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1.  An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples 
Clinical Proteomics  2015;12(1):3.
Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing.
We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low μg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples.
We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.
Electronic supplementary material
The online version of this article (doi:10.1186/1559-0275-12-3) contains supplementary material, which is available to authorized users.
PMCID: PMC4363461  PMID: 25838814
Multiple reaction monitoring; Plasma or serum analysis; Quantitative proteomics; Clinical proteomics; Mass spectrometry; Immunoaffinity depletion; Bioinformatics
2.  Quantitative matrix-assisted laser desorption/ionization mass spectrometry 
This review summarizes the essential characteristics of matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (TOF MS), especially as they relate to its applications in quantitative analysis. Approaches to quantification by MALDI-TOF MS are presented and published applications are critically reviewed.
PMCID: PMC2722264  PMID: 19106161
quantification; quantitative analysis; MALDI; mass spectrometry; biomarkers
3.  A Blood-Based Proteomic Classifier for the Molecular Characterization of Pulmonary Nodules 
Science translational medicine  2013;5(207):207ra142.
Each year millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. As the majority of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, 371 protein candidates were identified and a multiple reaction monitoring (MRM) assay was developed for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and Stage IA cancer matched on nodule size, age, gender and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a high negative predictive value (NPV) of 90%. Validation performance on samples from a non-discovery clinical site showed NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, FOS) that are associated with lung cancer, lung inflammation and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history and age, which are risk factors used for clinical management of pulmonary nodules. Thus this molecular test can provide a powerful complementary tool for physicians in lung cancer diagnosis.
PMCID: PMC4114963  PMID: 24132637
4.  Quantitative and Qualitative Differences in Protein Expression Between Papillary Thyroid Carcinoma and Normal Thyroid Tissue† 
Molecular carcinogenesis  2006;45(8):613-626.
In order to better understand basic mechanisms of tumor development and identify potential new biomarkers, we have performed difference gel electrophoresis (DIGE) and peptide mass fingerprinting on pooled protein extracts from patients with papillary thyroid carcinoma (PTC) compared with matched normal thyroid tissue. Image analysis of DIGE gels comparing PTC and matched normal thyroid tissue protein indicated that 25% of the protein spots were differentially expressed at a 2.5-fold cutoff and 35% at two-fold. Comparison between two different pools of protein from normal thyroid tissues revealed differential protein expression of only 4% at 2.5-fold and 6% at two-fold cutoff. One hundred ninety-two protein spots were identified by MALDI-TOFMS, representing 90 distinct proteins. Excluding albumin, globins and thyroglobulin, imaging software determined 31 proteins to be differentially expressed at the two-fold (or greater) level. Individual gel comparisons (PTC vs. matched normal) from five patients established that 15/31 (48%) of these proteins exhibited statistically significant differential expression. Previously identified molecular markers in this group of proteins include cathepsin B, cytokeratin 19, and galectin-3. Novel differentially expressed proteins include S100A6, moesin, HSP70 (BiP), peroxiredoxin 2, protein phosphatase 2, selenium binding protein 1, vitamin D binding protein, and proteins involved in mitochondrial function. The use of two-dimensional gel electrophoresis (2DGE) revealed a significantly altered protein mass and/or pI in 10%–15% of proteins, suggesting alternatively spliced forms and other posttranslational modification of proteins revealed by this approach. We confirmed S100A6 as a potentially useful biomarker using immunohistochemical analysis (85% sensitivity and 69% specificity for distinguishing benign from malignant thyroid neoplasms). In summary, proteomic analysis of PTC using DIGE and mass spectrometry has confirmed several known biomarkers, uncovered novel potential biomarkers, and provided insights into global pathophysiologic changes in PTC. Many of the differences observed would not have been detected by genomic or other proteomic approaches
PMCID: PMC1899163  PMID: 16788983
thyroid cancer; proteomics; molecular markers; DIGE

Results 1-4 (4)