1. Predicting Degree of Benefit From Adjuvant Trastuzumab in NSABP Trial B-31
- Author
-
Debora Fumagalli, Ahwon Lee, Charles E. Geyer, Priya Rastogi, Edward H. Romond, Lynn C. Goldstein, Seung Il Kim, Joseph P. Costantino, Patrick G. Gavin, Megan L. Reilly, Chungyeul Kim, Noriko Tanaka, D. Lawrence Wickerham, Seong Rim Kim, Yusuke Taniyama, Louis Fehrenbacher, Eleftherios P. Mamounas, Olga L. Bohn, Sandra M. Swain, Soonmyung Paik, Hanna Bandos, Matthew Y. Remillard, Nicole L. Blackmon, Katherine L. Pogue-Geile, Nour Sneige, Norman Wolmark, Jong-Hyeon Jeong, Eike Burandt, and Zachary D. Horne
- Subjects
Oncology ,Cancer Research ,medicine.medical_specialty ,Receptor, ErbB-2 ,Antineoplastic Agents ,Breast Neoplasms ,Antibodies, Monoclonal, Humanized ,law.invention ,Cohort Studies ,Breast cancer ,Randomized controlled trial ,Predictive Value of Tests ,Trastuzumab ,law ,Internal medicine ,Odds Ratio ,medicine ,Humans ,RNA, Messenger ,skin and connective tissue diseases ,Proportional Hazards Models ,Principal Component Analysis ,business.industry ,Proportional hazards model ,Gene Expression Profiling ,Hazard ratio ,Estrogen Receptor alpha ,medicine.disease ,Confidence interval ,Gene Expression Regulation, Neoplastic ,Treatment Outcome ,Chemotherapy, Adjuvant ,Predictive value of tests ,Cohort ,Immunology ,Female ,business ,medicine.drug - Abstract
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.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.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).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).
- Published
- 2013