Molecular markers for cervical cancer screening.

INTRODUCTION: Cervical cancer remains a significant healthcare problem, notably in low- to middle-income countries. While a negative test for hrHPV has a predictive value of more than 99.5%, its positive predictive value is less than 10% for CIN2+ stages. This makes the use of a so-called triage test indispensable for population-based screening to avoid referring women, that are ultimately at low risk of developing cervical cancer, to a gynecologist. This review will give an overview of tests that are based on epigenetic marker panels and protein markers. AREAS COVERED: There is a medical need for molecular markers with a better predictive value to discriminate hrHPV-positive women that are at risk of developing cervical cancer from those that are not. Areas covered are epigenetic and protein markers as well as health economic considerations in view of the fact that most cases of cervical cancer arise in low-to-middle-income countries. EXPERT OPINION: While there are biomarker assays based on changes at the nucleic acid (DNA methylation patterns, miRNAs) and at the protein level, they are not widely used in population screening. Combining nucleic acid-based and protein-based tests could improve the overall specificity for discriminating CIN2+ lesions that carry a low risk of progressing to cervical cancer within the screening interval from those that carry an elevated risk. The challenge is to reduce unnecessary referrals without an undesired increase in false-negative diagnoses resulting in cases of cervical cancer that could have been prevented. A further challenge is to develop tests for low-and middle-income countries, which is critical to reduce the worldwide burden of cervical cancer.

Proteomic alterations in early stage cervical cancer.

Laser capture microdissection (LCM) allows the capture of cell types or well-defined structures in tissue. We compared in a semi-quantitative way the proteomes from an equivalent of 8,000 tumor cells from patients with squamous cell cervical cancer (SCC, n = 22) with healthy epithelial and stromal cells obtained from normal cervical tissue (n = 13). Proteins were enzymatically digested into peptides which were measured by high-resolution mass spectrometry and analyzed by "all-or-nothing" analysis, Bonferroni, and Benjamini-Hochberg correction for multiple testing. By comparing LCM cell type preparations, 31 proteins were exclusively found in early stage cervical cancer (n = 11) when compared with healthy epithelium and stroma, based on criteria that address specificity in a restrictive "all-or-nothing" way. By Bonferroni correction for multiple testing, 30 proteins were significantly up-regulated between early stage cervical cancer and healthy control, including six members of the MCM protein family. MCM proteins are involved in DNA repair and expected to be participating in the early stage of cancer. After a less stringent Benjamini-Hochberg correction for multiple testing, we found that the abundances of 319 proteins were significantly different between early stage cervical cancer and healthy controls. Four proteins were confirmed in digests of whole tissue lysates by Parallel Reaction Monitoring (PRM). Ingenuity Pathway Analysis using correction for multiple testing by permutation resulted in two networks that were differentially regulated in early stage cervical cancer compared with healthy tissue. From these networks, we learned that specific tumor mechanisms become effective during the early stage of cervical cancer.

Comparison of Targeted Mass Spectrometry Techniques with an Immunoassay: A Case Study for HSP90α.

PURPOSE: The objective of this study is to better understand factors governing the variability and sensitivity in SRM and PRM, compared to immunoassay. EXPERIMENTAL DESIGN: A 2D-LC-MS/MS-based SRM and PRM assay is developed for quantitative measurements of HSP90α in serum. Forty-three control sera are compared by SRM, PRM, and ELISA following the manufacturer's instructions. Serum samples are trypsin-digested and fractionated by strong cation exchange chromatography prior to SRM and PRM measurements. Analytical parameters such as linearity, LOD, LOQ, repeatability, and reproducibility of the SRM, PRM, and ELISA are determined. RESULTS: PRM data obtained by high-resolution MS correlate better with ELISA measurements than SRM data measured on a triple quadrupole mass spectrometer. While all three methods (SRM, PRM, and ELISA) are able to quantify HSP90α in serum at the ng mL-1 level, the use of PRM on a high-resolution mass spectrometer reduces variation and shows comparable sensitivity to immunoassay. CONCLUSIONS AND CLINICAL RELEVANCE: Using fractionation, it is possible to measure ng mL-1 levels of HSP90α in a reproducible, selective, and sensitive way using PRM in serum. This opens up the possibility to use PRM in a multiplexed way as an attractive alternative for immunoassays without the use of antibodies or comparable binders.

Influence of clotting time on the protein composition of serum samples based on LC-MS data.

Many large, disease-related biobanks of serum samples have been established prior to the widespread use of proteomics in biomarker research. These biobanks may contain relevant information about the disease process, response to therapy or patient classifications especially with respect to long-term follow-up that is otherwise very difficult to obtain based on newly initiated studies, particularly in the case of slowly developing diseases. An important parameter that may influence the composition of serum but that is often not exactly known is clotting time. We therefore investigated the influence of clotting time on the protein and peptide composition of serum by label-free and stable-isotope labeling techniques. The label-free analysis of trypsin-digested serum showed that the overall pattern of LC-MS data is not affected by clotting times varying from 2 to 8h. However, univariate and multivariate statistical analyses revealed that proteins that are directly involved in blood clot formation, such as the clotting-derived fibrinopeptides, change significantly. This is most easily detected in the supernatant of acid-precipitated, immunodepleted serum. Stable-isotope labeling techniques show that truncated or phosphorylated forms of fibrinopeptides A and B increase or decrease depending on clotting time. These patterns can be easily recognized and should be taken into consideration when analyzing LC-MS data using serum sample collections of which the clotting time is not known. Next to the fibrinopeptides, leucine-rich alpha-2-glycoprotein (P02750) was shown to be consistently decreased in samples with clotting times of more than 1h. For prospective studies, we recommend to let blood clot for at least 2h at room temperature using glass tubes with a separation gel and micronized silica to accelerate blood clotting.

Chip-LC-MS for label-free profiling of human serum.

The discovery of biomarkers in easily accessible body fluids such as serum is one of the most challenging topics in proteomics requiring highly efficient separation and detection methodologies. Here, we present the application of a microfluidics-based LC-MS system (chip-LC-MS) to the label-free profiling of immunodepleted, trypsin-digested serum in comparison to conventional capillary LC-MS (cap-LC-MS). Both systems proved to have a repeatability of approximately 20% RSD for peak area, all sample preparation steps included, while repeatability of the LC-MS part by itself was less than 10% RSD for the chip-LC-MS system. Importantly, the chip-LC-MS system had a two times higher resolution in the LC dimension and resulted in a lower average charge state of the tryptic peptide ions generated in the ESI interface when compared to cap-LC-MS while requiring approximately 30 times less (~5 pmol) sample. In order to characterize both systems for their capability to find discriminating peptides in trypsin-digested serum samples, five out of ten individually prepared, identical sera were spiked with horse heart cytochrome c. A comprehensive data processing methodology was applied including 2-D smoothing, resolution reduction, peak picking, time alignment, and matching of the individual peak lists to create an aligned peak matrix amenable for statistical analysis. Statistical analysis by supervised classification and variable selection showed that both LC-MS systems could discriminate the two sample groups. However, the chip-LC-MS system allowed to assign 55% of the overall signal to selected peaks against 32% for the cap-LC-MS system.

Multiple testing issues in discriminating compound-related peaks and chromatograms from high frequency noise, spikes and solvent-based noise in LC-MS data sets.

Liquid Chromatography--Mass Spectrometry (LC-MS) is a powerful method for sensitive detection and quantification of proteins and peptides in complex biological fluids like serum. LC-MS produces complex data sets, consisting of some hundreds of millions of data points per sample at a resolution of 0.1 amu in the m/z domain and 7000 data points in the time domain. However, the detection of the lower abundance proteins from this data is hampered by the presence of artefacts, such as high frequency noise and spikes. Moreover, not all of the tens of thousands of the chromatograms produced per sample are relevant for the pursuit of the biomarkers. Thus in analysing the LC-MS data, two critical pre-processing issues arise. Which of the thousands of the: 1. chromatograms per sample are relevant for the detection of the biomarkers?, and 2. signals per chromatogram are truly compound-related? Each of these issues involves assessing the significance (deviation from noise) of multiple observations and the issue of multiple comparisons arises. Current methods disregard the multiplicity and provide no concrete threshold for significance. However, with such procedures, the probability of one or more false-positives is high as the number of tests to be performed is large, and must be controlled. Realizing that the cut-offs for declaring a chromatogram (or a signal) to be compound-related can hugely influence which proteins are detected, it seems natural to define thresholds that are neither arbitrary nor subjective. We suggest the choice of thresholds guided by the critical aim of controlling the False Discovery Rate (FDR) in multiple hypotheses testing for significance over a large set of features produced per sample. This involves the use of the regression diagnostics to characterize the signals of a chromatogram (e.g. as outliers or influential) and to suggest suitable tests statistics for the multiple testing procedures (MTP) for discriminating noise and spikes from true signals. The role of the Generalized Linear Models (GLM) in this MTP is investigated. The method is applied to LC-MS datasets from trypsin-digested serum spiked with varying levels of horse heart cytochrome C (cytoc).