title: Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study.
abstract: BACKGROUND
  Some but not all patients with non-small-cell lung cancer (NSCLC) respond to treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). We developed and tested the ability of a predictive algorithm based on matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) analysis of pretreatment serum to identify patients who are likely to benefit from treatment with EGFR TKIs.
  
  
  METHODS
  Serum collected from NSCLC patients before treatment with gefitinib or erlotinib were analyzed by MALDI MS. Spectra were acquired independently at two institutions. An algorithm to predict outcomes after treatment with EGFR TKIs was developed from a training set of 139 patients from three cohorts. The algorithm was then tested in two independent validation cohorts of 67 and 96 patients who were treated with gefitinib and erlotinib, respectively, and in three control cohorts of patients who were not treated with EGFR TKIs. The clinical outcomes of survival and time to progression were analyzed.
  
  
  RESULTS
  An algorithm based on eight distinct m/z features was developed based on outcomes after EGFR TKI therapy in training set patients. Classifications based on spectra acquired at the two institutions had a concordance of 97.1%. For both validation cohorts, the classifier identified patients who showed improved outcomes after EGFR TKI treatment. In one cohort, median survival of patients in the predicted "good" and "poor" groups was 207 and 92 days, respectively (hazard ratio [HR] of death in the good versus poor groups = 0.50, 95% confidence interval [CI] = 0.24 to 0.78). In the other cohort, median survivals were 306 versus 107 days (HR = 0.41, 95% CI = 0.17 to 0.63). The classifier did not predict outcomes in patients who did not receive EGFR TKI treatment.
  
  
  CONCLUSIONS
  This MALDI MS algorithm was not merely prognostic but could classify NSCLC patients for good or poor outcomes after treatment with EGFR TKIs. This algorithm may thus assist in the pretreatment selection of appropriate subgroups of NSCLC patients for treatment with EGFR TKIs.
authors.affiliation: Department of Medicine, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232-6838, USA.
authors.forename: Fumiko
authors.initials: F
authors.lastname: Taguchi
authors.forename: Benjamin
authors.initials: B
authors.lastname: Solomon
authors.forename: Vanesa
authors.initials: V
authors.lastname: Gregorc
authors.forename: Heinrich
authors.initials: H
authors.lastname: Roder
authors.forename: Robert
authors.initials: R
authors.lastname: Gray
authors.forename: Kazuo
authors.initials: K
authors.lastname: Kasahara
authors.forename: Makoto
authors.initials: M
authors.lastname: Nishio
authors.forename: Julie
authors.initials: J
authors.lastname: Brahmer
authors.forename: Anna
authors.initials: A
authors.lastname: Spreafico
authors.forename: Vienna
authors.initials: V
authors.lastname: Ludovini
authors.forename: Pierre P
authors.initials: PP
authors.lastname: Massion
authors.forename: Rafal
authors.initials: R
authors.lastname: Dziadziuszko
authors.forename: Joan
authors.initials: J
authors.lastname: Schiller
authors.forename: Julia
authors.initials: J
authors.lastname: Grigorieva
authors.forename: Maxim
authors.initials: M
authors.lastname: Tsypin
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Hunsucker
authors.forename: Richard
authors.initials: R
authors.lastname: Caprioli
authors.forename: Mark W
authors.initials: MW
authors.lastname: Duncan
authors.forename: Fred R
authors.initials: FR
authors.lastname: Hirsch
authors.forename: Paul A
authors.initials: PA
authors.lastname: Bunn
authors.forename: David P
authors.initials: DP
authors.lastname: Carbone
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000097:blood*; D002289:Carcinoma, Non-Small-Cell Lung / Q000145:classification* / Q000188:drug therapy; D066246:ErbB Receptors / Q000037:antagonists & inhibitors*; D005260:Female; D000077156:Gefitinib; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification* / Q000188:drug therapy; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D047428:Protein Kinase Inhibitors / Q000627:therapeutic use*; D040901:Proteomics; D011799:Quinazolines / Q000627:therapeutic use; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D015996:Survival Rate; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor; D047428:Protein Kinase Inhibitors; D011799:Quinazolines; D066246:ErbB Receptors; D000077156:Gefitinib