title: Proteomic patterns of tumour subsets in non-small-cell lung cancer.
abstract: BACKGROUND
  Proteomics-based approaches complement the genome initiatives and may be the next step in attempts to understand the biology of cancer. We used matrix-assisted laser desorption/ionisation mass spectrometry directly from 1-mm regions of single frozen tissue sections for profiling of protein expression from surgically resected tissues to classify lung tumours.
  
  
  METHODS
  Proteomic spectra were obtained and aligned from 79 lung tumours and 14 normal lung tissues. We built a class-prediction model with the proteomic patterns in a training cohort of 42 lung tumours and eight normal lung samples, and assessed their statistical significance. We then applied this model to a blinded test cohort, including 37 lung tumours and six normal lung samples, to estimate the misclassification rate.
  
  
  RESULTS
  We obtained more than 1600 protein peaks from histologically selected 1 mm diameter regions of single frozen sections from each tissue. Class-prediction models based on differentially expressed peaks enabled us to perfectly classify lung cancer histologies, distinguish primary tumours from metastases to the lung from other sites, and classify nodal involvement with 85% accuracy in the training cohort. This model nearly perfectly classified samples in the independent blinded test cohort. We also obtained a proteomic pattern comprised of 15 distinct mass spectrometry peaks that distinguished between patients with resected non-small-cell lung cancer who had poor prognosis (median survival 6 months, n=25) and those who had good prognosis (median survival 33 months, n=41, p<0.0001).
  
  
  CONCLUSIONS
  Proteomic patterns obtained directly from small amounts of fresh frozen lung-tumour tissue could be used to accurately classify and predict histological groups as well as nodal involvement and survival in resected non-small-cell lung cancer.
authors.affiliation: Vanderbilt-Ingram Cancer Center, Nashville, Tennessee 37232-6838, USA.
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Yanagisawa
authors.forename: Yu
authors.initials: Y
authors.lastname: Shyr
authors.forename: Baogang J
authors.initials: BJ
authors.lastname: Xu
authors.forename: Pierre P
authors.initials: PP
authors.lastname: Massion
authors.forename: Paul H
authors.initials: PH
authors.lastname: Larsen
authors.forename: Bill C
authors.initials: BC
authors.lastname: White
authors.forename: John R
authors.initials: JR
authors.lastname: Roberts
authors.forename: Mary
authors.initials: M
authors.lastname: Edgerton
authors.forename: Adriana
authors.initials: A
authors.lastname: Gonzalez
authors.forename: Sorena
authors.initials: S
authors.lastname: Nadaf
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.forename: Richard M
authors.initials: RM
authors.lastname: Caprioli
authors.forename: David P
authors.initials: DP
authors.lastname: Carbone
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis; D002289:Carcinoma, Non-Small-Cell Lung / Q000737:chemistry / Q000175:diagnosis* / Q000556:secondary; D003937:Diagnosis, Differential; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008175:Lung Neoplasms / Q000737:chemistry / Q000175:diagnosis* / Q000556:secondary; D009363:Neoplasm Proteins / Q000032:analysis* / Q000145:classification; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods* / Q000706:statistics & numerical data; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000706:statistics & numerical data
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D020543:Proteome