01.03.2016

The MSACL 2016 US, the 8th Annual Conference on mass spectrometry applications to the clinical lab took place in Palm Springs, California on February 21–25, 2016. SCiLS presented recent results on automated tumor typing of tissue sections.

  1. Boskamp, D. Lachmund, J. Oetjen, R. Casadonte, J.H. Kobarg, J. Kriegsmann, P. Maass. Automated tumor typing of tissue sections based on MALDI mass spectrometry imaging data and machine learning using characteristic spectral patterns. (download)

We present an automated classification method for MALDI mass spectrometry imaging data with applications to tumor typing of FFPE tissue sections. The proposed method consists of a) data pre-processing, b) identification of characteristic spectral patterns using non-negative matrix factorization (NMF), and c) applying linear discriminant analysis (LDA) for classification. We apply this method to the discrimination of breast, lung, colon and pancreas cancer. MALDI data has been acquired from eight tissue micro arrays (TMAs), two for each tumor type, with a total of 943 cores from 285 patients. Four TMAs have been used for training, the remaining four TMAs for validation. A sensitivity on core level of 100.0% (lung), 99.5% (pancreas), 100.0% (colon), and 100.0% (breast) was achieved. Only limited effects of different preprocessing variants (normalization, filtering) were observed.

NEWS_ABSTRACT

Automated tumor typing of tissue sections based on MALDI mass spectrometry imaging data and machine learning using characteristic spectral patterns