Proteomic patterns of preinvasive bronchial lesions.


A proteomics approach is warranted to further elucidate the molecular steps involved in lung tumor development. We asked whether we could classify preinvasive lesions of airway epithelium according to their proteomic profile.

We obtained matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiles from 10-microm sections of fresh-frozen tissue samples: 25 normal lung, 29 normal bronchial epithelium, and 20 preinvasive and 36 invasive lung tumor tissue samples from 53 patients. Proteomic profiles were calibrated, binned, and normalized before analysis. We performed class comparison, class prediction, and supervised hierarchic cluster analysis. We tested a set of discriminatory features obtained in a previously published dataset to classify this independent set of normal, preinvasive, and invasive lung tissues.

We found a specific proteomic profile that allows an overall predictive accuracy of over 90% of normal, preinvasive, and invasive lung tissues. The proteomic profiles of these tissues were distinct from each other within a disease continuum. We trained our prediction model in a previously published dataset and tested it in a new blinded test set to reach an overall 74% accuracy in classifying tumors from normal tissues.

We found specific patterns of protein expression of the airway epithelium that accurately classify bronchial and alveolar tissue with normal histology from preinvasive bronchial lesions and from invasive lung cancer. Although further study is needed to validate this approach and to identify biomarkers of tumor development, this is a first step toward a new proteomic characterization of the human model of lung cancer tumorigenesis.

  • Caprioli RM
  • Carbone DP
  • Chaurand P
  • Franklin WA
  • Gonzalez AL
  • Li H
  • Massion PP
  • Miller RF
  • Miller YE
  • Ninan M
  • Rahman SM
  • Shyr Y
  • Slovis BS
  • Yanagisawa K
  • Yildiz PB
  • Zhang X
PubMed ID
Appears In
Am J Respir Crit Care Med, 2005, 172 (12)