The “omics” Haystack: Defining Sources of Sample Bias in Expression Profiling

Semmes, O. John

doi:10.1373/clinchem.2005.053405

Cited by 21 publications

(14 citation statements)

References 9 publications

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“…Serum protein expression profiling using time-of-flight mass spectrometry approaches to identify biomarkers of disease has reached a nexus of technology development, clinical study design, and bioinformatics. Following a period of promising initial work using MALDI and SELDI mass spectrometry (1)(2)(3)(4), new strategies to increase the utility of this approach for clinical biomarker assay development are needed (5,6). Largely using simple chemical affinity beads or surfaces to decrease the sample complexities, these methods offer an automated, sensitive technique that consumes small amounts of clinical sample with relatively high throughput (2,(7)(8)(9).…”

mentioning

confidence: 99%

“…Some of these concerns have subsequently been attributed to study design bias, chance, an overgeneralization of results, and sample processing issues (13,14). On the other hand, when careful study design and sample handling is combined with carefully controlled instrument calibration, automated sample preparation, and supervised bioinformatic data analysis, serum expression profiling can be reproducible and portable across multiple laboratories (5,8,(15)(16)(17). However, the remaining issues of protein dynamic range and complexity continue to plague these and indeed all proteomic approaches.…”

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confidence: 99%

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Lectin Capture Strategies Combined with Mass Spectrometry for the Discovery of Serum Glycoprotein Biomarkers

Drake

Schwegler

Malik

et al. 2006

Molecular & Cellular Proteomics

209

160

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The application of mass spectrometry to identify disease biomarkers in clinical fluids like serum using high throughput protein expression profiling continues to evolve as technology development, clinical study design, and bioinformatics improve. Previous protein expression profiling studies have offered needed insight into issues of technical reproducibility, instrument calibration, sample preparation, study design, and supervised bioinformatic data analysis. In this overview, new strategies to increase the utility of protein expression profiling for clinical biomarker assay development are discussed with an emphasis on utilizing differential lectin-based glycoprotein capture and targeted immunoassays. The carbohydrate binding specificities of different lectins offer a biological affinity approach that complements existing mass spectrometer capabilities and retains automated throughput options. Specific examples using serum samples from prostate cancer and hepatocellular carcinoma subjects are provided along with suggested experimental strategies for integration of lectin-based methods into clinical fluid expression profiling strategies. Our example workflow incorporates the necessity of early validation in biomarker discovery using an immunoaffinity-based targeted analytical approach that integrates well with upstream discovery technologies.

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mentioning

confidence: 99%

mentioning

confidence: 99%

Lectin Capture Strategies Combined with Mass Spectrometry for the Discovery of Serum Glycoprotein Biomarkers

Drake

Schwegler

Malik

et al. 2006

Molecular & Cellular Proteomics

209

160

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show abstract

“…The search for biomarkers should be based on possible targets for screening, diagnosis, prognostication, and therapeutic monitoring. Although the search of a unique biomarker has been compared to "finding a needle in a haystack," the complex proteomic signature of the disease-host microenvironment may represent a "biomarker amplification cascade" (Semmes, 2005). The latter (i.e., innovative approach) is based on the so-called proteomic/genomic pattern diagnostics or proteomic/genomic profiling (Anderson, 2005).…”

Section: Discoverymentioning

confidence: 99%

“Omics” Translation

Plebani¹,

Zaninotto²,

Lippi³

2015

Principles of Translational Science in Medicine

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“…Some of these concerns have subsequently been attributed to study design bias, chance, an overgeneralization of results and sample processing issues (13,14). On the other hand, when careful study design and sample handling is combined with carefully controlled instrument calibration, automated sample preparation, and supervised bioinformatic data analysis, serum expression profiling can be reproducible and portable across multiple laboratories (5,8,(15)(16)(17).…”

Section: Ms-based Approaches To Clinical Proteomicsmentioning

confidence: 99%

Identification and Characterization of Prostate Cancer Associated Protein Biomarkers Using High-Throughput Mass Spectrometry

Malik¹

2006

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show abstract

The “omics” Haystack: Defining Sources of Sample Bias in Expression Profiling

Cited by 21 publications

References 9 publications

Lectin Capture Strategies Combined with Mass Spectrometry for the Discovery of Serum Glycoprotein Biomarkers

Lectin Capture Strategies Combined with Mass Spectrometry for the Discovery of Serum Glycoprotein Biomarkers

“Omics” Translation

Identification and Characterization of Prostate Cancer Associated Protein Biomarkers Using High-Throughput Mass Spectrometry

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