Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31

Katherine L. Pogue-Geile, Chungyeul Kim, Jong Hyeon Jeong, Noriko Tanaka, Hanna Bandos, Patrick G. Gavin, Debora Fumagalli, Lynn C. Goldstein, Nour Sneige, Eike Burandt, Yusuke Taniyama, Olga L. Bohn, Ahwon Lee, Seung Il Kim, Megan L. Reilly, Matthew Y. Remillard, Nicole L. Blackmon, Seong Rim Kim, Zachary D. Horne, Priya Rastogi & 9 others Louis Fehrenbacher, Edward H. Romond, Sandra M. Swain, Eleftherios P. Mamounas, D. Lawrence Wickerham, Charles E. Geyer, Joseph P. Costantino, Norman Wolmark, Soonmyung Paik

Research output: Contribution to journalArticle

59 Citations (Scopus)

Abstract

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).

Original languageEnglish (US)
Pages (from-to)1782-1788
Number of pages7
JournalJournal of the National Cancer Institute
Volume105
Issue number23
DOIs
StatePublished - Dec 4 2013

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Breast
Confidence Intervals
Genes
Gene Components
Neoplasms
Gene Expression Profiling
B 31
Trastuzumab
Principal Component Analysis
Proportional Hazards Models
Breast Neoplasms
Gene Expression
Messenger RNA
Therapeutics

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

Pogue-Geile, K. L., Kim, C., Jeong, J. H., Tanaka, N., Bandos, H., Gavin, P. G., ... Paik, S. (2013). Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31. Journal of the National Cancer Institute, 105(23), 1782-1788. https://doi.org/10.1093/jnci/djt321

Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31. / Pogue-Geile, Katherine L.; Kim, Chungyeul; Jeong, Jong Hyeon; Tanaka, Noriko; Bandos, Hanna; Gavin, Patrick G.; Fumagalli, Debora; Goldstein, Lynn C.; Sneige, Nour; Burandt, Eike; Taniyama, Yusuke; Bohn, Olga L.; Lee, Ahwon; Kim, Seung Il; Reilly, Megan L.; Remillard, Matthew Y.; Blackmon, Nicole L.; Kim, Seong Rim; Horne, Zachary D.; Rastogi, Priya; Fehrenbacher, Louis; Romond, Edward H.; Swain, Sandra M.; Mamounas, Eleftherios P.; Wickerham, D. Lawrence; Geyer, Charles E.; Costantino, Joseph P.; Wolmark, Norman; Paik, Soonmyung.

In: Journal of the National Cancer Institute, Vol. 105, No. 23, 04.12.2013, p. 1782-1788.

Research output: Contribution to journalArticle

Pogue-Geile, KL, Kim, C, Jeong, JH, Tanaka, N, Bandos, H, Gavin, PG, Fumagalli, D, Goldstein, LC, Sneige, N, Burandt, E, Taniyama, Y, Bohn, OL, Lee, A, Kim, SI, Reilly, ML, Remillard, MY, Blackmon, NL, Kim, SR, Horne, ZD, Rastogi, P, Fehrenbacher, L, Romond, EH, Swain, SM, Mamounas, EP, Wickerham, DL, Geyer, CE, Costantino, JP, Wolmark, N & Paik, S 2013, 'Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31', Journal of the National Cancer Institute, vol. 105, no. 23, pp. 1782-1788. https://doi.org/10.1093/jnci/djt321
Pogue-Geile KL, Kim C, Jeong JH, Tanaka N, Bandos H, Gavin PG et al. Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31. Journal of the National Cancer Institute. 2013 Dec 4;105(23):1782-1788. https://doi.org/10.1093/jnci/djt321
Pogue-Geile, Katherine L. ; Kim, Chungyeul ; Jeong, Jong Hyeon ; Tanaka, Noriko ; Bandos, Hanna ; Gavin, Patrick G. ; Fumagalli, Debora ; Goldstein, Lynn C. ; Sneige, Nour ; Burandt, Eike ; Taniyama, Yusuke ; Bohn, Olga L. ; Lee, Ahwon ; Kim, Seung Il ; Reilly, Megan L. ; Remillard, Matthew Y. ; Blackmon, Nicole L. ; Kim, Seong Rim ; Horne, Zachary D. ; Rastogi, Priya ; Fehrenbacher, Louis ; Romond, Edward H. ; Swain, Sandra M. ; Mamounas, Eleftherios P. ; Wickerham, D. Lawrence ; Geyer, Charles E. ; Costantino, Joseph P. ; Wolmark, Norman ; Paik, Soonmyung. / Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31. In: Journal of the National Cancer Institute. 2013 ; Vol. 105, No. 23. pp. 1782-1788.
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title = "Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31",
abstract = "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).",
author = "Pogue-Geile, {Katherine L.} and Chungyeul Kim and Jeong, {Jong Hyeon} and Noriko Tanaka and Hanna Bandos and Gavin, {Patrick G.} and Debora Fumagalli and Goldstein, {Lynn C.} and Nour Sneige and Eike Burandt and Yusuke Taniyama and Bohn, {Olga L.} and Ahwon Lee and Kim, {Seung Il} and Reilly, {Megan L.} and Remillard, {Matthew Y.} and Blackmon, {Nicole L.} and Kim, {Seong Rim} and Horne, {Zachary D.} and Priya Rastogi and Louis Fehrenbacher and Romond, {Edward H.} and Swain, {Sandra M.} and Mamounas, {Eleftherios P.} and Wickerham, {D. Lawrence} and Geyer, {Charles E.} and Costantino, {Joseph P.} and Norman Wolmark and Soonmyung Paik",
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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

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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