TY - JOUR
T1 - Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31
AU - Pogue-Geile, Katherine L.
AU - Kim, Chungyeul
AU - Jeong, Jong Hyeon
AU - Tanaka, Noriko
AU - Bandos, Hanna
AU - Gavin, Patrick G.
AU - Fumagalli, Debora
AU - Goldstein, Lynn C.
AU - Sneige, Nour
AU - Burandt, Eike
AU - Taniyama, Yusuke
AU - Bohn, Olga L.
AU - Lee, Ahwon
AU - Kim, Seung Il
AU - Reilly, Megan L.
AU - Remillard, Matthew Y.
AU - Blackmon, Nicole L.
AU - Kim, Seong Rim
AU - Horne, Zachary D.
AU - Rastogi, Priya
AU - Fehrenbacher, Louis
AU - Romond, Edward H.
AU - Swain, Sandra M.
AU - Mamounas, Eleftherios P.
AU - Wickerham, D. Lawrence
AU - Geyer, Charles E.
AU - Costantino, Joseph P.
AU - Wolmark, Norman
AU - Paik, Soonmyung
N1 - Funding Information:
This work was supported by the National Cancer Institute, Department of Health and Human Services, Public Health Service (grants U10-CA-12027, U10-CA-69651, U10-CA-37377, and U10-CA-69974) and by a grant from the Pennsylvania Department of Health. The latter Department specifically disclaims responsibility for any analysis, interpretations, or conclusions.
PY - 2013/12/4
Y1 - 2013/12/4
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. 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; Pinteraction between the model and trastuzumab < .001). 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).
AB - 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. 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; Pinteraction between the model and trastuzumab < .001). 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).
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U2 - 10.1093/jnci/djt321
DO - 10.1093/jnci/djt321
M3 - Article
C2 - 24262440
AN - SCOPUS:84890482594
SN - 0027-8874
VL - 105
SP - 1782
EP - 1788
JO - Journal of the National Cancer Institute
JF - Journal of the National Cancer Institute
IS - 23
ER -