title: 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.
abstract: 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.
authors.affiliation: Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland.
authors.forename: Piotr
authors.initials: P
authors.lastname: Widlak
authors.affiliation: Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.
authors.forename: Grzegorz
authors.initials: G
authors.lastname: Mrukwa
authors.affiliation: Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland.
authors.forename: Magdalena
authors.initials: M
authors.lastname: Kalinowska
authors.affiliation: Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland.
authors.forename: Monika
authors.initials: M
authors.lastname: Pietrowska
authors.affiliation: Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland.
authors.forename: Mykola
authors.initials: M
authors.lastname: Chekan
authors.affiliation: Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland.
authors.forename: Janusz
authors.initials: J
authors.lastname: Wierzgon
authors.affiliation: Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch, Gliwice, Poland.
authors.forename: Marta
authors.initials: M
authors.lastname: Gawin
authors.affiliation: Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.
authors.forename: Grzegorz
authors.initials: G
authors.lastname: Drazek
authors.affiliation: Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.
authors.forename: Joanna
authors.initials: J
authors.lastname: Polanska
mesh_terms: D000328:Adult; D000465:Algorithms; D002294:Carcinoma, Squamous Cell / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000235:genetics / Q000473:pathology; D016000:Cluster Analysis; D004848:Epithelium / Q000000981:diagnostic imaging / Q000473:pathology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D057054:Molecular Imaging / Q000379:methods*; D009062:Mouth Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000235:genetics / Q000473:pathology; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
keywords: Data clustering; Gaussian mixture model; Head and neck cancer; Imaging mass spectrometry; Technology; Unsupervised analysis