title: Postoperative serum proteomic profiles may predict metastatic relapse in high-risk primary breast cancer patients receiving adjuvant chemotherapy.
abstract: A total of 30-50% of early breast cancer (EBC) patients considered as high risk using standard prognostic factors develop metastatic recurrence despite standard adjuvant systemic treatment. A means to better predict clinical outcome is needed to optimize and individualize therapeutic decisions. To identify a protein signature correlating with metastatic relapse, we performed surface-enhanced laser desorption/ionization-time of flight mass spectrometry profiling of early postoperative serum from 81 high-risk EBC patients. Denatured and fractionated serum samples were incubated with IMAC30 and CM10 ProteinChip arrays. Several protein peaks were differentially expressed according to clinical outcome. By combining partial least squares and logistic regression methods, we built a multiprotein model that correctly predicted outcome in 83% of patients. The 5-year metastasis-free survival in 'good prognosis' and 'poor prognosis' patients as defined using the multiprotein index were strikingly different (83 and 22%, respectively; P<0.0001, log-rank test). In a multivariate Cox regression including conventional pathological factors and multiprotein index, the latter retained the strongest independent prognostic significance for metastatic relapse. Major components of the multiprotein index included haptoglobin, C3a complement fraction, transferrin, apolipoprotein C1 and apolipoprotein A1. Therefore, postoperative serum protein pattern may have an important prognostic value in high-risk EBC.
authors.affiliation: Department of Molecular Pharmacology, Institut Paoli-Calmettes, UMR599 Institut National de la Santé et de la Recherche Médicale (INSERM), Marseille, France. goncalvesa@marseille.fnclcc.fr
authors.forename: A
authors.initials: A
authors.lastname: Gonçalves
authors.forename: B
authors.initials: B
authors.lastname: Esterni
authors.forename: F
authors.initials: F
authors.lastname: Bertucci
authors.forename: R
authors.initials: R
authors.lastname: Sauvan
authors.forename: C
authors.initials: C
authors.lastname: Chabannon
authors.forename: M
authors.initials: M
authors.lastname: Cubizolles
authors.forename: V J
authors.initials: VJ
authors.lastname: Bardou
authors.forename: G
authors.initials: G
authors.lastname: Houvenaegel
authors.forename: J
authors.initials: J
authors.lastname: Jacquemier
authors.forename: S
authors.initials: S
authors.lastname: Granjeaud
authors.forename: X-Y
authors.initials: XY
authors.lastname: Meng
authors.forename: E T
authors.initials: ET
authors.lastname: Fung
authors.forename: D
authors.initials: D
authors.lastname: Birnbaum
authors.forename: D
authors.initials: D
authors.lastname: Maraninchi
authors.forename: P
authors.initials: P
authors.lastname: Viens
authors.forename: J-P
authors.initials: JP
authors.lastname: Borg
mesh_terms: D000328:Adult; D000368:Aged; D001798:Blood Proteins / Q000032:analysis*; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000473:pathology / Q000601:surgery; D017024:Chemotherapy, Adjuvant; D005260:Female; D006801:Humans; D008875:Middle Aged; D009362:Neoplasm Metastasis; D011184:Postoperative Period; D011379:Prognosis; D040081:Protein Array Analysis; D040901:Proteomics; D012008:Recurrence
chemical_list: D001798:Blood Proteins