Premium
Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium – application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data
Author(s) -
Widlak Piotr,
Mrukwa Grzegorz,
Kalinowska Magdalena,
Pietrowska Monika,
Chekan Mykola,
Wierzgon Janusz,
Gawin Marta,
Drazek Grzegorz,
Polanska Joanna
Publication year - 2016
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201500458
Subject(s) - mass spectrometry imaging , cancer , epithelium , pathology , computational biology , cluster analysis , mass spectrometry , segmentation , biology , computer science , artificial intelligence , chemistry , cancer research , medicine , genetics , chromatography
Intra‐tumor heterogeneity is a vivid problem of molecular oncology that could be addressed by imaging mass spectrometry. Here we aimed to assess molecular heterogeneity of oral squamous cell carcinoma and to detect signatures discriminating normal and cancerous epithelium. Tryptic peptides were analyzed by MALDI‐IMS in tissue specimens from five patients with oral cancer. Novel algorithm of IMS data analysis was developed and implemented, which included Gaussian mixture modeling for detection of spectral components and iterative k‐means algorithm for unsupervised spectra clustering performed in domain reduced to a subset of the most dispersed components. About 4% of the detected peptides showed significantly different abundances between normal epithelium and tumor, and could be considered as a molecular signature of oral cancer. Moreover, unsupervised clustering revealed two major sub‐regions within expert‐defined tumor areas. One of them showed molecular similarity with histologically normal epithelium. The other one showed similarity with connective tissue, yet was markedly different from normal epithelium. Pathologist's re‐inspection of tissue specimens confirmed distinct features in both tumor sub‐regions: foci of actual cancer cells or cancer microenvironment‐related cells prevailed in corresponding areas. Hence, molecular differences detected during automated segmentation of IMS data had an apparent reflection in real structures present in tumor.