Bregeon F., Brioude G., De Dominicis F., Atieh T., D'Journo X. B., Flaudrops C., Rolain J. M., Raoult Didier, Thomas P. A. (2014). MALDI-ToF mass spectrometry for the rapid diagnosis of cancerous lung nodules. Plos One, 9 (5), p. e97511. ISSN 1932-6203.
Titre du document
MALDI-ToF mass spectrometry for the rapid diagnosis of cancerous lung nodules
Bregeon F., Brioude G., De Dominicis F., Atieh T., D'Journo X. B., Flaudrops C., Rolain J. M., Raoult Didier, Thomas P. A.
Source
Plos One, 2014,
9 (5), p. e97511 ISSN 1932-6203
Recently, tissue-based methods for proteomic analysis have been used in clinical research and appear reliable for digestive, brain, lymphomatous, and lung cancers classification. However simple, tissue-based methods that couple signal analysis to tissue imaging are time consuming. To assess the reliability of a method involving rapid tissue preparation and analysis to discriminate cancerous from non-cancerous tissues, we tested 141 lung cancer/non-tumor pairs and 8 unique lung cancer samples among the stored frozen samples of 138 patients operated on during 2012. Samples were crushed in water, and 1.5 mu l was spotted onto a steel target for analysis with the Microflex LT analyzer (Bruker Daltonics). Spectra were analyzed using ClinProTools software. A set of samples was used to generate a random classification model on the basis of a list of discriminant peaks sorted with the k-nearest neighbor genetic algorithm. The rest of the samples (n = 43 cancerous and n = 41 non-tumoral) was used to verify the classification capability and calculate the diagnostic performance indices relative to the histological diagnosis. The analysis found 53 m/z valid peaks, 40 of which were significantly different between cancerous and non-tumoral samples. The selected genetic algorithm model identified 20 potential peaks from the training set and had 98.81% recognition capability and 89.17% positive predictive value. In the blinded set, this method accurately discriminated the two classes with a sensitivity of 86.7% and a specificity of 95.1% for the cancer tissues and a sensitivity of 87.8% and a specificity of 95.3% for the non-tumor tissues. The second model generated to discriminate primary lung cancer from metastases was of lower quality. The reliability of MALDI-ToF analysis coupled with a very simple lung preparation procedure appears promising and should be tested in the operating room on fresh samples coupled with the pathological examination.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Santé : généralités [050]
