title: Immunohistochemical profiling of node negative breast carcinomas allows prediction of metastatic risk.
abstract: The aim of this study was to identify a prognostic immunohistochemical signature indicative of risk of early metastasis in node-negative breast carcinomas that would also be relevant to the development of new tailored therapy. Quantitative measurements of the immunohistochemical expression of 64 markers (selected from literature data) using high-throughput densitometry (as a continuous variable) of digitised microscopic micro-array images were correlated with clinical outcome in 667 node-negative breast carcinomas (mean follow-up 102 months). Multivariable fractional polynomials model of logistic regression allowed the selection of the best combination of markers (in terms of sensitivity and specificity) to predict patient outcome without any categorisation using predefined cut-points for individual marker measurements. A highly predictive ten-marker (out of 64) signature was identified comprising PI3K, pmTOR, pMAPKAPK-2, SHARP-2, P21, HIF-1alpha, Moesin, p4EBP-1, pAKT and P27 that well classified 91.4% of node-negative patients (specificity 90.9%, sensitivity 93.7%, area under ROC curve 0.958) independently of estrogen receptors (ER), and progesterone receptors (PR) and HER-2 status (91.6% well classified patients when ER, PR, HER-2 excluded). It is concluded that quantitative immunoprofiling of node-negative breast carcinomas is helpful in selecting patients who should not receive aggressive adjuvant chemotherapy and provides data for the development of tailored therapy.
authors.affiliation: Department of Pathology, Hôpital Nord and Université de la Méditerranée, Marseille, France.
authors.forename: Sophie
authors.initials: S
authors.lastname: Giusiano
authors.forename: Veronique
authors.initials: V
authors.lastname: Secq
authors.forename: Xavier
authors.initials: X
authors.lastname: Carcopino
authors.forename: Severine
authors.initials: S
authors.lastname: Carpentier
authors.forename: Lucile
authors.initials: L
authors.lastname: Andrac
authors.forename: Marie-Noelle
authors.initials: MN
authors.lastname: Lavaut
authors.forename: Claude
authors.initials: C
authors.lastname: Allasia
authors.forename: Pascal
authors.initials: P
authors.lastname: Bonnier
authors.forename: Juan Lucio
authors.initials: JL
authors.lastname: Iovanna
authors.forename: Stephane
authors.initials: S
authors.lastname: Garcia
authors.forename: Leon
authors.initials: L
authors.lastname: Boubli
authors.forename: Daniel
authors.initials: D
authors.lastname: Birnbaum
authors.forename: Colette
authors.initials: C
authors.lastname: Charpin
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000737:chemistry* / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D018270:Carcinoma, Ductal, Breast / Q000737:chemistry* / Q000401:mortality / Q000556:secondary / Q000628:therapy*; D017024:Chemotherapy, Adjuvant; D016000:Cluster Analysis; D018572:Disease-Free Survival; D005260:Female; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D007150:Immunohistochemistry; D053208:Kaplan-Meier Estimate; D016015:Logistic Models; D008875:Middle Aged; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D018579:Patient Selection; D057285:Precision Medicine; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D046888:Tissue Array Analysis; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor