Pathway-based biomarker search by high-throughput proteomics profiling of secretomes.
An efficient means for the identification of prognostic and predictive biomarkers is essential in today's cancer management. A new approach toward biomarker discovery has therefore been proposed, where pathways instead of individual proteins would be monitored and targeted. Recently, the 'secretome', a biological fluid that may be enriched with secreted and/or shed proteins from adjacent disease-relevant cancer cells, has been targeted for biomarker discovery. We describe a novel method for secretome analysis using "stacking gels", label-free relative quantitation, and pathway analysis. The protocol presented here increases the throughput of secretome analysis by approximately 1 order of magnitude compared to earlier methodologies. In the first application, six cancer cell lines from three different tissues were studied. The global secretome data sets obtained were analyzed using pathway analysis software to attempt integrating the experimental findings into a cellular signaling context. This suggested that several secretome proteins might be interconnected with intracellular canonical pathways. This, in turn, may eventually allow the use of secretomes for discovery of pathway-based biomarkers. When this strategy was applied to two breast cancer cell lines, it appeared that the IGF signaling and the plasminogen activating system may be differentially regulated in invasive breast cancer, but this remains speculative until it is verified in a clinical setting. In summary, the methodology proposed optimizes cell culture with sample fractionation and LC-MS to obtain the highest yield from cultured cell secretomes, with a focus on rational biomarker discovery through putative linkage with cancer relevant pathways.
Lawlor
Kevin
K
Protein Center and Molecular Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.
Nazarian
Arpi
A
Lacomis
Lynne
L
Tempst
Paul
P
Villanueva
Josep
J
D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000378:metabolism; D045744:Cell Line, Tumor; D019295:Computational Biology; D005260:Female; D006801:Humans; D008297:Male; D009363:Neoplasm Proteins / Q000378:metabolism*; D011471:Prostatic Neoplasms / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology*; D001749:Urinary Bladder Neoplasms / Q000378:metabolism
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins