title: Serum proteomic profiling of lung cancer in high-risk groups and determination of clinical outcomes.
abstract: OBJECTIVE
  Lung cancer remains the leading cause of cancer-related mortality worldwide. Currently known serum markers do not efficiently diagnose lung cancer at early stage.
  
  
  METHODS
  In the present study, we developed a serum proteomic fingerprinting approach coupled with a three-step classification method to address two important clinical questions: (i) to determine whether or not proteomic profiling differs between lung cancer and benign lung diseases in a population of smokers and (ii) to assess the prognostic impact of this profiling in lung cancer. Proteomic spectra were obtained from 170 pathologically confirmed lung cancer or smoking patients with benign chronic lung disease serum samples.
  
  
  RESULTS
  Among the 228 protein peaks differentially expressed in the whole population, 88 differed significantly between lung cancer patients and benign lung disease, with area under the curve diagnostic values ranging from 0.63 to 0.84. Multiprotein classifiers based on differentially expressed peaks allowed the classification of lung cancer and benign disease with an area under the curve ranging from 0.991 to 0.994. Using a cross-validation methodology, diagnostic accuracy was 93.1% (sensitivity 94.3%, specificity 85.9%), and more than 90% of the stage I/II lung cancers were correctly classified. Finally, in the prognosis part of the study, a 4628 Da protein was found to be significantly and independently associated with prognosis in advanced stage non-small cell lung cancer patients (p = 0.0005).
  
  
  CONCLUSIONS
  The potential markers that we identified through proteomic fingerprinting could accurately classify lung cancers in a high-risk population and predict survival in a non-small cell lung cancer population.
authors.affiliation: Department of Thoracic Oncology, CHU Montpellier, Hôpital Arnaud de Villeneuve, Montpellier, France.
authors.forename: William
authors.initials: W
authors.lastname: Jacot
authors.forename: Ludovic
authors.initials: L
authors.lastname: Lhermitte
authors.forename: Nadège
authors.initials: N
authors.lastname: Dossat
authors.forename: Jean-Louis
authors.initials: JL
authors.lastname: Pujol
authors.forename: Nicolas
authors.initials: N
authors.lastname: Molinari
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Daurès
authors.forename: Thierry
authors.initials: T
authors.lastname: Maudelonde
authors.forename: Alain
authors.initials: A
authors.lastname: Mangé
authors.forename: Jérôme
authors.initials: J
authors.lastname: Solassol
mesh_terms: D000230:Adenocarcinoma / Q000097:blood / Q000145:classification / Q000556:secondary; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000097:blood*; D018287:Carcinoma, Large Cell / Q000145:classification / Q000175:diagnosis / Q000556:secondary; D002289:Carcinoma, Non-Small-Cell Lung / Q000097:blood* / Q000145:classification / Q000556:secondary; D002294:Carcinoma, Squamous Cell / Q000145:classification / Q000175:diagnosis / Q000556:secondary; D016172:DNA Fingerprinting / Q000379:methods; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000097:blood* / Q000145:classification / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D011379:Prognosis; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods; D012372:ROC Curve; D012307:Risk Factors; D055752:Small Cell Lung Carcinoma / Q000145:classification / Q000175:diagnosis / Q000556:secondary; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor; D020543:Proteome