title: Do serum biomarkers really measure breast cancer?
abstract: BACKGROUND
  Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins.
  
  
  METHODS
  This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis.
  
  
  RESULTS
  The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 +/- 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 +/- 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 +/- 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer.
  
  
  CONCLUSIONS
  Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.
authors.affiliation: Duke Advanced Imaging Labs, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710, USA. JonathanL_Jesneck@dfci.harvard.edu
authors.forename: Jonathan L
authors.initials: JL
authors.lastname: Jesneck
authors.forename: Sayan
authors.initials: S
authors.lastname: Mukherjee
authors.forename: Zoya
authors.initials: Z
authors.lastname: Yurkovetsky
authors.forename: Merlise
authors.initials: M
authors.lastname: Clyde
authors.forename: Jeffrey R
authors.initials: JR
authors.lastname: Marks
authors.forename: Anna E
authors.initials: AE
authors.lastname: Lokshin
authors.forename: Joseph Y
authors.initials: JY
authors.lastname: Lo
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000097:blood*; D001943:Breast Neoplasms / Q000097:blood* / Q000175:diagnosis / Q000473:pathology; D004797:Enzyme-Linked Immunosorbent Assay; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000097:blood; D012372:ROC Curve
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins