Quantitative proteomics in lung cancer

Abstract

Lung cancer is the most common cause of cancer-related death worldwide, less than 7% of patients survive 10 years following diagnosis across all stages of lung cancer. Late stage of diagnosis and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms that underlie lung cancer progression. Quantitative proteomics provides the relative different protein abundance in normal and cancer patients which offers the information for molecular interactions, signaling pathways, and biomarker identification. Here we introduce both theoretical and practical applications in the use of quantitative proteomics approaches, with principles of current technologies and methodologies including gel-based, label free, stable isotope labeling as well as targeted proteomics. Predictive markers of drug resistance, candidate biomarkers for diagnosis, and prognostic markers in lung cancer have also been discovered and analyzed by quantitative proteomic analysis. Moreover, construction of protein networks enables to provide an opportunity to interpret disease pathway and improve our understanding in cancer therapeutic strategies, allowing the discovery of molecular markers and new therapeutic targets for lung cancer.

Keywords: Biomarkers; Drug targets; Functional network; Lung cancer; Quantitative proteomics.

Fig. 1

The applications of quantitative proteomics for discovery of biomarkers in lung cancer study. Quantitative proteomics not only provides a list of identified proteins, it also quantifies the changes between normal and disease sample profiles which enables to generate classification models or biomarkers. Biomarkers are measurable biological indicators found in tissue, cells, blood or other body fluids that may be used for detection, diagnosis treatment and monitoring in cancer research by the means of advanced quantitative proteomic approaches: gel-based, stable isotope labeling, targeted proteomics, and label free. In gel-based proteomics, one-dimensional (1D) gel electrophoresis, two-dimensional (2D) polyacrylamide gel electrophoresis, and difference gel electrophoresis (DIGE) approaches have been developed and utilized to separate protein from protein mixtures and identification. In vitro labeling, the peptides are modified by stable isotope labeling (ICAT, iTRAQ, TMT) prior to MS analysis. In vivo labeling, isotope labeling (SILAC and SILAM), specific supplements containing distinct forms of amino acid are given to living cells or mammals prior to MS analysis. The resulting spectrum is able to generate peptide intensity for both identification and quantitation. Targeted proteomics (SRM, MRM, and DIA) using triple quadrupole mass spectrometers systems where the mass of the intact targeted analyte is selected in the first quadrupole (Q1), and then the fragmentation of the Q1 mass-selected precursor ion by collision-induced dissociation in the second quadrupole (Q2), finally a desired product ion is selected in the third quadrupole (Q3), which is then transmitted to the detector. This method of absolute quantitation in targeted proteomics analyses is suitable for identification and quantitation of target peptides within complex mixtures independent on peptide-specific manner. Label-free quantification is an alternative method for samples that cannot directly label and enables the comparison of protein expression across different samples or treatment regardless the number of samples. Protein microarray is another label-free method which is a high-density and high-throughput microarray containing thousands of unique proteins to identify the interactions on a large scale

Fig. 2

The depiction of the mathematical modeling in the conceptual world to the real world. Mathematical modeling empowers the researchers to examine the relationship between the biological processes in the real world and the predictions in the conceptual world. With the advent of high-throughput omics data, bioinformatics and mathematical modeling have become viable tools to improve our knowledge of molecular mechanism of cancer related phenomenon. It is a computational simulation that applied mathematical approaches of quantitative calculation for hundreds of components and their interactions and thus have the potential of truly explanation for complex diseases such as lung cancer. Researchers are able to systematically investigate systems perturbations, develop hypotheses to design new experiments, and ultimately predict the reliable candidates as novel therapeutic targets