Cancer Biomarker Discovery Using N-terminal Peptides and Multiple Reaction Monitoring-MS Techniques

Abstract

Introduction: Cancer is the leading cause of death in the worldwide, and the major cause of cancer death is the difficulty for early diagnosis. To overcome this problem, the discovery of cancer biomarkers is useful for early diagnosis, outcome monitoring, or predicting recurrence. For biomarker discovery, proteomics technique is powerful tools with high-throughput and high sensitivity. Thus, proteomics study can help variable cancer biomarker discovery and understand of cancer mechanisms in body.

Methods: In chapter I, to examine metastatic events in lung cancer, we performed a proteomics study by label-free quantitative analysis and N-terminal analysis in 2 human non-small-cell lung cancer cell lines with disparate metastatic potentials?NCI-H1703 (primary cell, stage I) and NCI-H1755 (metastatic cell, stage IV). In chapter II, we performed to identify new marker-candidate proteins from LiverAtlas database. And abundance of marker-candidate proteins were quantified in individual patients by multiple reaction monitoring assay.

Results: In chapter I, we identified 2130 proteins, 1355 of which were common to both cell lines. In the label-free quantitative analysis, we used the NSAF normalization method, resulting in 242 differential expressed proteins. For the N-terminal proteome analysis, 325 N-terminal peptides, including 45 novel fragments, were identified in the 2 cell lines. Based on two proteomic analysis, 11 quantitatively expressed proteins and 8 N-terminal peptides were enriched for the focal adhesion pathway. Most proteins from the quantitative analysis were upregulated in metastatic cancer cells, whereas novel fragment of CRKL was detected only in primary cancer cells. In chapter II, we selected quantitative 104 marker candidate proteins with reference labeled peptides. Among them, we found that 17 proteins with AUC more than 0.60 were able to effectively discriminate poor responders from total patients underwent TACE. Also, we discovered powerful ensemble model panel with protein markers and clinical variables.

Conclusions: In chapter I, our datasets of proteins and fragment peptides in lung cells might be valuable in discovering and validating lung cancer biomarkers and metastasis markers. This study increases our understanding of the NSCLC metastasis proteome. In chapter II, we discovered three new marker proteins that are associated with prognosis prediction after TACE in the first time. Our study can help to identify useful biomarkers for prediction of prognosis with multi-panel modeling.