The Mass Spectrometry society of Japan - The 68th Annual Conference on Mass Spectrometry, Japan
Japanese

Abstract

Table of contents

Timetable

Plenary Lectures

Oral Sessions (Day1, Day2, Day3)

Poster Presentations (Day1, Day2, Day3)

Workshops

Luncheon Seminars (Day1, Day2, Day3)

Poster Presentations

Day 3, May 13（Wed.）　　Poster (1008/09)

3P-07　　PDF

Diagnosis of breast cancer based on lipid profiling by probe electrospray ionization mass spectrometry

(1Univ. Yamanashi, 2Shimadzu)
oTomohiko Iwano1, Kentaro Yoshimura1, Shingo Inoue1, Toru Odate1, Koretsugu Ogata2, Shinji Funatsu2, Hiroshi Tanihata2, Tetsuo Kondo1, Daiisuke Ichikawa1, Sen Takeda1

Detection in the early stages of the breast cancer (BC) assures good prognosis. For this purpose, imaging techniques sometimes show less sensitivity and pathological examination needs multiple steps and the duration of preparation. For more accurate and less time-consuming diagnostic method using small specimen, we have applied probe electrospray ionization mass spectrometry (PESI-MS) and machine learning (Logistic regression) to biological systems. This system enables us to distinguish a cancerous tissue from a non-cancerous tissue using almost raw samples without specific laborious pretreatments.　In this study, we undertook two directions of study in BC. In both cases, mass spectra were acquired from non- cancerous and cancerous tissues. In the first part of study using PESI-MS, we established a diagnostic algorithm by machine learning using mass spectra. In the second part of study, we used LC-MS/MS to identify several specific molecules that were markedly increased in BC and used them as a collective biomarker. Collectively, we demonstrated that PESI-MS combined with machine learning has the potential to establish a lipid-based diagnosis of BC with higher accuracy using a simpler approach.