TY  - BOOK
AU  - Swain, Sandra M
TI  - Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31
SN  - 0027-8874
PY  - 2013///
KW  - *Antibodies, Monoclonal, Humanized/tu [Therapeutic Use]
KW  - *Antineoplastic Agents/tu [Therapeutic Use]
KW  - *Breast Neoplasms/dt [Drug Therapy]
KW  - *Breast Neoplasms/me [Metabolism]
KW  - *Estrogen Receptor alpha/ge [Genetics]
KW  - *Gene Expression Regulation, Neoplastic
KW  - *Receptor, erbB-2/ge [Genetics]
KW  - Chemotherapy, Adjuvant
KW  - Cohort Studies
KW  - Female
KW  - Gene Expression Profiling
KW  - Humans
KW  - Odds Ratio
KW  - Predictive Value of Tests
KW  - Principal Component Analysis
KW  - Proportional Hazards Models
KW  - RNA, Messenger/me [Metabolism]
KW  - Treatment Outcome
KW  - Washington Cancer Institute
KW  - Journal Article
KW  - Randomized Controlled Trial
KW  - Research Support, N.I.H., Extramural
KW  - Research Support, Non-U.S. Gov't
N1  - Available online from MWHC library: 1996 - present (after 1 year), Available in print through MWHC library: 1999 - 2006
N2  - BACKGROUND: National Surgical Adjuvant Breast and Bowel Project (NSABP) trial B-31 suggested the efficacy of adjuvant trastuzumab, even in HER2-negative breast cancer. This finding prompted us to develop a predictive model for degree of benefit from trastuzumab using archived tumor blocks from B-31; CONCLUSIONS: We developed a gene expression-based predictive model for degree of benefit from trastuzumab and demonstrated that HER2-negative tumors belong to the moderate benefit group, thus providing justification for testing trastuzumab in HER2-negative patients (NSABP B-47); METHODS: Case subjects with tumor blocks were randomly divided into discovery (n = 588) and confirmation cohorts (n = 991). A predictive model was built from the discovery cohort through gene expression profiling of 462 genes with nCounter assay. A predefined cut point for the predictive model was tested in the confirmation cohort. Gene-by-treatment interaction was tested with Cox models, and correlations between variables were assessed with Spearman correlation. Principal component analysis was performed on the final set of selected genes. All statistical tests were two-sided; RESULTS: Eight predictive genes associated with HER2 (ERBB2, c17orf37, GRB7) or ER (ESR1, NAT1, GATA3, CA12, IGF1R) were selected for model building. Three-dimensional subset treatment effect pattern plot using two principal components of these genes was used to identify a subset with no benefit from trastuzumab, characterized by intermediate-level ERBB2 and high-level ESR1 mRNA expression. In the confirmation set, the predefined cut points for this model classified patients into three subsets with differential benefit from trastuzumab with hazard ratios of 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29; n = 100), 0.60 (95% CI = 0.41 to 0.89; P = .01; n = 449), and 0.28 (95% CI = 0.20 to 0.41; P < .001; n = 442; P(interaction) between the model and trastuzumab < .001)
UR  - http://dx.doi.org/10.1093/jnci/djt321
ER  -