The role of quantitative mass spectrometry in the discovery of pancreatic cancer biomarkers for translational science

Abstract

In the post-genomic era, it has become evident that genetic changes alone are not sufficient to understand most disease processes including pancreatic cancer. Genome sequencing has revealed a complex set of genetic alterations in pancreatic cancer such as point mutations, chromosomal losses, gene amplifications and telomere shortening that drive cancerous growth through specific signaling pathways. Proteome-based approaches are important complements to genomic data and provide crucial information of the target driver molecules and their post-translational modifications. By applying quantitative mass spectrometry, this is an alternative way to identify biomarkers for early diagnosis and personalized medicine. We review the current quantitative mass spectrometric technologies and analyses that have been developed and applied in the last decade in the context of pancreatic cancer. Examples of candidate biomarkers that have been identified from these pancreas studies include among others, asporin, CD9, CXC chemokine ligand 7, fibronectin 1, galectin-1, gelsolin, intercellular adhesion molecule 1, insulin-like growth factor binding protein 2, metalloproteinase inhibitor 1, stromal cell derived factor 4, and transforming growth factor beta-induced protein. Many of these proteins are involved in various steps in pancreatic tumor progression including cell proliferation, adhesion, migration, invasion, metastasis, immune response and angiogenesis. These new protein candidates may provide essential information for the development of protein diagnostics and targeted therapies. We further argue that new strategies must be advanced and established for the integration of proteomic, transcriptomic and genomic data, in order to enhance biomarker translation. Large scale studies with meta data processing will pave the way for novel and unexpected correlations within pancreatic cancer, that will benefit the patient, with targeted treatment.

Asporin participates in gastric cancer cell growth and migration by influencing EGF receptor signaling

“…ASPN, a new member of the SLRPs, is expressed abundantly in several malignant tumors, suggesting its involvement in the tumorigenesis and progression of cancer (6). Our present results, consistent with previous studies, revealed a significantly elevated ASPN expression in carcinoma tissues relative to the matched adjacent non-tumor tissues (11)(12)(13)(14). In addition, ASPN had varied expression in an inverse trend according to the differentiation degree of gastric cancer epithelial cell lines, hinting its oncogenic property, to some extent.…”

“…Particularly, Satoyoshi et al recently concluded that ASPN activates the coordinated invasion of scirrhous gastric cancer and cancer-associated fibroblasts by activation of the CD44-Rac1 pathway (7). In addition, by applying quantitative mass spectrometry, innovative proteomic technologies or microarray analysis, ASPN was proven to be upregulated in a series of cancers, including pancreatic cancer (11), pancreatic ductal adenocarcinoma (12), prostate cancer (13) and breast carcinoma (14). In osteoarthritis, Kizawa et al found a direct interaction between ASPN and TGF-β.…”

Fibulin-5 Blocks Microenvironmental ROS in Pancreatic Cancer

“…The expression of Fbln5 in each model is similar to the expression level and pattern of Fbln5 expression in human PDA. Furthermore, FN and α5β1 integrin are expressed abundantly in animal models of PDA as well as human PDA (38, 39). To study Fbln5-integrin interaction we took advantage of the fact that 1) Fbln5 binds but does not activate α5β1 (8, 14), suggesting that it can function to reduce binding of other ligands of the integrin; and 2) knockin mice carrying a point mutation in the integrin binding domain of Fbln5 ( RGE mice) are viable and fertile (15).…”

Integrated proteo-genomic approach for early diagnosis and prognosis of cancer

“…Thus, the integration of proteomic, transcriptomic and genomic data, could provide vital information of the target driver molecules and their post-translational modifications status could immensely help in biomarker translation [11].…”