A multiplex serum protein assay for determining the probability of colorectal cancer.

Our purpose is to develop a serum assay to determine an individual's probability of having colorectal cancer (CRC). We have discovered a protein panel yielding encouraging, clinically significant results. We evaluated 431 serum samples from donors screened for CRC by colonoscopy. We compared the concentration of seven proteins in individuals with CRC versus individuals found to be CRC free. The assay monitored a single peptide from each of seven proteins. Comparing CRC to normal samples in univariate two-sample t-tests, 6 of the 7 proteins yielded a p-value less than 0.01. Logistic regression was used to construct a model for determination of CRC probability. The model was fit on a randomly chosen training set of 321 samples. Using 6 of the 7 proteins (ORM1, GSN, C9, HABP2, SAA2, and C3) and a cut point of 0.4, an independent test set of 110 samples yielded a sensitivity of 93.75%, a specificity of 82.89% and a prevalence-adjusted negative predictive value (NPV) of 99.9775% for the assay. The results demonstrate that the assay has promise as a sensitive, non-invasive diagnostic test to provide individuals with an understanding of their own probability of having CRC.

Improved image analysis workflow for 2-D gels enables large-scale 2-D gel-based proteomics studies--COPD biomarker discovery study.

2-D gel electrophoresis has been used for more than three decades to study the protein complement of organisms, tissues, and cells. Three issues are holding back large-scale proteomics studies: low-throughput, high technical variation, and study designs lacking statistical power. We identified image analysis as the central factor connecting these three issues. By developing an improved image analysis workflow we shortened project timelines, decreased technical variation, and thus enabled large-scale proteomics studies that are statistically powered. Rather than detecting protein spots on each gel image and matching spots across gel images, the improved workflow is based on aligning images first, then creating a consensus spot pattern and finally propagating the consensus spot pattern to all gel images for quantitation. This results in a data table without gaps. As an example we show here a study aimed at discovering circulating biomarkers for chronic obstructive pulmonary disease (COPD). Eight candidate biomarkers were identified by comparing plasma from 24 smokers with COPD and 24 smokers without COPD. Among the candidates are proteins such as plasma retinal-binding protein (RETB) and fibrinogen that had previously been linked to the disease and are frequently monitored in COPD patients, as well as other proteins such as apolipoprotein E (ApoE), inter-alpha-trypsininhibitor heavy chain H4 (ITIH4), and glutathione peroxidase.

Overcoming technical variation and biological variation in quantitative proteomics.

Quantitative proteomics investigates physiology at the molecular level by measuring relative differences in protein expression between samples under different experimental conditions. A major obstacle to reliably determining quantitative changes in protein expression is to overcome error imposed by technical variation and biological variation. In drug discovery and development the issue of biological variation often rises in concordance with the developmental stage of research, spanning from in vitro assays to clinical trials. In this paper we present case studies to raise awareness to the issues of technical variation and biological variation and the impact this places on applying quantitative proteomics. We defined the degree of technical variation from the process of two-dimensional electrophoresis as 20-30% coefficient of variation. On the other hand, biological variation observed experiment-to-experiment showed a broader degree of variation depending upon the sample type. This was demonstrated with case studies where variation was monitored across experiments with bacteria, established cell lines, primary cultures, and with drug treated human subjects. We discuss technical variation and biological variation as key factors to consider during experimental design, and offer insight into preparing experiments that overcome this challenge to provide statistically significant outcomes for conducting quantitative proteomic research.

Probing the molecular physiology of the microbial organism, Escherichia coli using proteomics.

Genomics and proteomics technologies have yielded volumes of data for more than 20 years, and they continues to produce data at an astounding rate. Has all of this data helped us understand more about life, or it is just bogging us down in details that cannot be assembled into meaningful ideas? This review of the proteomics efforts over the last couple of decades is meant to emphasize that a new scientific discipline has emerged, Molecular Physiology, and that, indeed, this discipline is contributing to our understanding of life. Molecular physiology offers the reductionisms details of individual cellular molecules and offers the systems biology multivariant and high-dimensional datasets of cellular molecules.