Identification of Serum Amyloid A as a Biomarker to Distinguish Prostate Cancer Patients with Bone Lesions

Le, Lyly; N., Kim; Tyldesley, Scott; Flibotte, Stéphane; Diamond, Deborah L.; Kuzyk, Michael A.; Sadar, Marianne D.

doi:10.1373/clinchem.2004.041087

Cited by 103 publications

(56 citation statements)

References 50 publications

(37 reference statements)

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“…The balance between dynamic range (quantitation of signal over orders of magnitude in target concentration) and sensitivity (both in terms of limit of detection, as well as signal disparity distinguishing comparable concentrations) is often difficult to maintain, as many proteins exist in low copy number (Anderson and Anderson, 2002), or the concentration span between basal and upregulated levels is large (Laack et al, 2002;Le et al, 2005). This difficulty is enhanced when attempting to selectively detect the target molecule in a complex solution (Yocum et al, 2005;Gong et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Detection of human proteins using arrayed imaging reflectometry

Mace

Striemer

Miller

2008

Biosensors and Bioelectronics

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Assays built upon protein arrays are critical tools in determining the basic nature of biology, and have considerable promise in diagnosing human disease. These protein arrays aid in the elucidation of mapping pathway interactions, disease biomarker discovery, and regulatory processes. The solutions used in these experiments, including cellular lysate and serum, are inherently complex mixtures and are high in total protein content. Therefore, array based assays must be robust and maintain a high level of selectivity and sensitivity. We report herein that Arrayed Imaging Reflectometry (AIR), a label-free biosensing platform we have previously disclosed, is highly suitable for the detection of human proteins in complex solutions. In particular, we demonstrate arraybased detection of cytokines in buffered solutions, and in undiluted human serum.

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Section: Introductionmentioning

confidence: 99%

Detection of human proteins using arrayed imaging reflectometry

Mace

Striemer

Miller

2008

Biosensors and Bioelectronics

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“…More recently, SELDI and other MALDI-based approaches have been used to detect proteins that are differentially expressed between patient groups that can then be isolated and identified, often using MS/MS approaches (Le et al, 2005;Malik et al, 2005;Paradis et al, 2005). This information could be useful in the design of more specific diagnostic tests or inform us about the disease process.…”

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confidence: 99%

Identification of serum biomarkers for colon cancer by proteomic analysis

et al. 2006

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Colorectal cancer (CRC) is often diagnosed at a late stage with concomitant poor prognosis. Early detection greatly improves prognosis; however, the invasive, unpleasant and inconvenient nature of current diagnostic procedures limits their applicability. No serum-based test is currently of sufficient sensitivity or specificity for widespread use. In the best currently available blood test, carcinoembryonic antigen exhibits low sensitivity and specificity particularly in the setting of early disease. Hence, there is great need for new biomarkers for early detection of CRC. We have used surface-enhanced laser desorbtion/ionisation (SELDI) to investigate the serum proteome of 62 CRC patients and 31 noncancer subjects. We have identified proteins (complement C3a des-arg, a1-antitrypsin and transferrin) with diagnostic potential. Artificial neural networks trained using only the intensities of the SELDI peaks corresponding to identified proteins were able to classify the patients used in this study with 95% sensitivity and 91% specificity.

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“…Interlaboratory SELDI experiments performed recently alleviated some of the reproducibility concerns (24,25). Furthermore, current research efforts have led to identification of several proteins behind the discriminating patterns peaks, including serum amyloid A (26), vitamin D-binding protein (27), and apolipoprotein A-II (28), which were identified as potential biomarkers for prostate cancer; haptoglobin (29), apolipoprotein A-I, transthyretin, and inter-␣-trypsin inhibitor (30) as biomarkers for ovarian cancer; and complement component C3a as a biomarker for breast cancer (31). Interestingly, common to all those putative cancer biomarkers is the fact that they are all relatively high abundance plasma proteins.…”

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confidence: 99%

Population Proteomics

Nedelkov

Kiernan

Niederkofler

et al. 2006

Molecular & Cellular Proteomics

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This review outlines the concept of population proteomics and its implication in the discovery and validation of cancer-specific protein modulations. Population proteomics is an applied subdiscipline of proteomics engaging in the investigation of human proteins across and within populations to define and better understand protein diversity. Population proteomics focuses on interrogation of specific proteins from large number of individuals, utilizing top-down, targeted affinity mass spectrometry approaches to probe protein modifications. Deglycosylation, sequence truncations, side-chain residue modifications, and other modifications have been reported for myriad of proteins, yet little is know about their incidence rate in the general population. Such information can be gathered via population proteomics and would greatly aid the biomarker discovery efforts. Discovery of novel protein modifications is also expected from such large scale population proteomics, expanding the protein knowledge database. In regard to cancer protein biomarkers, their validation via population proteomics-based approaches is advantageous as mass spectrometry detection is used both in the discovery and validation process, which is essential for the detection of those structurally modified protein biomarkers. Molecular & Cellular Proteomics 5: 1811-1818, 2006.In the past 5 years proteomics and its subdiscipline clinical proteomics (1-3) have taken a leading role in the discovery of new and improved cancer biomarkers. In its inception, clinical proteomics was an application of high end MS tools for evaluation of proteomic differences in the proteomes between healthy and cancer-bearing individuals. The tools were mainly based on the SELDI technology and utilization of wide specificity surfaces (e.g. hydrophobic or hydrophilic mass spectrometer targets) to bind groups of proteins from biological samples and generate distinct mass spectra containing hundreds of peaks (4, 5). These so-called proteomic patterns entered mainstream proteomics with the publication of a Lancet study in 2002 (6), which was quickly followed by a significant number of research studies (7-11) and reviews (12-15) describing proteomic patterns capable of discriminating between disease (e.g. ovarian cancer, prostate cancer, etc.) and healthy cohorts with relatively high sensitivity and specificity. However, critical assessment of those results quickly followed, outlining significant shortcomings and uncertainties in regard to the reproducibility of the findings, identity of the proteins behind the pattern peaks, and validation of the results (16 -23). Interlaboratory SELDI experiments performed recently alleviated some of the reproducibility concerns (24, 25). Furthermore, current research efforts have led to identification of several proteins behind the discriminating patterns peaks, including serum amyloid A (26), vitamin D-binding protein (27), and apolipoprotein A-II (28), which were identified as potential biomarkers for prostate cancer; haptoglobin (29), apolipopro...

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Identification of Serum Amyloid A as a Biomarker to Distinguish Prostate Cancer Patients with Bone Lesions

Cited by 103 publications

References 50 publications

Detection of human proteins using arrayed imaging reflectometry

Detection of human proteins using arrayed imaging reflectometry

Identification of serum biomarkers for colon cancer by proteomic analysis

Population Proteomics

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