Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31
Katherine L Pogue-Geile1, Chungyeul Kim, Jong-Hyeon Jeong
1Affiliations of authors: National Surgical Adjuvant Breast and Bowel Project Operations and Biostatistical Centers, Pittsburgh, PA (J-HJ, HB, PR, LF, EHR, SMS, EPM, DLW, CEG,Jr, JPC, NW, SP); Division of Pathology, NSABP (KLP-G, CK, NT, PGG, DF, YT, OLB, AL, S-IK, MLR, MYR, NLB, S-RK, ZDH); Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA (J-HJ, HB, JPC); PhenoPath Laboratories, PLLC, Seattle, WA (LCG); Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX (NS); Department of Pathology, University Medical Center Hamburg-Eppendorf, University Cancer Center, Hamburg, Germany (EB); Department of Pathology, Catholic University of Korea, Seoul, Republic of South Korea (AL); University of Pittsburgh Cancer Institute, Pittsburgh, PA (PR); Department of Oncology, Kaiser Permanente, Northern California, Vallejo, CA (LF); Department of Internal Medicine, Markey Cancer Center, University of Kentucky, Lexington, KY (EHR); Department of Medicine, Washington Cancer Institute, MedStar Washington Hospital Center, Washington, DC (SMS); Department of Surgery, MD Anderson Cancer Center Orlando, Orlando, FL (EPM); Department of Medicine, Virginia Commonwealth University, Richmond, VA (CEG, Jr); Department of Surgery, Allegheny Cancer Center at Allegheny General Hospital, Pittsburgh, PA (DLW, NW); Department of Biomedical Science, Severance Biomedical Science Institute, and Department of Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea (SP).
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; P(interaction) 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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