[Fragments Woven into a Whole: A New Approach to the Diagnosis of Cancer through Mass Spectrometry and Machine-Learning].

Conventionally, a definitive diagnosis of cancer is derived from histopathological diagnostics based on morphological criteria that are difficult to standardize on a quantifiable basis. On the other hand, while molecular tumor markers and blood biochemical profiles give quantitative values evaluated by objective criteria, these parameters are usually generated by deductive methods such as peak extraction. Therefore, some of the data that may contain useful information on specimens are discarded. To overcome the disadvantages of these methods, we have developed a new approach by employing both mass spectrometry and machine-learning for cancer diagnosis. Probe electrospray ionization (PESI) is a derivative of electrospray ionization that uses a fine acupuncture needle as a sample picker as well as an ion emitter for mass spectrometry. This method enables us to ionize very small tissue samples up to a few pico liters in the presence of physiological concentrations of inorganic salts, without the need for any sample pretreatment. Moreover, as this technique makes it possible to ionize all components with minimal suppression effects, we can retrieve much more molecular information from specimens. To make the most of data enriched with lipid compounds and substances with lower molecular weights such as carbohydrates, we employed machine-learning named the dual penalized logistic regression machine (dPLRM). This method is completely different from pattern-matching in that it discriminates categories by projecting the spectral data into a mathematical space with very high dimensions, where final judgment is made. We are now approaching the final clinical trial to validate the usefulness of our system.