genes and 42 proteins, four genes (PRDX1, EEF1A2, CALR and KCIP-1) were correlated with elevated protein expression. Following further validation experiments, the results showed that the amplification of EEF1A2 and KCIP-1 was associated with elevated protein expression, strongly suggesting that the two genes could be potential biomarkers for the diagnosis and therapy of lung cancer. Biomarker validation After biomarker discovery with the various omics technologies, the next phase is to validate the usefulness of these biomarkers for medical purpose. Currently, there is a large gap between the ability to discover potential biomarkers and the ability to validate these candidates for clinical applications. In cancer diagnostics, one major challenge for biomarker validation is the high level of variability of biomarker levels across the human population, and the considerable molecular heterogeneity of individual cancers, even from a single tissue. Another major challenge is the low incidence of cancer in the general population (approximately 5/1000); therefore, thousands of samples are required, to evaluate the true potential of a biomarker or a panel of biomarkers. To realize multiplexed gene or protein measurement in biomarker validation, the multiplexed assays should meet the following technology requirements: (i) high-throughput capability; (ii) be available for most biomarker candidates; (iii) have high precision (typically coefficients of variation <10%) and sufficient sensitivity to measure analytes within the biological dynamic range. PCR arrays PCR array technology shows promise for the validation of multiple gene-biomarkers. The latest PCR array technique combines the quantitative performance of real-time PCR with the multiple gene profiling capabilities of microarrays, to detect the expression of panels of genes simultaneously. For example, in a PCR array representing the 84 genes involved in oncogenesis, 24 genes demonstrate a >4-fold increase or decrease in expression between the normal breast and the tumorous breast (http;//www.superarray. com). This high-performance array platform has good reproducibility and specificity, high sensitivity and wide dynamic ranges. The flexibility and simplicity of the PCR arrays make them accessible for routine use in every laboratory, which is beneficial for the large-scale analysis of biomarker validation across different laboratories. Therefore, PCR arrays have the potential to be used for the validation of multiple gene biomarkers. Protein microarrays Another promising technology is the protein microarray, which can be used to simultaneously detect changes in many target proteins, such as a panel of biomarkers in
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Figure 2. Integration of transcriptomics and metabonomics for biomarker discovery.
cancer diagnosis. The details of this approach are described in recent reviews [51,52]. The advantages of protein microarrays include high sensitivity, good reproducibility, quantitative accuracy, the requirement for small sample volumes, and parallelization – useful for large populations of samples. In particular, high-throughput ELISA microarray technology holds promise for cancer biomarker validation [53]. Tissue microarrays Finally, tissue microarray (TMA) technology is emerging as a powerful tool to validate potential biomarkers. TMAs are arrays of core biopsies obtained from paraffin-embedded tissues, which can provide a method for high-throughput gene or protein expression analysis of large cohorts of cancer patients on a single slide. Using this method, Su et al. [54] reliably assessed the expression levels of 5 biomarkers (Ki-67, cyclin 131, b-catenin, cyclooxygenase-2, and epidermal-growth factor receptor) in colorectal adenoma. By combining the genetic algorithms with tissue microarrays, Dolled-Filhart et al. [55] discovered and validated the smallest set of biomarkers (GATA3, NAT1 and estrogen receptor) with the maximum prognostic value for breast cancer. Some reviews are available for the potential applications of TMA in biomarker development [51,56,57]. www.sciencedirect.com
Translation of biomarkers into clinical diagnostic tests After biomarkers are validated, there is much to be done before they can be used in clinical laboratories for cancer diagnostics. Many exciting methods are under development. For example, electrochemical immunosensors can be used to detect some tumor biomarkers (e.g. CEA, AFP, a-1-fetoprotein and b2-microglobulin), with a detection limit down to 1 ng/ml [58]. A surface plasmon resonance (SPR)-based microfluidic system for monitoring small-molecule analytes in whole saliva is being developed at the University of Washington (http://www.washington.edu/) [59]. In this salivary diagnostics system, many steps of the analysis are integrated into a disposable diagnostic card. Such a system enables measurement of the binding rates of analytes in saliva to the surface in just a few minutes; hence, it has enormous potential to be developed into integrated, portable, clinical diagnostic devices for home and bedside use. In particular, electronic nose is a small, inexpensive and portable system. Tumors produce volatile organic compounds (VOC), such as alkanes, methylated alkanes, aromatic compounds, and benzene derivatives; these have been identified in the exhaled breath of patients with lung and breast cancers using GC–MS [60,61]. Furthermore, VOCs, such as, hexanal and heptanal, were also identified
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Box 1. Outstanding questions in omics-based biomarker discovery and cancer diagnosis
Figure 3. Integration of genomics, transcriptomics and proteomics for biomarker discovery.
as biomarkers of lung cancer in blood [62]. These VOCs are likely to have distinctive odors that can be detected by the electronic nose (gas sensor array), with a low detection limit (in the range 5–0.1 ppm). To date, electronic nose has been used for the detection of lung cancers based on the breath of a patient [63]. It should be feasible to expand the use of electronic nose for the detection of human cancers, based on volatile biomarkers in breath, urine or other biological fluids. Conclusions In the post-genomics era, omics technologies offer exciting opportunities in biomarker discovery and cancer diagnostics. However, the data generated by these technologies is not reproducible or robust enough for clinical use. Some of the outstanding barriers to more focused studies are summarized in Box 1. One challenge is to validate omics findings in prospective, well-controlled clinical studies of diverse patients across multiple institutions – at least thousands of patients are required. Another is the integration of biochemical, genetic, clinical and various omics data to better understand organisms and disease states. The final challenge is how to implement these data into clinical practice. To achieve these goals, effective interdisciplinary communication and collaboration involving the fields of molecular biology, epidemiology, electronic engineering, physics, chemistry, biostatistics, computer science, mathematics with clinicians, is required, to perform successful and efficient research into biomarker discovery and molecular www.sciencedirect.com
(i) Mass spectrometry-based omics technology is biased towards high-abundance proteins in biological samples, and has low sensitivity to low-abundance molecules. Many classical biomarkers, such as a-fetoprotein in hepatoma, PSA in prostate cancer, and carcinoembryonic antigen in colon, lung, breast and pancreatic cancers, are not detected by the present technologies. (ii) The widely used immunodepletion pretreatment of blood samples might concentrate low-abundance proteins and also remove some of them, owing to their binding to highabundance proteins. New strategies should aim to equalize the concentration and better reduce the concentration differences in biological samples, rather than removing the most abundant proteins. Examples of promising approaches includes solid-phase ligand libraries. (iii) Experimental bias exists throughout the entire process – from sample collection to data generation. The source of variation includes: different platforms; different experimental protocols, such as specimen collection, processing and storage; sampling of different patient populations (e.g. genotype and physiological attributes, such as age, gender and reproductive status); and lifestyle effects (diet, smoking, alcohol and drugs). This calls for the standardization of experimental protocols and automation of all sample preparation steps for each omic platform. (iv) Data overfitting is a substantial problem in omics research, where the number of parameters in a model is too large relative to the number of samples available. Consequently, the model fits the original data but might predict poorly for independent data. Frequently, two methods are used to avoid data overfitting: cross-validation and validation of independent datasets. (v) Small numbers of samples are used in omics studies; consequently, different data analysis methods for the same omics data generate different predictive results, which might hinder the identification of true cancer signatures and lead to misinterpretations. A mathematical method has determined that, for microarray experiments, thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Similar frameworks for other omics should be developed, to estimate the optimum sample sizes that are needed to achieve a desired level of reproducibility.
diagnosis. However, such an effective collaboration will be challenging, owing to the fact that the researchers from different disciplines speak intrinsically different languages. In summary, there are many challenges, but also many opportunities ahead. As more data are accumulated from various laboratories around the world, more cancer biomarkers will be identified and validated. Hopefully, simple, fast, robust, portable, and cost-effective clinical diagnosis devices will move from the bench to the bedside in near future. Acknowledgements The authors wish to acknowledge the support of HKU Genomics, Proteomics and Bioinformatics Strategic Research Theme.
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32 Kim, J.E. et al. (2005) Global phosphoproteome of HT-29 human colon adenocarcinoma cells. J. Proteome Res. 4, 1339–1346 33 Reinders, J. and Sickmann, A. (2005) State-of-the-art in phosphoproteomics. Proteomics 5, 4052–4061 34 Amanchy, R. et al. (2005) Phosphoproteome analysis of HeLa cells using stable-isotope labeling with amino acids in cell culture (SILAC). J. Proteome Res. 4, 1661–1671 35 Mann, M. (2006) Functional and quantitative proteomics using SILAC. Nat. Rev. Mol. Cell Biol. 7, 952–958 36 Umezu-Goto, M. et al. (2004) Lysophosphatidic acid production and action: validated targets in cancer. J. Cell. Biochem. 92, 1115–1140 37 Wenk, M.R. (2005) The emerging field of lipidomics. Nat. Rev. Drug Discov. 4, 594–610 38 Masoodi, M. and Nicolaou, A. (2006) Lipidomic analysis of twenty-seven prostanoids and isoprostanes by liquid chromatography/electrospray tandem mass spectrometry. Rapid Commun. Mass Spectrom. 20, 3023–3029 39 Zhang, X.W. et al. (2005) Molecular diagnosis of human cancer type by gene expression profiles and independent component analysis. Eur. J. Hum. Genet. 13, 1303–1311 40 Dudoit, S. et al. (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc. 97, 77–87 41 Hilario, M. et al. (2006) Processing and classification of protein mass spectra. Mass Spectrom. Rev. 25, 409–449 42 Shin, H. and Markey, M.K. (2006) A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples. J. Biomed. Inform. 39, 227–248 43 Cloarec, O. et al. (2005) Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1HNMR data sets. Anal. Chem. 77, 1282–1289 44 Ein-Dor, L. et al. (2005) Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21, 171–178 45 Ein-Dor, L. et al. (2006) Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl. Acad. Sci. U. S. A. 103, 5923–5928 46 Kremer, A. et al. (2005) A bioinformatics perspective on proteomics: data storage, analysis and integration. Biosci. Rep. 25, 95–106 47 Bernal, A. et al. (2003) Emerging opportunities in functional informatics. Pharma Genomics 9, 38–44 48 Llyin, S.E. et al. (2004) Biomarker discovery and validation: technologies and integrative approaches. Trends Biotechnol. 22, 411–416 49 Ippolito, J.E. et al. (2005) An integrated functional genomics and metabolomics approach for defining poor prognosis in human neuroendocrine cancers. Proc. Natl. Acad. Sci. U. S. A. 102, 9901–9906 50 Li, R. et al. (2006) Identification of putative oncogenes in lung adenocarcinoma by a comprehensive functional genomic approach. Oncogene 25, 2628–2635 51 Aguilar-Mahecha, A. et al. (2006) Microarrays as validation strategies in clinical samples: tissue and protein microarrays. Omics: A Journal of Integrative Biology 10, 311–326 52 Kricka, L.J. et al. (2006) Current perspectives in protein array technology. Ann. Clin. Biochem. 457–467 53 Zangar, R.C. et al. (2006) ELISA microarray technology as a highthroughput system for cancer biomarker validation. Expert Rev. Proteomics 3, 37–44 54 Su, Y. et al. (2006) Immunohistochemical expressions of Ki-67, cyclin D1, beta-catenin, cyclooxygenase-2, and epidermal growth factor receptor in human colorectal adenoma: a validation study of tissue microarrays. Cancer Epidemiol. Biomarkers Prev. 15, 1719– 1726 55 Dolled-Filhart, M. et al. (2006) Classification of breast cancer using genetic algorithms and tissue microarrays. Clin. Cancer Res. 12, 6459– 6468 56 Giltnane, J.M. and Rimm, D.L. (2005) Technology insight: identification of biomarkers with tissue microarray technology. Nat. Clin. Pract. Urol. 2, A104–A111 57 Merseburger, A.S. et al. (2006) Tissue microarrays: applications in urological cancer research. World J. Urol. 24, 579–584 58 Wilson, M.S. (2005) Electrochemical immunosensors for the simultaneous detection of two tumor markers. Anal. Chem. 77, 1496–1502
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62 Deng, C. et al. (2004) Development of headspace solid-phase microextraction with on-fiber derivatization for determination of hexanal and heptanal in human blood. J. Chromatogr. B: Analyt. Technol. Biomed. Life Sci. 813, 47–52 63 Machado, R.F. et al. (2005) Detection of lung cancer by sensor array analyses of exhaled breath. Am. J. Respir. Crit. Care Med. 171, 1286–1291
Elsevier celebrates two anniversaries with a gift to university libraries in the developing world In 1580, the Elzevir family began their printing and bookselling business in the Netherlands, publishing works by scholars such as John Locke, Galileo Galilei and Hugo Grotius. On 4 March 1880, Jacobus George Robbers founded the modern Elsevier company intending, just like the original Elzevir family, to reproduce fine editions of literary classics for the edification of others who shared his passion, other ‘Elzevirians’. Robbers co-opted the Elzevir family printer’s mark, stamping the new Elsevier products with a classic symbol of the symbiotic relationship between publisher and scholar. Elsevier has since become a leader in the dissemination of scientific, technical and medical (STM) information, building a reputation for excellence in publishing, new product innovation and commitment to its STM communities. In celebration of the House of Elzevir’s 425th anniversary and the 125th anniversary of the modern Elsevier company, Elsevier donated books to ten university libraries in the developing world. Entitled ‘A Book in Your Name’, each of the 6700 Elsevier employees worldwide was invited to select one of the chosen libraries to receive a book donated by Elsevier. The core gift collection contains the company’s most important and widely used STM publications, including Gray’s Anatomy, Dorland’s Illustrated Medical Dictionary, Essential Medical Physiology, Cecil Essentials of Medicine, Mosby’s Medical, Nursing and Allied Health Dictionary, The Vaccine Book, Fundamentals of Neuroscience, and Myles Textbook for Midwives. The ten beneficiary libraries are located in Africa, South America and Asia. They include the Library of the Sciences of the University of Sierra Leone; the library of the Muhimbili University College of Health Sciences of the University of Dar es Salaam, Tanzania; the library of the College of Medicine of the University of Malawi; and the University of Zambia; Universite du Mali; Universidade Eduardo Mondlane, Mozambique; Makerere University, Uganda; Universidad San Francisco de Quito, Ecuador; Universidad Francisco Marroquin, Guatemala; and the National Centre for Scientific and Technological Information (NACESTI), Vietnam. Through ‘A Book in Your Name’, these libraries received books with a total retail value of approximately one million US dollars.
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Moving cancer diagnostics from bench to bedside Xuewu Zhang1, Lin Li1, Dong Wei1, Yeeleng Yap2 and Feng Chen3 1
College of Light Industry and Food Sciences, South China University of Technology, 381 Wushan Road, Guangzhou 510640, China Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, Singapore 138671 3 Department of Botany, University of Hong Kong, Pokfulam Road, Hong Kong, China 2
To improve treatment and reduce the mortality from cancer, a key task is to detect the disease as early as possible. To achieve this, many new technologies have been developed for biomarker discovery and validation. This review provides an overview of omics technologies in biomarker discovery and cancer detection, and highlights recent applications and future trends in cancer diagnostics. Although the present omic methods are not ready for immediate clinical use as diagnostic tools, it can be envisaged that simple, fast, robust, portable and cost-effective clinical diagnosis systems could be available in near future, for home and bedside use. Introduction Cancer is commonly described as a genetic disease. Although we have witnessed the development of many drugs against cancer, the death rates for the most prevalent cancers have not been reduced. A key challenge in cancer medicine is to detect the disease as early as possible; however, the majority of patients are diagnosed as having cancer at a late stage. For example, 72% of lung cancer patients, 57% of colorectal cancer patients, and 34% of breast cancer patients in the US are diagnosed at late stage. If these cancers are diagnosed at an early stage, the survival rate exceeds 85% [1,2]. To realize the benefits of early diagnosis, highly sensitive and specific assays for biomarkers are needed: a biomarker is an in vivo, biologically derived molecule, which indicates the progress or status of a disease. Recent omics technologies have opened the door to discovering new biomarkers for the diagnosis, prognosis, therapeuticresponse prediction and population screening of human cancers. In this article we focus on the recent advances and future directions in omics-based cancer diagnostics. Biomarker discovery with omics technologies In the post-genome era, efforts are focused on biomarker discovery and the early diagnosis of cancer through the application of various omics technologies – transcriptomics, proteomics, metabonomics, peptidomics, glycomics, phosphoproteomics or lipidomics – on tissue samples and body fluids. Currently, the biological samples analyzed include blood, urine, sputum, saliva, nipple-aspirate fluid, breath, tear fluid, cerebrospinal fluid and tissue samples. The Corresponding authors: Zhang, X. ([email protected]); Chen, F. ([email protected]). Available online 20 February 2007. www.sciencedirect.com
diversity of components in these samples (e.g. amino acids, peptides, proteins or metabolites) can further increase the analytical complexity. The primary steps for omics-based cancer diagnosis are summarized in Figure 1, and a systematic comparison of these technologies is provided in Table 1. Transcriptomics The transcriptome is the complete set of RNA products transcribed in a given organism, and transcriptomics is the study of the transcriptome. Microarray technology is a powerful tool for transcriptomics analysis that has been used to identify biomarkers associated with some tumor types – the patterns of gene expression can be used to classify types of tumors and predict the outcome. Numerous reports have demonstrated the potential power of expression profiling for the molecular diagnosis of human cancers (reviewed in [3–7]). In particular, using large-scale meta-analysis of cancer microarrays some common cancer biomarkers have been identified. For example, TOP2A is present in 18 cancer versus normal signatures, representing ten types of cancer [8]. Similarly, seven gene pairs were identified for common cancer biomarkers (colon, melanoma, ovarian and esophageal cancers); these biomarkers could be broadly used to increase the sensitivity and accuracy of cancer diagnosis [9]. Proteomics Proteomics is the large-scale identification and functional characterization of all the expressed proteins in a given cell or tissue, including all protein isoforms and modifications. The frequently used tools for proteomic investigations include two-dimensional gel electrophoresis (2D-gel) and mass spectrometry (MS). However, 2D-gel technology has many inherent drawbacks: (i) poor resolution for less abundant proteins; (ii) the inability to detect proteins with extreme properties (small, large, hydrophobic and acidic or basic properties); and (iii) identification of the proteins is difficult, time-consuming and costly. Mass spectrometry-based proteomics technology has been considered as a promising approach for the early diagnosis of cancers. Up to now, this technology has been applied to many types of cancers for biomarker discovery and diagnosis, including ovarian, prostate, breast, bladder, renal, lung, pancreas and astroglial tumors (reviewed in [10–16]). Recently, many new strategies have been developed for cancer biomarker discovery. For example, the
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high-sensitivity identification of biomarkers from complex biological samples [19]. Metabonomics In this context, two terms are frequently used: metabolomics and metabonomics. Metabolomics refers to the measurement of all metabolite concentrations in cells and tissues. Metabonomics is the quantitative measurement of the metabolic responses of multicellular systems to pathophysiological stimuli or genetic modification. Nuclear magnetic resonance (NMR) spectroscopy and MS, both in combination with modern separation approaches, are two primary analytical methods for conducting metabonomic measurements. Currently, only limited metabonomic investigations are available in cancer biomarker discovery. For example, HPLC-based metabonomics was applied to the diagnosis of liver cancer [20]. The results showed that a subset of the identified urinary nucleosides correlate better with cancer diagnosis than a-fetoprotein (AFP), the traditional single tumor-marker. Odunsi et al. [21] used 1 H–NMR-based metabonomics for the detection of ovarian cancer, and demonstrated that the sera from patients with ovarian cancer and healthy postmenopausal women could be discriminated with 100% sensitivity and specificity. A recent study has indicated that the simultaneous determination of modified nucleosides and creatinine in urine samples of cancer patients is a useful method for clinical cancer diagnosis and therapy [22].
Figure 1. Workflow for omics-based cancer diagnostics.
combination of multi-dimensional liquid chromatography and 2D-gels has been applied to plasma proteomics of lung cancer [17]; 2D-gel coupled with matrix assisted laser desorption ionisation time-of-flight/time-of-flight (MALDITOF–TOF) was used to screen biomarker candidates in serum samples of breast cancer patients [18]; coupling 2DLC/MS/MS with automated genome-assisted spectra interpretation enables the direct, high-throughput and
Peptidomics The aim of peptidomics is the simultaneous visualization and identification of the whole peptidome of a cell or tissue, that is, all expressed peptides with their post-translational modifications. In general, there are two sources of the candidate peptidome biomarkers: one is the peptides and fragments derived from parental protein molecules; the other is the cleavage products generated, ex vivo, after blood clotting. Both can make contributions to cancer diagnostics. Villanueva et al. [23] developed an automated procedure for serum peptide profiling that uses magnetic, reverse-phase beads to capture peptides, followed by MALDI-TOF–MS analysis. The results demonstrated that a pattern of 274 peptides can be used to correctly predict
Table 1. Comparison between omics technologies for biomarker discovery Technique Transcriptomics
Proteomics
Metabonomics
Peptidomics Glycomics Phosphoproteomics Lipidomics a
Advantages Well-established technology Few genes and/or transcripts (25 000 in humans) relative to proteins Suitable for various biological samples
Suitable for various biological samples Fewer metabolites (10 000 in humans) relative to transcripts or proteins Low molecular weight Increased stability and solubility of glycoprotiens relative to unmodified protiens Sub-proteome: reduces the amount of proteins that can be analyzed Sub-metabonomics: reduces the amount of metabolites that can be measured
Disadvantages Tissue materials required
Many different approaches More proteins (>500 000 in humans) relative to transcripts or metabolites Technology is in development Environmental impacts are ignored a Proteolysis in ex vivo samples complicate the results Difficulty in glycosylation analysis, particularly structure identification Difficulty in the identification of phosphorylated proteins Technology in development
The effects of environmental factors that will change metabonomic profiles, such as diet, drugs, smoking and physical activity, are too complicated to be considered.
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96.4% of samples from patients with or without brain tumors. Recently, using highly optimized peptide extraction and MALDI-TOF–MS, Villanueva et al. [24] profiled 106 serum samples from patients with advanced prostate cancer, bladder cancer and breast cancer, and identified 61 signature peptides that provide accurate prediction between the cancerous patients and controls. Surprisingly, the peptides identified as cancer-type-specific markers proved to be products generated from enzymatic breakdown after patient blood collection. Glycomics The emerging field of glycomics has been attracting interest in the area of cancer research. The aim of glycomics is to identify and study all the glycan molecules produced by an organism, encompassing all glycoconjugates (glycolipids, glycoproteins, lipopolysaccharides, peptidoglycans and proteoglycans), whereas glycoproteomics refers only to the characterization of the glycosylation of proteins. Comparative studies of the specific carbohydrate chains of glycoproteins can provide useful information for the diagnosis, prognosis and immunotherapy of tumors [25]. For example, glycoproteomic analysis was used to discover serum markers in liver cancer [26]: a glycoprotein, Golgi protein 73 (GP73), was found to be elevated in the serum of people with hepatocellular carcinoma (HCC). Serum GP73 levels correlate with a diagnosis of HCC, with a positive predictive value equal to or greater than the currently used marker, AFP [27]. In another study, 19 glycoproteins were found to be hyperfucosylated in HCC and are potential biomarkers for HCC diagnosis [28]. MS combined with lectin-based glycoprotein capture strategies for the discovery of serum glycoprotein biomarkers has been reviewed in [29]. Recently, a novel strategy – natural glycoprotein microarray using multi-lectin fluorescence detection – was shown to be useful for the identification of potential cancer biomarkers [30]. Phosphoproteomics Phosphoproteomics is the characterization of the phosphorylation of proteins. Is it possible to mine potential cancer biomarkers for molecular diagnosis and prognosis from the tumor phosphoproteome? Preliminary studies indicated that a distinct pattern could exist in the phosphotyrosine proteome of cancer patients. For example, Lim et al. [31] reported that three proteins (vimentin, hsp70, and actin) were consistently hyperphosphorylated at tyrosine residues in breast tumors but not in normal tissues. Kim et al. [32] identified 238 phosphorylation sites in 116 proteins from the HT-29 human colon adenocarcinoma cell line, using immobilized metal-affinity chromatography combined with liquid chromatography-tandem mass spectrometry (IMAC-LC-MS/MS) analysis. However, it is noted that the identification of phosphorylated proteins remains a difficult task. Currently, there are several emerging strategies for the enrichment and quantification of phosphoproteins. Reinders and Sickmann [33] reviewed the most frequently used methods for the isolation and detection of phospho-proteins and -peptides, such as specific enrichment or separation strategies as well as the localization of the phosphorylated residues www.sciencedirect.com
by various mass spectrometric techniques. In particular, the coupling of stable-isotope labeling with amino acids in cell culture (SILAC) to MS is considered to be a powerful, simple and quantitative phosphoproteomics technology [34,35]. Lipidomics Lipidomics is the systems-level analysis and characterization of lipids and their interacting partners. Lipids have been implicated in many human diseases, including cancer. For example, the simplest phospholipid, lysophosphatidic acid (LPA), was found to be markedly elevated in the ascetic fluid of ovarian cancer patients [36]. Furthermore, levels of sphingolipids are altered in various types of cancers [37]. Recently, to facilitate the understanding of the role of lipid mediators in cancer, a liquid chromatography–electrospray ionization-mass spectrometry–mass spectrometry (LC–ESI-MS–MS) assay was developed to conduct lipidomic analysis of 27 mediators, including prostaglandins, prostacyclines, thomboxanes, dihydroprostaglandins and isoprostanes [38]. It is possible that a global analysis of lipid patterns could provide diagnostic information for particular cancers, although, at present, little research is available in this field. Bioinformatics No matter which omics technology is used in biomarker development, bioinformatics tools are required to extract the diagnostic or prognostic information from the complex data. Based on pattern recognition technologies, discriminatory patterns (a panel of gene, protein or peptide patterns) can be identified for the diagnosis of persons with and without a cancer. For transcriptomics investigations, numerous approaches are used to conduct diagnostic and prognostic predictions for cancer patients, based on gene expression profiles – for example, the recently developed independent component analysis (ICA) [39]. The performance of different algorithms has been compared by Dudoit et al. [40]. For proteomics experiments, the computational issues in the processing and classification of protein mass spectra have been reviewed in detail by Hilario et al. [41], and the performance of various methods has been evaluated by Shin and Markey et al. [42]. For metabonomic data analysis, a few studies are available, such as a principle component analysis (PCA) method that was used for the diagnosis of liver cancer [20], and the statistical total correlation spectroscopy analysis method for biomarker identification from metabolic NMR datasets [43]. There are, however, still some bioinformatics challenges in omics research. One substantial problem is the overfitting of data – when the number of parameters in a model is too great relative to the number of samples, and the outcome is that the model fits the original data but might predict poorly for independent data. Two methods are frequently used to avoid data overfitting: cross-validation (applied to the entire data-analysis process) and validation of independent datasets. Another major concern is that different bioinformatic analyses generate different predictive patterns. The main problem for these discrepancies could be due to the small number of samples [44]. Recently, Ein-Dor et al. [45] demonstrated that thousands of samples
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are needed for transcriptomic analysis to generate a robust gene list for predicting outcome in cancer. Unfortunately, no such framework is available for estimating efficient sample sizes in other omics research. Globally, the main similarity for all the omics technologies is that they all rely on analytical chemistry methods and generate complex datasets. In particular, MS technology is a common and powerful tool for all omics platforms except transcriptomics. Alternatively, in omics research the development of robust data analysis approaches is important to generate discriminatory patterns (e.g. gene, protein or metabolite). In principle, the bioinformatic tools developed for microarray analysis should be transferable to other omics, such as proteomics. In particular, the lessons learned from analysis of DNA microarray data, including clustering, compendium, and pattern-matching approaches, should be helpful for designing new methods in proteomics [46], but are limited owing to the unique attributes of proteomics data. For example, the number of interrogations in microarrays is determined pre-experimentally by the number of genes or gene-specific probes on the array, whereas the number of targets in the proteomic or metabonomic analysis of complex samples is unknown. Hence, it is difficult to estimate the degree of confidence of the findings in such omics experiments. Future prospects for cancer diagnostics Integrative use of various omics platforms for biomarker discovery It is becoming increasingly clear that, to fulfill the dream of individualized cancer care and treatment, we must move from the use of a single omic platform to the integration of multiple omic platforms and finally to systems biology. However, it is difficult to establish a direct link between genes and/or proteins and metabolites: multiple mRNAs could be formed from one gene; multiple proteins from one mRNA; multiple metabolites can be formed from one enzyme; and the same metabolite can participate in many different pathways. This complicates the interpretation and integration of the various omics data. Bernal et al. [47] developed a new experimental method, integrative functional informatics, which is based on the convergence and integration of proteomics, bioinformatics and highthroughput screening techniques, to accelerate the discovery and validation of novel biomarkers. This approach enables high-throughput testing of potential biomarkers without compromising high-specificity and sensitivity; hence it is an ideal tool for the validation of novel biomarkers [48]. Ippolito et al. [49] combined transcriptomics with metabolomics to identify features of neuroendocrine (NE) cancers associated with a poor prognosis (Figure 2). In this study, GeneChip was first used to yield a signature of 446 genes, and this signature was then used for in silico metabolic reconstructions of NE cell metabolism. Finally, these reconstructions, in turn, were used to direct GC–MS/ MS and LC–MS/MS analysis of metabolites in NE tumors and cell lines: dopa decarboxylase (DDC) was identified as a general biomarker for NE tumors, and amiloride-binding protein 1 (ABP1) as a biomarker of poor-prognosis NE tumors. Recently, Li et al. [50] used an integrative omics approach (genomics, transcriptomics and proteomics) to www.sciencedirect.com
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identify, directly, potential biomarkers for the diagnosis and therapy of lung cancer (Figure 3). Initially, 183 genes, with increases in both genomic copy number and transcript, in six lung cell lines were identified. Then, 2D-gel electrophoresis and MS was used to identify 42 overexpressed proteins in the cancer cells relative to normal cells. Based on the comparison between the 183 genes and 42 proteins, four genes (PRDX1, EEF1A2, CALR and KCIP-1) were correlated with elevated protein expression. Following further validation experiments, the results showed that the amplification of EEF1A2 and KCIP-1 was associated with elevated protein expression, strongly suggesting that the two genes could be potential biomarkers for the diagnosis and therapy of lung cancer. Biomarker validation After biomarker discovery with the various omics technologies, the next phase is to validate the usefulness of these biomarkers for medical purpose. Currently, there is a large gap between the ability to discover potential biomarkers and the ability to validate these candidates for clinical applications. In cancer diagnostics, one major challenge for biomarker validation is the high level of variability of biomarker levels across the human population, and the considerable molecular heterogeneity of individual cancers, even from a single tissue. Another major challenge is the low incidence of cancer in the general population (approximately 5/1000); therefore, thousands of samples are required, to evaluate the true potential of a biomarker or a panel of biomarkers. To realize multiplexed gene or protein measurement in biomarker validation, the multiplexed assays should meet the following technology requirements: (i) high-throughput capability; (ii) be available for most biomarker candidates; (iii) have high precision (typically coefficients of variation <10%) and sufficient sensitivity to measure analytes within the biological dynamic range. PCR arrays PCR array technology shows promise for the validation of multiple gene-biomarkers. The latest PCR array technique combines the quantitative performance of real-time PCR with the multiple gene profiling capabilities of microarrays, to detect the expression of panels of genes simultaneously. For example, in a PCR array representing the 84 genes involved in oncogenesis, 24 genes demonstrate a >4-fold increase or decrease in expression between the normal breast and the tumorous breast (http;//www.superarray. com). This high-performance array platform has good reproducibility and specificity, high sensitivity and wide dynamic ranges. The flexibility and simplicity of the PCR arrays make them accessible for routine use in every laboratory, which is beneficial for the large-scale analysis of biomarker validation across different laboratories. Therefore, PCR arrays have the potential to be used for the validation of multiple gene biomarkers. Protein microarrays Another promising technology is the protein microarray, which can be used to simultaneously detect changes in many target proteins, such as a panel of biomarkers in
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Figure 2. Integration of transcriptomics and metabonomics for biomarker discovery.
cancer diagnosis. The details of this approach are described in recent reviews [51,52]. The advantages of protein microarrays include high sensitivity, good reproducibility, quantitative accuracy, the requirement for small sample volumes, and parallelization – useful for large populations of samples. In particular, high-throughput ELISA microarray technology holds promise for cancer biomarker validation [53]. Tissue microarrays Finally, tissue microarray (TMA) technology is emerging as a powerful tool to validate potential biomarkers. TMAs are arrays of core biopsies obtained from paraffin-embedded tissues, which can provide a method for high-throughput gene or protein expression analysis of large cohorts of cancer patients on a single slide. Using this method, Su et al. [54] reliably assessed the expression levels of 5 biomarkers (Ki-67, cyclin 131, b-catenin, cyclooxygenase-2, and epidermal-growth factor receptor) in colorectal adenoma. By combining the genetic algorithms with tissue microarrays, Dolled-Filhart et al. [55] discovered and validated the smallest set of biomarkers (GATA3, NAT1 and estrogen receptor) with the maximum prognostic value for breast cancer. Some reviews are available for the potential applications of TMA in biomarker development [51,56,57]. www.sciencedirect.com
Translation of biomarkers into clinical diagnostic tests After biomarkers are validated, there is much to be done before they can be used in clinical laboratories for cancer diagnostics. Many exciting methods are under development. For example, electrochemical immunosensors can be used to detect some tumor biomarkers (e.g. CEA, AFP, a-1-fetoprotein and b2-microglobulin), with a detection limit down to 1 ng/ml [58]. A surface plasmon resonance (SPR)-based microfluidic system for monitoring small-molecule analytes in whole saliva is being developed at the University of Washington (http://www.washington.edu/) [59]. In this salivary diagnostics system, many steps of the analysis are integrated into a disposable diagnostic card. Such a system enables measurement of the binding rates of analytes in saliva to the surface in just a few minutes; hence, it has enormous potential to be developed into integrated, portable, clinical diagnostic devices for home and bedside use. In particular, electronic nose is a small, inexpensive and portable system. Tumors produce volatile organic compounds (VOC), such as alkanes, methylated alkanes, aromatic compounds, and benzene derivatives; these have been identified in the exhaled breath of patients with lung and breast cancers using GC–MS [60,61]. Furthermore, VOCs, such as, hexanal and heptanal, were also identified
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Box 1. Outstanding questions in omics-based biomarker discovery and cancer diagnosis
Figure 3. Integration of genomics, transcriptomics and proteomics for biomarker discovery.
as biomarkers of lung cancer in blood [62]. These VOCs are likely to have distinctive odors that can be detected by the electronic nose (gas sensor array), with a low detection limit (in the range 5–0.1 ppm). To date, electronic nose has been used for the detection of lung cancers based on the breath of a patient [63]. It should be feasible to expand the use of electronic nose for the detection of human cancers, based on volatile biomarkers in breath, urine or other biological fluids. Conclusions In the post-genomics era, omics technologies offer exciting opportunities in biomarker discovery and cancer diagnostics. However, the data generated by these technologies is not reproducible or robust enough for clinical use. Some of the outstanding barriers to more focused studies are summarized in Box 1. One challenge is to validate omics findings in prospective, well-controlled clinical studies of diverse patients across multiple institutions – at least thousands of patients are required. Another is the integration of biochemical, genetic, clinical and various omics data to better understand organisms and disease states. The final challenge is how to implement these data into clinical practice. To achieve these goals, effective interdisciplinary communication and collaboration involving the fields of molecular biology, epidemiology, electronic engineering, physics, chemistry, biostatistics, computer science, mathematics with clinicians, is required, to perform successful and efficient research into biomarker discovery and molecular www.sciencedirect.com
(i) Mass spectrometry-based omics technology is biased towards high-abundance proteins in biological samples, and has low sensitivity to low-abundance molecules. Many classical biomarkers, such as a-fetoprotein in hepatoma, PSA in prostate cancer, and carcinoembryonic antigen in colon, lung, breast and pancreatic cancers, are not detected by the present technologies. (ii) The widely used immunodepletion pretreatment of blood samples might concentrate low-abundance proteins and also remove some of them, owing to their binding to highabundance proteins. New strategies should aim to equalize the concentration and better reduce the concentration differences in biological samples, rather than removing the most abundant proteins. Examples of promising approaches includes solid-phase ligand libraries. (iii) Experimental bias exists throughout the entire process – from sample collection to data generation. The source of variation includes: different platforms; different experimental protocols, such as specimen collection, processing and storage; sampling of different patient populations (e.g. genotype and physiological attributes, such as age, gender and reproductive status); and lifestyle effects (diet, smoking, alcohol and drugs). This calls for the standardization of experimental protocols and automation of all sample preparation steps for each omic platform. (iv) Data overfitting is a substantial problem in omics research, where the number of parameters in a model is too large relative to the number of samples available. Consequently, the model fits the original data but might predict poorly for independent data. Frequently, two methods are used to avoid data overfitting: cross-validation and validation of independent datasets. (v) Small numbers of samples are used in omics studies; consequently, different data analysis methods for the same omics data generate different predictive results, which might hinder the identification of true cancer signatures and lead to misinterpretations. A mathematical method has determined that, for microarray experiments, thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Similar frameworks for other omics should be developed, to estimate the optimum sample sizes that are needed to achieve a desired level of reproducibility.
diagnosis. However, such an effective collaboration will be challenging, owing to the fact that the researchers from different disciplines speak intrinsically different languages. In summary, there are many challenges, but also many opportunities ahead. As more data are accumulated from various laboratories around the world, more cancer biomarkers will be identified and validated. Hopefully, simple, fast, robust, portable, and cost-effective clinical diagnosis devices will move from the bench to the bedside in near future. Acknowledgements The authors wish to acknowledge the support of HKU Genomics, Proteomics and Bioinformatics Strategic Research Theme.
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Elsevier celebrates two anniversaries with a gift to university libraries in the developing world In 1580, the Elzevir family began their printing and bookselling business in the Netherlands, publishing works by scholars such as John Locke, Galileo Galilei and Hugo Grotius. On 4 March 1880, Jacobus George Robbers founded the modern Elsevier company intending, just like the original Elzevir family, to reproduce fine editions of literary classics for the edification of others who shared his passion, other ‘Elzevirians’. Robbers co-opted the Elzevir family printer’s mark, stamping the new Elsevier products with a classic symbol of the symbiotic relationship between publisher and scholar. Elsevier has since become a leader in the dissemination of scientific, technical and medical (STM) information, building a reputation for excellence in publishing, new product innovation and commitment to its STM communities. In celebration of the House of Elzevir’s 425th anniversary and the 125th anniversary of the modern Elsevier company, Elsevier donated books to ten university libraries in the developing world. Entitled ‘A Book in Your Name’, each of the 6700 Elsevier employees worldwide was invited to select one of the chosen libraries to receive a book donated by Elsevier. The core gift collection contains the company’s most important and widely used STM publications, including Gray’s Anatomy, Dorland’s Illustrated Medical Dictionary, Essential Medical Physiology, Cecil Essentials of Medicine, Mosby’s Medical, Nursing and Allied Health Dictionary, The Vaccine Book, Fundamentals of Neuroscience, and Myles Textbook for Midwives. The ten beneficiary libraries are located in Africa, South America and Asia. They include the Library of the Sciences of the University of Sierra Leone; the library of the Muhimbili University College of Health Sciences of the University of Dar es Salaam, Tanzania; the library of the College of Medicine of the University of Malawi; and the University of Zambia; Universite du Mali; Universidade Eduardo Mondlane, Mozambique; Makerere University, Uganda; Universidad San Francisco de Quito, Ecuador; Universidad Francisco Marroquin, Guatemala; and the National Centre for Scientific and Technological Information (NACESTI), Vietnam. Through ‘A Book in Your Name’, these libraries received books with a total retail value of approximately one million US dollars.
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