Objectives: Intact cell peptidome profiling (ICPP) with MALDI-ToF Mass-Spectrometry holds promise as a non invasive method to detect head and neck squamous cell carcinoma (HNSCC) objectively, which may improve the early diagnosis of oral cancer tremendously. The present study was designed to discriminate between tumour samples and non-cancer controls (healthy mucosa and oral lesions) by analysing complete spectral patterns of intact cells using MALDI-ToF MS.
Material and Methods: In the first step, a data base consisting of 26 patients suffering from HNSCC was established by taking brush biopsy samples of the diseased area and of the healthy buccal mucosa of the respective contralateral area. After performing MALDI-ToF MS on these samples, classification analysis was used as a basis for further classification of the blind study composed of additional 26 samples including HNSCC, oral lesions and healthy mucosa.
Results: By analyzing spectral patterns of the blind study, all cancerous lesions were defined accurately. One incorrect evaluation (false positive) occurred in the lesion cohort, leading to a sensitivity of 100%, a specificity of 93% and an overall accuracy of 96.5%.
Conclusion: ICPP using MALDI-ToF MS is able to distinguish between healthy and cancerous mucosa and between oral lesions and oral cancer with excellent sensitivity and specificity, which may lead to a more impartial early diagnosis of HNSCC.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:15-qucosa-116691 |
| Date | 27 June 2013 |
| Creators | Maurer, Katja |
| Contributors | Universitätsklinikum Leipzig, Sektion für Klinische und Experimentelle Orale Medizin an der Klinik für Mund-, Kiefer- und Plastische Gesichtschirurgie, Univ.-Professor Dr. med. dent. habil. Torsten W. Remmerbach, Univ.-Professor Dr. rer. nat. habil. Klaus Eschrich, Univ.-Prof. Dr. med. dent. Rainer Haak, Univ.-Prof. Dr. med. dent. Andrea-Maria Schmidt-Westhausen |
| Publisher | Universitätsbibliothek Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | deu |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf |
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