Application of proteomics to cancer early detection

Abstract

Strategies to achieve personalized medicine and improve public health encompass assessment of an individual's risk for disease, early detection, and molecular classification of disease resulting in an informed choice of the most appropriate treatment instituted at an early stage of disease development. An unmet need in this field for which proteomics is well suited to make a major contribution is the development of blood-based tests for early cancer detection. This is illustrated in proteomic studies of epithelial cancer that encompass analysis of specimens collected both at the time of diagnosis and specimens collected before onset of symptoms that are particularly suited for the identification of early detection markers. This overarching effort benefits from the availability of plasmas from subject cohorts and of engineered mouse models that are sampled at early stages of tumor development. Integration of findings from plasma with tumor tissue and cancer cell proteomic and genomic data allows elucidation of signatures in plasma for altered signaling pathways. The discovery and further development of early detection markers take advantage of the availability of in-depth quantitative proteomics methods and bioinformatics resources for data mining.

Figure 1. In-depth quantitative analysis by means of liquid chromatography-mass spectrometry of proteins in a pair of plasmas from a cancer case and a control

Plasmas from case and from control are subjected to isotopic labeling of proteins followed by pooling and fractionation of the pool prior to mass spectrometry analysis of digested fraction. Columns represent individual fractions; rows represent individual proteins identified in particular fractions. The color scheme is indicative of case to control concentrations ratios (red= increased, yellow= no change and green= decreased) for individual proteins based on differential isotope labeling. The ratio for an individual protein is determined based on the isotopic envelopes of case and control for a given peptide as shown for an EGFR peptide.

Figure 2. A mouse to human search for lung cancer plasma protein markers

Unsupervised hierarchical clustering of plasma proteome profile of mouse models of cancer resulted in clustering of proteomes from lung adenocarcinomas and small cell lung cancer. Validation studies of a panel of candidate markers (EGFR, SFTPB, WFDC2, and ANGPTL3) identified in mouse models of lung adenocarcinoma yielded a significant area under the curve (AUC) for the individual markers and the combined panel in plasmas from newly diagnosed subjects with lung cancer and in pre-diagnostic plasmas relative to controls. Likewise a validation study of ROBO1, a candidate marker identified in plasma from the small cell lung cancer mouse model, resulted in significantly increase levels in plasmas from subjects with small cell lung cancer relative to controls.