2P-16 PDF
PESI-MSの新たな展開：消化器がんの代謝経路解析に基づく創薬への展望
Nowadays, there are many research groups working on the establishment of MS-based cancer diagnosis system, while our group has been a frontrunner in the diagnosis of renal cell carcinoma and hepatocellular carcinoma, by probe electrospray ionization (PESI). Since PESI does not need vacuum assistance and cumbersome sample pretreatment, it is suitable for rapid analysis of biological samples. In this study, we present the versatility of PESI diagnosis system for oral squamous cell carcinoma and colorectal carcinoma.
We acquired the mass spectra from surgically excised human tissues (mucosal epithelia), and constructed a mass spectra database (1566 mass spectra). These data were fed into learning machine (logistic regression, LR) to determine if the tissue is cancerous or not. Subsequent cross-validation trial gave an accuracy of over 80% in cancer diagnosis, the results suggesting that our system is applicable for diagnosis of gastrointestinal malignancies.
While learning machine-based diagnosis algorithm makes judgment without annotating each peak, we took an alternative way to look into specific peaks showing significant elevation or depression in cancerous tissues in terms of ion intensity. This enables us to get molecular information valuable for identifying tumor markers or developing the molecularly targeted therapy.